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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Related Articles

Research Methodology

Mixed Methods Research

Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

Educational resources and simple solutions for your research journey

Qualitative vs Quantitative Research: Differences, Examples, and Methods

There are two broad kinds of research approaches: qualitative and quantitative research that are used to study and analyze phenomena in various fields such as natural sciences, social sciences, and humanities. Whether you have realized it or not, your research must have followed either or both research types. In this article we will discuss what qualitative vs quantitative research is, their applications, pros and cons, and when to use qualitative vs quantitative research . Before we get into the details, it is important to understand the differences between the qualitative and quantitative research.     

Table of Contents

Qualitative v s Quantitative Research  

Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality – characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. These two methodologies serve complementary roles in the research process, each offering unique insights and methods suited to different research questions and objectives.    

Qualitative and quantitative research approaches have their own unique characteristics, drawbacks, advantages, and uses. Where quantitative research is mostly employed to validate theories or assumptions with the goal of generalizing facts to the larger population, qualitative research is used to study concepts, thoughts, or experiences for the purpose of gaining the underlying reasons, motivations, and meanings behind human behavior .   

What Are the Differences Between Qualitative and Quantitative Research  

Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches across various aspects are listed in the table below.    

Data Collection Methods  

There are differences between qualitative and quantitative research when it comes to data collection as they deal with different types of data. Qualitative research is concerned with personal or descriptive accounts to understand human behavior within society. Quantitative research deals with numerical or measurable data to delineate relations among variables. Hence, the qualitative data collection methods differ significantly from quantitative data collection methods due to the nature of data being collected and the research objectives. Below is the list of data collection methods for each research approach:    

Qualitative Research Data Collection  

• Interviews  
• Focus g roups  
• Content a nalysis  
• Literature review  
• Observation  
• Ethnography  

Qualitative research data collection can involve one-on-one group interviews to capture in-depth perspectives of participants using open-ended questions. These interviews could be structured, semi-structured or unstructured depending upon the nature of the study. Focus groups can be used to explore specific topics and generate rich data through discussions among participants. Another qualitative data collection method is content analysis, which involves systematically analyzing text documents, audio, and video files or visual content to uncover patterns, themes, and meanings. This can be done through coding and categorization of raw data to draw meaningful insights. Data can be collected through observation studies where the goal is to simply observe and document behaviors, interaction, and phenomena in natural settings without interference. Lastly, ethnography allows one to immerse themselves in the culture or environment under study for a prolonged period to gain a deep understanding of the social phenomena.   

Quantitative Research Data Collection  

• Surveys/ q uestionnaires  
• Experiments
• Secondary data analysis  
• Structured o bservations  
• Case studies   
• Tests and a ssessments  

Quantitative research data collection approaches comprise of fundamental methods for generating numerical data that can be analyzed using statistical or mathematical tools. The most common quantitative data collection approach is the usage of structured surveys with close-ended questions to collect quantifiable data from a large sample of participants. These can be conducted online, over the phone, or in person.   

Performing experiments is another important data collection approach, in which variables are manipulated under controlled conditions to observe their effects on dependent variables. This often involves random assignment of participants to different conditions or groups. Such experimental settings are employed to gauge cause-and-effect relationships and understand a complex phenomenon. At times, instead of acquiring original data, researchers may deal with secondary data, which is the dataset curated by others, such as government agencies, research organizations, or academic institute. With structured observations, subjects in a natural environment can be studied by controlling the variables which aids in understanding the relationship among various variables. The secondary data is then analyzed to identify patterns and relationships among variables. Observational studies provide a means to systematically observe and record behaviors or phenomena as they occur in controlled environments. Case studies form an interesting study methodology in which a researcher studies a single entity or a small number of entities (individuals or organizations) in detail to understand complex phenomena within a specific context.   

Qualitative vs Quantitative Research Outcomes  

Qualitative research and quantitative research lead to varied research outcomes, each with its own strengths and limitations. For example, qualitative research outcomes provide deep descriptive accounts of human experiences, motivations, and perspectives that allow us to identify themes or narratives and context in which behavior, attitudes, or phenomena occurs.  Quantitative research outcomes on the other hand produce numerical data that is analyzed statistically to establish patterns and relationships objectively, to form generalizations about the larger population and make predictions. This numerical data can be presented in the form of graphs, tables, or charts. Both approaches offer valuable perspectives on complex phenomena, with qualitative research focusing on depth and interpretation, while quantitative research emphasizes numerical analysis and objectivity.  

When to Use Qualitative vs Quantitative Research Approach  

The decision to choose between qualitative and quantitative research depends on various factors, such as the research question, objectives, whether you are taking an inductive or deductive approach, available resources, practical considerations such as time and money, and the nature of the phenomenon under investigation. To simplify, quantitative research can be used if the aim of the research is to prove or test a hypothesis, while qualitative research should be used if the research question is more exploratory and an in-depth understanding of the concepts, behavior, or experiences is needed.     

Qualitative research approach  

Qualitative research approach is used under following scenarios:   

• To study complex phenomena: When the research requires understanding the depth, complexity, and context of a phenomenon.  
• Collecting participant perspectives: When the goal is to understand the why behind a certain behavior, and a need to capture subjective experiences and perceptions of participants.  
• Generating hypotheses or theories: When generating hypotheses, theories, or conceptual frameworks based on exploratory research.  

Example: If you have a research question “What obstacles do expatriate students encounter when acquiring a new language in their host country?”  

This research question can be addressed using the qualitative research approach by conducting in-depth interviews with 15-25 expatriate university students. Ask open-ended questions such as “What are the major challenges you face while attempting to learn the new language?”, “Do you find it difficult to learn the language as an adult?”, and “Do you feel practicing with a native friend or colleague helps the learning process”?  

Based on the findings of these answers, a follow-up questionnaire can be planned to clarify things. Next step will be to transcribe all interviews using transcription software and identify themes and patterns.   

Quantitative research approach  

Quantitative research approach is used under following scenarios:   

• Testing hypotheses or proving theories: When aiming to test hypotheses, establish relationships, or examine cause-and-effect relationships.   
• Generalizability: When needing findings that can be generalized to broader populations using large, representative samples.  
• Statistical analysis: When requiring rigorous statistical analysis to quantify relationships, patterns, or trends in data.   

Example : Considering the above example, you can conduct a survey of 200-300 expatriate university students and ask them specific questions such as: “On a scale of 1-10 how difficult is it to learn a new language?”  

Next, statistical analysis can be performed on the responses to draw conclusions like, on an average expatriate students rated the difficulty of learning a language 6.5 on the scale of 10.    

Mixed methods approach  

In many cases, researchers may opt for a mixed methods approach , combining qualitative and quantitative methods to leverage the strengths of both approaches. Researchers may use qualitative data to explore phenomena in-depth and generate hypotheses, while quantitative data can be used to test these hypotheses and generalize findings to broader populations.  

Example: Both qualitative and quantitative research methods can be used in combination to address the above research question. Through open-ended questions you can gain insights about different perspectives and experiences while quantitative research allows you to test that knowledge and prove/disprove your hypothesis.   

How to Analyze Qualitative and Quantitative Data  

When it comes to analyzing qualitative and quantitative data, the focus is on identifying patterns in the data to highlight the relationship between elements. The best research method for any given study should be chosen based on the study aim. A few methods to analyze qualitative and quantitative data are listed below.  

Analyzing qualitative data  

Qualitative data analysis is challenging as it is not expressed in numbers and consists majorly of texts, images, or videos. Hence, care must be taken while using any analytical approach. Some common approaches to analyze qualitative data include:  

• Organization: The first step is data (transcripts or notes) organization into different categories with similar concepts, themes, and patterns to find inter-relationships.  
• Coding: Data can be arranged in categories based on themes/concepts using coding.  
• Theme development: Utilize higher-level organization to group related codes into broader themes.  
• Interpretation: Explore the meaning behind different emerging themes to understand connections. Use different perspectives like culture, environment, and status to evaluate emerging themes.  
• Reporting: Present findings with quotes or excerpts to illustrate key themes.   

Analyzing quantitative data  

Quantitative data analysis is more direct compared to qualitative data as it primarily deals with numbers. Data can be evaluated using simple math or advanced statistics (descriptive or inferential). Some common approaches to analyze quantitative data include:  

• Processing raw data: Check missing values, outliers, or inconsistencies in raw data.  
• Descriptive statistics: Summarize data with means, standard deviations, or standard error using programs such as Excel, SPSS, or R language.  
• Exploratory data analysis: Usage of visuals to deduce patterns and trends.  
• Hypothesis testing: Apply statistical tests to find significance and test hypothesis (Student’s t-test or ANOVA).  
• Interpretation: Analyze results considering significance and practical implications.  
• Validation: Data validation through replication or literature review.  
• Reporting: Present findings by means of tables, figures, or graphs.   

Benefits and limitations of qualitative vs quantitative research  

There are significant differences between qualitative and quantitative research; we have listed the benefits and limitations of both methods below:  

Benefits of qualitative research  

• Rich insights: As qualitative research often produces information-rich data, it aids in gaining in-depth insights into complex phenomena, allowing researchers to explore nuances and meanings of the topic of study.  
• Flexibility: One of the most important benefits of qualitative research is flexibility in acquiring and analyzing data that allows researchers to adapt to the context and explore more unconventional aspects.  
• Contextual understanding: With descriptive and comprehensive data, understanding the context in which behaviors or phenomena occur becomes accessible.   
• Capturing different perspectives: Qualitative research allows for capturing different participant perspectives with open-ended question formats that further enrich data.   
• Hypothesis/theory generation: Qualitative research is often the first step in generating theory/hypothesis, which leads to future investigation thereby contributing to the field of research.

Limitations of qualitative research  

• Subjectivity: It is difficult to have objective interpretation with qualitative research, as research findings might be influenced by the expertise of researchers. The risk of researcher bias or interpretations affects the reliability and validity of the results.   
• Limited generalizability: Due to the presence of small, non-representative samples, the qualitative data cannot be used to make generalizations to a broader population.  
• Cost and time intensive: Qualitative data collection can be time-consuming and resource-intensive, therefore, it requires strategic planning and commitment.   
• Complex analysis: Analyzing qualitative data needs specialized skills and techniques, hence, it’s challenging for researchers without sufficient training or experience.   
• Potential misinterpretation: There is a risk of sampling bias and misinterpretation in data collection and analysis if researchers lack cultural or contextual understanding.   

Benefits of quantitative research  

• Objectivity: A key benefit of quantitative research approach, this objectivity reduces researcher bias and subjectivity, enhancing the reliability and validity of findings.   
• Generalizability: For quantitative research, the sample size must be large and representative enough to allow for generalization to broader populations.   
• Statistical analysis: Quantitative research enables rigorous statistical analysis (increasing power of the analysis), aiding hypothesis testing and finding patterns or relationship among variables.   
• Efficiency: Quantitative data collection and analysis is usually more efficient compared to the qualitative methods, especially when dealing with large datasets.   
• Clarity and Precision: The findings are usually clear and precise, making it easier to present them as graphs, tables, and figures to convey them to a larger audience.  

Limitations of quantitative research  

• Lacks depth and details: Due to its objective nature, quantitative research might lack the depth and richness of qualitative approaches, potentially overlooking important contextual factors or nuances.   
• Limited exploration: By not considering the subjective experiences of participants in depth , there’s a limited chance to study complex phenomenon in detail.   
• Potential oversimplification: Quantitative research may oversimplify complex phenomena by boiling them down to numbers, which might ignore key nuances.   
• Inflexibility: Quantitative research deals with predecided varibales and measures , which limits the ability of researchers to explore unexpected findings or adjust the research design as new findings become available .  
• Ethical consideration: Quantitative research may raise ethical concerns especially regarding privacy, informed consent, and the potential for harm, when dealing with sensitive topics or vulnerable populations.   

Frequently asked questions  

• What is the difference between qualitative and quantitative research? 

Quantitative methods use numerical data and statistical analysis for objective measurement and hypothesis testing, emphasizing generalizability. Qualitative methods gather non-numerical data to explore subjective experiences and contexts, providing rich, nuanced insights.  

• What are the types of qualitative research? 

Qualitative research methods include interviews, observations, focus groups, and case studies. They provide rich insights into participants’ perspectives and behaviors within their contexts, enabling exploration of complex phenomena.  

• What are the types of quantitative research? 

Quantitative research methods include surveys, experiments, observations, correlational studies, and longitudinal research. They gather numerical data for statistical analysis, aiming for objectivity and generalizability.  

• Can you give me examples for qualitative and quantitative research? 

Qualitative Research Example: 

Research Question: What are the experiences of parents with autistic children in accessing support services?  

Method: Conducting in-depth interviews with parents to explore their perspectives, challenges, and needs.  

Quantitative Research Example: 

Research Question: What is the correlation between sleep duration and academic performance in college students?  

Method: Distributing surveys to a large sample of college students to collect data on their sleep habits and academic performance, then analyzing the data statistically to determine any correlations.  

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Quantitative vs. Qualitative Research in Psychology

Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena⁠—things that happen because of and through human behavior⁠—are especially difficult to grasp with typical scientific models.

At a Glance

Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.

• Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
• Quantitative research involves collecting and evaluating numerical data. 

This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.

Qualitative Research vs. Quantitative Research

In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.

Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:

• Self-reports , like surveys or questionnaires
• Observation (often used in experiments or fieldwork)
• Implicit attitude tests that measure timing in responding to prompts

Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.

However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.

Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.

Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."

The scientific method follows this general process. A researcher must:

• Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
• Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
• Develop experiments to manipulate the variables
• Collect empirical (measured) data
• Analyze data

Quantitative methods are about measuring phenomena, not explaining them.

Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.

These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.

Basic Assumptions

Quantitative methods assume:

• That the world is measurable
• That humans can observe objectively
• That we can know things for certain about the world from observation

In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.

As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .

Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.

Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.

Correlation and Causation

A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:

• The study was a true experiment.
• The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
• The dependent variable can be measured through a ratio or a scale.

So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.

Pitfalls of Quantitative Research

Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?

As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.

Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.

Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.

While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."

Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.

These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.

Qualitative Approaches

There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:

• Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
• Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
• Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
• Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.

Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.

Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.

There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.

Interpretation

Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).

The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.

Relationship Between Qualitative and Quantitative Research

It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.

These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.

For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).

After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.

By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.

Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.

Gough B, Madill A. Subjectivity in psychological science: From problem to prospect . Psychol Methods . 2012;17(3):374-384. doi:10.1037/a0029313

Pearce T. “Science organized”: Positivism and the metaphysical club, 1865–1875 . J Hist Ideas . 2015;76(3):441-465.

Adams G. Context in person, person in context: A cultural psychology approach to social-personality psychology . In: Deaux K, Snyder M, eds. The Oxford Handbook of Personality and Social Psychology . Oxford University Press; 2012:182-208.

Brady HE. Causation and explanation in social science . In: Goodin RE, ed. The Oxford Handbook of Political Science. Oxford University Press; 2011. doi:10.1093/oxfordhb/9780199604456.013.0049

Chun Tie Y, Birks M, Francis K. Grounded theory research: A design framework for novice researchers .  SAGE Open Med . 2019;7:2050312118822927. doi:10.1177/2050312118822927

Reeves S, Peller J, Goldman J, Kitto S. Ethnography in qualitative educational research: AMEE Guide No. 80 . Medical Teacher . 2013;35(8):e1365-e1379. doi:10.3109/0142159X.2013.804977

Salkind NJ, ed. Encyclopedia of Research Design . Sage Publishing.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS.  Research Methods in Psychology . McGraw Hill Education.

By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

The differences between qualitative and quantitative research methods

Last updated

15 January 2023

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Two approaches to this systematic information gathering are qualitative and quantitative research. Each of these has its place in data collection, but each one approaches from a different direction. Here's what you need to know about qualitative and quantitative research.

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• The differences between quantitative and qualitative research

The main difference between these two approaches is the type of data you collect and how you interpret it. Qualitative research focuses on word-based data, aiming to define and understand ideas. This study allows researchers to collect information in an open-ended way through interviews, ethnography, and observation. You’ll study this information to determine patterns and the interplay of variables.

On the other hand, quantitative research focuses on numerical data and using it to determine relationships between variables. Researchers use easily quantifiable forms of data collection, such as experiments that measure the effect of one or several variables on one another.

• Qualitative vs. quantitative data collection

Focusing on different types of data means that the data collection methods vary. 

Quantitative data collection methods

As previously stated, quantitative data collection focuses on numbers. You gather information through experiments, database reports, or surveys with multiple-choice answers. The goal is to have data you can use in numerical analysis to determine relationships.

Qualitative data collection methods

On the other hand, the data collected for qualitative research is an exploration of a subject's attributes, thoughts, actions, or viewpoints. Researchers will typically conduct interviews , hold focus groups, or observe behavior in a natural setting to assemble this information. Other options include studying personal accounts or cultural records. 

• Qualitative vs. quantitative outcomes

The two approaches naturally produce different types of outcomes. Qualitative research gains a better understanding of the reason something happens. For example, researchers may comb through feedback and statements to ascertain the reasoning behind certain behaviors or actions.

On the other hand, quantitative research focuses on the numerical analysis of data, which may show cause-and-effect relationships. Put another way, qualitative research investigates why something happens, while quantitative research looks at what happens.

• How to analyze qualitative and quantitative data

Because the two research methods focus on different types of information, analyzing the data you've collected will look different, depending on your approach.

Analyzing quantitative data

As this data is often numerical, you’ll likely use statistical analysis to identify patterns. Researchers may use computer programs to generate data such as averages or rate changes, illustrating the results in tables or graphs.

Analyzing qualitative data

Qualitative data is more complex and time-consuming to process as it may include written texts, videos, or images to study. Finding patterns in thinking, actions, and beliefs is more nuanced and subject to interpretation. 

Researchers may use techniques such as thematic analysis , combing through the data to identify core themes or patterns. Another tool is discourse analysis , which studies how communication functions in different contexts.

• When to use qualitative vs. quantitative research

Choosing between the two approaches comes down to understanding what your goal is with the research.

Qualitative research approach

Qualitative research is useful for understanding a concept, such as what people think about certain experiences or how cultural beliefs affect perceptions of events. It can help you formulate a hypothesis or clarify general questions about the topic.

Quantitative research approach

On the other hand, quantitative research verifies or tests a hypothesis you've developed, or you can use it to find answers to those questions. 

Mixed methods approach

Often, researchers use elements of both types of research to provide complex and targeted information. This may look like a survey with multiple-choice and open-ended questions.

• Benefits and limitations

Of course, each type of research has drawbacks and strengths. It's essential to be aware of the pros and cons.

Qualitative studies: Pros and cons

This approach lets you consider your subject creatively and examine big-picture questions. It can advance your global understanding of topics that are challenging to quantify.

On the other hand, the wide-open possibilities of qualitative research can make it tricky to focus effectively on your subject of inquiry. It makes it easier for researchers to skew the data with social biases and personal assumptions. There’s also the tendency for people to behave differently under observation.

It can also be more difficult to get a large sample size because it's generally more complex and expensive to conduct qualitative research. The process usually takes longer, as well. 

Quantitative studies: Pros and cons

The quantitative methodology produces data you can communicate and present without bias. The methods are direct and generally easier to reproduce on a larger scale, enabling researchers to get accurate results. It can be instrumental in pinning down precise facts about a topic. 

It is also a restrictive form of inquiry. Researchers cannot add context to this type of data collection or expand their focus in a different direction within a single study. They must be alert for biases. Quantitative research is more susceptible to selection bias and omitting or incorrectly measuring variables.

• How to balance qualitative and quantitative research

Although people tend to gravitate to one form of inquiry over another, each has its place in studying a subject. Both approaches can identify patterns illustrating the connection between multiple elements, and they can each advance your understanding of subjects in important ways. 

Understanding how each option will serve you will help you decide how and when to use each. Generally, qualitative research can help you develop and refine questions, while quantitative research helps you get targeted answers to those questions. Which element do you need to advance your study of the subject? Can both of them hone your knowledge?

Open-ended vs. close-ended questions

One way to use techniques from both approaches is with open-ended and close-ended questions in surveys. Because quantitative analysis requires defined sets of data that you can represent numerically, the questions must be close-ended. On the other hand, qualitative inquiry is naturally open-ended, allowing room for complex ideas.

An example of this is a survey on the impact of inflation. You could include both multiple-choice questions and open-response questions:

1. How do you compensate for higher prices at the grocery store? (Select all that apply)

A. Purchase fewer items

B. Opt for less expensive choices

C. Take money from other parts of the budget

D. Use a food bank or other charity to fill the gaps

E. Make more food from scratch

2. How do rising prices affect your grocery shopping habits? (Write your answer)

We need qualitative and quantitative forms of research to advance our understanding of the world. Neither is the "right" way to go, but one may be better for you depending on your needs. 

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Qualitative vs. quantitative research - what’s the difference?

What is quantitative research?

What is quantitative research used for, how to collect data for quantitative research, what is qualitative research, what is qualitative research used for, how to collect data for qualitative research, when to use which approach, how to analyze qualitative and quantitative research, analyzing quantitative data, analyzing qualitative data, differences between qualitative and quantitative research, frequently asked questions about qualitative vs. quantitative research, related articles.

Both qualitative and quantitative research are valid and effective approaches to study a particular subject. However, it is important to know that these research approaches serve different purposes and provide different results. This guide will help illustrate quantitative and qualitative research, what they are used for, and the difference between them.

Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

To illustrate what quantitative research is used for, let’s look at a simple example. Let’s assume you want to research the reading habits of a specific part of a population.

With this research, you would like to establish what they read. In other words, do they read fiction, non-fiction, magazines, blogs, and so on? Also, you want to establish what they read about. For example, if they read fiction, is it thrillers, romance novels, or period dramas?

With quantitative research, you can gather concrete data about these reading habits. Your research will then, for example, show that 40% of the audience reads fiction and, of that 40%, 60% prefer romance novels.

In other studies and research projects, quantitative research will work in much the same way. That is, you use it to quantify variables, opinions, behaviors, and more.

Now that we've seen what quantitative research is and what it's used for, let's look at how you'll collect data for it. Because quantitative research is structured and statistical, its data collection methods focus on collecting numerical data.

Some methods to collect this data include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. These can include anything from online surveys to paper surveys. It’s important to remember that, to collect quantitative data, you won’t be able to ask open-ended questions.
• Interviews . As is the case with qualitative data, you’ll be able to use interviews to collect quantitative data with the proviso that the data will not be based on open-ended questions.
• Observations . You’ll also be able to use observations to collect quantitative data. However, here you’ll need to make observations in an environment where variables can’t be controlled.
• Website interceptors . With website interceptors, you’ll be able to get real-time insights into a specific product, service, or subject. In most cases, these interceptors take the form of surveys displayed on websites or invitations on the website to complete the survey.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences and include, for instance, diet studies. It’s important to remember that, for the results to be reliable, you’ll have to collect data from the same subjects.
• Online polls . Similar to website interceptors, online polls allow you to gather data from websites or social media platforms. These polls are short with only a few options and can give you valuable insights into a very specific question or topic.
• Experiments . With experiments, you’ll manipulate some variables (your independent variables) and gather data on causal relationships between others (your dependent variables). You’ll then measure what effect the manipulation of the independent variables has on the dependent variables.

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects.

The easiest way to describe qualitative research is that it answers the question " why ".

Considering that qualitative research aims to provide more profound insights and understanding into specific subjects, we’ll use our example mentioned earlier to explain what qualitative research is used for.

Based on this example, you’ve now established that 40% of the population reads fiction. You’ve probably also discovered in what proportion the population consumes other reading materials.

Qualitative research will now enable you to learn the reasons for these reading habits. For example, it will show you why 40% of the readers prefer fiction, while, for instance, only 10% prefer thrillers. It thus gives you an understanding of your participants’ behaviors and actions.

We've now recapped what qualitative research is and what it's used for. Let's now consider some methods to collect data for this type of research.

Some of these data collection methods include:

• Interviews . These include one-on-one interviews with respondents where you ask open-ended questions. You’ll then record the answers from every respondent and analyze these answers later.
• Open-ended survey questions . Open-ended survey questions give you insights into why respondents feel the way they do about a particular aspect.
• Focus groups . Focus groups allow you to have conversations with small groups of people and record their opinions and views about a specific topic.
• Observations . Observations like ethnography require that you participate in a specific organization or group in order to record their routines and interactions. This will, for instance, be the case where you want to establish how customers use a product in real-life scenarios.
• Literature reviews . With literature reviews, you’ll analyze the published works of other authors to analyze the prevailing view regarding a specific subject.
• Diary studies . Diary studies allow you to collect data about peoples’ habits, activities, and experiences over time. This will, for example, show you how customers use a product, when they use it, and what motivates them.

Now, the immediate question is: When should you use qualitative research, and when should you use quantitative research? As mentioned earlier, in its simplest form:

• Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality.
• Qualitative research allows you to understand concepts or experiences.

Let's look at how you'll use these approaches in a research project a bit closer:

• Formulating a hypothesis . As mentioned earlier, qualitative research gives you a deeper understanding of a topic. Apart from learning more profound insights about your research findings, you can also use it to formulate a hypothesis when you start your research.
• Confirming a hypothesis . Once you’ve formulated a hypothesis, you can test it with quantitative research. As mentioned, you can also use it to quantify trends and behavior.
• Finding general answers . Quantitative research can help you answer broad questions. This is because it uses a larger sample size and thus makes it easier to gather simple binary or numeric data on a specific subject.
• Getting a deeper understanding . Once you have the broad answers mentioned above, qualitative research will help you find reasons for these answers. In other words, quantitative research shows you the motives behind actions or behaviors.

Considering the above, why not consider a mixed approach ? You certainly can because these approaches are not mutually exclusive. In other words, using one does not necessarily exclude the other. Moreover, both these approaches are useful for different reasons.

This means you could use both approaches in one project to achieve different goals. For example, you could use qualitative to formulate a hypothesis. Once formulated, quantitative research will allow you to confirm the hypothesis.

So, to answer the initial question, the approach you use is up to you.  However, when deciding on the right approach, you should consider the specific research project, the data you'll gather, and what you want to achieve.

No matter what approach you choose, you should design your research in such a way that it delivers results that are objective, reliable, and valid.

Both these research approaches are based on data. Once you have this data, however, you need to analyze it to answer your research questions. The method to do this depends on the research approach you use.

To analyze quantitative data, you'll need to use mathematical or statistical analysis. This can involve anything from calculating simple averages to applying complex and advanced methods to calculate the statistical significance of the results. No matter what analysis methods you use, it will enable you to spot trends and patterns in your data.

Considering the above, you can use tools, applications, and programming languages like R to calculate:

• The average of a set of numbers . This could, for instance, be the case where you calculate the average scores students obtained in a test or the average time people spend on a website.
• The frequency of a specific response . This will be the case where you, for example, use open-ended survey questions during qualitative analysis. You could then calculate the frequency of a specific response for deeper insights.
• Any correlation between different variables . Through mathematical analysis, you can calculate whether two or more variables are directly or indirectly correlated. In turn, this could help you identify trends in the data.
• The statistical significance of your results . By analyzing the data and calculating the statistical significance of the results, you'll be able to see whether certain occurrences happen randomly or because of specific factors.

Analyzing qualitative data is more complex than quantitative data. This is simply because it's not based on numerical values but rather text, images, video, and the like. As such, you won't be able to use mathematical analysis to analyze and interpret your results.

Because of this, it relies on a more interpretive analysis style and a strict analytical framework to analyze data and extract insights from it.

Some of the most common ways to analyze qualitative data include:

• Qualitative content analysis . In a content analysis, you'll analyze the language used in a specific piece of text. This allows you to understand the intentions of the author, who the audience is, and find patterns and correlations in how different concepts are communicated. A major benefit of this approach is that it follows a systematic and transparent process that other researchers will be able to replicate. As such, your research will produce highly reliable results. Keep in mind, however, that content analysis can be time-intensive and difficult to automate. ➡️  Learn how to do a content analysis in the guide.
• Thematic analysis . In a thematic analysis, you'll analyze data with a view of extracting themes, topics, and patterns in the data. Although thematic analysis can encompass a range of diverse approaches, it's usually used to analyze a collection of texts like survey responses, focus group discussions, or transcriptions of interviews. One of the main benefits of thematic analysis is that it's flexible in its approach. However, in some cases, thematic analysis can be highly subjective, which, in turn, impacts the reliability of the results. ➡️  Learn how to do a thematic analysis in this guide.
• Discourse analysis . In a discourse analysis, you'll analyze written or spoken language to understand how language is used in real-life social situations. As such, you'll be able to determine how meaning is given to language in different contexts. This is an especially effective approach if you want to gain a deeper understanding of different social groups and how they communicate with each other. As such, it's commonly used in humanities and social science disciplines.

We’ve now given a broad overview of both qualitative and quantitative research. Based on this, we can summarize the differences between these two approaches as follows:

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

3 examples of qualitative research would be:

• Interviews . These include one-on-one interviews with respondents with open-ended questions. You’ll then record the answers and analyze them later.
• Observations . Observations require that you participate in a specific organization or group in order to record their routines and interactions.

3 examples of quantitative research include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. To collect quantitative data, you won’t be able to ask open-ended questions.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences.

The main purpose of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. The easiest way to describe qualitative research is that it answers the question " why ".

The purpose of quantitative research is to collect numerical data and use it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

• How it works

Qualitative Vs Quantitative Research – A Comprehensive Guide

Published by Carmen Troy at August 13th, 2021 , Revised On September 20, 2023

What is Quantitative Research?

Quantitative research is associated with numerical data or data that can be measured. It is used to study a large group of population. The information is gathered by performing statistical, mathematical, or computational techniques.

Quantitative research isn’t simply based on  statistical analysis or quantitative techniques but rather uses a certain approach to theory to address research hypotheses or questions, establish an appropriate research methodology, and draw findings & conclusions .

Characteristics of Quantitative Research

Some most commonly employed quantitative research strategies include data-driven dissertations, theory-driven studies, and reflection-driven research. Regardless of the chosen approach, there are some common quantitative research features as listed below.

• Quantitative research tests or builds on other researchers’ existing theories whilst taking a reflective or extensive route.
• Quantitative research aims to test the research hypothesis or answer established research questions.
• It is primarily justified by positivist or post-positivist research paradigms.
• The  research design can be relationship-based, quasi-experimental, experimental, or descriptive.
• It draws on a small sample to make generalisations to a wider population using probability sampling techniques.
• Quantitative data is gathered according to the established research questions using research vehicles such as structured observation, structured interviews, surveys, questionnaires, and laboratory results.
• The researcher uses  statistical analysis tools and techniques to measure variables and gather inferential or descriptive data. In some cases, your tutor or dissertation committee members might find it easier to verify your study results with numbers and statistical analysis.
• The study results’ accuracy is based on external and internal validity and authenticity of the data used.
• Quantitative research answers research questions or tests the hypothesis using charts, graphs, tables, data, and statements.
• It underpins  research questions or hypotheses and findings to make conclusions.
• The researcher can provide recommendations for future research and expand or test existing theories.

What is Qualitative Research?

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a  question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

As the term suggests,  qualitative research  is based on qualitative research methods, including participants’ observations, focus groups, and unstructured interviews.

Qualitative research is very different in nature when compared to quantitative research. It takes an established path towards the  research process , how  research questions  are set up, how existing theories are built upon, what research methods are employed, and how the  findings  are unveiled to the readers.

You may adopt conventional methods, including phenomenological research, narrative-based research, grounded theory research, ethnographies, case studies, and auto-ethnographies.

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Characteristics of Qualitative Research

Again, regardless of the chosen approach to qualitative research, your dissertation will have unique key features as listed below.

• The research questions that you aim to answer will expand or even change as the  dissertation writing process continues . This aspect of the research is typically known as an emergent design where the research objectives evolve with time.
• Qualitative research may use existing theories to cultivate new theoretical understandings or fall back on existing theories to support the research process. However, the original goal of testing a certain theoretical understanding remains the same.
• It can be based on various research models, such as critical theory, constructivism, and interpretivism.
• The chosen research design largely influences the analysis and discussion of results and the choices you make . Research design depends on the adopted research path: phenomenological research, narrative-based research, grounded theory-based research, ethnography, case study-based research, or auto-ethnography.
• Qualitative research answers research questions with theoretical sampling, where data gathered from the organisation or people are studied.
• It involves various research methods to gather qualitative data from participants belonging to the field of study. As indicated previously, some of the most notable qualitative research methods include participant observation, focus groups, and unstructured interviews.
• It incorporates an  inductive process where the researcher analyses and understands the data through his own eyes and judgments to identify concepts and themes that comprehensively depict the researched material.
• The key quality characteristics of qualitative research are transferability, conformity, confirmability, and reliability.
• Results and discussions are largely based on narratives, case study and personal experiences, which help detect inconsistencies, observations, processes, and ideas.
• Qualitative research discusses theoretical concepts obtained from the results whilst taking research questions and/or hypotheses to  draw general  conclusions .

Confused between qualitative and quantitative methods of data analysis? No idea what discourse and content analysis are?

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• Whether you want a full dissertation written or need help forming a dissertation proposal, we can help you with both.
• Get different dissertation services at ResearchProspect and score amazing grades!

When to Use Qualitative and Quantitative Research Model?

• The research  title, research questions,  hypothesis , objectives, and study area generally determine the dissertation’s best research method.
• If the primary aim of your research is to test a hypothesis, validate an existing theory or perhaps measure some variables, then the quantitative research model will be the more appropriate choice because it might be easier for you to convince your supervisor or members of the dissertation committee with the use of statistics and numbers.
• On the other hand, oftentimes, statistics and a collection of numbers are not the answer, especially where there is a need to understand meanings, experiences, and beliefs.
• If your research questions or hypothesis can be better addressed through people’s observations and experiences, you should consider qualitative data.
• If you select an inappropriate research method, you will not prove your findings’ accuracy, and your dissertation will be pretty much meaningless. To prove that your research is authentic and reliable, choose a research method that best suits your study’s requirements.
• In the sections that follow, we explain the most commonly employed research methods for the dissertation, including quantitative, qualitative, and mixed research methods.

Now that you know the unique differences between quantitative and qualitative research methods, you may want to learn a bit about primary and secondary research methods.

Here is an article that will help you  distinguish between primary and secondary research  and decide whether you need to use quantitative and/or qualitative methods of primary research in your dissertation.

Alternatively, you can base your dissertation on secondary research, which is descriptive and explanatory.

Limitations of Quantitative and Qualitative Research

What is quantitative research?

What is qualitative research.

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

Qualitative or quantitative, which research type should I use?

The research title, research questions, hypothesis , objectives, and study area generally determine the dissertation’s best research method.

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Qualitative vs Quantitative Research 101

A plain-language explanation (with examples).

By: Kerryn Warren (PhD, MSc, BSc) | June 2020

So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative . And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.

However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives , not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.

In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.

Qualitative vs Quantitative: Overview  

• Qualitative analysis 101
• Quantitative analysis 101
• How to choose which one to use
• Data collection and analysis for qualitative and quantitative research
• The pros and cons of both qualitative and quantitative research
• A quick word on mixed methods

Qualitative Research 101: The Basics

The bathwater is hot.

Let us unpack that a bit. What does that sentence mean? And is it useful?

The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat .

Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences  by systematically coding and analysing the data .

With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.

In this way, qualitative research can be incredibly rich and detailed , and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception , world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.

But that’s not the only way to analyse bathwater, of course…

Quantitative Research 101: The Basics

The bathwater is 45 degrees Celsius.

Now, what does this mean? How can this be used?

I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat .

If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).

With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs , and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…

In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:

Hypothesis: Men prefer bathing in lower temperature water than women do.

This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.

So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs .

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Qualitative vs Quantitative Research: Which one should you use?

And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research. 

If you want to understand a situation with richness and depth , and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.

On the other hand, if you need to test an already-theorised hypothesis , or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.

Basically, this means that your research approach should be chosen based on your broader research aims , objectives and research questions . If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement , using a scale, machine or… a thermometer… is going to need a quantitative method.

Let’s look at an example.

Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:

• How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
• What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
• Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).

The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.

Qualitative vs Quantitative Research: Data collection and analysis

The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.

A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.

To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.

On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.

So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.

Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.

There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis  may involve simple calculations for averages , or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.

Qualitative vs Quantitative Research: The pros & cons on both sides

Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.

Let’s start with qualitative “pros”

Qualitative research allows for richer , more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question . When we want to find out what and how people are thinking and feeling , qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.

Qualitative research can be used to explain previously observed phenomena , providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.

There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability . Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.

Qualitative research also has its “cons”:

A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?

But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?

Adding to this, researcher inexperience is an additional limitation . Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.

Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.

Now let’s turn to quantitative “pros”:

Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses . Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.

Quantitative research is respected as being objective and viable . This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.

Quantitative research can be pretty fast . The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.

Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity , which is an important ethical consideration . A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.

But there are also quantitative “cons”:

Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…

It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.

Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.

Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.

Mixed methods: a happy medium?

Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds .

In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.

A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully , where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!

Recap: Qualitative vs Quantitative Research

So, just to recap what we have learned in this post about the great qual vs quant debate:

• Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
• Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size .
• Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
• Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
• Mixed methods can be a happy medium, but should be used purposefully.
• Bathwater temperature is a contentious and severely under-studied research topic.

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Qualitative vs quantitative research.

13 min read You’ll use both quantitative and qualitative research methods to gather survey data. What are they exactly, and how can you best use them to gain the most accurate insights?

What is qualitative research?

Qualitative research  is all about  language, expression, body language and other forms of human communication . That covers words, meanings and understanding. Qualitative research is used to describe WHY. Why do people  feel  the way they do, why do they  act  in a certain way, what  opinions  do they have and what  motivates  them?

Qualitative data is used to understand phenomena – things that happen, situations that exist, and most importantly the meanings associated with them. It can help add a ‘why’ element to factual, objective data.

Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyse than quantitative data.

This qualitative data is called  unstructured data by researchers. This is because it has not traditionally had the type of structure that can be processed by computers, until today. It has, until recently at least, been exclusively accessible to human brains. And although our brains are highly sophisticated, they have limited processing power. What can help analyse this structured data to assist computers and the human brain?

Discover the 2023 Market Research Trends Transforming the industry

What is quantitative research?

Quantitative data refers to numerical information. Quantitative research gathers information that can be counted, measured, or rated numerically – AKA quantitative data. Scores, measurements, financial records, temperature charts and receipts or ledgers are all examples of quantitative data.

Quantitative data is often structured data, because it follows a consistent, predictable pattern that computers and calculating devices are able to process with ease. Humans can process it too, although we are now able to pass it over to machines to process on our behalf. This is partly what has made quantitative data so important historically, and why quantitative data – sometimes called ‘hard data’ – has dominated over qualitative data in fields like business, finance and economics.

It’s easy to ‘crunch the numbers’ of quantitative data and produce results visually in graphs, tables and on data analysis dashboards. Thanks to today’s abundance and accessibility of processing power, combined with our ability to store huge amounts of information, quantitative data has fuelled the Big Data phenomenon, putting quantitative methods and vast amounts of quantitative data at our fingertips.

As we’ve indicated, quantitative and qualitative data are entirely different and mutually exclusive categories. Here are a few of the differences between them.

1. Data collection

Data collection methods for quantitative data and qualitative data vary, but there are also some places where they overlap.

2. Data analysis

Quantitative data suits statistical analysis techniques like linear regression, T-tests and ANOVA. These are quite easy to automate, and large quantities of quantitative data can be analyzed quickly.

Analyzing qualitative data needs a higher degree of human judgement, since unlike quantitative data, non numerical data of a subjective nature has certain characteristics that inferential statistics can’t perceive. Working at a human scale has historically meant that qualitative data is lower in volume – although it can be richer in insights.

3. Strengths and weaknesses

When weighing up qualitative vs quantitative research, it’s largely a matter of choosing the method appropriate to your research goals. If you’re in the position of having to choose one method over another, it’s worth knowing the strengths and limitations of each, so that you know what to expect from your results.

Qualitative vs quantitative – the role of research questions

How do you know whether you need qualitative or quantitative research techniques? By finding out what kind of data you’re going to be collecting.

You’ll do this as you develop your research question, one of the first steps to any research program. It’s a single sentence that sums up the purpose of your research, who you’re going to gather data from, and what results you’re looking for.

As you formulate your question, you’ll get a sense of the sort of answer you’re working towards, and whether it will be expressed in numerical data or qualitative data.

For example, your research question might be “How often does a poor customer experience cause shoppers to abandon their shopping carts?” – this is a quantitative topic, as you’re looking for numerical values.

Or it might be “What is the emotional impact of a poor customer experience on regular customers in our supermarket?” This is a qualitative topic, concerned with thoughts and feelings and answered in personal, subjective ways that vary between respondents.

Here’s how to evaluate your research question and decide which method to use:

• Qualitative research:

Use this if your goal is to  understand  something – experiences, problems, ideas.

For example, you may want to understand how poor experiences in a supermarket make your customers feel. You might carry out this research through focus groups or in depth interviews (IDI’s). For a larger scale research method you could start  by surveying supermarket loyalty card holders, asking open text questions, like “How would you describe your experience today?” or “What could be improved about your experience?” This research will provide context and understanding that quantitative research will not.

• Quantitative research:

Use this if your goal is to  test or confirm  a hypothesis, or to study cause and effect relationships. For example, you want to find out what percentage of your returning customers are happy with the customer experience at your store. You can collect data to answer this via a survey.

For example, you could recruit 1,000 loyalty card holders as participants, asking them, “On a scale of 1-5, how happy are you with our store?” You can then make simple mathematical calculations to find the average score. The larger sample size will help make sure your results aren’t skewed by anomalous data or outliers, so you can draw conclusions with confidence.

Qualitative and quantitative research combined?

Do you always have to choose between qualitative or quantitative data?

In some cases you can get the best of both worlds by combining both quantitative and qualitative data.You could use pre quantitative data to understand the landscape of your research. Here you can gain  insights around a topic  and propose a  hypothesis.  Then adopt a quantitative research method to test it out. Here you’ll discover where to focus your survey appropriately or to pre-test your survey, to ensure your questions are understood as you intended. Finally, using a round of qualitative research methods to bring your insights and story to life. This mixed methods approach is becoming increasingly popular with businesses who are looking for in depth insights.

For example, in the supermarket scenario we’ve described, you could start out with a qualitative data collection phase where you use focus groups and conduct interviews with customers. You might find suggestions in your qualitative data that customers would like to be able to buy children’s clothes in the store.

In response, the supermarket might pilot a children’s clothing range. Targeted  quantitative  research could then reveal whether or not those stores selling children’s clothes achieve higher  customer satisfaction  scores  and a  rise in profits  for clothing.

Together, qualitative and quantitative data, combined with statistical analysis, have provided important insights about customer experience, and have proven the effectiveness of a solution to business problems.

Qualitative vs quantitative question types

As we’ve noted, surveys are one of the data collection methods suitable for both quantitative and qualitative research. Depending on the types of questions you choose to include, you can generate qualitative and quantitative data. Here we have summarized some of the survey question types you can use for each purpose.

Qualitative data survey questions

There are fewer survey  question  options for collecting qualitative data, since they all essentially do the same thing – provide the respondent with space to enter information in their own words. Qualitative research is not typically done with surveys alone, and researchers may use a mix of qualitative methods. As well as a survey, they might conduct in depth interviews, use observational studies or hold focus groups.

Open text ‘Other’ box (can be used with multiple choice questions)

Text box (space for short written answer)

Essay box (space for longer, more detailed written answers)

Quantitative data survey questions

These questions will yield quantitative data – i.e. a numerical value.

Net Promoter Score (NPS)

Likert Scale

Radio buttons (respondents choose just one option)

Check boxes (respondents can choose multiple options)

Sliding scale

Star rating

Analysing data (quantitative or qualitative) using technology

We are currently at an exciting point in the history of qualitative analysis. Digital analysis and other methods that were formerly exclusively used for  quantitative data  are now used for interpreting non numerical data too.

Artificial intelligence programs can now be used to analyse open text, and turn qualitative data into structured and semi structured quantitative data that relates to qualitative data topics such as emotion and sentiment, opinion and experience.

Research that in the past would have meant qualitative researchers conducting time-intensive studies using analysis methods like thematic analysis can now be done in a very short space of time. This not only saves time and money, but opens up qualitative data analysis to a much wider range of businesses and organisations.

The most advanced tools can even be used for real-time statistical analysis, forecasting and prediction, making them a powerful asset for businesses.

Qualitative or quantitative – which is better for data analysis?

Historically, quantitative data was much easier to analyse than qualitative data. But as we’ve seen, modern technology is helping qualitative analysis to catch up, making it quicker and less labor-intensive than before.

That means the choice between qualitative and quantitative studies no longer needs to factor in ease of analysis, provided you have the right tools at your disposal. With an integrated platform like Qualtrics, which incorporates data collection, data cleaning, data coding and a powerful suite of analysis tools for both qualitative and quantitative data, you have a wide range of options at your fingertips.

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Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research

No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.

Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.

Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.

Qualitative vs. Quantitative Research in Education: Definitions

Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.

Generate Hypotheses with Qualitative Research

Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.

Form Strong Conclusions with Quantitative Research

Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.

Differences in Data Collection Methods

Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.

Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.

An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.

Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.

On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.

Qualitative Research Methods

• Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
• Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
• Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
• Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.

Quantitative Research Methods

• Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
• Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
• Observations : Researchers look at quantifiable patterns and behavior.
• Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.

Choosing a Research Strategy

When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.

For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.

It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.

In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.

Learn How to Put Education Research into Action

Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .

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American University, EdD in Education Policy and Leadership

Edutopia, “2019 Education Research Highlights”

Formplus, “Qualitative vs. Quantitative Data: 15 Key Differences and Similarities”

iMotion, “Qualitative vs. Quantitative Research: What Is What?”

Scribbr, “Qualitative vs. Quantitative Research”

Simply Psychology, “What’s the Difference Between Quantitative and Qualitative Research?”

Typeform, “A Simple Guide to Qualitative and Quantitative Research”

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Qualitative and Quantitative Research: Differences and Similarities

Qualitative research and quantitative research are two complementary approaches for understanding the world around us.

Qualitative research collects non-numerical data , and the results are typically presented as written descriptions, photographs, videos, and/or sound recordings.

In contrast, quantitative research collects numerical data , and the results are typically presented in tables, graphs, and charts.

Debates about whether to use qualitative or quantitative research methods are common in the social sciences (i.e. anthropology, archaeology, economics, geography, history, law, linguistics, politics, psychology, sociology), which aim to understand a broad range of human conditions. Qualitative observations may be used to gain an understanding of unique situations, which may lead to quantitative research that aims to find commonalities.

Within the natural and physical sciences (i.e. physics, chemistry, geology, biology), qualitative observations often lead to a plethora of quantitative studies. For example, unusual observations through a microscope or telescope can immediately lead to counting and measuring. In other situations, meaningful numbers cannot immediately be obtained, and the qualitative research must stand on its own (e.g. The patient presented with an abnormally enlarged spleen (Figure 1), and complained of pain in the left shoulder.)

For both qualitative and quantitative research, the researcher's assumptions shape the direction of the study and thereby influence the results that can be obtained. Let's consider some prominent examples of qualitative and quantitative research, and how these two methods can complement each other.

Qualitative research example

In 1960, Jane Goodall started her decades-long study of chimpanzees in the wild at Gombe Stream National Park in Tanzania. Her work is an example of qualitative research that has fundamentally changed our understanding of non-human primates, and has influenced our understanding of other animals, their abilities, and their social interactions.

Dr. Goodall was by no means the first person to study non-human primates, but she took a highly unusual approach in her research. For example, she named individual chimpanzees instead of numbering them, and used terms such as "childhood", "adolescence", "motivation", "excitement", and "mood". She also described the distinct "personalities" of individual chimpanzees. Dr. Goodall was heavily criticized for describing chimpanzees in ways that are regularly used to describe humans, which perfectly illustrates how the assumptions of the researcher can heavily influence their work.

The quality of qualitative research is largely determined by the researcher's ability, knowledge, creativity, and interpretation of the results. One of the hallmarks of good qualitative research is that nothing is predefined or taken for granted, and that the study subjects teach the researcher about their lives. As a result, qualitative research studies evolve over time, and the focus or techniques used can shift as the study progresses.

Qualitative research methods

Dr. Goodall immersed herself in the chimpanzees' natural surroundings, and used direct observation to learn about their daily life. She used photographs, videos, sound recordings, and written descriptions to present her data. These are all well-established methods of qualitative research, with direct observation within the natural setting considered a gold standard. These methods are time-intensive for the researcher (and therefore monetarily expensive) and limit the number of individuals that can be studied at one time.

When studying humans, a wider variety of research methods are available to understand how people perceive and navigate their world—past or present. These techniques include: in-depth interviews (e.g. Can you discuss your experience of growing up in the Deep South in the 1950s?), open-ended survey questions (e.g. What do you enjoy most about being part of the Church of Latter Day Saints?), focus group discussions, researcher participation (e.g. in military training), review of written documents (e.g. social media accounts, diaries, school records, etc), and analysis of cultural records (e.g. anything left behind including trash, clothing, buildings, etc).

Qualitative research can lead to quantitative research

Qualitative research is largely exploratory. The goal is to gain a better understanding of an unknown situation. Qualitative research in humans may lead to a better understanding of underlying reasons, opinions, motivations, experiences, etc. The information generated through qualitative research can provide new hypotheses to test through quantitative research. Quantitative research studies are typically more focused and less exploratory, involve a larger sample size, and by definition produce numerical data.

Dr. Goodall's qualitative research clearly established periods of childhood and adolescence in chimpanzees. Quantitative studies could better characterize these time periods, for example by recording the amount of time individual chimpanzees spend with their mothers, with peers, or alone each day during childhood compared to adolescence.

For studies involving humans, quantitative data might be collected through a questionnaire with a limited number of answers (e.g. If you were being bullied, what is the likelihood that you would tell at least one parent? A) Very likely, B) Somewhat likely, C) Somewhat unlikely, D) Unlikely).

Quantitative research example

One of the most influential examples of quantitative research began with a simple qualitative observation: Some peas are round, and other peas are wrinkled. Gregor Mendel was not the first to make this observation, but he was the first to carry out rigorous quantitative experiments to better understand this characteristic of garden peas.

As described in his 1865 research paper, Mendel carried out carefully controlled genetic crosses and counted thousands of resulting peas. He discovered that the ratio of round peas to wrinkled peas matched the ratio expected if pea shape were determined by two copies of a gene for pea shape, one inherited from each parent. These experiments and calculations became the foundation of modern genetics, and Mendel's ratios became the default hypothesis for experiments involving thousands of different genes in hundreds of different organisms.

The quality of quantitative research is largely determined by the researcher's ability to design a feasible experiment, that will provide clear evidence to support or refute the working hypothesis. The hallmarks of good quantitative research include: a study that can be replicated by an independent group and produce similar results, a sample population that is representative of the population under study, a sample size that is large enough to reveal any expected statistical significance.

Quantitative research methods

The basic methods of quantitative research involve measuring or counting things (size, weight, distance, offspring, light intensity, participants, number of times a specific phrase is used, etc). In the social sciences especially, responses are often be split into somewhat arbitrary categories (e.g. How much time do you spend on social media during a typical weekday? A) 0-15 min, B) 15-30 min, C) 30-60 min, D) 1-2 hrs, E) more than 2 hrs).

These quantitative data can be displayed in a table, graph, or chart, and grouped in ways that highlight patterns and relationships. The quantitative data should also be subjected to mathematical and statistical analysis. To reveal overall trends, the average (or most common survey answer) and standard deviation can be determined for different groups (e.g. with treatment A and without treatment B).

Typically, the most important result from a quantitative experiment is the test of statistical significance. There are many different methods for determining statistical significance (e.g. t-test, chi square test, ANOVA, etc.), and the appropriate method will depend on the specific experiment.

Statistical significance provides an answer to the question: What is the probably that the difference observed between two groups is due to chance alone, and the two groups are actually the same? For example, your initial results might show that 32% of Friday grocery shoppers buy alcohol, while only 16% of Monday grocery shoppers buy alcohol. If this result reflects a true difference between Friday shoppers and Monday shoppers, grocery store managers might want to offer Friday specials to increase sales.

After the appropriate statistical test is conducted (which incorporates sample size and other variables), the probability that the observed difference is due to chance alone might be more than 5%, or less than 5%. If the probability is less than 5%, the convention is that the result is considered statistically significant. (The researcher is also likely to cheer and have at least a small celebration.) Otherwise, the result is considered statistically insignificant. (If the value is close to 5%, the researcher may try to group the data in different ways to achieve statistical significance. For example, by comparing alcohol sales after 5pm on Friday and Monday.) While it is important to reveal differences that may not be immediately obvious, the desire to manipulate information until it becomes statistically significant can also contribute to bias in research.

So how often do results from two groups that are actually the same give a probability of less than 5%? A bit less than 5% of the time (by definition). This is one of the reasons why it is so important that quantitative research can be replicated by different groups.

Which research method should I choose?

Choose the research methods that will allow you to produce the best results for a meaningful question, while acknowledging any unknowns and controlling for any bias. In many situations, this will involve a mixed methods approach. Qualitative research may allow you to learn about a poorly understood topic, and then quantitative research may allow you to obtain results that can be subjected to rigorous statistical tests to find true and meaningful patterns. Many different approaches are required to understand the complex world around us.

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Qualitative vs Quantitative Research: Differences and Examples

Understanding the differences between qualitative vs quantitative research is essential when conducting a research project, as both methods underpin the two key approaches in conducting a study.

In recent blogs, we elaborately discussed quantitative and qualitative research methods b ut what is the difference between the two? Which one is the best? Let’s find out.

Qualitative Research In a nutshell

Qualitative research is a research methodology where “quality” or opinion based research is conducted to derive research conclusions. This type of research is often conversational in nature rather than being quantifiable through empirical research and measurements.

Qualitative research: Methods & Characteristics

1. Conversation : A conversation takes place between the researcher and the respondent. This can be in the form of focus groups , in-depth interviews using telephonic / video / face-to-face conversations.

However, with the rise of online platforms, a bulk of steps in qualitative research involves creating and maintaining online community portals for a more quantifiable and recordable qualitative study.

LEARN ABOUT: Qualitative Interview

2. Conclusions : Research conclusions are subjective in nature when conducting qualitative research. The researcher may derive conclusions based on in-depth analysis of respondent attitude, reason behind responses and understanding of psychological motivations.

Quantitative Research In a nutshell

Quantitative research is a research methodology which uses questions and questionnaires to gather quantifiable data and perform statistical analysis to derive meaningful research conclusions.

Quantitative research: Methods & Characteristics

1. Questions : Quantitative research method uses surveys and polls to gather information on a given subject. There are a variety of question types used based on a nature of the research study.

For Example: If you want to conduct a customer satisfaction quantitative research, the Net Promoter Score is one of the critically acclaimed survey questions for this purpose.

2. Distribution : Quantitative research uses email surveys as the primary mode of gathering responses to questions. Alternatively, technology has given rise to offline distribution methods for relatively remote locations using offline mobile data capture apps. For social sciences and psychological quantitative research, social media surveys are also used to gather data.

3. Statistical Analysis : Quantitative research uses a wide range of data analysis techniques such as Conjoint Analysis , Cross Tabulation and Trend Analysis .

Qualitative vs Quantitative Research

Now let’s compare the qualitative and quantitative research methods in different aspects so that you can choose the right one in your next investigation.:

1. Objective and flow of research

Quantitative research is used in data-oriented research where the objective of research design is to derive “measurable empirical evidence” based on fixed and pre-determined questions. The flow of research, is therefore, decided before the research is conducted.

Where as, qualitative research is used where the objective is research is to keep probing the respondents based on previous answers under the complete discretion of the interviewer. The flow of research is not determined and the researcher / interviewer has the liberty to frame and ask new questions.

2. Respondent sample size

Respondents or sample of a particular panel is much larger for quantitative research such that enough verifiable information is gather to reach a conclusion without opinion bias. In large scale quantitative research, sample size can be in thousands.

Where as, qualitative research inherently uses less sample size because a large sample size makes it difficult of the research to probe respondents. For instance, a typical political focus group study evaluating election candidates involves no more than 5-10 panelists.

3. Information gathering

Quantitative research uses information gathering methods that can be quantified and processed for statistical analysis techniques. Simply put – quantitative research is heavily dependent on “numbers”, data and stats.

LEARN ABOUT: Research Process Steps

Where as, qualitative research uses conversational methods to gather relevant information on a given subject.

4. Post-research response analysis and conclusions

Quantitative research uses a variety of statistical analysis methods to derive quantifiable research conclusions. These are based on mathematical processes applied on the gather data.

Where as, qualitative researc h depends on the interviewer to derive research conclusions based on qualitative conversations held with the respondents. This conclusion is effectively subjective in nature. This is why quantitative research recordings are often reviewed by senior researchers before the final research conclusion is drawn.

Differences between qualitative vs quantitative research

We hope that this information helps you choose your next research method and achieve your goals.

If you want to carry out any qualitative or qualitative research questions , ask about the tools that QuestionPro has available to help you with the qualitative data collection of the data you need. We have functions for all types of research!.

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Home » Qualitative Vs Quantitative Research

Qualitative Vs Quantitative Research

Table of Contents

Qualitative research and quantitative research are two different approaches used in conducting research. Here’s a brief explanation of the differences between the two:

Qualitative Research is exploratory research that seeks to understand a phenomenon in its natural setting from the perspective of the people involved. It uses methods like interviews, focus groups, and observation to gather data.

Quantitative Research is structured research that focuses on measuring and analyzing numerical data. It uses methods like surveys, experiments, and statistical analysis to gather and analyze data.

Data Collection

Qualitative Research uses non-numeric data, such as words, images, and observations, to gather data. This data is often subjective and can be difficult to analyze.

Quantitative Research, on the other hand, uses numerical data, such as survey responses or experimental measurements, to gather data. This data is objective and easier to analyze.

Data Analysis

Qualitative Research uses an interpretive approach to analyze data, meaning that the researcher is interested in understanding the meaning behind the data. This often involves identifying patterns, themes, and relationships in the data.

Quantitative Research, on the other hand, uses statistical analysis to identify patterns and relationships in the data. This involves using mathematical formulas and statistical tests to analyze the data.

Qualitative Research is often used to gain a deeper understanding of a phenomenon or to generate hypotheses for further research. It is commonly used in fields like anthropology, sociology, and psychology.

Quantitative Research is often used to test hypotheses or to make predictions about a phenomenon. It is commonly used in fields like economics, engineering, and biology.

Differences between Qualitative and Quantitative Research

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Frequently asked questions

What’s the difference between quantitative and qualitative methods.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Frequently asked questions: Knowledge Base

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

The literature review usually comes near the beginning of your  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

Harvard referencing uses an author–date system. Sources are cited by the author’s last name and the publication year in brackets. Each Harvard in-text citation corresponds to an entry in the alphabetised reference list at the end of the paper.

Vancouver referencing uses a numerical system. Sources are cited by a number in parentheses or superscript. Each number corresponds to a full reference at the end of the paper.

A Harvard in-text citation should appear in brackets every time you quote, paraphrase, or refer to information from a source.

The citation can appear immediately after the quotation or paraphrase, or at the end of the sentence. If you’re quoting, place the citation outside of the quotation marks but before any other punctuation like a comma or full stop.

In Harvard referencing, up to three author names are included in an in-text citation or reference list entry. When there are four or more authors, include only the first, followed by ‘ et al. ’

A bibliography should always contain every source you cited in your text. Sometimes a bibliography also contains other sources that you used in your research, but did not cite in the text.

MHRA doesn’t specify a rule about this, so check with your supervisor to find out exactly what should be included in your bibliography.

Footnote numbers should appear in superscript (e.g. 11 ). You can use the ‘Insert footnote’ button in Word to do this automatically; it’s in the ‘References’ tab at the top.

Footnotes always appear after the quote or paraphrase they relate to. MHRA generally recommends placing footnote numbers at the end of the sentence, immediately after any closing punctuation, like this. 12

In situations where this might be awkward or misleading, such as a long sentence containing multiple quotations, footnotes can also be placed at the end of a clause mid-sentence, like this; 13 note that they still come after any punctuation.

When a source has two or three authors, name all of them in your MHRA references . When there are four or more, use only the first name, followed by ‘and others’:

Note that in the bibliography, only the author listed first has their name inverted. The names of additional authors and those of translators or editors are written normally.

A citation should appear wherever you use information or ideas from a source, whether by quoting or paraphrasing its content.

In Vancouver style , you have some flexibility about where the citation number appears in the sentence – usually directly after mentioning the author’s name is best, but simply placing it at the end of the sentence is an acceptable alternative, as long as it’s clear what it relates to.

In Vancouver style , when you refer to a source with multiple authors in your text, you should only name the first author followed by ‘et al.’. This applies even when there are only two authors.

In your reference list, include up to six authors. For sources with seven or more authors, list the first six followed by ‘et al.’.

The words ‘ dissertation ’ and ‘thesis’ both refer to a large written research project undertaken to complete a degree, but they are used differently depending on the country:

• In the UK, you write a dissertation at the end of a bachelor’s or master’s degree, and you write a thesis to complete a PhD.
• In the US, it’s the other way around: you may write a thesis at the end of a bachelor’s or master’s degree, and you write a dissertation to complete a PhD.

The main difference is in terms of scale – a dissertation is usually much longer than the other essays you complete during your degree.

Another key difference is that you are given much more independence when working on a dissertation. You choose your own dissertation topic , and you have to conduct the research and write the dissertation yourself (with some assistance from your supervisor).

Dissertation word counts vary widely across different fields, institutions, and levels of education:

• An undergraduate dissertation is typically 8,000–15,000 words
• A master’s dissertation is typically 12,000–50,000 words
• A PhD thesis is typically book-length: 70,000–100,000 words

However, none of these are strict guidelines – your word count may be lower or higher than the numbers stated here. Always check the guidelines provided by your university to determine how long your own dissertation should be.

At the bachelor’s and master’s levels, the dissertation is usually the main focus of your final year. You might work on it (alongside other classes) for the entirety of the final year, or for the last six months. This includes formulating an idea, doing the research, and writing up.

A PhD thesis takes a longer time, as the thesis is the main focus of the degree. A PhD thesis might be being formulated and worked on for the whole four years of the degree program. The writing process alone can take around 18 months.

References should be included in your text whenever you use words, ideas, or information from a source. A source can be anything from a book or journal article to a website or YouTube video.

If you don’t acknowledge your sources, you can get in trouble for plagiarism .

Your university should tell you which referencing style to follow. If you’re unsure, check with a supervisor. Commonly used styles include:

• Harvard referencing , the most commonly used style in UK universities.
• MHRA , used in humanities subjects.
• APA , used in the social sciences.
• Vancouver , used in biomedicine.
• OSCOLA , used in law.

Your university may have its own referencing style guide.

If you are allowed to choose which style to follow, we recommend Harvard referencing, as it is a straightforward and widely used style.

To avoid plagiarism , always include a reference when you use words, ideas or information from a source. This shows that you are not trying to pass the work of others off as your own.

You must also properly quote or paraphrase the source. If you’re not sure whether you’ve done this correctly, you can use the Scribbr Plagiarism Checker to find and correct any mistakes.

In Harvard style , when you quote directly from a source that includes page numbers, your in-text citation must include a page number. For example: (Smith, 2014, p. 33).

You can also include page numbers to point the reader towards a passage that you paraphrased . If you refer to the general ideas or findings of the source as a whole, you don’t need to include a page number.

When you want to use a quote but can’t access the original source, you can cite it indirectly. In the in-text citation , first mention the source you want to refer to, and then the source in which you found it. For example:

It’s advisable to avoid indirect citations wherever possible, because they suggest you don’t have full knowledge of the sources you’re citing. Only use an indirect citation if you can’t reasonably gain access to the original source.

In Harvard style referencing , to distinguish between two sources by the same author that were published in the same year, you add a different letter after the year for each source:

• (Smith, 2019a)
• (Smith, 2019b)

Add ‘a’ to the first one you cite, ‘b’ to the second, and so on. Do the same in your bibliography or reference list .

To create a hanging indent for your bibliography or reference list :

• Highlight all the entries
• Click on the arrow in the bottom-right corner of the ‘Paragraph’ tab in the top menu.
• In the pop-up window, under ‘Special’ in the ‘Indentation’ section, use the drop-down menu to select ‘Hanging’.
• Then close the window with ‘OK’.

Though the terms are sometimes used interchangeably, there is a difference in meaning:

• A reference list only includes sources cited in the text – every entry corresponds to an in-text citation .
• A bibliography also includes other sources which were consulted during the research but not cited.

It’s important to assess the reliability of information found online. Look for sources from established publications and institutions with expertise (e.g. peer-reviewed journals and government agencies).

The CRAAP test (currency, relevance, authority, accuracy, purpose) can aid you in assessing sources, as can our list of credible sources . You should generally avoid citing websites like Wikipedia that can be edited by anyone – instead, look for the original source of the information in the “References” section.

You can generally omit page numbers in your in-text citations of online sources which don’t have them. But when you quote or paraphrase a specific passage from a particularly long online source, it’s useful to find an alternate location marker.

For text-based sources, you can use paragraph numbers (e.g. ‘para. 4’) or headings (e.g. ‘under “Methodology”’). With video or audio sources, use a timestamp (e.g. ‘10:15’).

In the acknowledgements of your thesis or dissertation, you should first thank those who helped you academically or professionally, such as your supervisor, funders, and other academics.

Then you can include personal thanks to friends, family members, or anyone else who supported you during the process.

Yes, it’s important to thank your supervisor(s) in the acknowledgements section of your thesis or dissertation .

Even if you feel your supervisor did not contribute greatly to the final product, you still should acknowledge them, if only for a very brief thank you. If you do not include your supervisor, it may be seen as a snub.

The acknowledgements are generally included at the very beginning of your thesis or dissertation, directly after the title page and before the abstract .

In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length.

You may acknowledge God in your thesis or dissertation acknowledgements , but be sure to follow academic convention by also thanking the relevant members of academia, as well as family, colleagues, and friends who helped you.

All level 1 and 2 headings should be included in your table of contents . That means the titles of your chapters and the main sections within them.

The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list .

Do not include the acknowledgements or abstract   in the table of contents.

To automatically insert a table of contents in Microsoft Word, follow these steps:

• Apply heading styles throughout the document.
• In the references section in the ribbon, locate the Table of Contents group.
• Click the arrow next to the Table of Contents icon and select Custom Table of Contents.
• Select which levels of headings you would like to include in the table of contents.

Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.

The table of contents in a thesis or dissertation always goes between your abstract and your introduction.

An abbreviation is a shortened version of an existing word, such as Dr for Doctor. In contrast, an acronym uses the first letter of each word to create a wholly new word, such as UNESCO (an acronym for the United Nations Educational, Scientific and Cultural Organization).

Your dissertation sometimes contains a list of abbreviations .

As a rule of thumb, write the explanation in full the first time you use an acronym or abbreviation. You can then proceed with the shortened version. However, if the abbreviation is very common (like UK or PC), then you can just use the abbreviated version straight away.

Be sure to add each abbreviation in your list of abbreviations !

If you only used a few abbreviations in your thesis or dissertation, you don’t necessarily need to include a list of abbreviations .

If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimising confusion about abbreviations unfamiliar to your reader.

A list of abbreviations is a list of all the abbreviations you used in your thesis or dissertation. It should appear at the beginning of your document, immediately after your table of contents . It should always be in alphabetical order.

Fishbone diagrams have a few different names that are used interchangeably, including herringbone diagram, cause-and-effect diagram, and Ishikawa diagram.

These are all ways to refer to the same thing– a problem-solving approach that uses a fish-shaped diagram to model possible root causes of problems and troubleshoot solutions.

Fishbone diagrams (also called herringbone diagrams, cause-and-effect diagrams, and Ishikawa diagrams) are most popular in fields of quality management. They are also commonly used in nursing and healthcare, or as a brainstorming technique for students.

Some synonyms and near synonyms of among include:

• In the company of
• In the middle of
• Surrounded by

Some synonyms and near synonyms of between  include:

• In the space separating
• In the time separating

In spite of   is a preposition used to mean ‘ regardless of ‘, ‘notwithstanding’, or ‘even though’.

It’s always used in a subordinate clause to contrast with the information given in the main clause of a sentence (e.g., ‘Amy continued to watch TV, in spite of the time’).

Despite   is a preposition used to mean ‘ regardless of ‘, ‘notwithstanding’, or ‘even though’.

It’s used in a subordinate clause to contrast with information given in the main clause of a sentence (e.g., ‘Despite the stress, Joe loves his job’).

‘Log in’ is a phrasal verb meaning ‘connect to an electronic device, system, or app’. The preposition ‘to’ is often used directly after the verb; ‘in’ and ‘to’ should be written as two separate words (e.g., ‘ log in to the app to update privacy settings’).

‘Log into’ is sometimes used instead of ‘log in to’, but this is generally considered incorrect (as is ‘login to’).

Some synonyms and near synonyms of ensure include:

• Make certain

Some synonyms and near synonyms of assure  include:

Rest assured is an expression meaning ‘you can be certain’ (e.g., ‘Rest assured, I will find your cat’). ‘Assured’ is the adjectival form of the verb assure , meaning ‘convince’ or ‘persuade’.

Some synonyms and near synonyms for council include:

There are numerous synonyms and near synonyms for the two meanings of counsel :

AI writing tools can be used to perform a variety of tasks.

Generative AI writing tools (like ChatGPT ) generate text based on human inputs and can be used for interactive learning, to provide feedback, or to generate research questions or outlines.

These tools can also be used to paraphrase or summarise text or to identify grammar and punctuation mistakes. Y ou can also use Scribbr’s free paraphrasing tool , summarising tool , and grammar checker , which are designed specifically for these purposes.

Using AI writing tools (like ChatGPT ) to write your essay is usually considered plagiarism and may result in penalisation, unless it is allowed by your university. Text generated by AI tools is based on existing texts and therefore cannot provide unique insights. Furthermore, these outputs sometimes contain factual inaccuracies or grammar mistakes.

However, AI writing tools can be used effectively as a source of feedback and inspiration for your writing (e.g., to generate research questions ). Other AI tools, like grammar checkers, can help identify and eliminate grammar and punctuation mistakes to enhance your writing.

The Scribbr Knowledge Base is a collection of free resources to help you succeed in academic research, writing, and citation. Every week, we publish helpful step-by-step guides, clear examples, simple templates, engaging videos, and more.

The Knowledge Base is for students at all levels. Whether you’re writing your first essay, working on your bachelor’s or master’s dissertation, or getting to grips with your PhD research, we’ve got you covered.

As well as the Knowledge Base, Scribbr provides many other tools and services to support you in academic writing and citation:

• Create your citations and manage your reference list with our free Reference Generators in APA and MLA style.
• Scan your paper for in-text citation errors and inconsistencies with our innovative APA Citation Checker .
• Avoid accidental plagiarism with our reliable Plagiarism Checker .
• Polish your writing and get feedback on structure and clarity with our Proofreading & Editing services .

Yes! We’re happy for educators to use our content, and we’ve even adapted some of our articles into ready-made lecture slides .

You are free to display, distribute, and adapt Scribbr materials in your classes or upload them in private learning environments like Blackboard. We only ask that you credit Scribbr for any content you use.

We’re always striving to improve the Knowledge Base. If you have an idea for a topic we should cover, or you notice a mistake in any of our articles, let us know by emailing [email protected] .

The consequences of plagiarism vary depending on the type of plagiarism and the context in which it occurs. For example, submitting a whole paper by someone else will have the most severe consequences, while accidental citation errors are considered less serious.

If you’re a student, then you might fail the course, be suspended or expelled, or be obligated to attend a workshop on plagiarism. It depends on whether it’s your first offence or you’ve done it before.

As an academic or professional, plagiarising seriously damages your reputation. You might also lose your research funding or your job, and you could even face legal consequences for copyright infringement.

Paraphrasing without crediting the original author is a form of plagiarism , because you’re presenting someone else’s ideas as if they were your own.

However, paraphrasing is not plagiarism if you correctly reference the source . This means including an in-text referencing and a full reference , formatted according to your required citation style (e.g., Harvard , Vancouver ).

As well as referencing your source, make sure that any paraphrased text is completely rewritten in your own words.

Accidental plagiarism is one of the most common examples of plagiarism . Perhaps you forgot to cite a source, or paraphrased something a bit too closely. Maybe you can’t remember where you got an idea from, and aren’t totally sure if it’s original or not.

These all count as plagiarism, even though you didn’t do it on purpose. When in doubt, make sure you’re citing your sources . Also consider running your work through a plagiarism checker tool prior to submission, which work by using advanced database software to scan for matches between your text and existing texts.

Scribbr’s Plagiarism Checker takes less than 10 minutes and can help you turn in your paper with confidence.

The accuracy depends on the plagiarism checker you use. Per our in-depth research , Scribbr is the most accurate plagiarism checker. Many free plagiarism checkers fail to detect all plagiarism or falsely flag text as plagiarism.

Plagiarism checkers work by using advanced database software to scan for matches between your text and existing texts. Their accuracy is determined by two factors: the algorithm (which recognises the plagiarism) and the size of the database (with which your document is compared).

To avoid plagiarism when summarising an article or other source, follow these two rules:

• Write the summary entirely in your own words by   paraphrasing the author’s ideas.
• Reference the source with an in-text citation and a full reference so your reader can easily find the original text.

Plagiarism can be detected by your professor or readers if the tone, formatting, or style of your text is different in different parts of your paper, or if they’re familiar with the plagiarised source.

Many universities also use   plagiarism detection software like Turnitin’s, which compares your text to a large database of other sources, flagging any similarities that come up.

It can be easier than you think to commit plagiarism by accident. Consider using a   plagiarism checker prior to submitting your essay to ensure you haven’t missed any citations.

Some examples of plagiarism include:

• Copying and pasting a Wikipedia article into the body of an assignment
• Quoting a source without including a citation
• Not paraphrasing a source properly (e.g. maintaining wording too close to the original)
• Forgetting to cite the source of an idea

The most surefire way to   avoid plagiarism is to always cite your sources . When in doubt, cite!

Global plagiarism means taking an entire work written by someone else and passing it off as your own. This can include getting someone else to write an essay or assignment for you, or submitting a text you found online as your own work.

Global plagiarism is one of the most serious types of plagiarism because it involves deliberately and directly lying about the authorship of a work. It can have severe consequences for students and professionals alike.

Verbatim plagiarism means copying text from a source and pasting it directly into your own document without giving proper credit.

If the structure and the majority of the words are the same as in the original source, then you are committing verbatim plagiarism. This is the case even if you delete a few words or replace them with synonyms.

If you want to use an author’s exact words, you need to quote the original source by putting the copied text in quotation marks and including an   in-text citation .

Patchwork plagiarism , also called mosaic plagiarism, means copying phrases, passages, or ideas from various existing sources and combining them to create a new text. This includes slightly rephrasing some of the content, while keeping many of the same words and the same structure as the original.

While this type of plagiarism is more insidious than simply copying and pasting directly from a source, plagiarism checkers like Turnitin’s can still easily detect it.

To avoid plagiarism in any form, remember to reference your sources .

Yes, reusing your own work without citation is considered self-plagiarism . This can range from resubmitting an entire assignment to reusing passages or data from something you’ve handed in previously.

Self-plagiarism often has the same consequences as other types of plagiarism . If you want to reuse content you wrote in the past, make sure to check your university’s policy or consult your professor.

If you are reusing content or data you used in a previous assignment, make sure to cite yourself. You can cite yourself the same way you would cite any other source: simply follow the directions for the citation style you are using.

Keep in mind that reusing prior content can be considered self-plagiarism , so make sure you ask your instructor or consult your university’s handbook prior to doing so.

Most institutions have an internal database of previously submitted student assignments. Turnitin can check for self-plagiarism by comparing your paper against this database. If you’ve reused parts of an assignment you already submitted, it will flag any similarities as potential plagiarism.

Online plagiarism checkers don’t have access to your institution’s database, so they can’t detect self-plagiarism of unpublished work. If you’re worried about accidentally self-plagiarising, you can use Scribbr’s Self-Plagiarism Checker to upload your unpublished documents and check them for similarities.

Plagiarism has serious consequences and can be illegal in certain scenarios.

While most of the time plagiarism in an undergraduate setting is not illegal, plagiarism or self-plagiarism in a professional academic setting can lead to legal action, including copyright infringement and fraud. Many scholarly journals do not allow you to submit the same work to more than one journal, and if you do not credit a coauthor, you could be legally defrauding them.

Even if you aren’t breaking the law, plagiarism can seriously impact your academic career. While the exact consequences of plagiarism vary by institution and severity, common consequences include a lower grade, automatically failing a course, academic suspension or probation, and even expulsion.

Self-plagiarism means recycling work that you’ve previously published or submitted as an assignment. It’s considered academic dishonesty to present something as brand new when you’ve already gotten credit and perhaps feedback for it in the past.

If you want to refer to ideas or data from previous work, be sure to cite yourself.

Academic integrity means being honest, ethical, and thorough in your academic work. To maintain academic integrity, you should avoid misleading your readers about any part of your research and refrain from offences like plagiarism and contract cheating, which are examples of academic misconduct.

Academic dishonesty refers to deceitful or misleading behavior in an academic setting. Academic dishonesty can occur intentionally or unintentionally, and it varies in severity.

It can encompass paying for a pre-written essay, cheating on an exam, or committing plagiarism . It can also include helping others cheat, copying a friend’s homework answers, or even pretending to be sick to miss an exam.

Academic dishonesty doesn’t just occur in a classroom setting, but also in research and other academic-adjacent fields.

Consequences of academic dishonesty depend on the severity of the offence and your institution’s policy. They can range from a warning for a first offence to a failing grade in a course to expulsion from your university.

For those in certain fields, such as nursing, engineering, or lab sciences, not learning fundamentals properly can directly impact the health and safety of others. For those working in academia or research, academic dishonesty impacts your professional reputation, leading others to doubt your future work.

Academic dishonesty can be intentional or unintentional, ranging from something as simple as claiming to have read something you didn’t to copying your neighbour’s answers on an exam.

You can commit academic dishonesty with the best of intentions, such as helping a friend cheat on a paper. Severe academic dishonesty can include buying a pre-written essay or the answers to a multiple-choice test, or falsifying a medical emergency to avoid taking a final exam.

Plagiarism means presenting someone else’s work as your own without giving proper credit to the original author. In academic writing, plagiarism involves using words, ideas, or information from a source without including a citation .

Plagiarism can have serious consequences , even when it’s done accidentally. To avoid plagiarism, it’s important to keep track of your sources and cite them correctly.

Common knowledge does not need to be cited. However, you should be extra careful when deciding what counts as common knowledge.

Common knowledge encompasses information that the average educated reader would accept as true without needing the extra validation of a source or citation.

Common knowledge should be widely known, undisputed, and easily verified. When in doubt, always cite your sources.

Most online plagiarism checkers only have access to public databases, whose software doesn’t allow you to compare two documents for plagiarism.

However, in addition to our Plagiarism Checker , Scribbr also offers an Self-Plagiarism Checker . This is an add-on tool that lets you compare your paper with unpublished or private documents. This way you can rest assured that you haven’t unintentionally plagiarised or self-plagiarised .

Compare two sources for plagiarism

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation)

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

The Scribbr Reference Generator is developed using the open-source Citation Style Language (CSL) project and Frank Bennett’s citeproc-js . It’s the same technology used by dozens of other popular citation tools, including Mendeley and Zotero.

You can find all the citation styles and locales used in the Scribbr Reference Generator in our publicly accessible repository on Github .

To paraphrase effectively, don’t just take the original sentence and swap out some of the words for synonyms. Instead, try:

• Reformulating the sentence (e.g., change active to passive , or start from a different point)
• Combining information from multiple sentences into one
• Leaving out information from the original that isn’t relevant to your point
• Using synonyms where they don’t distort the meaning

The main point is to ensure you don’t just copy the structure of the original text, but instead reformulate the idea in your own words.

Plagiarism means using someone else’s words or ideas and passing them off as your own. Paraphrasing means putting someone else’s ideas into your own words.

So when does paraphrasing count as plagiarism?

• Paraphrasing is plagiarism if you don’t properly credit the original author.
• Paraphrasing is plagiarism if your text is too close to the original wording (even if you cite the source). If you directly copy a sentence or phrase, you should quote it instead.
• Paraphrasing  is not plagiarism if you put the author’s ideas completely into your own words and properly reference the source .

To present information from other sources in academic writing , it’s best to paraphrase in most cases. This shows that you’ve understood the ideas you’re discussing and incorporates them into your text smoothly.

It’s appropriate to quote when:

• Changing the phrasing would distort the meaning of the original text
• You want to discuss the author’s language choices (e.g., in literary analysis )
• You’re presenting a precise definition
• You’re looking in depth at a specific claim

A quote is an exact copy of someone else’s words, usually enclosed in quotation marks and credited to the original author or speaker.

Every time you quote a source , you must include a correctly formatted in-text citation . This looks slightly different depending on the citation style .

For example, a direct quote in APA is cited like this: ‘This is a quote’ (Streefkerk, 2020, p. 5).

Every in-text citation should also correspond to a full reference at the end of your paper.

In scientific subjects, the information itself is more important than how it was expressed, so quoting should generally be kept to a minimum. In the arts and humanities, however, well-chosen quotes are often essential to a good paper.

In social sciences, it varies. If your research is mainly quantitative , you won’t include many quotes, but if it’s more qualitative , you may need to quote from the data you collected .

As a general guideline, quotes should take up no more than 5–10% of your paper. If in doubt, check with your instructor or supervisor how much quoting is appropriate in your field.

If you’re quoting from a text that paraphrases or summarises other sources and cites them in parentheses , APA  recommends retaining the citations as part of the quote:

• Smith states that ‘the literature on this topic (Jones, 2015; Sill, 2019; Paulson, 2020) shows no clear consensus’ (Smith, 2019, p. 4).

Footnote or endnote numbers that appear within quoted text should be omitted.

If you want to cite an indirect source (one you’ve only seen quoted in another source), either locate the original source or use the phrase ‘as cited in’ in your citation.

A block quote is a long quote formatted as a separate ‘block’ of text. Instead of using quotation marks , you place the quote on a new line, and indent the entire quote to mark it apart from your own words.

APA uses block quotes for quotes that are 40 words or longer.

A credible source should pass the CRAAP test  and follow these guidelines:

• The information should be up to date and current.
• The author and publication should be a trusted authority on the subject you are researching.
• The sources the author cited should be easy to find, clear, and unbiased.
• For a web source, the URL and layout should signify that it is trustworthy.

Common examples of primary sources include interview transcripts , photographs, novels, paintings, films, historical documents, and official statistics.

Anything you directly analyze or use as first-hand evidence can be a primary source, including qualitative or quantitative data that you collected yourself.

Common examples of secondary sources include academic books, journal articles , reviews, essays , and textbooks.

Anything that summarizes, evaluates or interprets primary sources can be a secondary source. If a source gives you an overview of background information or presents another researcher’s ideas on your topic, it is probably a secondary source.

To determine if a source is primary or secondary, ask yourself:

• Was the source created by someone directly involved in the events you’re studying (primary), or by another researcher (secondary)?
• Does the source provide original information (primary), or does it summarize information from other sources (secondary)?
• Are you directly analyzing the source itself (primary), or only using it for background information (secondary)?

Some types of sources are nearly always primary: works of art and literature, raw statistical data, official documents and records, and personal communications (e.g. letters, interviews ). If you use one of these in your research, it is probably a primary source.

Primary sources are often considered the most credible in terms of providing evidence for your argument, as they give you direct evidence of what you are researching. However, it’s up to you to ensure the information they provide is reliable and accurate.

Always make sure to properly cite your sources to avoid plagiarism .

A fictional movie is usually a primary source. A documentary can be either primary or secondary depending on the context.

If you are directly analysing some aspect of the movie itself – for example, the cinematography, narrative techniques, or social context – the movie is a primary source.

If you use the movie for background information or analysis about your topic – for example, to learn about a historical event or a scientific discovery – the movie is a secondary source.

Whether it’s primary or secondary, always properly cite the movie in the citation style you are using. Learn how to create an MLA movie citation or an APA movie citation .

Articles in newspapers and magazines can be primary or secondary depending on the focus of your research.

In historical studies, old articles are used as primary sources that give direct evidence about the time period. In social and communication studies, articles are used as primary sources to analyse language and social relations (for example, by conducting content analysis or discourse analysis ).

If you are not analysing the article itself, but only using it for background information or facts about your topic, then the article is a secondary source.

In academic writing , there are three main situations where quoting is the best choice:

• To analyse the author’s language (e.g., in a literary analysis essay )
• To give evidence from primary sources
• To accurately present a precise definition or argument

Don’t overuse quotes; your own voice should be dominant. If you just want to provide information from a source, it’s usually better to paraphrase or summarise .

Your list of tables and figures should go directly after your table of contents in your thesis or dissertation.

Lists of figures and tables are often not required, and they aren’t particularly common. They specifically aren’t required for APA Style, though you should be careful to follow their other guidelines for figures and tables .

If you have many figures and tables in your thesis or dissertation, include one may help you stay organised. Your educational institution may require them, so be sure to check their guidelines.

Copyright information can usually be found wherever the table or figure was published. For example, for a diagram in a journal article , look on the journal’s website or the database where you found the article. Images found on sites like Flickr are listed with clear copyright information.

If you find that permission is required to reproduce the material, be sure to contact the author or publisher and ask for it.

A list of figures and tables compiles all of the figures and tables that you used in your thesis or dissertation and displays them with the page number where they can be found.

APA doesn’t require you to include a list of tables or a list of figures . However, it is advisable to do so if your text is long enough to feature a table of contents and it includes a lot of tables and/or figures .

A list of tables and list of figures appear (in that order) after your table of contents, and are presented in a similar way.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. Your glossary only needs to include terms that your reader may not be familiar with, and is intended to enhance their understanding of your work.

Definitional terms often fall into the category of common knowledge , meaning that they don’t necessarily have to be cited. This guidance can apply to your thesis or dissertation glossary as well.

However, if you’d prefer to cite your sources , you can follow guidance for citing dictionary entries in MLA or APA style for your glossary.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, an index is a list of the contents of your work organised by page number.

Glossaries are not mandatory, but if you use a lot of technical or field-specific terms, it may improve readability to add one to your thesis or dissertation. Your educational institution may also require them, so be sure to check their specific guidelines.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, dictionaries are more general collections of words.

The title page of your thesis or dissertation should include your name, department, institution, degree program, and submission date.

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

Usually, no title page is needed in an MLA paper . A header is generally included at the top of the first page instead. The exceptions are when:

• Your instructor requires one, or
• Your paper is a group project

In those cases, you should use a title page instead of a header, listing the same information but on a separate page.

When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organise your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation, such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

While a theoretical framework describes the theoretical underpinnings of your work based on existing research, a conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.

A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You’ll likely need both in your dissertation .

A theoretical framework can sometimes be integrated into a  literature review chapter , but it can also be included as its own chapter or section in your dissertation . As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

An abstract is a concise summary of an academic text (such as a journal article or dissertation ). It serves two main purposes:

• To help potential readers determine the relevance of your paper for their own research.
• To communicate your key findings to those who don’t have time to read the whole paper.

Abstracts are often indexed along with keywords on academic databases, so they make your work more easily findable. Since the abstract is the first thing any reader sees, it’s important that it clearly and accurately summarises the contents of your paper.

The abstract is the very last thing you write. You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis or paper.

Avoid citing sources in your abstract . There are two reasons for this:

• The abstract should focus on your original research, not on the work of others.
• The abstract should be self-contained and fully understandable without reference to other sources.

There are some circumstances where you might need to mention other sources in an abstract: for example, if your research responds directly to another study or focuses on the work of a single theorist. In general, though, don’t include citations unless absolutely necessary.

The abstract appears on its own page, after the title page and acknowledgements but before the table of contents .

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

A noun is a word that represents a person, thing, concept, or place (e.g., ‘John’, ‘house’, ‘affinity’, ‘river’). Most sentences contain at least one noun or pronoun .

Nouns are often, but not always, preceded by an article (‘the’, ‘a’, or ‘an’) and/or another determiner such as an adjective.

There are many ways to categorize nouns into various types, and the same noun can fall into multiple categories or even change types depending on context.

Some of the main types of nouns are:

• Common nouns and proper nouns
• Countable and uncountable nouns
• Concrete and abstract nouns
• Collective nouns
• Possessive nouns
• Attributive nouns
• Appositive nouns
• Generic nouns

Pronouns are words like ‘I’, ‘she’, and ‘they’ that are used in a similar way to nouns . They stand in for a noun that has already been mentioned or refer to yourself and other people.

Pronouns can function just like nouns as the head of a noun phrase and as the subject or object of a verb. However, pronouns change their forms (e.g., from ‘I’ to ‘me’) depending on the grammatical context they’re used in, whereas nouns usually don’t.

Common nouns are words for types of things, people, and places, such as ‘dog’, ‘professor’, and ‘city’. They are not capitalised and are typically used in combination with articles and other determiners.

Proper nouns are words for specific things, people, and places, such as ‘Max’, ‘Dr Prakash’, and ‘London’. They are always capitalised and usually aren’t combined with articles and other determiners.

A proper adjective is an adjective that was derived from a proper noun and is therefore capitalised .

Proper adjectives include words for nationalities, languages, and ethnicities (e.g., ‘Japanese’, ‘Inuit’, ‘French’) and words derived from people’s names (e.g., ‘Bayesian’, ‘Orwellian’).

The names of seasons (e.g., ‘spring’) are treated as common nouns in English and therefore not capitalised . People often assume they are proper nouns, but this is an error.

The names of days and months, however, are capitalised since they’re treated as proper nouns in English (e.g., ‘Wednesday’, ‘January’).

No, as a general rule, academic concepts, disciplines, theories, models, etc. are treated as common nouns , not proper nouns , and therefore not capitalised . For example, ‘five-factor model of personality’ or ‘analytic philosophy’.

However, proper nouns that appear within the name of an academic concept (such as the name of the inventor) are capitalised as usual. For example, ‘Darwin’s theory of evolution’ or ‘ Student’s t table ‘.

Collective nouns are most commonly treated as singular (e.g., ‘the herd is grazing’), but usage differs between US and UK English :

• In US English, it’s standard to treat all collective nouns as singular, even when they are plural in appearance (e.g., ‘The Rolling Stones is …’). Using the plural form is usually seen as incorrect.
• In UK English, collective nouns can be treated as singular or plural depending on context. It’s quite common to use the plural form, especially when the noun looks plural (e.g., ‘The Rolling Stones are …’).

The plural of “crisis” is “crises”. It’s a loanword from Latin and retains its original Latin plural noun form (similar to “analyses” and “bases”). It’s wrong to write “crisises”.

For example, you might write “Several crises destabilized the regime.”

Normally, the plural of “fish” is the same as the singular: “fish”. It’s one of a group of irregular plural nouns in English that are identical to the corresponding singular nouns (e.g., “moose”, “sheep”). For example, you might write “The fish scatter as the shark approaches.”

If you’re referring to several species of fish, though, the regular plural “fishes” is often used instead. For example, “The aquarium contains many different fishes , including trout and carp.”

The correct plural of “octopus” is “octopuses”.

People often write “octopi” instead because they assume that the plural noun is formed in the same way as Latin loanwords such as “fungus/fungi”. But “octopus” actually comes from Greek, where its original plural is “octopodes”. In English, it instead has the regular plural form “octopuses”.

For example, you might write “There are four octopuses in the aquarium.”

The plural of “moose” is the same as the singular: “moose”. It’s one of a group of plural nouns in English that are identical to the corresponding singular nouns. So it’s wrong to write “mooses”.

For example, you might write “There are several moose in the forest.”

Bias in research affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.

Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behaviour and external factors (difficult circumstances) to justify the same behaviour in themselves.

Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews . These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.

Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen either because people are not willing or not able to participate.

In research, demand characteristics are cues that might indicate the aim of a study to participants. These cues can lead to participants changing their behaviors or responses based on what they think the research is about.

Demand characteristics are common problems in psychology experiments and other social science studies because they can bias your research findings.

Demand characteristics are a type of extraneous variable that can affect the outcomes of the study. They can invalidate studies by providing an alternative explanation for the results.

These cues may nudge participants to consciously or unconsciously change their responses, and they pose a threat to both internal and external validity . You can’t be sure that your independent variable manipulation worked, or that your findings can be applied to other people or settings.

You can control demand characteristics by taking a few precautions in your research design and materials.

Use these measures:

• Deception: Hide the purpose of the study from participants
• Between-groups design : Give each participant only one independent variable treatment
• Double-blind design : Conceal the assignment of groups from participants and yourself
• Implicit measures: Use indirect or hidden measurements for your variables

Some attrition is normal and to be expected in research. However, the type of attrition is important because systematic research bias can distort your findings. Attrition bias can lead to inaccurate results because it affects internal and/or external validity .

To avoid attrition bias , applying some of these measures can help you reduce participant dropout (attrition) by making it easy and appealing for participants to stay.

• Provide compensation (e.g., cash or gift cards) for attending every session
• Minimise the number of follow-ups as much as possible
• Make all follow-ups brief, flexible, and convenient for participants
• Send participants routine reminders to schedule follow-ups
• Recruit more participants than you need for your sample (oversample)
• Maintain detailed contact information so you can get in touch with participants even if they move

If you have a small amount of attrition bias , you can use a few statistical methods to try to make up for this research bias .

Multiple imputation involves using simulations to replace the missing data with likely values. Alternatively, you can use sample weighting to make up for the uneven balance of participants in your sample.

Placebos are used in medical research for new medication or therapies, called clinical trials. In these trials some people are given a placebo, while others are given the new medication being tested.

The purpose is to determine how effective the new medication is: if it benefits people beyond a predefined threshold as compared to the placebo, it’s considered effective.

Although there is no definite answer to what causes the placebo effect , researchers propose a number of explanations such as the power of suggestion, doctor-patient interaction, classical conditioning, etc.

Belief bias and confirmation bias are both types of cognitive bias that impact our judgment and decision-making.

Confirmation bias relates to how we perceive and judge evidence. We tend to seek out and prefer information that supports our preexisting beliefs, ignoring any information that contradicts those beliefs.

Belief bias describes the tendency to judge an argument based on how plausible the conclusion seems to us, rather than how much evidence is provided to support it during the course of the argument.

Positivity bias is phenomenon that occurs when a person judges individual members of a group positively, even when they have negative impressions or judgments of the group as a whole. Positivity bias is closely related to optimism bias , or the e xpectation that things will work out well, even if rationality suggests that problems are inevitable in life.

Perception bias is a problem because it prevents us from seeing situations or people objectively. Rather, our expectations, beliefs, or emotions interfere with how we interpret reality. This, in turn, can cause us to misjudge ourselves or others. For example, our prejudices can interfere with whether we perceive people’s faces as friendly or unfriendly.

There are many ways to categorize adjectives into various types. An adjective can fall into one or more of these categories depending on how it is used.

Some of the main types of adjectives are:

• Attributive adjectives
• Predicative adjectives
• Comparative adjectives
• Superlative adjectives
• Coordinate adjectives
• Appositive adjectives
• Compound adjectives
• Participial adjectives
• Proper adjectives
• Denominal adjectives
• Nominal adjectives

Cardinal numbers (e.g., one, two, three) can be placed before a noun to indicate quantity (e.g., one apple). While these are sometimes referred to as ‘numeral adjectives ‘, they are more accurately categorised as determiners or quantifiers.

Proper adjectives are adjectives formed from a proper noun (i.e., the name of a specific person, place, or thing) that are used to indicate origin. Like proper nouns, proper adjectives are always capitalised (e.g., Newtonian, Marxian, African).

The cost of proofreading depends on the type and length of text, the turnaround time, and the level of services required. Most proofreading companies charge per word or page, while freelancers sometimes charge an hourly rate.

For proofreading alone, which involves only basic corrections of typos and formatting mistakes, you might pay as little as £0.01 per word, but in many cases, your text will also require some level of editing , which costs slightly more.

It’s often possible to purchase combined proofreading and editing services and calculate the price in advance based on your requirements.

Then and than are two commonly confused words . In the context of ‘better than’, you use ‘than’ with an ‘a’.

• Julie is better than Jesse.
• I’d rather spend my time with you than with him.
• I understand Eoghan’s point of view better than Claudia’s.

Use to and used to are commonly confused words . In the case of ‘used to do’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.

• I used to do laundry once a week.
• They used to do each other’s hair.
• We used to do the dishes every day .

There are numerous synonyms and near synonyms for the various meanings of “ favour ”:

There are numerous synonyms and near synonyms for the two meanings of “ favoured ”:

No one (two words) is an indefinite pronoun meaning ‘nobody’. People sometimes mistakenly write ‘noone’, but this is incorrect and should be avoided. ‘No-one’, with a hyphen, is also acceptable in UK English .

Nobody and no one are both indefinite pronouns meaning ‘no person’. They can be used interchangeably (e.g., ‘nobody is home’ means the same as ‘no one is home’).

Some synonyms and near synonyms of  every time include:

• Without exception

‘Everytime’ is sometimes used to mean ‘each time’ or ‘whenever’. However, this is incorrect and should be avoided. The correct phrase is every time   (two words).

Yes, the conjunction because is a compound word , but one with a long history. It originates in Middle English from the preposition “bi” (“by”) and the noun “cause”. Over time, the open compound “bi cause” became the closed compound “because”, which we use today.

Though it’s spelled this way now, the verb “be” is not one of the words that makes up “because”.

Yes, today is a compound word , but a very old one. It wasn’t originally formed from the preposition “to” and the noun “day”; rather, it originates from their Old English equivalents, “tō” and “dæġe”.

In the past, it was sometimes written as a hyphenated compound: “to-day”. But the hyphen is no longer included; it’s always “today” now (“to day” is also wrong).

IEEE citation format is defined by the Institute of Electrical and Electronics Engineers and used in their publications.

It’s also a widely used citation style for students in technical fields like electrical and electronic engineering, computer science, telecommunications, and computer engineering.

An IEEE in-text citation consists of a number in brackets at the relevant point in the text, which points the reader to the right entry in the numbered reference list at the end of the paper. For example, ‘Smith [1] states that …’

A location marker such as a page number is also included within the brackets when needed: ‘Smith [1, p. 13] argues …’

The IEEE reference page consists of a list of references numbered in the order they were cited in the text. The title ‘References’ appears in bold at the top, either left-aligned or centered.

The numbers appear in square brackets on the left-hand side of the page. The reference entries are indented consistently to separate them from the numbers. Entries are single-spaced, with a normal paragraph break between them.

If you cite the same source more than once in your writing, use the same number for all of the IEEE in-text citations for that source, and only include it on the IEEE reference page once. The source is numbered based on the first time you cite it.

For example, the fourth source you cite in your paper is numbered [4]. If you cite it again later, you still cite it as [4]. You can cite different parts of the source each time by adding page numbers [4, p. 15].

A verb is a word that indicates a physical action (e.g., ‘drive’), a mental action (e.g., ‘think’) or a state of being (e.g., ‘exist’). Every sentence contains a verb.

Verbs are almost always used along with a noun or pronoun to describe what the noun or pronoun is doing.

There are many ways to categorize verbs into various types. A verb can fall into one or more of these categories depending on how it is used.

Some of the main types of verbs are:

• Regular verbs
• Irregular verbs
• Transitive verbs
• Intransitive verbs
• Dynamic verbs
• Stative verbs
• Linking verbs
• Auxiliary verbs
• Modal verbs
• Phrasal verbs

Regular verbs are verbs whose simple past and past participle are formed by adding the suffix ‘-ed’ (e.g., ‘walked’).

Irregular verbs are verbs that form their simple past and past participles in some way other than by adding the suffix ‘-ed’ (e.g., ‘sat’).

The indefinite articles a and an are used to refer to a general or unspecified version of a noun (e.g., a house). Which indefinite article you use depends on the pronunciation of the word that follows it.

• A is used for words that begin with a consonant sound (e.g., a bear).
• An is used for words that begin with a vowel sound (e.g., an eagle).

Indefinite articles can only be used with singular countable nouns . Like definite articles, they are a type of determiner .

Editing and proofreading are different steps in the process of revising a text.

Editing comes first, and can involve major changes to content, structure and language. The first stages of editing are often done by authors themselves, while a professional editor makes the final improvements to grammar and style (for example, by improving sentence structure and word choice ).

Proofreading is the final stage of checking a text before it is published or shared. It focuses on correcting minor errors and inconsistencies (for example, in punctuation and capitalization ). Proofreaders often also check for formatting issues, especially in print publishing.

Whether you’re publishing a blog, submitting a research paper , or even just writing an important email, there are a few techniques you can use to make sure it’s error-free:

• Take a break : Set your work aside for at least a few hours so that you can look at it with fresh eyes.
• Proofread a printout : Staring at a screen for too long can cause fatigue – sit down with a pen and paper to check the final version.
• Use digital shortcuts : Take note of any recurring mistakes (for example, misspelling a particular word, switching between US and UK English , or inconsistently capitalizing a term), and use Find and Replace to fix it throughout the document.

If you want to be confident that an important text is error-free, it might be worth choosing a professional proofreading service instead.

There are many different routes to becoming a professional proofreader or editor. The necessary qualifications depend on the field – to be an academic or scientific proofreader, for example, you will need at least a university degree in a relevant subject.

For most proofreading jobs, experience and demonstrated skills are more important than specific qualifications. Often your skills will be tested as part of the application process.

To learn practical proofreading skills, you can choose to take a course with a professional organisation such as the Society for Editors and Proofreaders . Alternatively, you can apply to companies that offer specialised on-the-job training programmes, such as the Scribbr Academy .

Though they’re pronounced the same, there’s a big difference in meaning between its and it’s .

• ‘The cat ate its food’.
• ‘It’s almost Christmas’.

Its and it’s are often confused, but its (without apostrophe) is the possessive form of ‘it’ (e.g., its tail, its argument, its wing). You use ‘its’ instead of ‘his’ and ‘her’ for neuter, inanimate nouns.

Then and than are two commonly confused words with different meanings and grammatical roles.

• Then (pronounced with a short ‘e’ sound) refers to time. It’s often an adverb , but it can also be used as a noun meaning ‘that time’ and as an adjective referring to a previous status.
• Than (pronounced with a short ‘a’ sound) is used for comparisons. Grammatically, it usually functions as a conjunction , but sometimes it’s a preposition .

Use to and used to are commonly confused words . In the case of ‘used to be’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.

• I used to be the new coworker.
• There used to be 4 cookies left.
• We used to walk to school every day .

A grammar checker is a tool designed to automatically check your text for spelling errors, grammatical issues, punctuation mistakes , and problems with sentence structure . You can check out our analysis of the best free grammar checkers to learn more.

A paraphrasing tool edits your text more actively, changing things whether they were grammatically incorrect or not. It can paraphrase your sentences to make them more concise and readable or for other purposes. You can check out our analysis of the best free paraphrasing tools to learn more.

Some tools available online combine both functions. Others, such as QuillBot , have separate grammar checker and paraphrasing tools. Be aware of what exactly the tool you’re using does to avoid introducing unwanted changes.

Good grammar is the key to expressing yourself clearly and fluently, especially in professional communication and academic writing . Word processors, browsers, and email programs typically have built-in grammar checkers, but they’re quite limited in the kinds of problems they can fix.

If you want to go beyond detecting basic spelling errors, there are many online grammar checkers with more advanced functionality. They can often detect issues with punctuation , word choice, and sentence structure that more basic tools would miss.

Not all of these tools are reliable, though. You can check out our research into the best free grammar checkers to explore the options.

Our research indicates that the best free grammar checker available online is the QuillBot grammar checker .

We tested 10 of the most popular checkers with the same sample text (containing 20 grammatical errors) and found that QuillBot easily outperformed the competition, scoring 18 out of 20, a drastic improvement over the second-place score of 13 out of 20.

It even appeared to outperform the premium versions of other grammar checkers, despite being entirely free.

A teacher’s aide is a person who assists in teaching classes but is not a qualified teacher. Aide is a noun meaning ‘assistant’, so it will always refer to a person.

‘Teacher’s aid’ is incorrect.

A visual aid is an instructional device (e.g., a photo, a chart) that appeals to vision to help you understand written or spoken information. Aid is often placed after an attributive noun or adjective (like ‘visual’) that describes the type of help provided.

‘Visual aide’ is incorrect.

A job aid is an instructional tool (e.g., a checklist, a cheat sheet) that helps you work efficiently. Aid is a noun meaning ‘assistance’. It’s often placed after an adjective or attributive noun (like ‘job’) that describes the specific type of help provided.

‘Job aide’ is incorrect.

There are numerous synonyms for the various meanings of truly :

Yours truly is a phrase used at the end of a formal letter or email. It can also be used (typically in a humorous way) as a pronoun to refer to oneself (e.g., ‘The dinner was cooked by yours truly ‘). The latter usage should be avoided in formal writing.

It’s formed by combining the second-person possessive pronoun ‘yours’ with the adverb ‘ truly ‘.

A pathetic fallacy can be a short phrase or a whole sentence and is often used in novels and poetry. Pathetic fallacies serve multiple purposes, such as:

• Conveying the emotional state of the characters or the narrator
• Creating an atmosphere or set the mood of a scene
• Foreshadowing events to come
• Giving texture and vividness to a piece of writing
• Communicating emotion to the reader in a subtle way, by describing the external world.
• Bringing inanimate objects to life so that they seem more relatable.

AMA citation format is a citation style designed by the American Medical Association. It’s frequently used in the field of medicine.

You may be told to use AMA style for your student papers. You will also have to follow this style if you’re submitting a paper to a journal published by the AMA.

An AMA in-text citation consists of the number of the relevant reference on your AMA reference page , written in superscript 1 at the point in the text where the source is used.

It may also include the page number or range of the relevant material in the source (e.g., the part you quoted 2(p46) ). Multiple sources can be cited at one point, presented as a range or list (with no spaces 3,5–9 ).

An AMA reference usually includes the author’s last name and initials, the title of the source, information about the publisher or the publication it’s contained in, and the publication date. The specific details included, and the formatting, depend on the source type.

References in AMA style are presented in numerical order (numbered by the order in which they were first cited in the text) on your reference page. A source that’s cited repeatedly in the text still only appears once on the reference page.

An AMA in-text citation just consists of the number of the relevant entry on your AMA reference page , written in superscript at the point in the text where the source is referred to.

You don’t need to mention the author of the source in your sentence, but you can do so if you want. It’s not an official part of the citation, but it can be useful as part of a signal phrase introducing the source.

On your AMA reference page , author names are written with the last name first, followed by the initial(s) of their first name and middle name if mentioned.

There’s a space between the last name and the initials, but no space or punctuation between the initials themselves. The names of multiple authors are separated by commas , and the whole list ends in a period, e.g., ‘Andreessen F, Smith PW, Gonzalez E’.

The names of up to six authors should be listed for each source on your AMA reference page , separated by commas . For a source with seven or more authors, you should list the first three followed by ‘ et al’ : ‘Isidore, Gilbert, Gunvor, et al’.

In the text, mentioning author names is optional (as they aren’t an official part of AMA in-text citations ). If you do mention them, though, you should use the first author’s name followed by ‘et al’ when there are three or more : ‘Isidore et al argue that …’

Note that according to AMA’s rather minimalistic punctuation guidelines, there’s no period after ‘et al’ unless it appears at the end of a sentence. This is different from most other styles, where there is normally a period.

Yes, you should normally include an access date in an AMA website citation (or when citing any source with a URL). This is because webpages can change their content over time, so it’s useful for the reader to know when you accessed the page.

When a publication or update date is provided on the page, you should include it in addition to the access date. The access date appears second in this case, e.g., ‘Published June 19, 2021. Accessed August 29, 2022.’

Don’t include an access date when citing a source with a DOI (such as in an AMA journal article citation ).

Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.

However, for other variables, you can choose the level of measurement . For example, income is a variable that can be recorded on an ordinal or a ratio scale:

• At an ordinal level , you could create 5 income groupings and code the incomes that fall within them from 1–5.
• At a ratio level , you would record exact numbers for income.

If you have a choice, the ratio level is always preferable because you can analyse data in more ways. The higher the level of measurement, the more precise your data is.

The level at which you measure a variable determines how you can analyse your data.

Depending on the level of measurement , you can perform different descriptive statistics to get an overall summary of your data and inferential statistics to see if your results support or refute your hypothesis .

Levels of measurement tell you how precisely variables are recorded. There are 4 levels of measurement, which can be ranked from low to high:

• Nominal : the data can only be categorised.
• Ordinal : the data can be categorised and ranked.
• Interval : the data can be categorised and ranked, and evenly spaced.
• Ratio : the data can be categorised, ranked, evenly spaced and has a natural zero.

Statistical analysis is the main method for analyzing quantitative research data . It uses probabilities and models to test predictions about a population from sample data.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

As the degrees of freedom increase, Student’s t distribution becomes less leptokurtic , meaning that the probability of extreme values decreases. The distribution becomes more and more similar to a standard normal distribution .

When there are only one or two degrees of freedom , the chi-square distribution is shaped like a backwards ‘J’. When there are three or more degrees of freedom, the distribution is shaped like a right-skewed hump. As the degrees of freedom increase, the hump becomes less right-skewed and the peak of the hump moves to the right. The distribution becomes more and more similar to a normal distribution .

‘Looking forward in hearing from you’ is an incorrect version of the phrase looking forward to hearing from you . The phrasal verb ‘looking forward to’ always needs the preposition ‘to’, not ‘in’.

• I am looking forward in hearing from you.
• I am looking forward to hearing from you.

Some synonyms and near synonyms for the expression looking forward to hearing from you include:

• Eagerly awaiting your response
• Hoping to hear from you soon
• It would be great to hear back from you
• Thanks in advance for your reply

People sometimes mistakenly write ‘looking forward to hear from you’, but this is incorrect. The correct phrase is looking forward to hearing from you .

The phrasal verb ‘look forward to’ is always followed by a direct object, the thing you’re looking forward to. As the direct object has to be a noun phrase , it should be the gerund ‘hearing’, not the verb ‘hear’.

• I’m looking forward to hear from you soon.
• I’m looking forward to hearing from you soon.

Traditionally, the sign-off Yours sincerely is used in an email message or letter when you are writing to someone you have interacted with before, not a complete stranger.

Yours faithfully is used instead when you are writing to someone you have had no previous correspondence with, especially if you greeted them as ‘ Dear Sir or Madam ’.

Just checking in   is a standard phrase used to start an email (or other message) that’s intended to ask someone for a response or follow-up action in a friendly, informal way. However, it’s a cliché opening that can come across as passive-aggressive, so we recommend avoiding it in favor of a more direct opening like “We previously discussed …”

In a more personal context, you might encounter “just checking in” as part of a longer phrase such as “I’m just checking in to see how you’re doing”. In this case, it’s not asking the other person to do anything but rather asking about their well-being (emotional or physical) in a friendly way.

“Earliest convenience” is part of the phrase at your earliest convenience , meaning “as soon as you can”. 

It’s typically used to end an email in a formal context by asking the recipient to do something when it’s convenient for them to do so.

ASAP is an abbreviation of the phrase “as soon as possible”. 

It’s typically used to indicate a sense of urgency in highly informal contexts (e.g., “Let me know ASAP if you need me to drive you to the airport”).

“ASAP” should be avoided in more formal correspondence. Instead, use an alternative like at your earliest convenience .

Some synonyms and near synonyms of the verb   compose   (meaning “to make up”) are:

People increasingly use “comprise” as a synonym of “compose.” However, this is normally still seen as a mistake, and we recommend avoiding it in your academic writing . “Comprise” traditionally means “to be made up of,” not “to make up.”

Some synonyms and near synonyms of the verb comprise are:

• Be composed of
• Be made up of

People increasingly use “comprise” interchangeably with “compose,” meaning that they consider words like “compose,” “constitute,” and “form” to be synonymous with “comprise.” However, this is still normally regarded as an error, and we advise against using these words interchangeably in academic writing .

A fallacy is a mistaken belief, particularly one based on unsound arguments or one that lacks the evidence to support it. Common types of fallacy that may compromise the quality of your research are:

• Correlation/causation fallacy: Claiming that two events that occur together have a cause-and-effect relationship even though this can’t be proven
• Ecological fallacy : Making inferences about the nature of individuals based on aggregate data for the group
• The sunk cost fallacy : Following through on a project or decision because we have already invested time, effort, or money into it, even if the current costs outweigh the benefits
• The base-rate fallacy : Ignoring base-rate or statistically significant information, such as sample size or the relative frequency of an event, in favor of  less relevant information e.g., pertaining to a single case, or a small number of cases
• The planning fallacy : Underestimating the time needed to complete a future task, even when we know that similar tasks in the past have taken longer than planned

The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.

For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias or positivity bias.

Although both red herring fallacy and straw man fallacy are logical fallacies or reasoning errors, they denote different attempts to “win” an argument. More specifically:

• A red herring fallacy refers to an attempt to change the subject and divert attention from the original issue. In other words, a seemingly solid but ultimately irrelevant argument is introduced into the discussion, either on purpose or by mistake.
• A straw man argument involves the deliberate distortion of another person’s argument. By oversimplifying or exaggerating it, the other party creates an easy-to-refute argument and then attacks it.

The red herring fallacy is a problem because it is flawed reasoning. It is a distraction device that causes people to become sidetracked from the main issue and draw wrong conclusions.

Although a red herring may have some kernel of truth, it is used as a distraction to keep our eyes on a different matter. As a result, it can cause us to accept and spread misleading information.

The sunk cost fallacy and escalation of commitment (or commitment bias ) are two closely related terms. However, there is a slight difference between them:

• Escalation of commitment (aka commitment bias ) is the tendency to be consistent with what we have already done or said we will do in the past, especially if we did so in public. In other words, it is an attempt to save face and appear consistent.
• Sunk cost fallacy is the tendency to stick with a decision or a plan even when it’s failing. Because we have already invested valuable time, money, or energy, quitting feels like these resources were wasted.

In other words, escalating commitment is a manifestation of the sunk cost fallacy: an irrational escalation of commitment frequently occurs when people refuse to accept that the resources they’ve already invested cannot be recovered. Instead, they insist on more spending to justify the initial investment (and the incurred losses).

When you are faced with a straw man argument , the best way to respond is to draw attention to the fallacy and ask your discussion partner to show how your original statement and their distorted version are the same. Since these are different, your partner will either have to admit that their argument is invalid or try to justify it by using more flawed reasoning, which you can then attack.

The straw man argument is a problem because it occurs when we fail to take an opposing point of view seriously. Instead, we intentionally misrepresent our opponent’s ideas and avoid genuinely engaging with them. Due to this, resorting to straw man fallacy lowers the standard of constructive debate.

A straw man argument is a distorted (and weaker) version of another person’s argument that can easily be refuted (e.g., when a teacher proposes that the class spend more time on math exercises, a parent complains that the teacher doesn’t care about reading and writing).

This is a straw man argument because it misrepresents the teacher’s position, which didn’t mention anything about cutting down on reading and writing. The straw man argument is also known as the straw man fallacy .

A slippery slope argument is not always a fallacy.

• When someone claims adopting a certain policy or taking a certain action will automatically lead to a series of other policies or actions also being taken, this is a slippery slope argument.
• If they don’t show a causal connection between the advocated policy and the consequent policies, then they commit a slippery slope fallacy .

There are a number of ways you can deal with slippery slope arguments especially when you suspect these are fallacious:

• Slippery slope arguments take advantage of the gray area between an initial action or decision and the possible next steps that might lead to the undesirable outcome. You can point out these missing steps and ask your partner to indicate what evidence exists to support the claimed relationship between two or more events.
• Ask yourself if each link in the chain of events or action is valid. Every proposition has to be true for the overall argument to work, so even if one link is irrational or not supported by evidence, then the argument collapses.
• Sometimes people commit a slippery slope fallacy unintentionally. In these instances, use an example that demonstrates the problem with slippery slope arguments in general (e.g., by using statements to reach a conclusion that is not necessarily relevant to the initial statement). By attacking the concept of slippery slope arguments you can show that they are often fallacious.

People sometimes confuse cognitive bias and logical fallacies because they both relate to flawed thinking. However, they are not the same:

• Cognitive bias is the tendency to make decisions or take action in an illogical way because of our values, memory, socialization, and other personal attributes. In other words, it refers to a fixed pattern of thinking rooted in the way our brain works.
• Logical fallacies relate to how we make claims and construct our arguments in the moment. They are statements that sound convincing at first but can be disproven through logical reasoning.

In other words, cognitive bias refers to an ongoing predisposition, while logical fallacy refers to mistakes of reasoning that occur in the moment.

An appeal to ignorance (ignorance here meaning lack of evidence) is a type of informal logical fallacy .

It asserts that something must be true because it hasn’t been proven false—or that something must be false because it has not yet been proven true.

For example, “unicorns exist because there is no evidence that they don’t.” The appeal to ignorance is also called the burden of proof fallacy .

An ad hominem (Latin for “to the person”) is a type of informal logical fallacy . Instead of arguing against a person’s position, an ad hominem argument attacks the person’s character or actions in an effort to discredit them.

This rhetorical strategy is fallacious because a person’s character, motive, education, or other personal trait is logically irrelevant to whether their argument is true or false.

Name-calling is common in ad hominem fallacy (e.g., “environmental activists are ineffective because they’re all lazy tree-huggers”).

Ad hominem is a persuasive technique where someone tries to undermine the opponent’s argument by personally attacking them.

In this way, one can redirect the discussion away from the main topic and to the opponent’s personality without engaging with their viewpoint. When the opponent’s personality is irrelevant to the discussion, we call it an ad hominem fallacy .

Ad hominem tu quoque (‘you too”) is an attempt to rebut a claim by attacking its proponent on the grounds that they uphold a double standard or that they don’t practice what they preach. For example, someone is telling you that you should drive slowly otherwise you’ll get a speeding ticket one of these days, and you reply “but you used to get them all the time!”

Argumentum ad hominem means “argument to the person” in Latin and it is commonly referred to as ad hominem argument or personal attack. Ad hominem arguments are used in debates to refute an argument by attacking the character of the person making it, instead of the logic or premise of the argument itself.

The opposite of the hasty generalization fallacy is called slothful induction fallacy or appeal to coincidence .

It is the tendency to deny a conclusion even though there is sufficient evidence that supports it. Slothful induction occurs due to our natural tendency to dismiss events or facts that do not align with our personal biases and expectations. For example, a researcher may try to explain away unexpected results by claiming it is just a coincidence.

To avoid a hasty generalization fallacy we need to ensure that the conclusions drawn are well-supported by the appropriate evidence. More specifically:

• In statistics , if we want to draw inferences about an entire population, we need to make sure that the sample is random and representative of the population . We can achieve that by using a probability sampling method , like simple random sampling or stratified sampling .
• In academic writing , use precise language and measured phases. Try to avoid making absolute claims, cite specific instances and examples without applying the findings to a larger group.
• As readers, we need to ask ourselves “does the writer demonstrate sufficient knowledge of the situation or phenomenon that would allow them to make a generalization?”

The hasty generalization fallacy and the anecdotal evidence fallacy are similar in that they both result in conclusions drawn from insufficient evidence. However, there is a difference between the two:

• The hasty generalization fallacy involves genuinely considering an example or case (i.e., the evidence comes first and then an incorrect conclusion is drawn from this).
• The anecdotal evidence fallacy (also known as “cherry-picking” ) is knowing in advance what conclusion we want to support, and then selecting the story (or a few stories) that support it. By overemphasizing anecdotal evidence that fits well with the point we are trying to make, we overlook evidence that would undermine our argument.

Although many sources use circular reasoning fallacy and begging the question interchangeably, others point out that there is a subtle difference between the two:

• Begging the question fallacy occurs when you assume that an argument is true in order to justify a conclusion. If something begs the question, what you are actually asking is, “Is the premise of that argument actually true?” For example, the statement “Snakes make great pets. That’s why we should get a snake” begs the question “are snakes really great pets?”
• Circular reasoning fallacy on the other hand, occurs when the evidence used to support a claim is just a repetition of the claim itself.  For example, “People have free will because they can choose what to do.”

In other words, we could say begging the question is a form of circular reasoning.

Circular reasoning fallacy uses circular reasoning to support an argument. More specifically, the evidence used to support a claim is just a repetition of the claim itself. For example: “The President of the United States is a good leader (claim), because they are the leader of this country (supporting evidence)”.

An example of a non sequitur is the following statement:

“Giving up nuclear weapons weakened the United States’ military. Giving up nuclear weapons also weakened China. For this reason, it is wrong to try to outlaw firearms in the United States today.”

Clearly there is a step missing in this line of reasoning and the conclusion does not follow from the premise, resulting in a non sequitur fallacy .

The difference between the post hoc fallacy and the non sequitur fallacy is that post hoc fallacy infers a causal connection between two events where none exists, whereas the non sequitur fallacy infers a conclusion that lacks a logical connection to the premise.

In other words, a post hoc fallacy occurs when there is a lack of a cause-and-effect relationship, while a non sequitur fallacy occurs when there is a lack of logical connection.

An example of post hoc fallacy is the following line of reasoning:

“Yesterday I had ice cream, and today I have a terrible stomachache. I’m sure the ice cream caused this.”

Although it is possible that the ice cream had something to do with the stomachache, there is no proof to justify the conclusion other than the order of events. Therefore, this line of reasoning is fallacious.

Post hoc fallacy and hasty generalisation fallacy are similar in that they both involve jumping to conclusions. However, there is a difference between the two:

• Post hoc fallacy is assuming a cause and effect relationship between two events, simply because one happened after the other.
• Hasty generalisation fallacy is drawing a general conclusion from a small sample or little evidence.

In other words, post hoc fallacy involves a leap to a causal claim; hasty generalisation fallacy involves a leap to a general proposition.

The fallacy of composition is similar to and can be confused with the hasty generalization fallacy . However, there is a difference between the two:

• The fallacy of composition involves drawing an inference about the characteristics of a whole or group based on the characteristics of its individual members.
• The hasty generalization fallacy involves drawing an inference about a population or class of things on the basis of few atypical instances or a small sample of that population or thing.

In other words, the fallacy of composition is using an unwarranted assumption that we can infer something about a whole based on the characteristics of its parts, while the hasty generalization fallacy is using insufficient evidence to draw a conclusion.

The opposite of the fallacy of composition is the fallacy of division . In the fallacy of division, the assumption is that a characteristic which applies to a whole or a group must necessarily apply to the parts or individual members. For example, “Australians travel a lot. Gary is Australian, so he must travel a lot.”

Base rate fallacy can be avoided by following these steps:

• Avoid making an important decision in haste. When we are under pressure, we are more likely to resort to cognitive shortcuts like the availability heuristic and the representativeness heuristic . Due to this, we are more likely to factor in only current and vivid information, and ignore the actual probability of something happening (i.e., base rate).
• Take a long-term view on the decision or question at hand. Look for relevant statistical data, which can reveal long-term trends and give you the full picture.
• Talk to experts like professionals. They are more aware of probabilities related to specific decisions.

Suppose there is a population consisting of 90% psychologists and 10% engineers. Given that you know someone enjoyed physics at school, you may conclude that they are an engineer rather than a psychologist, even though you know that this person comes from a population consisting of far more psychologists than engineers.

When we ignore the rate of occurrence of some trait in a population (the base-rate information) we commit base rate fallacy .

Cost-benefit fallacy is a common error that occurs when allocating sources in project management. It is the fallacy of assuming that cost-benefit estimates are more or less accurate, when in fact they are highly inaccurate and biased. This means that cost-benefit analyses can be useful, but only after the cost-benefit fallacy has been acknowledged and corrected for. Cost-benefit fallacy is a type of base rate fallacy .

In advertising, the fallacy of equivocation is often used to create a pun. For example, a billboard company might advertise their billboards using a line like: “Looking for a sign? This is it!” The word sign has a literal meaning as billboard and a figurative one as a sign from God, the universe, etc.

Equivocation is a fallacy because it is a form of argumentation that is both misleading and logically unsound. When the meaning of a word or phrase shifts in the course of an argument, it causes confusion and also implies that the conclusion (which may be true) does not follow from the premise.

The fallacy of equivocation is an informal logical fallacy, meaning that the error lies in the content of the argument instead of the structure.

Fallacies of relevance are a group of fallacies that occur in arguments when the premises are logically irrelevant to the conclusion. Although at first there seems to be a connection between the premise and the conclusion, in reality fallacies of relevance use unrelated forms of appeal.

For example, the genetic fallacy makes an appeal to the source or origin of the claim in an attempt to assert or refute something.

The ad hominem fallacy and the genetic fallacy are closely related in that they are both fallacies of relevance. In other words, they both involve arguments that use evidence or examples that are not logically related to the argument at hand. However, there is a difference between the two:

• In the ad hominem fallacy , the goal is to discredit the argument by discrediting the person currently making the argument.
• In the genetic fallacy , the goal is to discredit the argument by discrediting the history or origin (i.e., genesis) of an argument.

False dilemma fallacy is also known as false dichotomy, false binary, and “either-or” fallacy. It is the fallacy of presenting only two choices, outcomes, or sides to an argument as the only possibilities, when more are available.

The false dilemma fallacy works in two ways:

• By presenting only two options as if these were the only ones available
• By presenting two options as mutually exclusive (i.e., only one option can be selected or can be true at a time)

In both cases, by using the false dilemma fallacy, one conceals alternative choices and doesn’t allow others to consider the full range of options. This is usually achieved through an“either-or” construction and polarised, divisive language (“you are either a friend or an enemy”).

The best way to avoid a false dilemma fallacy is to pause and reflect on two points:

• Are the options presented truly the only ones available ? It could be that another option has been deliberately omitted.
• Are the options mentioned mutually exclusive ? Perhaps all of the available options can be selected (or be true) at the same time, which shows that they aren’t mutually exclusive. Proving this is called “escaping between the horns of the dilemma.”

Begging the question fallacy is an argument in which you assume what you are trying to prove. In other words, your position and the justification of that position are the same, only slightly rephrased.

For example: “All freshmen should attend college orientation, because all college students should go to such an orientation.”

The complex question fallacy and begging the question fallacy are similar in that they are both based on assumptions. However, there is a difference between them:

• A complex question fallacy occurs when someone asks a question that presupposes the answer to another question that has not been established or accepted by the other person. For example, asking someone “Have you stopped cheating on tests?”, unless it has previously been established that the person is indeed cheating on tests, is a fallacy.
• Begging the question fallacy occurs when we assume the very thing as a premise that we’re trying to prove in our conclusion. In other words, the conclusion is used to support the premises, and the premises prove the validity of the conclusion. For example: “God exists because the Bible says so, and the Bible is true because it is the word of God.”

In other words, begging the question is about drawing a conclusion based on an assumption, while a complex question involves asking a question that presupposes the answer to a prior question.

“ No true Scotsman ” arguments aren’t always fallacious. When there is a generally accepted definition of who or what constitutes a group, it’s reasonable to use statements in the form of “no true Scotsman”.

For example, the statement that “no true pacifist would volunteer for military service” is not fallacious, since a pacifist is, by definition, someone who opposes war or violence as a means of settling disputes.

No true Scotsman arguments are fallacious because instead of logically refuting the counterexample, they simply assert that it doesn’t count. In other words, the counterexample is rejected for psychological, but not logical, reasons.

The appeal to purity or no true Scotsman fallacy is an attempt to defend a generalisation about a group from a counterexample by shifting the definition of the group in the middle of the argument. In this way, one can exclude the counterexample as not being “true”, “genuine”, or “pure” enough to be considered as part of the group in question.

To identify an appeal to authority fallacy , you can ask yourself the following questions:

• Is the authority cited really a qualified expert in this particular area under discussion? For example, someone who has formal education or years of experience can be an expert.
• Do experts disagree on this particular subject? If that is the case, then for almost any claim supported by one expert there will be a counterclaim that is supported by another expert. If there is no consensus, an appeal to authority is fallacious.
• Is the authority in question biased? If you suspect that an expert’s prejudice and bias could have influenced their views, then the expert is not reliable and an argument citing this expert will be fallacious.To identify an appeal to authority fallacy, you ask yourself whether the authority cited is a qualified expert in the particular area under discussion.

Appeal to authority is a fallacy when those who use it do not provide any justification to support their argument. Instead they cite someone famous who agrees with their viewpoint, but is not qualified to make reliable claims on the subject.

Appeal to authority fallacy is often convincing because of the effect authority figures have on us. When someone cites a famous person, a well-known scientist, a politician, etc. people tend to be distracted and often fail to critically examine whether the authority figure is indeed an expert in the area under discussion.

The ad populum fallacy is common in politics. One example is the following viewpoint: “The majority of our countrymen think we should have military operations overseas; therefore, it’s the right thing to do.”

This line of reasoning is fallacious, because popular acceptance of a belief or position does not amount to a justification of that belief. In other words, following the prevailing opinion without examining the underlying reasons is irrational.

The ad populum fallacy plays on our innate desire to fit in (known as “bandwagon effect”). If many people believe something, our common sense tells us that it must be true and we tend to accept it. However, in logic, the popularity of a proposition cannot serve as evidence of its truthfulness.

Ad populum (or appeal to popularity) fallacy and appeal to authority fallacy are similar in that they both conflate the validity of a belief with its popular acceptance among a specific group. However there is a key difference between the two:

• An ad populum fallacy tries to persuade others by claiming that something is true or right because a lot of people think so.
• An appeal to authority fallacy tries to persuade by claiming a group of experts believe something is true or right, therefore it must be so.

To identify a false cause fallacy , you need to carefully analyse the argument:

• When someone claims that one event directly causes another, ask if there is sufficient evidence to establish a cause-and-effect relationship. 
• Ask if the claim is based merely on the chronological order or co-occurrence of the two events. 
• Consider alternative possible explanations (are there other factors at play that could influence the outcome?).

By carefully analysing the reasoning, considering alternative explanations, and examining the evidence provided, you can identify a false cause fallacy and discern whether a causal claim is valid or flawed.

False cause fallacy examples include: 

• Believing that wearing your lucky jersey will help your team win 
• Thinking that everytime you wash your car, it rains
• Claiming that playing video games causes violent behavior 

In each of these examples, we falsely assume that one event causes another without any proof.

The planning fallacy and procrastination are not the same thing. Although they both relate to time and task management, they describe different challenges:

• The planning fallacy describes our inability to correctly estimate how long a future task will take, mainly due to optimism bias and a strong focus on the best-case scenario.
• Procrastination refers to postponing a task, usually by focusing on less urgent or more enjoyable activities. This is due to psychological reasons, like fear of failure.

In other words, the planning fallacy refers to inaccurate predictions about the time we need to finish a task, while procrastination is a deliberate delay due to psychological factors.

A real-life example of the planning fallacy is the construction of the Sydney Opera House in Australia. When construction began in the late 1950s, it was initially estimated that it would be completed in four years at a cost of around $7 million.

Because the government wanted the construction to start before political opposition would stop it and while public opinion was still favorable, a number of design issues had not been carefully studied in advance. Due to this, several problems appeared immediately after the project commenced.

The construction process eventually stretched over 14 years, with the Opera House being completed in 1973 at a cost of over $100 million, significantly exceeding the initial estimates.

An example of appeal to pity fallacy is the following appeal by a student to their professor:

“Professor, please consider raising my grade. I had a terrible semester: my car broke down, my laptop got stolen, and my cat got sick.”

While these circumstances may be unfortunate, they are not directly related to the student’s academic performance.

While both the appeal to pity fallacy and   red herring fallacy can serve as a distraction from the original discussion topic, they are distinct fallacies. More specifically:

• Appeal to pity fallacy attempts to evoke feelings of sympathy, pity, or guilt in an audience, so that they accept the speaker’s conclusion as truthful.
• Red herring fallacy attempts to introduce an irrelevant piece of information that diverts the audience’s attention to a different topic.

Both fallacies can be used as a tool of deception. However, they operate differently and serve distinct purposes in arguments.

Argumentum ad misericordiam (Latin for “argument from pity or misery”) is another name for appeal to pity fallacy . It occurs when someone evokes sympathy or guilt in an attempt to gain support for their claim, without providing any logical reasons to support the claim itself. Appeal to pity is a deceptive tactic of argumentation, playing on people’s emotions to sway their opinion.

Yes, it’s quite common to start a sentence with a preposition, and there’s no reason not to do so.

For example, the sentence “ To many, she was a hero” is perfectly grammatical. It could also be rephrased as “She was a hero to  many”, but there’s no particular reason to do so. Both versions are fine.

Some people argue that you shouldn’t end a sentence with a preposition , but that “rule” can also be ignored, since it’s not supported by serious language authorities.

Yes, it’s fine to end a sentence with a preposition . The “rule” against doing so is overwhelmingly rejected by modern style guides and language authorities and is based on the rules of Latin grammar, not English.

Trying to avoid ending a sentence with a preposition often results in very unnatural phrasings. For example, turning “He knows what he’s talking about ” into “He knows about what he’s talking” or “He knows that about which he’s talking” is definitely not an improvement.

No, ChatGPT is not a credible source of factual information and can’t be cited for this purpose in academic writing . While it tries to provide accurate answers, it often gets things wrong because its responses are based on patterns, not facts and data.

Specifically, the CRAAP test for evaluating sources includes five criteria: currency , relevance , authority , accuracy , and purpose . ChatGPT fails to meet at least three of them:

• Currency: The dataset that ChatGPT was trained on only extends to 2021, making it slightly outdated.
• Authority: It’s just a language model and is not considered a trustworthy source of factual information.
• Accuracy: It bases its responses on patterns rather than evidence and is unable to cite its sources .

So you shouldn’t cite ChatGPT as a trustworthy source for a factual claim. You might still cite ChatGPT for other reasons – for example, if you’re writing a paper about AI language models, ChatGPT responses are a relevant primary source .

ChatGPT is an AI language model that was trained on a large body of text from a variety of sources (e.g., Wikipedia, books, news articles, scientific journals). The dataset only went up to 2021, meaning that it lacks information on more recent events.

It’s also important to understand that ChatGPT doesn’t access a database of facts to answer your questions. Instead, its responses are based on patterns that it saw in the training data.

So ChatGPT is not always trustworthy . It can usually answer general knowledge questions accurately, but it can easily give misleading answers on more specialist topics.

Another consequence of this way of generating responses is that ChatGPT usually can’t cite its sources accurately. It doesn’t really know what source it’s basing any specific claim on. It’s best to check any information you get from it against a credible source .

No, it is not possible to cite your sources with ChatGPT . You can ask it to create citations, but it isn’t designed for this task and tends to make up sources that don’t exist or present information in the wrong format. ChatGPT also cannot add citations to direct quotes in your text.

Instead, use a tool designed for this purpose, like the Scribbr Citation Generator .

But you can use ChatGPT for assignments in other ways, to provide inspiration, feedback, and general writing advice.

GPT  stands for “generative pre-trained transformer”, which is a type of large language model: a neural network trained on a very large amount of text to produce convincing, human-like language outputs. The Chat part of the name just means “chat”: ChatGPT is a chatbot that you interact with by typing in text.

The technology behind ChatGPT is GPT-3.5 (in the free version) or GPT-4 (in the premium version). These are the names for the specific versions of the GPT model. GPT-4 is currently the most advanced model that OpenAI has created. It’s also the model used in Bing’s chatbot feature.

ChatGPT was created by OpenAI, an AI research company. It started as a nonprofit company in 2015 but became for-profit in 2019. Its CEO is Sam Altman, who also co-founded the company. OpenAI released ChatGPT as a free “research preview” in November 2022. Currently, it’s still available for free, although a more advanced premium version is available if you pay for it.

OpenAI is also known for developing DALL-E, an AI image generator that runs on similar technology to ChatGPT.

ChatGPT is owned by OpenAI, the company that developed and released it. OpenAI is a company dedicated to AI research. It started as a nonprofit company in 2015 but transitioned to for-profit in 2019. Its current CEO is Sam Altman, who also co-founded the company.

In terms of who owns the content generated by ChatGPT, OpenAI states that it will not claim copyright on this content , and the terms of use state that “you can use Content for any purpose, including commercial purposes such as sale or publication”. This means that you effectively own any content you generate with ChatGPT and can use it for your own purposes.

Be cautious about how you use ChatGPT content in an academic context. University policies on AI writing are still developing, so even if you “own” the content, you’re often not allowed to submit it as your own work according to your university or to publish it in a journal.

ChatGPT is a chatbot based on a large language model (LLM). These models are trained on huge datasets consisting of hundreds of billions of words of text, based on which the model learns to effectively predict natural responses to the prompts you enter.

ChatGPT was also refined through a process called reinforcement learning from human feedback (RLHF), which involves “rewarding” the model for providing useful answers and discouraging inappropriate answers – encouraging it to make fewer mistakes.

Essentially, ChatGPT’s answers are based on predicting the most likely responses to your inputs based on its training data, with a reward system on top of this to incentivise it to give you the most helpful answers possible. It’s a bit like an incredibly advanced version of predictive text. This is also one of ChatGPT’s limitations : because its answers are based on probabilities, they’re not always trustworthy .

OpenAI may store ChatGPT conversations for the purposes of future training. Additionally, these conversations may be monitored by human AI trainers.

Users can choose not to have their chat history saved. Unsaved chats are not used to train future models and are permanently deleted from ChatGPT’s system after 30 days.

The official ChatGPT app is currently only available on iOS devices. If you don’t have an iOS device, only use the official OpenAI website to access the tool. This helps to eliminate the potential risk of downloading fraudulent or malicious software.

ChatGPT conversations are generally used to train future models and to resolve issues/bugs. These chats may be monitored by human AI trainers.

However, users can opt out of having their conversations used for training. In these instances, chats are monitored only for potential abuse.

Yes, using ChatGPT as a conversation partner is a great way to practice a language in an interactive way.

Try using a prompt like this one:

“Please be my Spanish conversation partner. Only speak to me in Spanish. Keep your answers short (maximum 50 words). Ask me questions. Let’s start the conversation with the following topic: [conversation topic].”

Yes, there are a variety of ways to use ChatGPT for language learning , including treating it as a conversation partner, asking it for translations, and using it to generate a curriculum or practice exercises.

AI detectors aim to identify the presence of AI-generated text (e.g., from ChatGPT ) in a piece of writing, but they can’t do so with complete accuracy. In our comparison of the best AI detectors , we found that the 10 tools we tested had an average accuracy of 60%. The best free tool had 68% accuracy, the best premium tool 84%.

Because of how AI detectors work , they can never guarantee 100% accuracy, and there is always at least a small risk of false positives (human text being marked as AI-generated). Therefore, these tools should not be relied upon to provide absolute proof that a text is or isn’t AI-generated. Rather, they can provide a good indication in combination with other evidence.

Tools called AI detectors are designed to label text as AI-generated or human. AI detectors work by looking for specific characteristics in the text, such as a low level of randomness in word choice and sentence length. These characteristics are typical of AI writing, allowing the detector to make a good guess at when text is AI-generated.

But these tools can’t guarantee 100% accuracy. Check out our comparison of the best AI detectors to learn more.

You can also manually watch for clues that a text is AI-generated – for example, a very different style from the writer’s usual voice or a generic, overly polite tone.

Our research into the best summary generators (aka summarisers or summarising tools) found that the best summariser available in 2023 is the one offered by QuillBot.

While many summarisers just pick out some sentences from the text, QuillBot generates original summaries that are creative, clear, accurate, and concise. It can summarise texts of up to 1,200 words for free, or up to 6,000 with a premium subscription.

Try the QuillBot summarizer for free

Deep learning requires a large dataset (e.g., images or text) to learn from. The more diverse and representative the data, the better the model will learn to recognise objects or make predictions. Only when the training data is sufficiently varied can the model make accurate predictions or recognise objects from new data.

Deep learning models can be biased in their predictions if the training data consist of biased information. For example, if a deep learning model used for screening job applicants has been trained with a dataset consisting primarily of white male applicants, it will consistently favour this specific population over others.

A good ChatGPT prompt (i.e., one that will get you the kinds of responses you want):

• Gives the tool a role to explain what type of answer you expect from it
• Is precisely formulated and gives enough context
• Is free from bias
• Has been tested and improved by experimenting with the tool

ChatGPT prompts are the textual inputs (e.g., questions, instructions) that you enter into ChatGPT to get responses.

ChatGPT predicts an appropriate response to the prompt you entered. In general, a more specific and carefully worded prompt will get you better responses.

Yes, ChatGPT is currently available for free. You have to sign up for a free account to use the tool, and you should be aware that your data may be collected to train future versions of the model.

To sign up and use the tool for free, go to this page and click “Sign up”. You can do so with your email or with a Google account.

A premium version of the tool called ChatGPT Plus is available as a monthly subscription. It currently costs £16 and gets you access to features like GPT-4 (a more advanced version of the language model). But it’s optional: you can use the tool completely free if you’re not interested in the extra features.

You can access ChatGPT by signing up for a free account:

• Follow this link to the ChatGPT website.
• Click on “Sign up” and fill in the necessary details (or use your Google account). It’s free to sign up and use the tool.
• Type a prompt into the chat box to get started!

A ChatGPT app is also available for iOS, and an Android app is planned for the future. The app works similarly to the website, and you log in with the same account for both.

According to OpenAI’s terms of use, users have the right to reproduce text generated by ChatGPT during conversations.

However, publishing ChatGPT outputs may have legal implications , such as copyright infringement.

Users should be aware of such issues and use ChatGPT outputs as a source of inspiration instead.

According to OpenAI’s terms of use, users have the right to use outputs from their own ChatGPT conversations for any purpose (including commercial publication).

However, users should be aware of the potential legal implications of publishing ChatGPT outputs. ChatGPT responses are not always unique: different users may receive the same response.

Furthermore, ChatGPT outputs may contain copyrighted material. Users may be liable if they reproduce such material.

ChatGPT can sometimes reproduce biases from its training data , since it draws on the text it has “seen” to create plausible responses to your prompts.

For example, users have shown that it sometimes makes sexist assumptions such as that a doctor mentioned in a prompt must be a man rather than a woman. Some have also pointed out political bias in terms of which political figures the tool is willing to write positively or negatively about and which requests it refuses.

The tool is unlikely to be consistently biased toward a particular perspective or against a particular group. Rather, its responses are based on its training data and on the way you phrase your ChatGPT prompts . It’s sensitive to phrasing, so asking it the same question in different ways will result in quite different answers.

Information extraction  refers to the process of starting from unstructured sources (e.g., text documents written in ordinary English) and automatically extracting structured information (i.e., data in a clearly defined format that’s easily understood by computers). It’s an important concept in natural language processing (NLP) .

For example, you might think of using news articles full of celebrity gossip to automatically create a database of the relationships between the celebrities mentioned (e.g., married, dating, divorced, feuding). You would end up with data in a structured format, something like MarriageBetween(celebrity 1 ,celebrity 2 ,date) .

The challenge involves developing systems that can “understand” the text well enough to extract this kind of data from it.

Knowledge representation and reasoning (KRR) is the study of how to represent information about the world in a form that can be used by a computer system to solve and reason about complex problems. It is an important field of artificial intelligence (AI) research.

An example of a KRR application is a semantic network, a way of grouping words or concepts by how closely related they are and formally defining the relationships between them so that a machine can “understand” language in something like the way people do.

A related concept is information extraction , concerned with how to get structured information from unstructured sources.

Yes, you can use ChatGPT to summarise text . This can help you understand complex information more easily, summarise the central argument of your own paper, or clarify your research question.

You can also use Scribbr’s free text summariser , which is designed specifically for this purpose.

Yes, you can use ChatGPT to paraphrase text to help you express your ideas more clearly, explore different ways of phrasing your arguments, and avoid repetition.

However, it’s not specifically designed for this purpose. We recommend using a specialised tool like Scribbr’s free paraphrasing tool , which will provide a smoother user experience.

Yes, you use ChatGPT to help write your college essay by having it generate feedback on certain aspects of your work (consistency of tone, clarity of structure, etc.).

However, ChatGPT is not able to adequately judge qualities like vulnerability and authenticity. For this reason, it’s important to also ask for feedback from people who have experience with college essays and who know you well. Alternatively, you can get advice using Scribbr’s essay editing service .

No, having ChatGPT write your college essay can negatively impact your application in numerous ways. ChatGPT outputs are unoriginal and lack personal insight.

Furthermore, Passing off AI-generated text as your own work is considered academically dishonest . AI detectors may be used to detect this offense, and it’s highly unlikely that any university will accept you if you are caught submitting an AI-generated admission essay.

However, you can use ChatGPT to help write your college essay during the preparation and revision stages (e.g., for brainstorming ideas and generating feedback).

ChatGPT and other AI writing tools can have unethical uses. These include:

• Reproducing biases and false information
• Using ChatGPT to cheat in academic contexts
• Violating the privacy of others by inputting personal information

However, when used correctly, AI writing tools can be helpful resources for improving your academic writing and research skills. Some ways to use ChatGPT ethically include:

• Following your institution’s guidelines
• Critically evaluating outputs
• Being transparent about how you used the tool

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Qualitative vs Quantitative Research: Key Differences & Questions

Written by: Orana Velarde

There’s a common saying in business that goes, “Know your customer.”

And yes, it's crucial for many situations, such as customer experience, sales targets, marketing and communication.

But how can you truly know your customer? One way is by conducting qualitative and quantitative research.

Qualitative and quantitative research go hand in hand to help you successfully grasp what your customer's pain points are, what they expect from your brand and what their life situations are.

The answers and results from the research are critical to making fundamental decisions for your business and your brand.

In this guide, you’ll learn how qualitative and quantitative research differ, how to formulate questions for each type, how to conduct the research and how to present the results.

Plus, you’ll discover Visme Forms, the ideal way to engage your customers with your research questions.

Table of Contents

What is qualitative research, what is quantitative research, qualitative vs quantitative research.

• Examples of Qualitative and Quantitative Questions
• Conduct and Analyze Quantitative and Qualitative Research
• Quantitative and qualitative research are essential tools for gathering information about consumers, an audience or an employee base.
• Qualitative research is about people's thoughts, feelings and perspectives, while quantitative research concentrates on demographic, statistical and numerical data.
• Quantitative and qualitative data involve asking the right questions in a survey or form. Quantitative questions are simple questions with definite answers, while qualitative questions are open-ended.
• To conduct and analyze qualitative and quantitative data, prepare for the research process, gather and analyze the data and present it to your audience.
• Create both qualitative and quantitative forms and surveys using Visme Forms , an intuitive tool that gets you 2-3x more conversions than traditional forms.

Qualitative research is an investigation method that explores the “whys” and “hows” of human behavior and experiences concerning a specific situation.

Instead of focusing on numbers and statistics, it dives deep into people’s thoughts, feelings and perspectives. Through methods such as interviews, observation and text analysis, researchers can uncover the nuances and complexities of any situation.

Quantitative research is a systematic investigation method that uses statistical and mathematical techniques to analyze numerical data. This approach involves collecting data through methods like surveys and experiments with close-ended questions. The data is then analyzed using statistical tools to conclude patterns, trends and relationships between variables.

Qualitative vs quantitative research methods differ in their purpose but go hand in hand when you're looking to fully understand a situation. They complement each other to give researchers a wide view of the data they need to analyze critical information.

You should conduct qualitative research if you’re looking to understand concepts, thoughts and experiences. On the other hand, you should conduct quantitative research when you want to confirm or test a hypothesis or theory.

That said, what is a common goal of qualitative and quantitative research?

Both research methods help you gain a deeper understanding of a phenomenon or topic by generating knowledge through the collection and analysis of data. They’re complementary to each other and, therefore, have a common goal.

Here are some qualitative vs quantitative research examples:

Made with Visme Infographic Maker

Qualitative Research Examples

• Focus groups with target customers to explore their perceptions, attitudes and preferences regarding a new product or service. These groups can provide insights into potential market acceptance and areas for improvement.
• Case studies of successful businesses to understand the strategies, leadership styles and market positioning that contributed to their growth and competitiveness.
• Interviews with key stakeholders such as employees, managers and customers to uncover organizational strengths, weaknesses, opportunities and threats for strategic planning and decision-making.

Quantitative Research Examples

• Market research surveys to gather numerical data on consumer preferences, purchasing behavior and market trends to inform product development and marketing strategies.
• Financial analysis of company performance, including metrics such as revenue growth, profit margins and return on investment. This analysis can be used to assess business health and make investment decisions.
• Studies measuring employee engagement through surveys and analytical factors such as job satisfaction, work-life balance and organizational culture to improve retention and productivity.

Advantages and Disadvantages of Qualitative Research

Qualitative business research is crucial for understanding the customer pain points, goals and expectations concerning your business.

Conducting qualitative research within your business can help improve sales and marketing funnels, create better-oriented touchpoints and even improve your employees' experiences.

Here are some of the main advantages of qualitative research:

• Qualitative research lets you ask questions and get insights that can’t be easily understood with numbers.
• Feelings and thoughts, for example, can’t be measured with numerical data.
• Qualitative research is flexible and adaptive, so it’s ideal for a wide range of social aspects, including consumer satisfaction and brand loyalty.
• This research type provides deeper insights into the research study, no matter the topic.
• It helps researchers design more accurate research methods for future interviews and surveys.

Some disadvantages of qualitative research include the following:

• Large sample sizes can affect qualitative research results. Interviewing a large group can lead to overgeneralization. On the other hand, too small a group won’t reveal any outlying opinions.
• Responses are subjective; cognitive bias can affect interviewees, even if they are not aware of it.
• Qualitative research can be time-consuming due to the need for in-depth analysis and interpretation of the data.

Advantages and Disadvantages of Quantitative Research

In business, quantitative research is the backbone of all statistical decision-making.

Conducting quantitative research in your business is critical for determining its performance in terms of sales, distribution margins, profit and loss, ROI and demographic expansion.

These are the major advantages of quantitative research

• Quantitative analysis produces objective data that is factual and verifiable. The data can also be analyzed with the help of computing software.
• Since quantitative research uses standardized methods and questions, it's easier to replicate the study.
• Participants can remain anonymous; they don’t need to share personal information.
• Researchers don’t need to be present at all times. Surveys and polls can be conducted digitally or online at the respondent’s leisure.
• It can capture vast amounts of data in a short period and can be analyzed continuously.

Some of the disadvantages of quantitative research include the following:

• The standardized methods and questions used in quantitative research may limit the range of responses.
• Research models and modalities require careful attention to detail. One mistake in the questions can invalidate all the data.
• Quantitative research on its own can be limited without a view of underlying issues or information about the big picture.

Examples of Qualitative & Quantitative Questions

To conduct a comprehensive study, researchers must ask questions using surveys, feedback forms, tests and experiments.

When it comes to qualitative vs quantitative research questions, there’s a clear distinction.

Qualitative questions aim to understand behavior and thought, while quantitative questions aim to prove hypotheses and gain numerical data.

Here are some qualitative and quantitative examples of questions to get you started.

Qualitative Research Questions

Qualitative research questions are typically answered in sentences, long-form answers or rating systems. Start the research with a broad question and then work your way toward the details.

Here are some broad question examples:

• How do customers perceive the quality of our customer service interactions?
• How do customers describe their ideal experience with our brand?
• What are the key drivers of brand loyalty among our customers?
• What are the strategies employed by successful entrepreneurs to navigate market fluctuations?

Next, here are some detailed questions to research the first broad question:

• Can you describe a recent customer service interaction you had with our company?
• What aspects of the customer service interaction stood out to you the most?
• How would you rate the friendliness and professionalism of our customer service reps?
• Have you ever had a negative experience with our customer service? If so, could you describe what happened?

This customer service survey asks the customer about their overall shopping experience through several steps, including a rating system and personal thoughts.

Quantitative Research Questions

Quantitative research begins with one broad question that expands into detailed numerical questions.

Here are some sample broad quantitative research questions.

• What is the average customer retention rate over the past year?
• What is the breakdown of customer demographics (age, gender, location, etc.) within our customer base?
• What percentage of customers are satisfied with our product/service overall?
• How long is the average customer wait time for support inquiries to be resolved?

Here are some questions to delve deeper into the first broad quantitative research question above:

• What is the total number of customers who have continued using our services and products over the past year?
• How many new customers have been acquired in the past year?
• What is the percentage of customers who have renewed their subscriptions or contracts with us in the past year?
• What is the total number of customers who have churned or discontinued using our services/products in the past year?

Conduct & Analyze Qualitative & Quantitative Data

Let's take a look at how to conduct and analyze qualitative and quantitative data.

Prepare For The Research Process

Before conducting any research, you must set a goal. It’s at this point that you set the broad question that you will then expand on for more detailed data. For this to work, you have to brainstorm with your team or colleagues.

Visme’s online whiteboard makes it easy to collaborate with your team during the brainstorming process .

Use sticky notes to come up with possible broad questions and then pick the best ones.

Next, draft the questions you’ll ask participants and put together more detailed questions that pertain to your main question.

Once all the questions are finalized, start building the form, survey or worksheet you’ll use to gather your data.

Here’s an example whiteboard you can use for a brainstorming session with your team:

Gather Data

There are two ways to gather the research data; in-person or digital (optionally anonymous).

In-Person Research

In-person research, like interviews, focus groups, observation and experiments, is more suited for qualitative research. Some examples include:

• Hosting focus groups with target customers to explore their reactions and perceptions of a new advertising campaign.
• Conducting in-depth interviews with key stakeholders to understand their perspectives on market trends and competitors.
• Observing customer interactions with a product in a retail store to gather insights into their preferences and behaviors.
• Participate in user testing sessions to observe how individuals navigate through a website or mobile app and gather feedback on usability.
• Implementing a pilot program to test a new employee training initiative and gather feedback before company-wide implementation.

For more efficient in-person data collection, provide researchers with a digital interactive document where they can record their findings.

Use this workbook template to create a checklist-style document with your research questions and notes.

You can create this digital document on Visme and access it via a link on a tablet, mobile device or laptop. The digital worksheet should include informational interactive hotspots to help testers ask better, more nuanced qualitative questions.

Digital (Anonymous) Research

Digital forms and surveys work for quantitative and qualitative research questions and can have combined questions to get a broader grasp of the situation. Typically, the first questions are quantitative and then progress to qualitative.

With a Visme subscription, you have access to an interactive online form builder that lets you create digital research forms in a variety of styles.

Create pop-up forms that collect names and emails quickly or customer feedback forms that take several steps to fill out. With the same tool, you can create lead generation forms , registration forms and contact forms .

The example below is a feedback form for a software training program. The questions are both quantitative and qualitative, separated into steps.

When you create quantitative and qualitative research forms with Visme, you can design and publish forms that convert 2X better than traditional forms.

These forms come equipped with customizable animations and 3D characters that support your brand message so you can easily personalize them to match your buyer persona or ideal customer profile .

Choose from a wide variety of form templates by use case and style it to match your brand.

First, you have to publish your form to gather data. From within Visme Forms, you can share a live link or copy an embed code and insert your form into a webpage or Visme project in seconds.

Regardless of how it’s shared, a Visme form is 100% responsive and will adjust to screen size dynamically. Data collected through Visme forms is stored natively in your form analytics dashboard.

Track views, demographics, emails and form entries in an easy-to-read layout. You can also collect and store your data through multiple integrations , like Mailchimp, HubSpot and Google Sheets.

Analyze The Data

To analyze quantitative data, you typically use statistical methods to identify patterns and trends. You can calculate measures and descriptive statistics such as mean, median, mode, standard deviation and range to summarize the central tendency and variability of your data.

Conduct inferential statistics tests, such as t-tests, ANOVA, correlation analysis, or regression analysis, to test hypotheses, relationships, or differences between groups. Visualize your data using graphs, charts and histograms to identify patterns, trends and outliers.

To analyze qualitative data, you can use various techniques, such as coding, categorization and synthesis, to identify themes and patterns. Some popular tools include SPSS, SAS, R and Stata.

Qualitative data consists of non-numerical information, such as text, images, or observations, and is analyzed using thematic, content, or narrative analysis. By coding the data systematically, you can identify key themes, patterns, or categories within it. Use popular tools like NVivo, ATLAS.ti, MAXQDA and Dedoose.

Present The Data

One of the most important aspects of qualitative and quantitative research is presenting the data. Sharing the results with your team, higher-ups and other stakeholders is critical for growth and decision-making.

Once you have all the data collected, create a research presentation or research report and share it with relevant stakeholders.

Use one of Visme’s document or presentation templates to build a comprehensive report from which to share your results and analysis.

This research report template example includes data visualizations and text blurbs about quantitative and qualitative research results.

Alternatively, you can save time creating your report using the Visme AI Report Writer , which takes a text prompt and generates a report draft in a style of your choosing.

To create other reports or documents to share your qualitative and quantitative data, the AI Document Generator will generate any type of document you need.

While working on the report together, use the workflow feature to assign specific sections to their stakeholders. Ask them to add their survey data to charts, graphs, maps and tables by connecting to a spreadsheet or filling it in manually.

But what is the best way to showcase qualitative vs quantitative data?

Quantitative data is best represented with data visualizations , like charts, graphs and tables. Creating reports with Visme allows you to make your data beautiful, regardless of whether it's qualitative or quantitative.

When you create reports with Visme, your data can be static or live through a connection to a live Google Sheet or Google Analytics data capture.

Then take advantage of the AI Writer to help you write a concise and descriptive text that highlights your research analysis. This AI writing tool can help you summarize long analysis copy, rephrase complicated sentences or edit grammar mistakes.

Once you’re done, sharing your forms, surveys and reports is easy. Visme Forms are embeddable on websites, emails and even other Visme projects.

Reports can be shared as digital flipbook documents, interactive experiences, static PDFs or printed booklets. You can even select singular data visualizations from the report and share them on social media via the Visme social media scheduler .

Here’s an example of how a report is viewed when shared as a live Visme link.

When distributing qualitative and quantitative research reports, share them as digital Visme links. This allows you to track who opens them and from where.

Know if all stakeholders have seen the report and easily remind those who haven’t. You can track your report’s performance with Visme’s analytics dashboard .

Get inspired by Andrew Kitchner , CEO of New Wave Solutions, an employee surveying company. Andrew and his team use Visme to present surveying reports in what they call The Return to Work Sentiment Report.

Andrew Kitchner

CEO of New Wave Solutions

Improve Your Research Activities & Content with Visme

Don’t work on qualitative and quantitative research alone; do it with your team as a collaborative effort.

Create a dedicated Visme workspace where you can brainstorm, plan, and create surveys, polls and forms to conduct qualitative and quantitative research. Finally, create data reports together so that all relevant departments can double-check the information.

Use Visme’s form builder to create engaging forms that make qualitative and quantitative research easier and more effective. Personalize them and set their actions to match your brand style. Take advantage of integrated 3D characters, animations and other interactive elements to give your form personality and a unique feel.

Get started with Visme Forms today and uncover your customers’ (or your employees’) pain points, expectations and goals. You’ll see improvement in your decision-making process in no time.

Create beautiful forms that engage & convert.

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About the Author

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• Key Differences

Know the Differences & Comparisons

Difference Between Qualitative and Quantitative Research

In a qualitative research, there are only a few non-representative cases are used as a sample to develop an initial understanding. Unlike, quantitative research in which a sufficient number of representative cases are taken to consideration to recommend a final course of action.

There is a never-ending debate on, which research is better than the other, so in this article, we are going to shed light on the difference between qualitative and quantitative research.

Content: Qualitative Research Vs Quantitative Research

Comparison chart.

Definition of Qualitative Research

Qualitative research is one which provides insights and understanding of the problem setting. It is an unstructured, exploratory research method that studies highly complex phenomena that are impossible to elucidate with the quantitative research. Although, it generates ideas or hypothesis for later quantitative research.

Qualitative research is used to gain an in-depth understanding of human behaviour, experience, attitudes, intentions, and motivations, on the basis of observation and interpretation, to find out the way people think and feel. It is a form of research in which the researcher gives more weight to the views of the participants. Case study, grounded theory, ethnography, historical and phenomenology are the types of qualitative research.

Definition of Quantitative Research

Quantitative research is a form of research that relies on the methods of natural sciences, which produces numerical data and hard facts. It aims at establishing cause and effect relationship between two variables by using mathematical, computational and statistical methods. The research is also known as empirical research as it can be accurately and precisely measured.

The data collected by the researcher can be divided into categories or put into rank, or it can be measured in terms of units of measurement. Graphs and tables of raw data can be constructed with the help quantitative research, making it easier for the researcher to analyse the results.

Key Differences Between Qualitative And Quantitative Research

The differences between qualitative and quantitative research are provided can be drawn clearly on the following grounds:

• Qualitative research is a method of inquiry that develops understanding on human and social sciences, to find the way people think and feel. A scientific and empirical research method that is used to generate numerical data, by employing statistical, logical and mathematical technique is called quantitative research.
• Qualitative research is holistic in nature while quantitative research is particularistic.
• The qualitative research follows a subjective approach as the researcher is intimately involved, whereas the approach of quantitative research is objective, as the researcher is uninvolved and attempts to precise the observations and analysis on the topic to answer the inquiry.
• Qualitative research is exploratory. As opposed to quantitative research which is conclusive.
• The reasoning used to synthesise data in qualitative research is inductive whereas in the case of quantitative research the reasoning is deductive.
• Qualitative research is based on purposive sampling, where a small sample size is selected with a view to get a thorough understanding of the target concept. On the other hand, quantitative research relies on random sampling; wherein a large representative sample is chosen in order to extrapolate the results to the whole population.
• Verbal data are collected in qualitative research. Conversely, in quantitative research measurable data is gathered.
• Inquiry in qualitative research is a process-oriented, which is not in the case of quantitative research.
• Elements used in the analysis of qualitative research are words, pictures, and objects while that of quantitative research is numerical data.
• Qualitative Research is conducted with the aim of exploring and discovering ideas used in the ongoing processes. As opposed to quantitative research the purpose is to examine cause and effect relationship between variables.
• Lastly, the methods used in qualitative research are in-depth interviews, focus groups, etc. In contrast, the methods of conducting quantitative research are structured interviews and observations.
• Qualitative Research develops the initial understanding whereas quantitative research recommends a final course of action.

Video: Qualitative Vs Quantitative Research

An ideal research is one, which is conducted by considering both the methods, together. Although, there are some particular areas which require, only one type of research which mainly depends on the information required by the researcher.  While qualitative research tends to be interpretative, quantitative research is concrete.

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• How we work

What is the Difference Between Qualitative and Quantitative Analysis

The right choice of research methodology is the starting point of the project's success. So, let's dive in.

Explaining the Difference Between Qualitative and Quantitative Analysis

Every research project in academia involves some way of managing the collected information. Hence, the choice of qualitative vs quantitative analysis becomes actual. To choose the right methods, you need to understand the core meaning of each type of analysis.

Let’s briefly discuss the difference between qualitative and quantitative analysis.

Qualitative

• Uses words.
• Helpful when you need to get insights or clarify some unobvious moments.
• Some popular methods are ethnography, interviews, focus groups, and literature reviews.
• Question type: open-ended questions.
• Analysis types: categorization, generalization, interpretation.
• Often used for understanding something

Quantitative

• Uses numbers, graphs, and statistics.
• Helpful when you need to get general information on the topic.
• Some popular methods are experiments, observations in numbers, and surveys.
• Question type: close-ended questions.
• Analysis types: mathematical and statistical.
• Often used to test/confirm facts

As you can see, the difference between these research methods is evident. Still, they both have the same thing – the risk of particular biases. We consider them next to make it easier to choose one for qualitative research vs quantitative research.

How to Choose Quantitative vs Qualitative Research

The formulation of the research question and the objectives of the study are critical for good reason. The question and purpose are the key factors in choosing qualitative vs quantitative research methods for your project. If your PhD project aims to understand how something works, what reactions it triggers, and group this data, you need qualitative analysis. Quantitative analysis is the right choice if you must explore cause-and-effect connections, identify patterns, and count them.

But let’s mention that quantitative methods often go hand in hand with qualitative methods. Moreover, the way you collect, classify & organize data is the analysis itself, which is the basis for statistical data analysis. Furthermore, combining qualitative and quantitative research methods in the study is important for a complete and objective picture. This is known as a mixed modes approach.

Pitfalls of Qualitative Research Proposal to Consider

PhD investigations may have pitfalls or biases regardless of the chosen methodology. Here, we describe the most popular ones from all the possible biases. Considering them is essential both for the investigation process and data processing. You can also use them to get more information on the difference between qualitative and quantitative methods.

• Hawthorne Effect. This bias may occur when participants change their behavior after learning that they are part of a research study.
• Observer Bias. When researchers have their own expectations and beliefs, that might influence how they interpret collected results.
• Recall Bias. Research participants may recollect past events or experiences inaccurately or with distortions.
• Social Desirability Bias. This happens when participants try to present themselves in a positive light when answering questions or describing their experiences.

All of these biases can distort data, and making conclusions based on them would be a mistake. Such a study wouldn’t accurately reflect reality and couldn’t be the basis for valid conclusions. Therefore, it’s essential to consider all these types of biases when writing qualitative research proposal.

Obstacles of Quantitive Research Analysis to Consider

Like in the previous case, quantitative analysis has biases that can affect the results’ accuracy. In this section of the article, we’ll briefly go over them.

• Information bias. The analyst collects data that doesn’t reflect reality. This may have various reasons. For example, if the researcher asks participants questions they can’t or don’t want to answer correctly. The same happens when the analyst doesn’t consider all possible sources of information.
• Missing variable bias. The researcher doesn’t consider all the factors that could affect their study’s results. This way, they may make a wrong conclusion.
• Sampling bias. It happens when the analyst chooses participants for their study in a way that means the results of their study can’t be applied to everyone else.
• Selection bias. This happens when the researcher treats data differently. This can arise when the researcher pressures participants to answer in a certain way.

How do you reduce the risk of bias in quantitive and qualitative analysis methods to zero? You can avoid bias in quantitative analysis by following three principles:

• Objectivity in your approach to the study.
• Accuracy in collecting and analyzing data.
• The study’s completeness excludes the factors affecting the results.

How to Ask Research Questions for a Qualitative Research Proposal

As we mentioned before, the research question and goals are at the core of choosing the methodology. The way you frame your research question will determine the direction of your study and how you analyze your data.

How to ask research questions for a qualitative research proposal

If it comes to qualitative methodology, the question should be open-ended. This means that there should not be a single, definitive answer. You have to formulate a question relevant to your research topic in an easy-to-understand way.

How to ask research questions within a quantitative study

In turn, quantitative studies involve close-ended questions. They also can provide multiple choice. And while qualitative methods help provide the context, quantitative ones help test & experiment. If you’re still looking for a good topic, check out these quantitative research topics for inspiration!

Mixed Qualitative vs Quantitative Research Methods (MMR)

In some cases, it is necessary to use mixed quantitative vs qualitative research methods. It helps to get a complete overview of the study results. Using them together, this Mixed Methods Research (MMR) can provide the most comprehensive and objective results and allow for more accurate conclusions. The best combination for your research project will depend on the specific study.

There are many different combinations of MMR. However, the most common MMRs are:

• Convergent.
• Exploratory.
• Instrumental.
• Transformative.

In addition, depending on how you use the difference between quantitative research vs qualitative research and collected data in the study, they divide MMR into types based on their use. These include concurrent, sequential, and integrated use of MMR.

Need More Help With Your Qualitative Research Proposal?

When developing a PhD research design, you often choose between qualitative and quantitative analysis methods. The steps to decide are to ask the right questions and select the research object. But you don’t always have to. If MMR can help you get a more complete picture and make better conclusions, then it’s the way to go.

Today, we have explained what is the difference between qualitative and quantitative analysis. We hope you find it useful. But if you still have trouble with your PhD research proposal, there is always an option to get help from our experts. We can assist you with everything from the topic selection to polishing the finished copy. Just give us a direction and wait a little while we pick up the best-suited specialist.

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• Research Process
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Choosing the Right Research Methodology: A Guide for Researchers

• 3 minute read
• 43.1K views

Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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What is the difference in the research objective between Qualitative versus quantitative study while writing a proposal?

Research is the vital tool to increase the knowledge about the study. There are two standard method of research in all field of science and social science, they are Qualitative and Quantitative research . Qualitative and Quantitative research provides a deep understanding of the problem in separate manner. Qualitative researches are often to be of understanding the personal views of the people, whereas quantitative researches are of concrete statistics and generalization of the population through the samples. However, there has been an increasing trend from early 90’s in combining the quantitative and qualitative research, which in turns delivers significant insights about the problem(Black, 1999). Further, there is always a confusion among the researchers to choose between the Qualitative and Quantitative research objective according to the phenomena considered. Thus, in this blog, I will list out the difference of Qualitative and Quantitative study in research perspective.

Qualitative research

Qualitative research is a type of physical, empirical, exploratory, direct, and empathic research. It helps the researcher to understand the opinions, reasons, causes or trends that hide behind the data. The face to face interview method of data collection is the most commonly used method in qualitative research in which a small sample of respondents is interviewed for a long time, even for hours.

Quantitative research

Quantitative analysis lets the researcher to calculate, using empirical data or only data that can then be quickly converted into statistics, and it tests a wide sample of respondents’ behaviors, views and attitudes. The reason for collecting more sample is that if there is more data then the results will be more accurate(Denscombe, 2010). The Data collection methods takes various forms such as online surveys, telephonic surveys, etc. However, the most common method of data collection is that the telephonic research or web research and the time taken for this type of data collection will be approximately 10 minutes for each respondents.

Different research methods are used in Qualitative and Quantitative research to collect and analyze data, and allows the researcher to answer varioustypes of research questions.In order to write the valid research proposal, you should know the difference between the two types of research methods(Denzin & Lincoln, 1994). This list of difference allows the researcher to make the conclusion about which type of research they going to do and which method of data collection is suitable.

Furthermore, in the quantitative research, the researchers will not involve in the interview process and the data collected based on different modes are then analysed to draw a valid conclusions. However, in the qualitative case, the researcher is directly involved in the data collection process. In qualitative research, only small sample size calculation is selected to understand the concept thoroughly, whereas in the quantitative study large samples are collected and generalize the results to the population. Quantitative research focus on testing theories or hypothesis whereas qualitative research formulate the theories or hypothesis(Glaser & Strauss, 1967). Quantitative data is analysed using advanced mathematical and statistical tools and techniques whereas qualitative data is often the type of summarization and interpreting from it. Quantitative study needs large samples whereas qualitative requires small samples and the questionnaire is of multiple choice in quantitative and open-ended type in qualitative.

Data collection

Data can be collected for this two types of research in various ways and the main thing to keep in mind is that it should be helpful to answer the research questions(Glaser & Strauss, 1967). Mostly, data collection may be either qualitative or quantitative. For instance, in case studies, the data can be in number like frequencies or rating scale, and in surveys, the data can be in the form of text, etc. Here, I will list out most common methods in both the research methods.

Quantitative data collection

• Experiments : Experimental variables can be controlled and used to identify the cause-and-effect relationships.
• Surveys : Survey questionnaire involves a list of multiple choice questions and the data can be collected through online mode, in person, or over the phone.
• Observations: In this case, the variables cannot be controlled and it is natural.

Qualitative data collection

• Interviews : The data is collected orally by asking open-ended questions to targeted samples.
• Ethnography : Observing the cultural behaviour closely in a community for long period.
• Focus groups: Gathering data from discussion among a group of people about a topic.
• Literature review : Collecting the already published works and making a review.

Conclusions

In summary, an innovative and interesting research is the one which include both the methods in the research together. However, there are certain research areas which require only one type of research as in few medical examinations and the selection of research methods is purely depend upon the researcher. Qualitative research is interpretative research whereas quantitative research is the concrete one. On the whole, I would suggest a rule thumb for selecting the right research method is that, if the researcher wants to confirm or test a theory or hypothesis then quantitative research is the key, and if the researcher wants to understand any concepts, thoughts, etc., then the qualitative research is the best tool. Further, it is recommended to the researchers to decide upon which method of research is need for the study, deeply understand the differences, and choose appropriately.

• Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics. Sage. https://books.google.com/books?hl=en&lr=&id=rJA7CgAAQBAJ&oi=fnd&pg=PT6&dq=Doing+quantitative+research+in+the+social+sciences:+An+integrated+approach+to+research+design&ots=t2YtwJuuV6&sig=DamT3WSXWivFSflztbTi0hGmOMQ
• Denscombe, M. (2010). The Good Research Guide: For Small-Scale Social Research Projects: for small-scale social research projects. McGraw-Hill Education. https://books.google.co.in/books?id=woS5P2Y6HFUC
• Denzin, N. K., & Lincoln, Y. S. (1994). Handbook of qualitative research. Sage Publications. https://books.google.co.in/books?id=u8hpAAAAMAAJ
• Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine. https://books.google.co.in/books?id=_4BGvgEACAAJ

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Can you differentiate between quantitative and qualitative research proposal?

Depends what you are looking for? With this sort of question it is obvious you are not so sure!

Quantitative Research   is used to quantify the problem by way of generating numerical data or data that can be transformed into useable statistics.

Qualitative Research  is primarily exploratory research.  It is used to gain an understanding of underlying reasons, opinions, and motivations.

Qualitative research is primarily exploratory research.  It is used to gain an understanding of underlying reasons, opinions, and motivations.

While quantitative research is used to quantify the problem by way of generating numerical data or data that can be transformed into useable statistics.

Quantitative Research is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics. It is used to quantify attitudes, opinions, behaviors, and other defined variables – and generalize results from a larger sample population. Quantitative Research uses measurable data to formulate facts and uncover patterns in research. Quantitative data collection methods are much more structured than Qualitative data collection methods. Quantitative data collection methods include various forms of surveys – online surveys , paper surveys , mobile surveys and kiosk surveys , face-to-face interviews, telephone interviews , longitudinal studies, website interceptors, online polls , and systematic observations

Qualitative Research is primarily exploratory research.  It is used to gain an understanding of underlying reasons, opinions, and motivations. It provides insights into the problem or helps to develop ideas or hypotheses for potential quantitative research. Qualitative Research is also used to uncover trends in thought and opinions, and dive deeper into the problem. Qualitative data collection methods vary using unstructured or semi-structured techniques. Some common methods include focus groups (group discussions), individual interviews, and participation/observations. The sample size is typically small, and respondents are selected to fulfill a given quota

Quantitative number of proposals are more but quality is not there. The quality proposal no need more proposals one is enough. That is accurate and specific.

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What’s the difference between an MBA and a DBA?

A DBA (Doctor of Business Administration) is technically a higher level business qualification than an MBA – but what are the differences between these two programs?

To find out, we spoke to Dr. Marika Taishoff , MBA Program Director, and Dr. Mariateresa Torchia , DBA Program Director and Director of Research.

1. Student Profile

Entrepreneurs and managers of any organization who have acquired substantial managerial experience and want to deepen and broaden their academic and research skills to treat managerial problems.

Individuals with at least three years of work experience looking to:

• enhance their careers in management;
• acquire new skills, managerial toolkits, and perspectives;
• switch career paths;
• familiarize themselves with new trends and challenges facing businesses today and tomorrow;
• or start and grow their own business.

Average age 36.

2. Program Goals

It offers a platform of critical reflection for the cooperative treatment of problems of business and non-business organizations based on a rigorous academic methodology.

Equip participants with the quantitative and qualitative toolkits and critical and strategic thinking skills to be rapidly applied in the workplace.

The MBA Executive Coach prepares participants to develop their professional brand, master the interview process, and identify and hone the skillsets required for their desired corporate or entrepreneurial objectives.

Through the Mentorship Program , MBAs meet regularly with successful executives and entrepreneurs in their field of interest, thus gaining true insights and building their professional networks , an underlying goal of the MBA program.

3. Teaching Methods

Advisors support and advise students regarding their literature review and research questions, their research proposal, publications, and the planning of implementation actions concerning their specific research problem.

During each of the six Residential Weeks , scholarly academics having substantial managerial consulting experience lead several Research Seminars on complementary subjects to the formal courses concerning issues related to the state of the student’s project work or to hot topics in research.

In addition, special Business Insights sessions provide the opportunity to exchange with and learn from experienced practitioners who can be role models for the DBA students.

Highly applied and hands-on approach , characterized by case study debate and discussion, dynamic interactions with classmates and faculty, and the preparation of individual and group projects around real-world issues, frequently assigned to the MBAs by corporations, entrepreneurs, or NGOs.

The aim is to bridge theory and practice and to foster collaborative and action-based learning through extensive group work.

4. Faculty Teaching Options

DBA : Full-time, tenure-track positions at teaching institutions.

MBA : Typically, part-time or adjunct positions.

5. Courses and Research

The program includes a common core of taught courses that provide knowledge and skills generally needed for conducting doctoral research and implementing research results.

The core courses cover the following content:  

• The bases of the philosophy of science,
• The application of qualitative and quantitative methods in business research,
• The foundations of competitiveness of business organizations,
• The challenges that organizations currently face.

The program includes workshops on: 

• Developing Research Questions
• Developing a Research Proposal
• Academic Writing
• Teaching Skills
• Research Presentation Skills.

MBA courses are designed around four key themes, which together provide a transversal view of the firm, its functions and activities, and its environment.

These themes are:

• Business Environment
• Business Markets
• Finance & Operations

The Integrative Component, which is the MBA Capstone, and links all the themes, gives participants the choice of doing an Entrepreneurial Business Plan or the Corporate Consulting Project.

MBAs are also offered electives in Wealth Management and Luxury, linked to the theme of “Learning from Monaco”.

The research required for the MBA is essentially non-empirical.

DBA : Academia, executive leadership, or consulting.

MBA : Executives in corporations, NGOs, and start-ups across all sectors. Consultants. Entrepreneurs.

7. Residency

DBA : Students attend classes 2 times a year (residential week) with online classes and assignments between sessions.

MBA : Students typically attend full-time on campus, but part-time and online are also possible.

8. Time Commitment

DBA : 3 years, around 3-5 hours of work per week.

MBA : Full-time: 10 months, around 10-30 hours per week. Part-Time: 20 months, around 6-12 hours per week.

For more information, you can consult our website:  www.monaco.edu.   Feel free to also contact the Admissions Team directly at  [email protected]