qualitative and quantitative research methodologies pdf

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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.

Quantitative vs. Qualitative Research Design: Understanding the Differences

As a future professional in the social and education landscape, research design is one of the most critical strategies that you will master to identify challenges, ask questions and form data-driven solutions to address problems specific to your industry. 

Many approaches to research design exist, and not all work in every circumstance. While all data-focused research methods are valid in their own right, certain research design methods are more appropriate for specific study objectives.

Unlock our resource to learn more about jump starting a career in research design — Research Design and Data Analysis for the Social Good .

We will discuss the differences between quantitative (numerical and statistics-focused) and qualitative (non-numerical and human-focused) research design methods so that you can determine which approach is most strategic given your specific area of graduate-level study. 

Understanding Social Phenomena: Qualitative Research Design

Qualitative research focuses on understanding a phenomenon based on human experience and individual perception. It is a non-numerical methodology relying on interpreting a process or result. Qualitative research also paves the way for uncovering other hypotheses related to social phenomena. 

In its most basic form, qualitative research is exploratory in nature and seeks to understand the subjective experience of individuals based on social reality.

Qualitative data is…

• often used in fields related to education, sociology and anthropology; 
• designed to arrive at conclusions regarding social phenomena; 
• focused on data-gathering techniques like interviews, focus groups or case studies; 
• dedicated to perpetuating a flexible, adaptive approach to data gathering;
• known to lead professionals to deeper insights within the overall research study.

You want to use qualitative data research design if:

• you work in a field concerned with enhancing humankind through the lens of social change;
• your research focuses on understanding complex social trends and individual perceptions of those trends;
• you have interests related to human development and interpersonal relationships.

Examples of Qualitative Research Design in Education

Here are just a few examples of how qualitative research design methods can impact education:

Example 1: Former educators participate in in-depth interviews to help determine why a specific school is experiencing a higher-than-average turnover rate compared to other schools in the region. These interviews help determine the types of resources that will make a difference in teacher retention. 

Example 2: Focus group discussions occur to understand the challenges that neurodivergent students experience in the classroom daily. These discussions prepare administrators, staff, teachers and parents to understand the kinds of support that will augment and improve student outcomes.

Example 3: Case studies examine the impacts of a new education policy that limits the number of teacher aids required in a special needs classroom. These findings help policymakers determine whether the new policy affects the learning outcomes of a particular class of students.

Interpreting the Numbers: Quantitative Research Design

Quantitative research tests hypotheses and measures connections between variables. It relies on insights derived from numbers — countable, measurable and statistically sound data. Quantitative research is a strategic research design used when basing critical decisions on statistical conclusions and quantifiable data.

Quantitative research provides numerical-backed quantifiable data that may approve or discount a theory or hypothesis.

Quantitative data is…

• often used in fields related to education, data analysis and healthcare; 
• designed to arrive at numerical, statistical conclusions based on objective facts;
• focused on data-gathering techniques like experiments, surveys or observations;
• dedicated to using mathematical principles to arrive at conclusions;
• known to lead professionals to indisputable observations within the overall research study.

You want to use quantitative data research design if:

• you work in a field concerned with analyzing data to inform decisions;
• your research focuses on studying relationships between variables to form data-driven conclusions;
• you have interests related to mathematics, statistical analysis and data science.

Examples of Quantitative Research Design in Education

Here are just a few examples of how quantitative research design methods may impact education:

Example 1: Researchers compile data to understand the connection between class sizes and standardized test scores. Researchers can determine if and what the relationship is between smaller, intimate class sizes and higher test scores for grade-school children using statistical and data analysis.

Example 2: Professionals conduct an experiment in which a group of high school students must complete a certain number of community service hours before graduation. Researchers compare those students to another group of students who did not complete service hours — using statistical analysis to determine if the requirement increased college acceptance rates.

Example 3: Teachers take a survey to examine an education policy that restricts the number of extracurricular activities offered at a particular academic institution. The findings help better understand the far-reaching impacts of extracurricular opportunities on academic performance.

Making the Most of Research Design Methods for Good: Vanderbilt University’s Peabody College

Vanderbilt University's Peabody College of Education and Human Development offers a variety of respected, nationally-recognized graduate programs designed with future agents of social change in mind. We foster a culture of excellence and compassion and guide you to become the best you can be — both in the classroom and beyond.

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• Education Policy M.P.P — an analysis-focused Master of Public Policy program designed for future leaders in education policy and practice, allowing students to specialize in either K-12 Education Policy, Higher Education Policy or Quantitative Methods in Education Policy. 
• Quantitative Methods M.Ed. — a data-driven Master of Education degree that teaches the theory and application of quantitative analysis in behavioral, social and educational sciences.

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Quantitative, Qualitative, and Mixed-Methods Approaches to Research 

• Applied Research Methods in Public and Nonprofit Organizations Mitchell Brown, & Kathleen Hale. (2014). Applied Research Methods in Public and Nonprofit Organizations: Vol. First edition. Jossey-Bass.

Please review Chapter 3. 

• Research Methodology & Design - Qualitative Research Methods National University Library (2024). Qualitative research methods. This guide features NU Library resources for studying and planning qualitative research methodology and design.
• Research Methodology & Design - Quantitative Research Methods National University Library (2024). Quantitative research methods. This guide features NU Library resources for studying and planning quantitative research methodology and design.
• Doing Qualitative Research Creswell, J. (2015). Doing qualitative research [Video]. Sage Research Methods. https://doi.org/10.4135/9781506363448 This video discusses the characteristics of a qualitative researcher and defines qualitative research. (9:55).
• Top Tips for Conducting Qualitative Research Hein, W. (2020). Top tips for conducting qualitative research [Video]. Sage Research Methods. https://doi.org/10.4135/9781529730708 This video reviews common student research problems, how to identify gaps in your research, figuring out what methods you favor, and challenging research problems. (9:32).
• An Introduction to Reasoning in Qualitative & Quantitative Research  Rivas, C. (2017). An introduction to reasoning in qualitative & quantitative research [Video]. Sage Research Methods. https://doi.org/10.4135/9781473992269 This video reviews perspective and reasoning styles, deductive reasoning, inductive reasoning, and abductive reasoning. (7:47).
• What Is Best: Quantitative, Qualitative or Mixed Approaches? O'Leary, Z. (2017). What is best: Quantitative, qualitative or mixed approaches? [Video]. Sage Research Methods. https://doi.org/10.4135/9781526400147 This video discusses quantitative, qualitative, and mixed approaches. (10:32).
• An Introduction to Mixed Methods Research Hitchcock, J. (2017). An introduction to mixed methods research [Video]. Sage Research Methods. https://doi.org/10.4135/9781473991712 This video reviews mixed methods research, how it can be used to address complex questions, and more. (16:05).
• SAGE Research Methods: Qualitative vs Quantitative Research – Video Search Result List Spend some time exploring the videos in the search results list provided. This is a great place to start as you complete your own research for your culminating project proposal.

Statistics  

• Elementary Statistics: A QuickStudy Digital Reference Guide Rumsey, D. (2020). Elementary Statistics: A QuickStudy Digital Reference Guide. QuickStudy Reference Guides.
• Introductory Statistics Eusea, J., Okwan, P., Belmasrour, R., Patterson, S., & Andrus, S. (2024). Introductory Statistics. Pressbooks.
• Basic Statistics Sandals29, & Leon, A. (2022). Basic Statistics. Pressbooks.
• College Statistics LePine, M. (2022). College Statistics. Pressbooks.
• Statistics for Research Students Fein, E. C., Gilmour, J., Machin, T., & Hendry, L. (2022). Statistics for Research Students. Pressbooks.
• Introduction to Probability and Statistics Pardoe, I. (2022). Introduction to Probability and Statistics. Pressbooks.
• Introduction to Statistics Lumen Learning, & OpenStax (2023). Introduction to Statistics. Pressbooks.
• From Questions To Answers – Statistics For Everyone Roth, A. (2024). From Questions To Answers – Statistics For Everyone. Pressbooks.

Using Artificial Intelligence in Research

Resources  .

• Artificial Intelligence: OpenAI, ChatGPT, Large Language Models, and You National University Library (2024). Artificial intelligence: OpenAI, ChatGPT, Large language models, and you. Explore this resource provided by National University to guide the ethical use of AI in your research.
• Using Generative AI Academic Success Center (2024). Using generative AI. Familiarize yourself with the National University Academic Success Center’s resources for using generative AI. Please note that the misuse of AI in courses constitutes a breach of academic integrity as defined by the Student Code of Conduct.

Videos 

• Using AI in Research Projects [Cory Lebson]. (2023, October 10). Using AI in Research Projects [Video]. LinkedIn Learning. This course gives tips and techniques for using generative AI to enhance, extend, and improve research projects of all types. (25m). Please make sure you are signed into OKTA prior to accessing the following LinkedIn Learning resources. Faculty, staff, and students have access to LinkedIn Learning and do not need to create an account.
• How to Research and Write Using Generative AI Tools [Dave Birss]. (2023, March 15). How to Research and Write Using Generative AI Tools [Video]. LinkedIn Learning. This course teaches strategies for writing effective prompts for generative AI. (1h 15m). Please make sure you are signed into OKTA prior to accessing the following LinkedIn Learning resources. Faculty, staff, and students have access to LinkedIn Learning and do not need to create an account.
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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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Qualitative vs. Quantitative Research

Research can be done using either qualitative or quantitative data. The decision of which data to use depends on the purpose of the research.

Qualitative Research:  Explores an unfamiliar topic, concept, or idea. The data for this research is hard to measure, provided by open ended questionnaires for example. It is presented in the research in descriptive, narrative, exploratory way where a vague topic needs to be clearly defined, explained, the problem should be identified in details.

Quantitative Research:  Explores a problem or correlation with variables (measurable data, numbers, or statistics) provided by surveys and questionnaires with closed ended questions for example. Usually uses large samples of data to warrant generalization.

Empirical Research:  The terms quantitative and empirical research are often used interchangeably. Empirical research however simply means any form of research that is directly observed.  Therefore not all empirical research is quantitative.

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• Systematic review
• Open access
• Published: 15 July 2024

Teamwork and implementation of innovations in healthcare and human service settings: a systematic review

• Elizabeth A. McGuier   ORCID: orcid.org/0000-0002-6219-6358 1 ,
• David J. Kolko 1 ,
• Gregory A. Aarons 2 , 3 , 4 ,
• Allison Schachter 5 , 6 ,
• Mary Lou Klem 7 ,
• Matthew A. Diabes 8 ,
• Laurie R. Weingart 8 ,
• Eduardo Salas 9 &
• Courtney Benjamin Wolk 5 , 6  

Implementation Science volume  19 , Article number:  49 ( 2024 ) Cite this article

Metrics details

Implementation of new practices in team-based settings requires teams to work together to respond to new demands and changing expectations. However, team constructs and team-based implementation approaches have received little attention in the implementation science literature. This systematic review summarizes empirical research examining associations between teamwork and implementation outcomes when evidence-based practices and other innovations are implemented in healthcare and human service settings.

We searched MEDLINE, CINAHL, APA PsycINFO and ERIC for peer-reviewed empirical articles published from January 2000 to March 2022. Additional articles were identified by searches of reference lists and a cited reference search for included articles (completed in February 2023). We selected studies using quantitative, qualitative, or mixed methods to examine associations between team constructs and implementation outcomes in healthcare and human service settings. We used the Mixed Methods Appraisal Tool to assess methodological quality/risk of bias and conducted a narrative synthesis of included studies. GRADE and GRADE-CERQual were used to assess the strength of the body of evidence.

Searches identified 10,489 results. After review, 58 articles representing 55 studies were included. Relevant studies increased over time; 71% of articles were published after 2016. We were unable to generate estimates of effects for any quantitative associations because of very limited overlap in the reported associations between team variables and implementation outcomes. Qualitative findings with high confidence were: 1) Staffing shortages and turnover hinder implementation; 2) Adaptive team functioning (i.e., positive affective states, effective behavior processes, shared cognitive states) facilitates implementation and is associated with better implementation outcomes; Problems in team functioning (i.e., negative affective states, problematic behavioral processes, lack of shared cognitive states) act as barriers to implementation and are associated with poor implementation outcomes; and 3) Open, ongoing, and effective communication within teams facilitates implementation of new practices; poor communication is a barrier.

Conclusions

Teamwork matters for implementation. However, both team constructs and implementation outcomes were often poorly specified, and there was little overlap of team constructs and implementation outcomes studied in quantitative studies. Greater specificity and rigor are needed to understand how teamwork influences implementation processes and outcomes. We provide recommendations for improving the conceptualization, description, assessment, analysis, and interpretation of research on teams implementing innovations.

Trial registration

This systematic review was registered in PROSPERO, the international prospective register of systematic reviews. Registration number: CRD42020220168.

Peer Review reports

Contributions to the Literature:

This paper reviews more than 20 years of research on teams and implementation of new practices in healthcare and human service settings.

We concluded with high confidence that adaptive team functioning is associated with better implementation outcomes and problems in team functioning are associated with poorer implementation outcomes. While not surprising, the implementation science literature has lacked clear empirical evidence for this finding.

Use of the provided recommendations will improve the quality of future research on teams and implementation of evidence-based practices.

Healthcare and human service providers (e.g., clinicians, case managers) often work in team-based settings where professionals work collaboratively with one another and service recipients toward shared goals [ 1 , 2 ]. Team-based care is intended to include multiple professionals with varying skills and expertise [ 1 , 3 ]. It requires shared responsibility for outcomes and increases team members’ dependence on one another to complete work [ 1 , 3 , 4 ]. Effective team-based care and higher quality teamwork are associated with improvements in care access and quality, patient safety, patient satisfaction, clinical outcomes, and costs [ 2 , 4 , 5 , 6 , 7 , 8 , 9 ].

We use the term ‘teamwork’ to refer to an array of team constructs using the input-mediator-outcome-input (IMOI) framework (Fig.  1 ) [ 10 , 11 , 12 ]. The IMOI framework recognizes that team interactions are dynamic and complex, with processes unfolding over time and feedback loops between processes, outcomes, and inputs [ 10 ]. Team inputs include team structure and composition, task demands, and contextual features [ 13 ]. Mediators are aspects of team functioning (i.e., what team members think, feel, and do [ 12 ]) through which inputs influence outcomes. These processes and emergent states may be cognitive, affective, or behavioral [ 5 , 14 , 15 , 16 ]. Team effectiveness outcomes are multidimensional and include team performance as well as team viability and the impact of the team on members’ development [ 12 , 17 , 18 , 19 ].

Conceptual model of team effectiveness and key terminology. Figure adapted from “Advancing research on teams and team effectiveness in implementation science: An application of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework” by E.A. McGuier, D.J. Kolko, N.A. Stadnick, L. Brookman-Frazee, C.B. Wolk, C.T. Yuan, C.S. Burke, & G.A. Aarons, 2023, Implementation Research and Practice , 4 , 26334895231190855. [CC BY-NC]

Implementation of new practices in team-based service settings requires team members to work together to respond to changing demands and expectations. Extensive research has identified barriers and facilitators to implementation of new practices at the individual provider, organization, and system levels; however, the team level has received little empirical attention [ 20 , 21 ]. This is a problem because implementation efforts increasingly rely on teams, and responses to a new practice are likely to be influenced by team characteristics and processes. See McGuier and colleagues [ 20 ] for an overview of team constructs in the context of implementation science and the Exploration, Preparation, Implementation, Sustainment (EPIS) framework [ 22 , 23 ]. Given increasing use of team-based care and interest in implementation strategies targeting teams, examining how teamwork is associated with implementation processes and outcomes is critical. This systematic review identified and summarized empirical research examining associations between teamwork and implementation outcomes when evidence-based practices (EBPs) and other innovations were implemented in healthcare and human service settings.

This systematic review was registered (PROSPERO; registration number: CRD42020220168) and conducted following the published protocol [ 24 ]. The review was conducted in accordance with PRISMA and SWiM guidance [ 25 , 26 ]; relevant checklists are in Additional File 1.

Information sources and search strategy

We searched the following databases: MEDLINE (Ovid), CINAHL (Ebsco), APA PsycINFO (Ovid), and ERIC (Ebsco). Database searches were run on August 7, 2020, and again on March 8, 2022. For all searches, a publication date from 2000 to current was applied; there were no language restrictions (see [ 24 ]). An experienced health sciences librarian (MLK) designed the Ovid MEDLINE search and translated that search for use in the other databases (see additional file in [ 24 ]). The search strings consisted of controlled vocabulary (when available) and natural language terms representing concepts of teamwork and implementation science or innovation or evidence-based practice. Results were downloaded to an EndNote (version X9.3.3) library and duplicate records removed [ 27 ]. Additional relevant articles were identified by hand searches of reference lists of included articles, a cited reference search for included articles in the Web of Science (Clarivate) bibliographic database (completed in February 2023), and requests sent to implementation science listservs and centers for suggestions of relevant articles.

Eligibility criteria

We included empirical journal articles describing studies using quantitative, qualitative, or mixed methods. Study protocols, reviews, and commentaries were excluded. All studies were conducted in healthcare or human service settings (e.g., hospitals, clinics, child welfare) and described the implementation of a practice to improve patient care. Studies of interventions to improve teamwork (e.g., team building interventions) and studies of teams created to implement the innovation (e.g., quality improvement teams, implementation support teams) were excluded. Eligible studies assessed at least one team construct and described its influence on implementation processes and outcomes.

Changes from protocol

Several changes were made from our systematic review protocol (PROSPERO CRD42020220168; [ 24 ]). Specifically, during the full-text review stage, we broadened the scope from team functioning (i.e., processes and states) to include team structure and performance because of the small number of studies that assessed and reported specific processes or states. This change increased the number of included studies. Similarly, because implementation outcomes were often inconsistently defined and poorly reported [ 28 , 29 , 30 ], we broadened our scope to include studies that identified team constructs as implementation determinants (i.e., barriers/facilitators) without explicitly defining and measuring an implementation outcome. Because of changes in university access to bibliographic databases, the cited reference search was performed in the Web of Science only instead of the Web of Science and Scopus. This bibliographic database indexes more than 21,000 scientific journals [ 31 ]. Lastly, because of time and resource constraints, we did not search conference abstracts or contact authors for unreported data.

Selection process and data extraction

Title/abstract screening and review of full-text articles were conducted by pairs of trained independent reviewers in DistillerSR. Conflicts were resolved through re-review, discussion between reviewers, and when needed, discussion with a senior team member (EAM). A final review of all included articles was conducted by EAM. Relevant data from each article was extracted into an Excel spreadsheet by one reviewer (AS). A second reviewer (EAM) conducted a line-by-line review and verification. Our data extraction form was informed by existing forms and guides (e.g., [ 32 , 33 ]). For each included study, we extracted information on measures of teamwork and implementation-relevant outcomes, characteristics of the setting, teams, and participants, analysis methods, and results. For quantitative studies, we recorded correlation coefficients and/or regression coefficients as standardized metrics of association. For qualitative studies, we recorded themes [ 33 ].

Quality and risk of bias assessment

The Mixed Methods Appraisal Tool (MMAT) [ 34 ] was used to evaluate quality and risk of bias for each included study. Multiple publications from the same study were evaluated separately because they reported different outcomes. Consistent with Powell and colleagues [ 35 ], quality evaluations were only made for the components of the study relevant to our question. Quality evaluations were conducted by two independent reviewers (EAM, MAD) with discrepancies resolved through consensus discussion. After completing the MMAT, the reviewers jointly categorized each article as high, moderate, or low quality. High quality studies were those with affirmative responses to all MMAT questions. Moderate quality studies had at least one minor methodological problem, and low-quality studies had serious flaws (e.g., qualitative studies with poor coherence between data, analysis, and conclusions; quantitative studies with biased samples and/or inappropriate statistical analyses).

We rated the relevance of each publication to our research question as high, moderate, or low. Highly relevant studies reported implementation of a well-defined innovation, thoroughly described team constructs and implementation outcomes, and clearly linked team constructs to implementation outcomes. Most studies rated as low relevance provided very limited information about teamwork and/or implementation outcomes. Studies that only described barriers/facilitators were rated as low or moderate relevance. Ratings were conducted by two independent reviewers (EAM, CBW) with discrepancies resolved through consensus discussion.

Data synthesis

We conducted a narrative synthesis of included studies following guidelines for synthesis without meta-analysis (SWiM) [ 36 ]. We prioritized reporting of high quality, highly relevant studies. Studies categorized as low quality and/or low relevance were not included in the synthesis but are included in the description of study characteristics to convey the breadth of the literature. We organized studies based on the IMOI framework (i.e., team inputs, processes/states, and outputs) and organized studies of processes/states by affective, behavioral, and cognitive constructs when possible. Because of the heterogeneity in team constructs and implementation outcomes, we were unable to quantitatively synthesize results using meta-analysis or formally investigate heterogeneity; this challenge is common in implementation science systematic reviews [ 30 ]. We assessed the strength of the overall body of evidence with GRADE for quantitative studies [ 37 ] and GRADE-CERQual for qualitative studies [ 38 , 39 ]. GRADE results in ratings of high, moderate, low, or very low quality of evidence for each outcome of interest. GRADE-CERQual results in ratings of high, moderate, low, or very low confidence in each review finding. GRADE ratings were made independently with discrepancies resolved through consensus discussion; GRADE-CERQual ratings were made through iterative discussions as recommended [ 39 ]. All ratings and decisions were made by the first and senior authors.

Search results

Our initial search, after removal of duplicates, yielded 7181 results. The second search (August 2020-March 2022) captured an additional 1341 results. The cited reference search yielded 1961 results. A total of 10,489 results were included in title/abstract review. Figure  2 provides a PRISMA flow diagram for included studies. After full-text review, 58 articles from 55 studies were included in analyses [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ].

PRISMA flow diagram of included articles. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71 . For more information, visit: http://www.prisma-statement.org/

As shown in Fig.  3 , publications on teamwork and implementation have increased substantially since 2000. Three articles on this topic (5%) were published between 2000 and 2007, 14 (24%) between 2008 and 2015, and 41 (71%) between 2016 and early 2023.

Included articles by year of publication

Study characteristics

Interrater agreement was good for assessment of study quality (81% agreement on MMAT questions) and ratings of relevance (88% agreement). There were 20 high quality articles, 23 moderate quality articles, and 15 low quality articles. Fourteen articles were rated as high relevance, 22 as moderate, and 22 as low relevance. Only 4 were rated as both high quality and high relevance. We report study characteristics for all 58 eligible articles. Our narrative synthesis includes 32 articles categorized as moderate/high quality and moderate/high relevance; it excludes 26 articles categorized as low quality and/or relevance.

Studies were conducted in inpatient healthcare ( n  = 22), outpatient/ambulatory healthcare ( n  = 21), mental health settings ( n  = 9), and other settings (e.g., residential facilities, multiple settings; n  = 6). There were 33 qualitative, 15 quantitative, and 10 mixed methods studies. All quantitative studies were descriptive observational studies.

Most studies examined team processes/states ( n  = 53); fewer examined team inputs ( n  = 27). Only two studies examined a team effectiveness outcome. The most common implementation outcomes were fidelity ( n  = 16) and other specified implementation outcomes (e.g., “extent of use,” “implementation success”) ( n  = 15). Less frequently identified implementation outcomes included adoption ( n  = 5), sustainment ( n  = 4), reach ( n  = 4), and perceptions of the innovation (e.g., acceptability, appropriateness, feasibility; n  = 3). Approximately one-third of studies ( n  = 21) did not report specific implementation outcomes but described implementation determinants (i.e., barriers and facilitators).

Synthesis: team inputs & implementation outcomes

Team inputs examined in studies included team stability/instability and staffing shortages, aspects of team structure and composition, interdependence, and hierarchy and professional roles. Quantitative findings are presented in Table  1 . A CERQual Summary of Qualitative Findings related to team inputs is shown in Table  2 . A CERQual Evidence Profile is provided in Additional File 2 (Table A1).

Team stability/instability and staffing shortages

Team stability/instability (i.e., consistency in membership over time) was examined in one mixed methods study [ 48 , 49 ] and three qualitative studies [ 70 , 81 , 94 ]. A study of surgical teams found variations in membership stability but no association between stability and “implementation success” (i.e., composite measure based on number of uses of new technique, proportion of uses, and changes in use) [ 48 , 49 ]. The authors suggested that stability facilitates the development of team coordination but that selecting small and exclusive teams may limit the spread of innovations within the organization. Another study found that a dedicated and stable team in which members were selected and trained together in the use of a new surgical technique led to quicker uptake and better integration into practice, theorizing that dedicated and stable teams increased trust, motivation, and collaborative problem-solving [ 81 ]. However, dedicated teams were difficult to sustain, and some sites instead used rotating team members from a larger pool of trained staff. In rural primary care, stability of team members facilitated sustainment of memory care clinics [ 70 ]. Lastly, another study in primary care found mixed perceptions of stable vs. rotating staff when adding a new team role (i.e., health coach); some team members liked rotating through different roles while others wanted more stability [ 94 ]. Across studies, we found that dedicated and stable team members facilitate implementation while instability in team membership is a barrier to implementation (moderate confidence).

Qualitative studies identified staffing shortages and turnover on teams as barriers to implementation [ 50 , 67 , 75 , 78 , 92 ]. In Veterans Health Administration (VA) clinics, “inadequate staffing posed an insurmountable barrier,” hindering communication and delivery of optimal care during the implementation of the patient-centered medical home (PCMH) model [ 92 ]. Similarly, staff shortages, turnover, and high workloads hindered guideline implementation in Kenyan hospitals [ 75 ]. Two studies found negative impacts of staffing shortages and turnover on sustainment. Staff turnover contributed to discontinuity in Dialectical Behavior Therapy (DBT) team members [ 78 ], and appropriate staffing (i.e., ensuring manageable workloads) and blocking time for team members were identified as critical to sustainment of a team-based model in the VA [ 67 ]. We found that staffing shortages and turnover hinder implementation (high confidence).

Team structure/composition

Studies examined multiple aspects of team structure and composition, specifically team size, workload, longevity (i.e., how long team members had worked together), history of change, and team member characteristics. Team size was examined in two studies of DBT. In a mixed methods study, team size was positively correlated with fidelity, and qualitative data suggested that team size may increase as a result of successful implementation [ 47 ]. In contrast, another study found that DBT team size was not associated with the number of DBT components adopted and was negatively associated with reach, suggesting reach may reflect high workloads [ 72 ]. In VA mental health clinics, team workload (i.e., number of patients seen) was negatively associated with sustainment of trauma-focused therapies [ 68 ]. In these studies, team longevity and history of change were not associated with implementation outcomes [ 47 , 68 ]. Team member characteristics, specifically team member competency/expertise, experience, and commitment/engagement, were identified as facilitators of implementation in some qualitative studies [ 40 , 70 , 81 , 84 , 95 ].

Overall, few findings could be made from quantitative studies examining team structure and composition. Two studies of team size found mixed results, and workload, longevity, and history of change were examined in only one study each. Across qualitative studies, we found team member competency/expertise, experience, and commitment/engagement facilitate implementation (moderate confidence).

Team interdependence

One quantitative study examined team interdependence [ 65 ]. In multidisciplinary child abuse teams implementing a mental health screening/referral protocol, task interdependence (i.e., reliance on team members to share resources and coordinate workflows) was positively associated with reach but not time to adoption. Outcome interdependence (i.e., extent to which outcomes are evaluated at the team vs. individual level) was significantly negatively correlated with time to adoption but not reach. Neither task nor outcome interdependence were associated with team members’ perceptions of acceptability, appropriateness, or feasibility of the innovation [ 65 ]. Because only one study examined interdependence, no review findings were made.

Hierarchy & professional roles

Hierarchy, power distributions, and rigid roles were identified as barriers to implementation in several qualitative studies [ 50 , 53 , 74 , 97 ]. Flatter hierarchies (i.e., more equal distribution of power and authority) supported guideline implementation in pediatric primary care; practices with low compliance to guidelines had more hierarchical relationships while practices with high compliance had more shared decision-making [ 97 ]. In a setting with hierarchy and rigid division of roles, nurses trained in an innovation reported concern that their decisions would be questioned by physicians without expertise in the innovation but more authority [ 74 ]. Similarly, in surgical teams, rigid professional roles and a hierarchical team culture constrained open discussion and created contention over how and when a “time-out” should be completed, resulting in inconsistent use and poor fidelity [ 50 , 53 ]. Across studies, we found that in multidisciplinary settings, rigid professional roles, hierarchical relationships, and power differentials are barriers to implementation (moderate confidence).

Summary of team inputs & implementation outcomes

There was no overlap among team input variables and implementation outcomes examined in quantitative studies (Table  1 ). Accordingly, we were unable to generate estimates of effects or ratings of evidence quality. Qualitative review findings are shown in Table  2 . We found: 1) Dedicated and stable team members facilitate implementation while instability in team membership is a barrier to implementation (moderate confidence); 2) Staffing shortages and turnover hinder implementation (high confidence); 3) Team member competency/expertise, experience, and commitment/engagement facilitate implementation (moderate confidence); and 4) In multidisciplinary settings, rigid professional roles, hierarchical relationships, and power differentials are barriers to implementation (moderate confidence).

Synthesis: team processes/states & implementation outcomes

Studies examined overall team functioning as well as specific affective states, behavioral processes, and cognitive states. Quantitative findings are presented in Table  3 , and a GRADE Evidence Profile is provided in Additional File 2 (Table A2). A CERQual Summary of Qualitative Findings related to team processes and states is shown in Table  4 . A CERQual Evidence Profile is provided in Additional File 2 (Table A3).

Overall team functioning

Nine studies examined quantitative associations between overall team functioning and implementation outcomes. Team functioning was positively associated with intervention fidelity in 2 of 3 studies. One study examined implementation of transition programs for adolescents with chronic health conditions in 29 teams. More positive team climate, measured by the Team Climate Inventory (i.e., shared vision, participative safety, task orientation, support for innovation), at study start was associated with greater improvements in quality of chronic care delivery one year later [ 45 ]. Additionally, improvements in team climate were associated with greater improvement in care delivery [ 45 ]. These findings were consistent across teams working with different patient populations, suggesting the influence of team climate generalizes across teams and settings [ 45 ]. Greater team climate for innovation was also associated with greater fidelity (i.e., implementation of more program elements) among DBT teams [ 47 ]. In contrast, no significant associations were found between team climate and fidelity to a multifaceted cardiovascular disease management intervention, with qualitative data suggesting variation in the influence of teamwork across practices [ 77 ]. There was no overlap in the metrics of association reported in these studies; therefore, we were unable to generate an estimate of the effect of team functioning on fidelity. The quality of the evidence for fidelity was rated very low because of serious methodological limitations, serious inconsistency, and very serious imprecision due to the small number of studies.

Three studies examined associations between teamwork and adoption, with no significant associations found. The first study found that teamwork climate (i.e., perceived quality of collaboration between personnel) was not significantly associated with adoption of a comprehensive safety program in intensive care units, although there were associations between adoption and organizational constructs (e.g., lower safety climate, more management support) [ 59 ]. In a study of DBT teams, neither positive nor negative team functioning was associated with the number of DBT modes adopted [ 72 ]. The third study assessed relational coordination (i.e., shared goals, communication, respect) in primary care practices implementing patient engagement strategies. Relational coordination was high across practices initially and did not differ for practices with high vs. low adoption, although it increased over time in practices with high adoption [ 83 ]. There was no overlap in the metrics of association reported in these studies; therefore, we were unable to generate an estimate of the effect of team functioning on adoption. The quality of the evidence was rated very low because of serious methodological limitations and very serious imprecision due to the small number of studies.

Reach and sustainment were each examined in one quantitative study. DBT teams with more negative functioning had greater reach, suggesting that reach may reflect high workloads; positive functioning was not associated with reach [ 72 ]. In VA mental health clinics, team functioning was positively correlated with sustainment of evidence-based trauma-focused psychotherapies and significantly associated with sustainment after controlling for covariates [ 68 ]. Two studies examined other implementation outcomes. One found that better team functioning was associated with greater implementation of changes to improve access to care in VA clinics [ 62 ]. In the other, primary care practices reporting better teamwork were more likely to be in later stages of transformation to PCMHs than practices with poorer teamwork [ 88 ]. Because of the small number of studies examining reach, sustainment, and other implementation outcomes, we were unable to generate estimates of effects or ratings of evidence quality for these outcomes.

Our qualitative review findings are based on 12 studies describing how team functioning influenced implementation processes and outcomes. There was considerable variation across studies in how team functioning was defined and what implementation outcomes were examined. Most findings were based on relatively thin and superficial data. Studies occurred in a variety of healthcare settings with varying resources and implemented diverse interventions. We found with high confidence that 1) Adaptive team functioning, characterized by positive affective states (e.g., trust, mutual respect, belonging), effective behavior processes (e.g., frequent communication and coordination), and shared cognitive states (e.g., clear roles, shared mental models of how to provide care), facilitates implementation and is associated with better implementation outcomes; and 2) Problems in team functioning, including negative affective states (e.g., tension, lack of trust), problematic behavioral processes (e.g., conflict, competition, poor communication), and a lack of shared cognitive states (e.g., unclear roles, lack of shared awareness, competing goals), act as barriers to implementation and are associated with poor implementation outcomes.

Affective states

Specific affective states were examined in one quantitative study, three mixed methods studies, and one qualitative study. There was no overlap in the associations between affective states and implementation outcomes reported in quantitative studies (Table  3 ). In a study of multidisciplinary teams responding to child abuse, affective integration (i.e., liking, trust, respect) was positively associated with acceptability, appropriateness, and feasibility but not time to adoption or reach [ 65 ]. In DBT teams, cohesion was associated with fidelity, and qualitative data indicated that liking one’s team members and having a shared team identity were critical to effective implementation [ 47 ]. Another study of DBT teams described conflicts and lack of safety and trust within teams resulting in their dissolution [ 78 ].

Edmondson and colleagues found that psychological safety and ease of speaking up (i.e., interpersonal climate that allows members to share questions and concerns) were associated with implementation success [ 48 , 49 ]. In teams with low psychological safety, lower-status team members were hesitant to speak up, hindering change and proficiency in the new practice [ 49 ]. Psychological safety was closely related to learning behavior within the team, including speaking up with questions and concerns [ 48 , 49 ]. From the mixed methods and qualitative studies, we found that trust, cohesion, and psychological safety within teams facilitate implementation by contributing to team members’ willingness to speak up and share experiences and feedback. Negative affective states, fear of judgment, conflict, and lack of safety hinder implementation (moderate confidence).

Behavioral processes

Specific behavioral processes, including communication, learning behavior, and coordination, were examined in two quantitative studies, two mixed methods studies, and five qualitative studies. There was no overlap in the associations between behavioral processes and implementation outcomes reported in quantitative studies (Table  3 ).

Only one study reported quantitative findings for communication. Communication in DBT teams was positively associated with fidelity [ 47 ]. Qualitative studies frequently identified communication as a determinant of implementation (Table  4 ). From qualitative studies, we found that open, ongoing, and effective communication within teams facilitates implementation of new practices; poor communication is a barrier (high confidence).

Quantitative associations between team learning behavior and implementation outcomes were reported in three studies. Team learning behavior in child abuse teams was positively correlated with acceptability and feasibility; it was not associated with appropriateness, time to adoption, or reach [ 65 ]. Learning behavior was positively associated with knowledge and use of innovations in nursing teams [ 91 ] and with implementation success in surgical teams [ 48 ]. Because each of these studies examined different implementation outcomes, we were unable to generate an estimate of the effect of learning behavior or rate evidence quality.

Inter-team communication, specifically speaking up and learning from other teams (i.e., boundary spanning), was identified as a critical part of team learning processes associated with successful implementation [ 48 ]. Communication beyond the team was also identified as a facilitator of implementation in two qualitative studies [ 47 , 75 ]. We found that communication beyond the team facilitates implementation by providing opportunities for team learning (low confidence).

Lastly, two qualitative studies examined coordination among healthcare teams [ 40 , 95 ]. Findings were somewhat ambiguous and based on thin data. We found with low confidence that poor coordination among healthcare professionals interferes with providing high-quality care and can be a barrier to implementation of new approaches (low confidence).

Cognitive states

Specific cognitive states were examined in two quantitative studies. There was no overlap in the associations between cognitive states and implementation outcomes reported (Table  3 ). The first study found no significant associations between shared goals and implementation outcomes [ 65 ]. The second study found that greater team knowledge and skills were associated with greater implementation of key changes to improve access to care; team problem recognition was not associated with implementation [ 62 ].

Two studies reported qualitative findings related to shared goals. In VA mental health teams, shared mission differentiated teams with sustained high reach of EBPs from those with low reach [ 84 ]. Commitment to a shared goal consistent with the EBP supported sustainment [ 84 ]. Similarly, shared goals and vision were identified as a facilitator of DBT programs [ 47 ]. We found that shared goals, mission, and vision within teams facilitate implementation and sustainment (low confidence).

Summary of team processes/states & implementation outcomes

There was very little overlap in the reported associations between team processes/states and implementation outcomes (Table  3 ). We were unable to generate estimates of effects for any associations. When there was sufficient overlap to rate evidence quality, the evidence was rated very low quality (Table A2, Additional File 2).

Qualitative review findings are shown in Table  4 . We found the following: 1) Adaptive team functioning, characterized by positive affective states (e.g., trust, mutual respect, belonging), effective behavior processes (e.g., frequent communication and coordination), and shared cognitive states (e.g., clear roles, shared mental models of how to provide care), facilitates implementation and is associated with better implementation outcomes (high confidence); 2) Problems in team functioning, including negative affective states (e.g., tension, lack of trust), problematic behavioral processes (e.g., conflict, competition, poor communication), and a lack of shared cognitive states (e.g., unclear roles, lack of shared awareness, competing goals), act as barriers to implementation and are associated with poor implementation outcomes (high confidence); 3) Trust, cohesion, and psychological safety within teams facilitate implementation by contributing to team members’ willingness to speak up and openly share experiences and feedback. Negative affective states, fear of judgment, conflict, and lack of safety hinder implementation (moderate confidence); 4) Open, ongoing, and effective communication within teams facilitates implementation of new practices; poor communication is a barrier (high confidence); 5) Communication beyond the team facilitates implementation by providing opportunities for team learning (low confidence); 6) Poor coordination among healthcare professionals interferes with providing high-quality care and can be a barrier to implementation of new approaches (low confidence); and 7) Shared goals, mission, and vision within teams facilitate implementation and sustainment (low confidence).

Synthesis: team effectiveness outcomes & implementation outcomes

Team effectiveness outcomes are multidimensional and include performance (i.e., productivity, efficiency, and quality of the team’s work), team viability, and the impact of the team on members’ development [ 12 , 17 , 18 , 19 ]. Only two studies examined associations between team effectiveness and implementation outcomes. Quantitative findings are presented in Table  5 . One quantitative study found that team members’ ratings of team performance were associated with innovation acceptability, appropriateness, and feasibility; performance was not associated with time to adoption or reach [ 65 ]. One qualitative study found that positive outcomes for team members (e.g., increased comfort working together, greater knowledge) were associated with sustainment [ 70 ]. No studies examined associations of team viability and implementation outcomes.

Summary of team effectiveness outcomes & implementation outcomes

Only one quantitative study examined associations between a dimension of team effectiveness and implementation outcomes (Table  5 ). Accordingly, we were unable to generate ratings of evidence quality or estimates of any effects. Similarly, because there was only one qualitative study, we were unable to make a review finding.

This systematic review summarizes over 20 years of empirical literature on the associations between teamwork and implementation outcomes in the context of implementation of new practices in health and human services. Consistent with increased attention to teams and reliance on team-based models of care, as well as the growth of implementation science, studies increased substantially over time. We included studies that used quantitative, qualitative, or mixed methods, yielding a total of 58 articles representing 55 studies. Included studies spanned naturalistic implementation evaluations and planned implementation research.

Key findings with high confidence were: 1) Staffing shortages and turnover hinder implementation, 2) Adaptive team functioning, characterized by positive affective states, effective behavior processes, and shared cognitive states, facilitates implementation and is associated with better implementation outcomes. Problems in team functioning, including negative affective states, problematic behavioral processes, and a lack of shared cognitive states, act as barriers to implementation and are associated with poor implementation outcomes, and 3) Open, ongoing, and effective communication within teams facilitates implementation of new practices; poor communication is a barrier. Our results generally align with conventional wisdom and scientific research outside of healthcare, increasing confidence in the findings. Team effectiveness and change management research in other types of organizations and settings (e.g., military, aviation, space exploration) [ 98 , 99 , 100 , 101 , 102 , 103 ] is largely converging.

Overall, the literature was heterogeneous, and many studies lacked specificity regarding team composition and implementation activities and outcomes. Teamwork was defined and measured inconsistently and with limited precision across studies, which hindered our ability to draw conclusions about how teams influence implementation processes and outcomes. There was also poor measurement and reporting of implementation outcomes in many studies, consistent with a recent review of research on implementation outcomes [ 28 , 29 ]. Many studies used broad measures encompassing multiple dimensions of teamwork. Among studies that assessed specific team processes and states, there was very little overlap across constructs assessed. Qualitative studies identified a rich array of specific team processes and states; research to confirm the presence of these factors in other settings and determine their associations with implementation outcomes is needed.

In Table  6 , we summarize the limitations of existing research on teams and implementation and provide recommendations for future research. Notably, increased specificity and rigor in how teamwork is conceptualized and assessed is needed to advance our understanding of how teamwork affects implementation processes and outcomes. Limited inclusion of teams and team constructs in implementation theories, models, and frameworks has likely contributed to the neglect of teams in implementation science [ 20 ]. Updates to theories, models, and framework should consider integrating teams and team-level constructs [ 20 ]. In addition, there are well-established theories of team effectiveness that could inform hypotheses about how specific team constructs affect implementation [ 104 , 105 , 106 , 107 ].

There is considerable room for improvement in the definition and description of teams and analysis of data from teams. Describing the structure and purpose of teams, as well as interdependencies within the team, can help differentiate teams from groups of individuals who do not constitute a team, an important conceptual distinction that can be difficult to discern in study descriptions. Reporting of sampling and recruitment procedures for teams and team-level response rates is needed. For quantitative studies, use of standardized, validated measures of teamwork constructs is recommended. Researchers should be careful to base inferences about teams on team-level data. Lastly, future research should follow recommendations for improving measurement and reporting of implementation outcomes [ 29 , 108 ] and consider the multilevel context of teams in theory, measurement, analysis, and interpretation of results [ 109 ].

Limitations

As with all systematic reviews, it is possible that we failed to identify some relevant articles or data. We did not search gray literature or conference abstracts or contact authors for unreported data. Our organization of studies by the IMOI framework is likely imperfect given the broad array of team constructs included and poor reporting in many studies. We included diverse innovations intended to improve patient care, including specific EBPs, clinical practice guidelines, models of care, care bundles, procedural changes, and technological innovations. This diversity in objects of implementation reflects ongoing debates about the necessary strength of evidence for objects of implementation and varying thresholds in different contexts [ 110 ]. In this review, high quality studies tended to involve clinical interventions with strong research evidence (e.g., DBT) and clinics in structured and often team-based healthcare systems (e.g., VA). Diversity of innovations and settings provides greater external validity for our findings but may mask some findings specific to certain innovations or settings.

We only included studies of existing teams providing clinical services, however, many studies provided limited descriptions of teams, and in some cases the distinction between clinical teams and implementation/quality improvement teams was unclear. There is increasing attention to use of teams in implementation frameworks [ 20 , 111 ] and evidence that functioning of implementation teams matters [ 112 , 113 ]. Research on the composition and functioning of implementation teams is an important area for future research.

Our systematic review findings indicate that teamwork matters for implementation. However, greater specificity and rigor are needed to advance our understanding of how teamwork influences implementation processes and outcomes. We provide recommendations for improving the conceptualization, description, assessment, analysis, and interpretation of research on teams implementing new practices.

Availability of data and materials

All data cited in this review came from published papers and are therefore already available. The data created as part of the review process are included in this published article and its supplementary information files.

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Acknowledgements

Thank you to Ikzzui Chu, Jamie Feldman, Grace Kinkler, Rachael Park, and Jaely Wright for their assistance with article screening.

This work was supported by the National Institute of Mental Health grants MH123729 (EAM), MH124914 (DJK), and MH126231 (GAA), the National Cancer Institute U01CA275118 (GAA), National Institute on Drug Abuse R01DA049891 (GAA), the Agency for Healthcare Research and Quality grant R18HS026862 (CBW), and the Collaboration and Conflict Research Lab at Carnegie Mellon University Tepper School of Business. The content of this manuscript does not represent the views of funding agencies and is solely the responsibility of the authors.

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McGuier, E.A., Kolko, D.J., Aarons, G.A. et al. Teamwork and implementation of innovations in healthcare and human service settings: a systematic review. Implementation Sci 19 , 49 (2024). https://doi.org/10.1186/s13012-024-01381-9

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12 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analysed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in primary research . Qualitative research can also come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as biology, chemistry, psychology, economics, sociology, and marketing .

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will aim to use a sample size that is representative of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

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Qualitative vs quantitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Quantitative research focuses on the thoughts, feelings, and values of a participant , to understand why people act in the way they do . They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender , social class, political alignment, likely favoured products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behaviour and actions of people.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

• Surveys : A survey is a simple-to-create and easy-to-distribute research method , which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

• Face-to-face or phone interviews: Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions . The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

• Polls: Polls can be a shorter version of surveys , used to get a ‘flavour’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

• Test Scores
• Percent of training course completed
• Performance score out of 100
• Number of support calls active
• Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:

Descriptive

This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead, data analysis will suggest a conclusion , which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

• A description of child’s Christmas gifts they received that year
• A description of what businesses sell the most of during Black Friday
• A description of a product issue being experienced by a customer

Correlational

This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis methods looks at the variables in a natural environment.

Examples of correlational quantitative design include:

• The relationship between a child’s Christmas gifts and their perceived happiness level
• The relationship between a business’ sales during Black Friday and the total revenue generated over the year
• The relationship between a customer’s product issue and the reputation of the product

Causal-Comparative/Quasi-Experimental

This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

• The effect of children’s Christmas gifts on happiness
• The effect of Black Friday sales figures on the productivity of company yearly sales
• The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

• The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
• The effect of Black Friday sales on the success of the company
• The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarise our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

• Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
• Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
• Less bias : There are higher levels of control that can be applied to the research, so bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

• Can’t understand reasons: Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
• Useful for simpler situations: Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

• Identifying which advert or landing page performs better
• Identifying how satisfied your customers are
• How many customers are likely to recommend you
• Tracking how your brand ranks in awareness and customer purchase intent
• Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

• Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
• Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
• Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
• Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
• Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

How Qualtrics products can enhance & simplify the quantitative research process

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting quantitative research. From survey creation and data collection to statistical analysis methods and data reporting, it can help all your internal teams gain insights from your numerical data.

Quantitative methods are catered to your business through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of quantitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Stats IQ™ and Driver IQ™ make analysing numerical data easy and simple. Choose to highlight key findings based on variables or highlight statistically insignificant findings. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualisations quickly:

Find out how Qualtrics XM solution can help improve research team’s quantitative methods

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