Child Care and Early Education Research Connections

Descriptive research studies.

Descriptive research is a type of research that is used to describe the characteristics of a population. It collects data that are used to answer a wide range of what, when, and how questions pertaining to a particular population or group. For example, descriptive studies might be used to answer questions such as: What percentage of Head Start teachers have a bachelor's degree or higher? What is the average reading ability of 5-year-olds when they first enter kindergarten? What kinds of math activities are used in early childhood programs? When do children first receive regular child care from someone other than their parents? When are children with developmental disabilities first diagnosed and when do they first receive services? What factors do programs consider when making decisions about the type of assessments that will be used to assess the skills of the children in their programs? How do the types of services children receive from their early childhood program change as children age?

Descriptive research does not answer questions about why a certain phenomenon occurs or what the causes are. Answers to such questions are best obtained from  randomized and quasi-experimental studies . However, data from descriptive studies can be used to examine the relationships (correlations) among variables. While the findings from correlational analyses are not evidence of causality, they can help to distinguish variables that may be important in explaining a phenomenon from those that are not. Thus, descriptive research is often used to generate hypotheses that should be tested using more rigorous designs.

A variety of data collection methods may be used alone or in combination to answer the types of questions guiding descriptive research. Some of the more common methods include surveys, interviews, observations, case studies, and portfolios. The data collected through these methods can be either quantitative or qualitative. Quantitative data are typically analyzed and presenting using  descriptive statistics . Using quantitative data, researchers may describe the characteristics of a sample or population in terms of percentages (e.g., percentage of population that belong to different racial/ethnic groups, percentage of low-income families that receive different government services) or averages (e.g., average household income, average scores of reading, mathematics and language assessments). Quantitative data, such as narrative data collected as part of a case study, may be used to organize, classify, and used to identify patterns of behaviors, attitudes, and other characteristics of groups.

Descriptive studies have an important role in early care and education research. Studies such as the  National Survey of Early Care and Education  and the  National Household Education Surveys Program  have greatly increased our knowledge of the supply of and demand for child care in the U.S. The  Head Start Family and Child Experiences Survey  and the  Early Childhood Longitudinal Study Program  have provided researchers, policy makers and practitioners with rich information about school readiness skills of children in the U.S.

Each of the methods used to collect descriptive data have their own strengths and limitations. The following are some of the strengths and limitations of descriptive research studies in general.

Study participants are questioned or observed in a natural setting (e.g., their homes, child care or educational settings).

Study data can be used to identify the prevalence of particular problems and the need for new or additional services to address these problems.

Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research."

Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples.

Limitations:

Descriptive studies cannot be used to establish cause and effect relationships.

Respondents may not be truthful when answering survey questions or may give socially desirable responses.

The choice and wording of questions on a questionnaire may influence the descriptive findings.

Depending on the type and size of sample, the findings may not be generalizable or produce an accurate description of the population of interest.

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

  • Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
  • Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
  • Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
  • Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
  • Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
  • Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

  • A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
  • A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
  • An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
  • A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
  • An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

  • Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
  • Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
  • Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
  • Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
  • Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
  • I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
  • Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

  • Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
  • Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
  • Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
  • Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
  • Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
  • Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

  • To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
  • To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
  • To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
  • To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

  • Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
  • Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
  • Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
  • Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
  • Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
  • Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

  • Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
  • Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
  • Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
  • Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
  • Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
  • Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

  • Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
  • Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
  • Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
  • Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
  • Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
  • Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

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  • Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

  • How has the London housing market changed over the past 20 years?
  • Do customers of company X prefer product Y or product Z?
  • What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
  • What are the most popular online news sources among under-18s?
  • How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

  • Describing the demographics of a country or region
  • Gauging public opinion on political and social topics
  • Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA). Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

The results & analysis section in a dissertation

Overview: Quantitative Results Chapter

  • What exactly the results/findings/analysis chapter is
  • What you need to include in your results chapter
  • How to structure your results chapter
  • A few tips and tricks for writing top-notch chapter

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

The results and discussion chapter are typically split

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

There must be alignment between your research aims objectives and questions

As a general guide, your results chapter will typically include the following:

  • Some demographic data about your sample
  • Reliability tests (if you used measurement scales)
  • Descriptive statistics
  • Inferential statistics (if your research objectives and questions require these)
  • Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

dissertation descriptive study

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Your chapter must have a golden thread

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

  • What age range are they?
  • How is gender distributed?
  • How is ethnicity distributed?
  • What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

Communicate the data

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Descriptive statistics

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

  • The mean – this is simply the mathematical average of a range of numbers.
  • The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
  • The mode – this is the most commonly repeated number in the data set.
  • Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
  • Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
  • Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Dive into the inferential statistics

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

make it easy for your reader to understand your quantitative results

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

  • Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
  • Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
  • Set your significance level (this is usually 0.05)
  • Calculate your statistics and find your p-value (e.g., p=0.01)
  • Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

  • When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
  • Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
  • Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
  • Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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Characteristics of Qualitative Descriptive Studies: A Systematic Review

MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing

Justine S. Sefcik

MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing

Christine Bradway

PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing

Qualitative description (QD) is a term that is widely used to describe qualitative studies of health care and nursing-related phenomena. However, limited discussions regarding QD are found in the existing literature. In this systematic review, we identified characteristics of methods and findings reported in research articles published in 2014 whose authors identified the work as QD. After searching and screening, data were extracted from the sample of 55 QD articles and examined to characterize research objectives, design justification, theoretical/philosophical frameworks, sampling and sample size, data collection and sources, data analysis, and presentation of findings. In this review, three primary findings were identified. First, despite inconsistencies, most articles included characteristics consistent with limited, available QD definitions and descriptions. Next, flexibility or variability of methods was common and desirable for obtaining rich data and achieving understanding of a phenomenon. Finally, justification for how a QD approach was chosen and why it would be an appropriate fit for a particular study was limited in the sample and, therefore, in need of increased attention. Based on these findings, recommendations include encouragement to researchers to provide as many details as possible regarding the methods of their QD study so that readers can determine whether the methods used were reasonable and effective in producing useful findings.

Qualitative description (QD) is a label used in qualitative research for studies which are descriptive in nature, particularly for examining health care and nursing-related phenomena ( Polit & Beck, 2009 , 2014 ). QD is a widely cited research tradition and has been identified as important and appropriate for research questions focused on discovering the who, what, and where of events or experiences and gaining insights from informants regarding a poorly understood phenomenon. It is also the label of choice when a straight description of a phenomenon is desired or information is sought to develop and refine questionnaires or interventions ( Neergaard et al., 2009 ; Sullivan-Bolyai et al., 2005 ).

Despite many strengths and frequent citations of its use, limited discussions regarding QD are found in qualitative research textbooks and publications. To the best of our knowledge, only seven articles include specific guidance on how to design, implement, analyze, or report the results of a QD study ( Milne & Oberle, 2005 ; Neergaard, Olesen, Andersen, & Sondergaard, 2009 ; Sandelowski, 2000 , 2010 ; Sullivan-Bolyai, Bova, & Harper, 2005 ; Vaismoradi, Turunen, & Bondas, 2013 ; Willis, Sullivan-Bolyai, Knafl, & Zichi-Cohen, 2016 ). Furthermore, little is known about characteristics of QD as reported in journal-published, nursing-related, qualitative studies. Therefore, the purpose of this systematic review was to describe specific characteristics of methods and findings of studies reported in journal articles (published in 2014) self-labeled as QD. In this review, we did not have a goal to judge whether QD was done correctly but rather to report on the features of the methods and findings.

Features of QD

Several QD design features and techniques have been described in the literature. First, researchers generally draw from a naturalistic perspective and examine a phenomenon in its natural state ( Sandelowski, 2000 ). Second, QD has been described as less theoretical compared to other qualitative approaches ( Neergaard et al., 2009 ), facilitating flexibility in commitment to a theory or framework when designing and conducting a study ( Sandelowski, 2000 , 2010 ). For example, researchers may or may not decide to begin with a theory of the targeted phenomenon and do not need to stay committed to a theory or framework if their investigations take them down another path ( Sandelowski, 2010 ). Third, data collection strategies typically involve individual and/or focus group interviews with minimal to semi-structured interview guides ( Neergaard et al., 2009 ; Sandelowski, 2000 ). Fourth, researchers commonly employ purposeful sampling techniques such as maximum variation sampling which has been described as being useful for obtaining broad insights and rich information ( Neergaard et al., 2009 ; Sandelowski, 2000 ). Fifth, content analysis (and in many cases, supplemented by descriptive quantitative data to describe the study sample) is considered a primary strategy for data analysis ( Neergaard et al., 2009 ; Sandelowski, 2000 ). In some instances thematic analysis may also be used to analyze data; however, experts suggest care should be taken that this type of analysis is not confused with content analysis ( Vaismoradi et al., 2013 ). These data analysis approaches allow researchers to stay close to the data and as such, interpretation is of low-inference ( Neergaard et al., 2009 ), meaning that different researchers will agree more readily on the same findings even if they do not choose to present the findings in the same way ( Sandelowski, 2000 ). Finally, representation of study findings in published reports is expected to be straightforward, including comprehensive descriptive summaries and accurate details of the data collected, and presented in a way that makes sense to the reader ( Neergaard et al., 2009 ; Sandelowski, 2000 ).

It is also important to acknowledge that variations in methods or techniques may be appropriate across QD studies ( Sandelowski, 2010 ). For example, when consistent with the study goals, decisions may be made to use techniques from other qualitative traditions, such as employing a constant comparative analytic approach typically associated with grounded theory ( Sandelowski, 2000 ).

Search Strategy and Study Screening

The PubMed electronic database was searched for articles written in English and published from January 1, 2014 to December 31, 2014, using the terms, “qualitative descriptive study,” “qualitative descriptive design,” and “qualitative description,” combined with “nursing.” This specific publication year, “2014,” was chosen because it was the most recent full year at the time of beginning this systematic review. As we did not intend to identify trends in QD approaches over time, it seemed reasonable to focus on the nursing QD studies published in a certain year. The inclusion criterion for this review was data-based, nursing-related, research articles in which authors used the terms QD, qualitative descriptive study, or qualitative descriptive design in their titles or abstracts as well as in the main texts of the publication.

All articles yielded through an initial search in PubMed were exported into EndNote X7 ( Thomson Reuters, 2014 ), a reference management software, and duplicates were removed. Next, titles and abstracts were reviewed to determine if the publication met inclusion criteria; all articles meeting inclusion criteria were then read independently in full by two authors (HK and JS) to determine if the terms – QD or qualitative descriptive study/design – were clearly stated in the main texts. Any articles in which researchers did not specifically state these key terms in the main text were then excluded, even if the terms had been used in the study title or abstract. In one article, for example, although “qualitative descriptive study” was reported in the published abstract, the researchers reported a “qualitative exploratory design” in the main text of the article ( Sundqvist & Carlsson, 2014 ); therefore, this article was excluded from our review. Despite the possibility that there may be other QD studies published in 2014 that were not labeled as such, to facilitate our screening process we only included articles where the researchers clearly used our search terms for their approach. Finally, the two authors compared, discussed, and reconciled their lists of articles with a third author (CB).

Study Selection

Initially, although the year 2014 was specifically requested, 95 articles were identified (due to ahead of print/Epub) and exported into the EndNote program. Three duplicate publications were removed and the 20 articles with final publication dates of 2015 were also excluded. The remaining 72 articles were then screened by examining titles, abstracts, and full-texts. Based on our inclusion criteria, 15 (of 72) were then excluded because QD or QD design/study was not identified in the main text. We then re-examined the remaining 57 articles and excluded two additional articles that did not meet inclusion criteria (e.g., QD was only reported as an analytic approach in the data analysis section). The remaining 55 publications met inclusion criteria and comprised the sample for our systematic review (see Figure 1 ).

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Flow Diagram of Study Selection

Of the 55 publications, 23 originated from North America (17 in the United States; 6 in Canada), 12 from Asia, 11 from Europe, 7 from Australia and New Zealand, and 2 from South America. Eleven studies were part of larger research projects and two of them were reported as part of larger mixed-methods studies. Four were described as a secondary analysis.

Quality Appraisal Process

Following the identification of the 55 publications, two authors (HK and JS) independently examined each article using the Critical Appraisal Skills Programme (CASP) qualitative checklist ( CASP, 2013 ). The CASP was chosen to determine the general adequacy (or rigor) of the qualitative studies included in this review as the CASP criteria are generic and intend to be applied to qualitative studies in general. In addition, the CASP was useful because we were able to examine the internal consistency between study aims and methods and between study aims and findings as well as the usefulness of findings ( CASP, 2013 ). The CASP consists of 10 main questions with several sub-questions to consider when making a decision about the main question ( CASP, 2013 ). The first two questions have reviewers examine the clarity of study aims and appropriateness of using qualitative research to achieve the aims. With the next eight questions, reviewers assess study design, sampling, data collection, and analysis as well as the clarity of the study’s results statement and the value of the research. We used the seven questions and 17 sub-questions related to methods and statement of findings to evaluate the articles. The results of this process are presented in Table 1 .

CASP Questions and Quality Appraisal Results (N = 55)

Note . The CASP questions are adapted from “10 questions to help you make sense of qualitative research,” by Critical Appraisal Skills Programme, 2013, retrieved from http://media.wix.com/ugd/dded87_29c5b002d99342f788c6ac670e49f274.pdf . Its license can be found at http://creativecommons.org/licenses/by-nc-sa/3.0/

Once articles were assessed by the two authors independently, all three authors discussed and reconciled our assessment. No articles were excluded based on CASP results; rather, results were used to depict the general adequacy (or rigor) of all 55 articles meeting inclusion criteria for our systematic review. In addition, the CASP was included to enhance our examination of the relationship between the methods and the usefulness of the findings documented in each of the QD articles included in this review.

Process for Data Extraction and Analysis

To further assess each of the 55 articles, data were extracted on: (a) research objectives, (b) design justification, (c) theoretical or philosophical framework, (d) sampling and sample size, (e) data collection and data sources, (f) data analysis, and (g) presentation of findings (see Table 2 ). We discussed extracted data and identified common and unique features in the articles included in our systematic review. Findings are described in detail below and in Table 3 .

Elements for Data Extraction

Data Extraction and Analysis Results

Note . NR = not reported

Quality Appraisal Results

Justification for use of a QD design was evident in close to half (47.3%) of the 55 publications. While most researchers clearly described recruitment strategies (80%) and data collection methods (100%), justification for how the study setting was selected was only identified in 38.2% of the articles and almost 75% of the articles did not include any reason for the choice of data collection methods (e.g., focus-group interviews). In the vast majority (90.9%) of the articles, researchers did not explain their involvement and positionality during the process of recruitment and data collection or during data analysis (63.6%). Ethical standards were reported in greater than 89% of all articles and most articles included an in-depth description of data analysis (83.6%) and development of categories or themes (92.7%). Finally, all researchers clearly stated their findings in relation to research questions/objectives. Researchers of 83.3% of the articles discussed the credibility of their findings (see Table 1 ).

Research Objectives

In statements of study objectives and/or questions, the most frequently used verbs were “explore” ( n = 22) and “describe” ( n = 17). Researchers also used “identify” ( n = 3), “understand” ( n = 4), or “investigate” ( n = 2). Most articles focused on participants’ experiences related to certain phenomena ( n = 18), facilitators/challenges/factors/reasons ( n = 14), perceptions about specific care/nursing practice/interventions ( n = 11), and knowledge/attitudes/beliefs ( n = 3).

Design Justification

A total of 30 articles included references for QD. The most frequently cited references ( n = 23) were “Whatever happened to qualitative description?” ( Sandelowski, 2000 ) and “What’s in a name? Qualitative description revisited” ( Sandelowski, 2010 ). Other references cited included “Qualitative description – the poor cousin of health research?” ( Neergaard et al., 2009 ), “Reaching the parts other methods cannot reach: an introduction to qualitative methods in health and health services research” ( Pope & Mays, 1995 ), and general research textbooks ( Polit & Beck, 2004 , 2012 ).

In 26 articles (and not necessarily the same as those citing specific references to QD), researchers provided a rationale for selecting QD. Most researchers chose QD because this approach aims to produce a straight description and comprehensive summary of the phenomenon of interest using participants’ language and staying close to the data (or using low inference).

Authors of two articles distinctly stated a QD design, yet also acknowledged grounded-theory or phenomenological overtones by adopting some techniques from these qualitative traditions ( Michael, O'Callaghan, Baird, Hiscock, & Clayton, 2014 ; Peacock, Hammond-Collins, & Forbes, 2014 ). For example, Michael et al. (2014 , p. 1066) reported:

The research used a qualitative descriptive design with grounded theory overtones ( Sandelowski, 2000 ). We sought to provide a comprehensive summary of participants’ views through theoretical sampling; multiple data sources (focus groups [FGs] and interviews); inductive, cyclic, and constant comparative analysis; and condensation of data into thematic representations ( Corbin & Strauss, 1990 , 2008 ).

Authors of four additional articles included language suggestive of a grounded-theory or phenomenological tradition, e.g., by employing a constant comparison technique or translating themes stated in participants’ language into the primary language of the researchers during data analysis ( Asemani et al., 2014 ; Li, Lee, Chen, Jeng, & Chen, 2014 ; Ma, 2014 ; Soule, 2014 ). Additionally, Li et al. (2014) specifically reported use of a grounded-theory approach.

Theoretical or Philosophical Framework

In most (n = 48) articles, researchers did not specify any theoretical or philosophical framework. Of those articles in which a framework or philosophical stance was included, the authors of five articles described the framework as guiding the development of an interview guide ( Al-Zadjali, Keller, Larkey, & Evans, 2014 ; DeBruyn, Ochoa-Marin, & Semenic, 2014 ; Fantasia, Sutherland, Fontenot, & Ierardi, 2014 ; Ma, 2014 ; Wiens, Babenko-Mould, & Iwasiw, 2014 ). In two articles, data analysis was described as including key concepts of a framework being used as pre-determined codes or categories ( Al-Zadjali et al., 2014 ; Wiens et al., 2014 ). Oosterveld-Vlug et al. (2014) and Zhang, Shan, and Jiang (2014) discussed a conceptual model and underlying philosophy in detail in the background or discussion section, although the model and philosophy were not described as being used in developing interview questions or analyzing data.

Sampling and Sample Size

In 38 of the 55 articles, researchers reported ‘purposeful sampling’ or some derivation of purposeful sampling such as convenience ( n = 10), maximum variation ( n = 8), snowball ( n = 3), and theoretical sampling ( n = 1). In three instances ( Asemani et al., 2014 ; Chan & Lopez, 2014 ; Soule, 2014 ), multiple sampling strategies were described, for example, a combination of snowball, convenience, and maximum variation sampling. In articles where maximum variation sampling was employed, “variation” referred to seeking diversity in participants’ demographics ( n = 7; e.g., age, gender, and education level), while one article did not include details regarding how their maximum variation sampling strategy was operationalized ( Marcinowicz, Abramowicz, Zarzycka, Abramowicz, & Konstantynowicz, 2014 ). Authors of 17 articles did not specify their sampling techniques.

Sample sizes ranged from 8 to 1,932 with nine studies in the 8–10 participant range and 24 studies in the 11–20 participant range. The participant range of 21–30 and 31–50 was reported in eight articles each. Six studies included more than 50 participants. Two of these articles depicted quite large sample sizes (N=253, Hart & Mareno, 2014 ; N=1,932, Lyndon et al., 2014 ) and the authors of these articles described the use of survey instruments and analysis of responses to open-ended questions. This was in contrast to studies with smaller sample sizes where individual interviews and focus groups were more commonly employed.

Data Collection and Data Sources

In a majority of studies, researchers collected data through individual ( n = 39) and/or focus-group ( n = 14) interviews that were semistructured. Most researchers reported that interviews were audiotaped ( n = 51) and interview guides were described as the primary data collection tool in 29 of the 51 studies. In some cases, researchers also described additional data sources, for example, taking memos or field notes during participant observation sessions or as a way to reflect their thoughts about interviews ( n = 10). Written responses to open-ended questions in survey questionnaires were another type of data source in a small number of studies ( n = 4).

Data Analysis

The analysis strategy most commonly used in the QD studies included in this review was qualitative content analysis ( n = 30). Among the studies where this technique was used, most researchers described an inductive approach; researchers of two studies analyzed data both inductively and deductively. Thematic analysis was adopted in 14 studies and the constant comparison technique in 10 studies. In nine studies, researchers employed multiple techniques to analyze data including qualitative content analysis with constant comparison ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland, Christensen, Shone, Kearney, & Kitzman, 2014 ; Li et al., 2014 ) and thematic analysis with constant comparison ( Johansson, Hildingsson, & Fenwick, 2014 ; Oosterveld-Vlug et al., 2014 ). In addition, five teams conducted descriptive statistical analysis using both quantitative and qualitative data and counting the frequencies of codes/themes ( Ewens, Chapman, Tulloch, & Hendricks, 2014 ; Miller, 2014 ; Santos, Sandelowski, & Gualda, 2014 ; Villar, Celdran, Faba, & Serrat, 2014 ) or targeted events through video monitoring ( Martorella, Boitor, Michaud, & Gelinas, 2014 ). Tseng, Chen, and Wang (2014) cited Thorne, Reimer Kirkham, and O’Flynn-Magee (2004)’s interpretive description as the inductive analytic approach. In five out of 55 articles, researchers did not specifically name their analysis strategies, despite including descriptions about procedural aspects of data analysis. Researchers of 20 studies reported that data saturation for their themes was achieved.

Presentation of Findings

Researchers described participants’ experiences of health care, interventions, or illnesses in 18 articles and presented straightforward, focused, detailed descriptions of facilitators, challenges, factors, reasons, and causes in 15 articles. Participants’ perceptions of specific care, interventions, or programs were described in detail in 11 articles. All researchers presented their findings with extensive descriptions including themes or categories. In 25 of 55 articles, figures or tables were also presented to illustrate or summarize the findings. In addition, the authors of three articles summarized, organized, and described their data using key concepts of conceptual models ( Al-Zadjali et al., 2014 ; Oosterveld-Vlug et al., 2014 ; Wiens et al., 2014 ). Martorella et al. (2014) assessed acceptability and feasibility of hand massage therapy and arranged their findings in relation to pre-determined indicators of acceptability and feasibility. In one longitudinal QD study ( Kneck, Fagerberg, Eriksson, & Lundman, 2014 ), the researchers presented the findings as several key patterns of learning for persons living with diabetes; in another longitudinal QD study ( Stegenga & Macpherson, 2014 ), findings were presented as processes and themes regarding patients’ identity work across the cancer trajectory. In another two studies, the researchers described and compared themes or categories from two different perspectives, such as patients and nurses ( Canzan, Heilemann, Saiani, Mortari, & Ambrosi, 2014 ) or parents and children ( Marcinowicz et al., 2014 ). Additionally, Ma (2014) reported themes using both participants’ language and the researcher’s language.

In this systematic review, we examined and reported specific characteristics of methods and findings reported in journal articles self-identified as QD and published during one calendar year. To accomplish this we identified 55 articles that met inclusion criteria, performed a quality appraisal following CASP guidelines, and extracted and analyzed data focusing on QD features. In general, three primary findings emerged. First, despite inconsistencies, most QD publications had the characteristics that were originally observed by Sandelowski (2000) and summarized by other limited available QD literature. Next, there are no clear boundaries in methods used in the QD studies included in this review; in a number of studies, researchers adopted and combined techniques originating from other qualitative traditions to obtain rich data and increase their understanding of the phenomenon under investigation. Finally, justification for how QD was chosen and why it would be an appropriate fit for a particular study is an area in need of increased attention.

In general, the overall characteristics were consistent with design features of QD studies described in the literature ( Neergaard et al., 2009 ; Sandelowski, 2000 , 2010 ; Vaismoradi et al., 2013 ). For example, many authors reported that study objectives were to describe or explore participants’ experiences and factors related to certain phenomena, events, or interventions. In most cases, these authors cited Sandelowski (2000) as a reference for this particular characteristic. It was rare that theoretical or philosophical frameworks were identified, which also is consistent with descriptions of QD. In most studies, researchers used purposeful sampling and its derivative sampling techniques, collected data through interviews, and analyzed data using qualitative content analysis or thematic analysis. Moreover, all researchers presented focused or comprehensive, descriptive summaries of data including themes or categories answering their research questions. These characteristics do not indicate that there are correct ways to do QD studies; rather, they demonstrate how others designed and produced QD studies.

In several studies, researchers combined techniques that originated from other qualitative traditions for sampling, data collection, and analysis. This flexibility or variability, a key feature of recently published QD studies, may indicate that there are no clear boundaries in designing QD studies. Sandelowski (2010) articulated: “in the actual world of research practice, methods bleed into each other; they are so much messier than textbook depictions” (p. 81). Hammersley (2007) also observed:

“We are not so much faced with a set of clearly differentiated qualitative approaches as with a complex landscape of variable practice in which the inhabitants use a range of labels (‘ethnography’, ‘discourse analysis’, ‘life history work’, narrative study’, ……, and so on) in diverse and open-ended ways in order to characterize their orientation, and probably do this somewhat differently across audiences and occasions” (p. 293).

This concept of having no clear boundaries in methods when designing a QD study should enable researchers to obtain rich data and produce a comprehensive summary of data through various data collection and analysis approaches to answer their research questions. For example, using an ethnographical approach (e.g., participant observation) in data collection for a QD study may facilitate an in-depth description of participants’ nonverbal expressions and interactions with others and their environment as well as situations or events in which researchers are interested ( Kawulich, 2005 ). One example found in our review is that Adams et al. (2014) explored family members’ responses to nursing communication strategies for patients in intensive care units (ICUs). In this study, researchers conducted interviews with family members, observed interactions between healthcare providers, patients, and family members in ICUs, attended ICU rounds and family meetings, and took field notes about their observations and reflections. Accordingly, the variability in methods provided Adams and colleagues (2014) with many different aspects of data that were then used to complement participants’ interviews (i.e., data triangulation). Moreover, by using a constant comparison technique in addition to qualitative content analysis or thematic analysis in QD studies, researchers compare each case with others looking for similarities and differences as well as reasoning why differences exist, to generate more general understanding of phenomena of interest ( Thorne, 2000 ). In fact, this constant comparison analysis is compatible with qualitative content analysis and thematic analysis and we found several examples of using this approach in studies we reviewed ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland et al., 2014 ; Johansson et al., 2014 ; Li et al., 2014 ; Oosterveld-Vlug et al., 2014 ).

However, this flexibility or variability in methods of QD studies may cause readers’ as well as researchers’ confusion in designing and often labeling qualitative studies ( Neergaard et al., 2009 ). Especially, it could be difficult for scholars unfamiliar with qualitative studies to differentiate QD studies with “hues, tones, and textures” of qualitative traditions ( Sandelowski, 2000 , p. 337) from grounded theory, phenomenological, and ethnographical research. In fact, the major difference is in the presentation of the findings (or outcomes of qualitative research) ( Neergaard et al., 2009 ; Sandelowski, 2000 ). The final products of grounded theory, phenomenological, and ethnographical research are a generation of a theory, a description of the meaning or essence of people’s lived experience, and an in-depth, narrative description about certain culture, respectively, through researchers’ intensive/deep interpretations, reflections, and/or transformation of data ( Streubert & Carpenter, 2011 ). In contrast, QD studies result in “a rich, straight description” of experiences, perceptions, or events using language from the collected data ( Neergaard et al., 2009 ) through low-inference (or data-near) interpretations during data analysis ( Sandelowski, 2000 , 2010 ). This feature is consistent with our finding regarding presentation of findings: in all QD articles included in this systematic review, the researchers presented focused or comprehensive, descriptive summaries to their research questions.

Finally, an explanation or justification of why a QD approach was chosen or appropriate for the study aims was not found in more than half of studies in the sample. While other qualitative approaches, including grounded theory, phenomenology, ethnography, and narrative analysis, are used to better understand people’s thoughts, behaviors, and situations regarding certain phenomena ( Sullivan-Bolyai et al., 2005 ), as noted above, the results will likely read differently than those for a QD study ( Carter & Little, 2007 ). Therefore, it is important that researchers accurately label and justify their choices of approach, particularly for studies focused on participants’ experiences, which could be addressed with other qualitative traditions. Justifying one’s research epistemology, methodology, and methods allows readers to evaluate these choices for internal consistency, provides context to assist in understanding the findings, and contributes to the transparency of choices, all of which enhance the rigor of the study ( Carter & Little, 2007 ; Wu, Thompson, Aroian, McQuaid, & Deatrick, 2016 ).

Use of the CASP tool drew our attention to the credibility and usefulness of the findings of the QD studies included in this review. Although justification for study design and methods was lacking in many articles, most authors reported techniques of recruitment, data collection, and analysis that appeared. Internal consistencies among study objectives, methods, and findings were achieved in most studies, increasing readers’ confidence that the findings of these studies are credible and useful in understanding under-explored phenomenon of interest.

In summary, our findings support the notion that many scholars employ QD and include a variety of commonly observed characteristics in their study design and subsequent publications. Based on our review, we found that QD as a scholarly approach allows flexibility as research questions and study findings emerge. We encourage authors to provide as many details as possible regarding how QD was chosen for a particular study as well as details regarding methods to facilitate readers’ understanding and evaluation of the study design and rigor. We acknowledge the challenge of strict word limitation with submissions to print journals; potential solutions include collaboration with journal editors and staff to consider creative use of charts or tables, or using more citations and less text in background sections so that methods sections are robust.

Limitations

Several limitations of this review deserve mention. First, only articles where researchers explicitly stated in the main body of the article that a QD design was employed were included. In contrast, articles labeled as QD in only the title or abstract, or without their research design named were not examined due to the lack of certainty that the researchers actually carried out a QD study. As a result, we may have excluded some studies where a QD design was followed. Second, only one database was searched and therefore we did not identify or describe potential studies following a QD approach that were published in non-PubMed databases. Third, our review is limited by reliance on what was included in the published version of a study. In some cases, this may have been a result of word limits or specific styles imposed by journals, or inconsistent reporting preferences of authors and may have limited our ability to appraise the general adequacy with the CASP tool and examine specific characteristics of these studies.

Conclusions

A systematic review was conducted by examining QD research articles focused on nursing-related phenomena and published in one calendar year. Current patterns include some characteristics of QD studies consistent with the previous observations described in the literature, a focus on the flexibility or variability of methods in QD studies, and a need for increased explanations of why QD was an appropriate label for a particular study. Based on these findings, recommendations include encouragement to authors to provide as many details as possible regarding the methods of their QD study. In this way, readers can thoroughly consider and examine if the methods used were effective and reasonable in producing credible and useful findings.

Acknowledgments

This work was supported in part by the John A. Hartford Foundation’s National Hartford Centers of Gerontological Nursing Excellence Award Program.

Hyejin Kim is a Ruth L. Kirschstein NRSA Predoctoral Fellow (F31NR015702) and 2013–2015 National Hartford Centers of Gerontological Nursing Excellence Patricia G. Archbold Scholar. Justine Sefcik is a Ruth L. Kirschstein Predoctoral Fellow (F31NR015693) through the National Institutes of Health, National Institute of Nursing Research.

Conflict of Interest Statement

The Authors declare that there is no conflict of interest.

Contributor Information

Hyejin Kim, MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing.

Justine S. Sefcik, MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing.

Christine Bradway, PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing.

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Qualitative Research Designs

Case study design, using case study design in the applied doctoral experience (ade), applicability of case study design to applied problem of practice, case study design references.

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The field of qualitative research there are a number of research designs (also referred to as “traditions” or “genres”), including case study, phenomenology, narrative inquiry, action research, ethnography, grounded theory, as well as a number of critical genres including Feminist theory, indigenous research, critical race theory and cultural studies. The choice of research design is directly tied to and must be aligned with your research problem and purpose. As Bloomberg & Volpe (2019) explain:

Choice of research design is directly tied to research problem and purpose. As the researcher, you actively create the link among problem, purpose, and design through a process of reflecting on problem and purpose, focusing on researchable questions, and considering how to best address these questions. Thinking along these lines affords a research study methodological congruence (p. 38).

Case study is an in-depth exploration from multiple perspectives of a bounded social phenomenon, be this a social system such as a program, event, institution, organization, or community (Stake, 1995, 2005; Yin, 2018). Case study is employed across disciplines, including education, health care, social work, sociology, and organizational studies. The purpose is to generate understanding and deep insights to inform professional practice, policy development, and community or social action (Bloomberg 2018).

Yin (2018) and Stake (1995, 2005), two of the key proponents of case study methodology, use different terms to describe case studies. Yin categorizes case studies as exploratory or descriptive . The former is used to explore those situations in which the intervention being evaluated has no clear single set of outcomes. The latter is used to describe an intervention or phenomenon and the real-life context in which it occurred. Stake identifies case studies as intrinsic or instrumental , and he proposes that a primary distinction in designing case studies is between single and multiple (or collective) case study designs. A single case study may be an instrumental case study (research focuses on an issue or concern in one bounded case) or an intrinsic case study (the focus is on the case itself because the case presents a unique situation). A longitudinal case study design is chosen when the researcher seeks to examine the same single case at two or more different points in time or to capture trends over time. A multiple case study design is used when a researcher seeks to determine the prevalence or frequency of a particular phenomenon. This approach is useful when cases are used for purposes of a cross-case analysis in order to compare, contrast, and synthesize perspectives regarding the same issue. The focus is on the analysis of diverse cases to determine how these confirm the findings within or between cases, or call the findings into question.

Case study affords significant interaction with research participants, providing an in-depth picture of the phenomenon (Bloomberg & Volpe, 2019). Research is extensive, drawing on multiple methods of data collection, and involves multiple data sources. Triangulation is critical in attempting to obtain an in-depth understanding of the phenomenon under study and adds rigor, breadth, and depth to the study and provides corroborative evidence of the data obtained. Analysis of data can be holistic or embedded—that is, dealing with the whole or parts of the case (Yin, 2018). With multiple cases the typical analytic strategy is to provide detailed description of themes within each case (within-case analysis), followed by thematic analysis across cases (cross-case analysis), providing insights regarding how individual cases are comparable along important dimensions. Research culminates in the production of a detailed description of a setting and its participants, accompanied by an analysis of the data for themes or patterns (Stake, 1995, 2005; Yin, 2018). In addition to thick, rich description, the researcher’s interpretations, conclusions, and recommendations contribute to the reader’s overall understanding of the case study.

Analysis of findings should show that the researcher has attended to all the data, should address the most significant aspects of the case, and should demonstrate familiarity with the prevailing thinking and discourse about the topic. The goal of case study design (as with all qualitative designs) is not generalizability but rather transferability —that is, how (if at all) and in what ways understanding and knowledge can be applied in similar contexts and settings. The qualitative researcher attempts to address the issue of transferability by way of thick, rich description that will provide the basis for a case or cases to have relevance and potential application across a broader context.

Qualitative research methods ask the questions of "what" and "how" a phenomenon is understood in a real-life context (Bloomberg & Volpe, 2019). In the education field, qualitative research methods uncover educational experiences and practices because qualitative research allows the researcher to reveal new knowledge and understanding. Moreover, qualitative descriptive case studies describe, analyze and interpret events that explain the reasoning behind specific phenomena (Bloomberg, 2018). As such, case study design can be the foundation for a rigorous study within the Applied Doctoral Experience (ADE).

Case study design is an appropriate research design to consider when conceptualizing and conducting a dissertation research study that is based on an applied problem of practice with inherent real-life educational implications. Case study researchers study current, real-life cases that are in progress so that they can gather accurate information that is current. This fits well with the ADE program, as students are typically exploring a problem of practice. Because of the flexibility of the methods used, a descriptive design provides the researcher with the opportunity to choose data collection methods that are best suited to a practice-based research purpose, and can include individual interviews, focus groups, observation, surveys, and critical incident questionnaires. Methods are triangulated to contribute to the study’s trustworthiness. In selecting the set of data collection methods, it is important that the researcher carefully consider the alignment between research questions and the type of data that is needed to address these. Each data source is one piece of the “puzzle,” that contributes to the researcher’s holistic understanding of a phenomenon. The various strands of data are woven together holistically to promote a deeper understanding of the case and its application to an educationally-based problem of practice.

Research studies within the Applied Doctoral Experience (ADE) will be practical in nature and focus on problems and issues that inform educational practice.  Many of the types of studies that fall within the ADE framework are exploratory, and align with case study design. Case study design fits very well with applied problems related to educational practice, as the following set of examples illustrate:

Elementary Bilingual Education Teachers’ Self-Efficacy in Teaching English Language Learners: A Qualitative Case Study

The problem to be addressed in the proposed study is that some elementary bilingual education teachers’ beliefs about their lack of preparedness to teach the English language may negatively impact the language proficiency skills of Hispanic ELLs (Ernst-Slavit & Wenger, 2016; Fuchs et al., 2018; Hoque, 2016). The purpose of the proposed qualitative descriptive case study was to explore the perspectives and experiences of elementary bilingual education teachers regarding their perceived lack of preparedness to teach the English language and how this may impact the language proficiency of Hispanic ELLs.

Exploring Minority Teachers Experiences Pertaining to their Value in Education: A Single Case Study of Teachers in New York City

The problem is that minority K-12 teachers are underrepresented in the United States, with research indicating that school leaders and teachers in schools that are populated mainly by black students, staffed mostly by white teachers who may be unprepared to deal with biases and stereotypes that are ingrained in schools (Egalite, Kisida, & Winters, 2015; Milligan & Howley, 2015). The purpose of this qualitative exploratory single case study was to develop a clearer understanding of minority teachers’ experiences concerning the under-representation of minority K-12 teachers in urban school districts in the United States since there are so few of them.

Exploring the Impact of an Urban Teacher Residency Program on Teachers’ Cultural Intelligence: A Qualitative Case Study

The problem to be addressed by this case study is that teacher candidates often report being unprepared and ill-equipped to effectively educate culturally diverse students (Skepple, 2015; Beutel, 2018). The purpose of this study was to explore and gain an in-depth understanding of the perceived impact of an urban teacher residency program in urban Iowa on teachers’ cultural competence using the cultural intelligence (CQ) framework (Earley & Ang, 2003).

Qualitative Case Study that Explores Self-Efficacy and Mentorship on Women in Academic Administrative Leadership Roles

The problem was that female school-level administrators might be less likely to experience mentorship, thereby potentially decreasing their self-efficacy (Bing & Smith, 2019; Brown, 2020; Grant, 2021). The purpose of this case study was to determine to what extent female school-level administrators in the United States who had a mentor have a sense of self-efficacy and to examine the relationship between mentorship and self-efficacy.

Suburban Teacher and Administrator Perceptions of Culturally Responsive Teaching to Promote Connectedness in Students of Color: A Qualitative Case Study

The problem to be addressed in this study is the racial discrimination experienced by students of color in suburban schools and the resulting negative school experience (Jara & Bloomsbury, 2020; Jones, 2019; Kohli et al., 2017; Wandix-White, 2020). The purpose of this case study is to explore how culturally responsive practices can counteract systemic racism and discrimination in suburban schools thereby meeting the needs of students of color by creating positive learning experiences. 

As you can see, all of these studies were well suited to qualitative case study design. In each of these studies, the applied research problem and research purpose were clearly grounded in educational practice as well as directly aligned with qualitative case study methodology. In the Applied Doctoral Experience (ADE), you will be focused on addressing or resolving an educationally relevant research problem of practice. As such, your case study, with clear boundaries, will be one that centers on a real-life authentic problem in your field of practice that you believe is in need of resolution or improvement, and that the outcome thereof will be educationally valuable.

Bloomberg, L. D. (2018). Case study method. In B. B. Frey (Ed.), The SAGE Encyclopedia of educational research, measurement, and evaluation (pp. 237–239). SAGE. https://go.openathens.net/redirector/nu.edu?url=https%3A%2F%2Fmethods.sagepub.com%2FReference%2Fthe-sage-encyclopedia-of-educational-research-measurement-and-evaluation%2Fi4294.xml

Bloomberg, L. D. & Volpe, M. (2019). Completing your qualitative dissertation: A road map from beginning to end . (4th Ed.). SAGE.

Stake, R. E. (1995). The art of case study research. SAGE.

Stake, R. E. (2005). Qualitative case studies. In N. K. Denzin and Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (3rd ed., pp. 443–466). SAGE.

Yin, R. (2018). Case study research and applications: Designs and methods. SAGE.

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How to Structure a Dissertation – A Step by Step Guide

Published by Owen Ingram at August 11th, 2021 , Revised On September 20, 2023

A dissertation – sometimes called a thesis –  is a long piece of information backed up by extensive research. This one, huge piece of research is what matters the most when students – undergraduates and postgraduates – are in their final year of study.

On the other hand, some institutions, especially in the case of undergraduate students, may or may not require students to write a dissertation. Courses are offered instead. This generally depends on the requirements of that particular institution.

If you are unsure about how to structure your dissertation or thesis, this article will offer you some guidelines to work out what the most important segments of a dissertation paper are and how you should organise them. Why is structure so important in research, anyway?

One way to answer that, as Abbie Hoffman aptly put it, is because: “Structure is more important than content in the transmission of information.”

Also Read:   How to write a dissertation – step by step guide .

How to Structure a Dissertation or Thesis

It should be noted that the exact structure of your dissertation will depend on several factors, such as:

  • Your research approach (qualitative/quantitative)
  • The nature of your research design (exploratory/descriptive etc.)
  • The requirements set for forth by your academic institution.
  • The discipline or field your study belongs to. For instance, if you are a humanities student, you will need to develop your dissertation on the same pattern as any long essay .

This will include developing an overall argument to support the thesis statement and organizing chapters around theories or questions. The dissertation will be structured such that it starts with an introduction , develops on the main idea in its main body paragraphs and is then summarised in conclusion .

However, if you are basing your dissertation on primary or empirical research, you will be required to include each of the below components. In most cases of dissertation writing, each of these elements will have to be written as a separate chapter.

But depending on the word count you are provided with and academic subject, you may choose to combine some of these elements.

For example, sciences and engineering students often present results and discussions together in one chapter rather than two different chapters.

If you have any doubts about structuring your dissertation or thesis, it would be a good idea to consult with your academic supervisor and check your department’s requirements.

Parts of  a Dissertation or Thesis

Your dissertation will  start with a t itle page that will contain details of the author/researcher, research topic, degree program (the paper is to be submitted for), and research supervisor. In other words, a title page is the opening page containing all the names and title related to your research.

The name of your university, logo, student ID and submission date can also be presented on the title page. Many academic programs have stringent rules for formatting the dissertation title page.

Acknowledgements

The acknowledgments section allows you to thank those who helped you with your dissertation project. You might want to mention the names of your academic supervisor, family members, friends, God, and participants of your study whose contribution and support enabled you to complete your work.

However, the acknowledgments section is usually optional.

Tip: Many students wrongly assume that they need to thank everyone…even those who had little to no contributions towards the dissertation. This is not the case. You only need to thank those who were directly involved in the research process, such as your participants/volunteers, supervisor(s) etc.

Perhaps the smallest yet important part of a thesis, an abstract contains 5 parts:

  • A brief introduction of your research topic.
  • The significance of your research.
  •  A line or two about the methodology that was used.
  • The results and what they mean (briefly); their interpretation(s).
  • And lastly, a conclusive comment regarding the results’ interpretation(s) as conclusion .

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Tip: Make sure to highlight key points to help readers figure out the scope and findings of your research study without having to read the entire dissertation. The abstract is your first chance to impress your readers. So, make sure to get it right. Here are detailed guidelines on how to write abstract for dissertation .

Table of Contents

Table of contents is the section of a dissertation that guides each section of the dissertation paper’s contents. Depending on the level of detail in a table of contents, the most useful headings are listed to provide the reader the page number on which said information may be found at.

Table of contents can be inserted automatically as well as manually using the Microsoft Word Table of Contents feature.

List of Figures and Tables

If your dissertation paper uses several illustrations, tables and figures, you might want to present them in a numbered list in a separate section . Again, this list of tables and figures can be auto-created and auto inserted using the Microsoft Word built-in feature.

List of Abbreviations

Dissertations that include several abbreviations can also have an independent and separate alphabetised  list of abbreviations so readers can easily figure out their meanings.

If you think you have used terms and phrases in your dissertation that readers might not be familiar with, you can create a  glossary  that lists important phrases and terms with their meanings explained.

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Introduction

Introduction chapter  briefly introduces the purpose and relevance of your research topic.

Here, you will be expected to list the aim and key objectives of your research so your readers can easily understand what the following chapters of the dissertation will cover. A good dissertation introduction section incorporates the following information:

  • It provides background information to give context to your research.
  • It clearly specifies the research problem you wish to address with your research. When creating research questions , it is important to make sure your research’s focus and scope are neither too broad nor too narrow.
  • it demonstrates how your research is relevant and how it would contribute to the existing knowledge.
  • It provides an overview of the structure of your dissertation. The last section of an introduction contains an outline of the following chapters. It could start off with something like: “In the following chapter, past literature has been reviewed and critiqued. The proceeding section lays down major research findings…”
  • Theoretical framework – under a separate sub-heading – is also provided within the introductory chapter. Theoretical framework deals with the basic, underlying theory or theories that the research revolves around.

All the information presented under this section should be relevant, clear, and engaging. The readers should be able to figure out the what, why, when, and how of your study once they have read the introduction. Here are comprehensive guidelines on how to structure the introduction to the dissertation .

“Overwhelmed by tight deadlines and tons of assignments to write? There is no need to panic! Our expert academics can help you with every aspect of your dissertation – from topic creation and research problem identification to choosing the methodological approach and data analysis.”

Literature Review 

The  literature review chapter  presents previous research performed on the topic and improves your understanding of the existing literature on your chosen topic. This is usually organised to complement your  primary research  work completed at a later stage.

Make sure that your chosen academic sources are authentic and up-to-date. The literature review chapter must be comprehensive and address the aims and objectives as defined in the introduction chapter. Here is what your literature research chapter should aim to achieve:

  • Data collection from authentic and relevant academic sources such as books, journal articles and research papers.
  • Analytical assessment of the information collected from those sources; this would involve a critiquing the reviewed researches that is, what their strengths/weaknesses are, why the research method they employed is better than others, importance of their findings, etc.
  • Identifying key research gaps, conflicts, patterns, and theories to get your point across to the reader effectively.

While your literature review should summarise previous literature, it is equally important to make sure that you develop a comprehensible argument or structure to justify your research topic. It would help if you considered keeping the following questions in mind when writing the literature review:

  • How does your research work fill a certain gap in exiting literature?
  • Did you adopt/adapt a new research approach to investigate the topic?
  • Does your research solve an unresolved problem?
  • Is your research dealing with some groundbreaking topic or theory that others might have overlooked?
  • Is your research taking forward an existing theoretical discussion?
  • Does your research strengthen and build on current knowledge within your area of study? This is otherwise known as ‘adding to the existing body of knowledge’ in academic circles.

Tip: You might want to establish relationships between variables/concepts to provide descriptive answers to some or all of your research questions. For instance, in case of quantitative research, you might hypothesise that variable A is positively co-related to variable B that is, one increases and so does the other one.

Research Methodology

The methods and techniques ( secondary and/or primar y) employed to collect research data are discussed in detail in the  Methodology chapter. The most commonly used primary data collection methods are:

  • questionnaires
  • focus groups
  • observations

Essentially, the methodology chapter allows the researcher to explain how he/she achieved the findings, why they are reliable and how they helped him/her test the research hypotheses or address the research problem.

You might want to consider the following when writing methodology for the dissertation:

  • Type of research and approach your work is based on. Some of the most widely used types of research include experimental, quantitative and qualitative methodologies.
  • Data collection techniques that were employed such as questionnaires, surveys, focus groups, observations etc.
  • Details of how, when, where, and what of the research that was conducted.
  • Data analysis strategies employed (for instance, regression analysis).
  • Software and tools used for data analysis (Excel, STATA, SPSS, lab equipment, etc.).
  • Research limitations to highlight any hurdles you had to overcome when carrying our research. Limitations might or might not be mentioned within research methodology. Some institutions’ guidelines dictate they be mentioned under a separate section alongside recommendations.
  • Justification of your selection of research approach and research methodology.

Here is a comprehensive article on  how to structure a dissertation methodology .

Research Findings

In this section, you present your research findings. The dissertation findings chapter  is built around the research questions, as outlined in the introduction chapter. Report findings that are directly relevant to your research questions.

Any information that is not directly relevant to research questions or hypotheses but could be useful for the readers can be placed under the  Appendices .

As indicated above, you can either develop a  standalone chapter  to present your findings or combine them with the discussion chapter. This choice depends on  the type of research involved and the academic subject, as well as what your institution’s academic guidelines dictate.

For example, it is common to have both findings and discussion grouped under the same section, particularly if the dissertation is based on qualitative research data.

On the other hand, dissertations that use quantitative or experimental data should present findings and analysis/discussion in two separate chapters. Here are some sample dissertations to help you figure out the best structure for your own project.

Sample Dissertation

Tip: Try to present as many charts, graphs, illustrations and tables in the findings chapter to improve your data presentation. Provide their qualitative interpretations alongside, too. Refrain from explaining the information that is already evident from figures and tables.

The findings are followed by the  Discussion chapter , which is considered the heart of any dissertation paper. The discussion section is an opportunity for you to tie the knots together to address the research questions and present arguments, models and key themes.

This chapter can make or break your research.

The discussion chapter does not require any new data or information because it is more about the interpretation(s) of the data you have already collected and presented. Here are some questions for you to think over when writing the discussion chapter:

  • Did your work answer all the research questions or tested the hypothesis?
  • Did you come up with some unexpected results for which you have to provide an additional explanation or justification?
  • Are there any limitations that could have influenced your research findings?

Here is an article on how to  structure a dissertation discussion .

Conclusions corresponding to each research objective are provided in the  Conclusion section . This is usually done by revisiting the research questions to finally close the dissertation. Some institutions may specifically ask for recommendations to evaluate your critical thinking.

By the end, the readers should have a clear apprehension of your fundamental case with a focus on  what methods of research were employed  and what you achieved from this research.

Quick Question: Does the conclusion chapter reflect on the contributions your research work will make to existing knowledge?

Answer: Yes, the conclusion chapter of the research paper typically includes a reflection on the research’s contributions to existing knowledge.  In the “conclusion chapter”, you have to summarise the key findings and discuss how they add value to the existing literature on the current topic.

Reference list

All academic sources that you collected information from should be cited in-text and also presented in a  reference list (or a bibliography in case you include references that you read for the research but didn’t end up citing in the text), so the readers can easily locate the source of information when/if needed.

At most UK universities, Harvard referencing is the recommended style of referencing. It has strict and specific requirements on how to format a reference resource. Other common styles of referencing include MLA, APA, Footnotes, etc.

Each chapter of the dissertation should have relevant information. Any information that is not directly relevant to your research topic but your readers might be interested in (interview transcripts etc.) should be moved under the Appendices section .

Things like questionnaires, survey items or readings that were used in the study’s experiment are mostly included under appendices.

An Outline of Dissertation/Thesis Structure

An Outline of Dissertation

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FAQs About Structure a Dissertation

What does the title page of a dissertation contain.

The title page will contain details of the author/researcher, research topic , degree program (the paper is to be submitted for) and research supervisor’s name(s). The name of your university, logo, student number and submission date can also be presented on the title page.

What is the purpose of adding acknowledgement?

The acknowledgements section allows you to thank those who helped you with your dissertation project. You might want to mention the names of your academic supervisor, family members, friends, God and participants of your study whose contribution and support enabled you to complete your work.

Can I omit the glossary from the dissertation?

Yes, but only if you think that your paper does not contain any terms or phrases that the reader might not understand. If you think you have used them in the paper,  you must create a glossary that lists important phrases and terms with their meanings explained.

What is the purpose of appendices in a dissertation?

Any information that is not directly relevant to research questions or hypotheses but could be useful for the readers can be placed under the Appendices, such as questionnaire that was used in the study.

Which referencing style should I use in my dissertation?

You can use any of the referencing styles such as APA, MLA, and Harvard, according to the recommendation of your university; however, almost all UK institutions prefer Harvard referencing style .

What is the difference between references and bibliography?

References contain all the works that you read up and used and therefore, cited within the text of your thesis. However, in case you read on some works and resources that you didn’t end up citing in-text, they will be referenced in what is called a bibliography.

Additional readings might also be present alongside each bibliography entry for readers.

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  • Dissertation & Thesis Outline | Example & Free Templates

Dissertation & Thesis Outline | Example & Free Templates

Published on June 7, 2022 by Tegan George . Revised on November 21, 2023.

A thesis or dissertation outline is one of the most critical early steps in your writing process . It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding the specifics of your dissertation topic and showcasing its relevance to your field.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

  • Your anticipated title
  • Your abstract
  • Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

In the final product, you can also provide a chapter outline for your readers. This is a short paragraph at the end of your introduction to inform readers about the organizational structure of your thesis or dissertation. This chapter outline is also known as a reading guide or summary outline.

Table of contents

How to outline your thesis or dissertation, dissertation and thesis outline templates, chapter outline example, sample sentences for your chapter outline, sample verbs for variation in your chapter outline, other interesting articles, frequently asked questions about thesis and dissertation outlines.

While there are some inter-institutional differences, many outlines proceed in a fairly similar fashion.

  • Working Title
  • “Elevator pitch” of your work (often written last).
  • Introduce your area of study, sharing details about your research question, problem statement , and hypotheses . Situate your research within an existing paradigm or conceptual or theoretical framework .
  • Subdivide as you see fit into main topics and sub-topics.
  • Describe your research methods (e.g., your scope , population , and data collection ).
  • Present your research findings and share about your data analysis methods.
  • Answer the research question in a concise way.
  • Interpret your findings, discuss potential limitations of your own research and speculate about future implications or related opportunities.

For a more detailed overview of chapters and other elements, be sure to check out our article on the structure of a dissertation or download our template .

To help you get started, we’ve created a full thesis or dissertation template in Word or Google Docs format. It’s easy adapt it to your own requirements.

 Download Word template    Download Google Docs template

Chapter outline example American English

It can be easy to fall into a pattern of overusing the same words or sentence constructions, which can make your work monotonous and repetitive for your readers. Consider utilizing some of the alternative constructions presented below.

Example 1: Passive construction

The passive voice is a common choice for outlines and overviews because the context makes it clear who is carrying out the action (e.g., you are conducting the research ). However, overuse of the passive voice can make your text vague and imprecise.

Example 2: IS-AV construction

You can also present your information using the “IS-AV” (inanimate subject with an active verb ) construction.

A chapter is an inanimate object, so it is not capable of taking an action itself (e.g., presenting or discussing). However, the meaning of the sentence is still easily understandable, so the IS-AV construction can be a good way to add variety to your text.

Example 3: The “I” construction

Another option is to use the “I” construction, which is often recommended by style manuals (e.g., APA Style and Chicago style ). However, depending on your field of study, this construction is not always considered professional or academic. Ask your supervisor if you’re not sure.

Example 4: Mix-and-match

To truly make the most of these options, consider mixing and matching the passive voice , IS-AV construction , and “I” construction .This can help the flow of your argument and improve the readability of your text.

As you draft the chapter outline, you may also find yourself frequently repeating the same words, such as “discuss,” “present,” “prove,” or “show.” Consider branching out to add richness and nuance to your writing. Here are some examples of synonyms you can use.

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When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

  • Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

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Stressors, emotions, and social support systems among respiratory nurses during the Omicron outbreak in China: a qualitative study

  • Wenzhen Yu 1 ,
  • Ying Zhang 1 ,
  • Yunyan Xianyu 1 &
  • Dan Cheng 1  

BMC Nursing volume  23 , Article number:  188 ( 2024 ) Cite this article

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Respiratory nurses faced tremendous challenges when the Omicron variant spread rapidly in China from late 2022 to early 2023. An in-depth understanding of respiratory nurses’ experiences during challenging times can help to develop better management and support strategies. The present study was conducted to explore and describe the work experiences of nurses working in the Department of Pulmonary and Critical Care Medicine (PCCM) during the Omicron outbreak in China.

This study utilized a descriptive phenomenological method. Between January 9 and 22, 2023, semistructured and individual in-depth interviews were conducted with 11 respiratory nurses at a tertiary hospital in Wuhan, Hubei Province. A purposive sampling method was used to select the participants, and the sample size was determined based on data saturation. The data analysis was carried out using Colaizzi’s method.

Three themes with ten subthemes emerged: (a) multiple stressors (intense workload due to high variability in COVID patients; worry about not having enough ability and energy to care for critically ill patients; fighting for anxious clients, colleagues, and selves); (b) mixed emotions (feelings of loss and responsibility; feelings of frustration and achievement; feelings of nervousness and security); and (c) a perceived social support system (team cohesion; family support; head nurse leadership; and the impact of social media).

Nursing managers should be attentive to frontline nurses’ needs and occupational stress during novel coronavirus disease 2019 (COVID-19) outbreaks. Management should strengthen psychological and social support systems, optimize nursing leadership styles, and proactively consider the application of artificial intelligence (AI) technologies and products in clinical care to improve the ability of nurses to effectively respond to future public health crises.

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Introduction

As of January 29, 2023, more than 753 million confirmed cases of COVID-19 have been reported globally, with more than 6.8 million deaths [ 1 ]. The Omicron variant (Omicron, B.1.1.529) is one of the five World Health Organization (WHO) variants of concern (VOCs). Compared with other VOCs, the Omicron variant has significantly increased transmission and immune escape [ 2 ]. An analysis by the Chinese Center for Disease Control and Prevention (CCDC) revealed that from December 1, 2022, to early January 2023, Omicron BA.5.2 and BF.7 were the prevalent strains in China, with these two lineages accounting for 97.5% of all indigenous cases [ 3 ]. At the press conference of the Joint COVID-19 Prevention and Control Mechanism of the State Council on January 14, 2023, the number of hospitalizations due to COVID-19 reached a peak of 1.63 million on January 5, and from December 8, 2022, to January 12, 2023, a total of 59,938 deaths related to hospitalizations due to COVID-19 occurred in medical institutions across the country. There were 5,503 deaths due to respiratory failure [ 4 ].

Nurses play a vital role in rescuing and treating COVID-19 patients. Nurses are at the forefront of the fight against disease, facing enormous physical and mental pressure while adopting effective strategies to overcome unprecedented challenges [ 5 – 6 ]. Research has shown that frontline nurses faced numerous challenges during the COVID-19 pandemic. A systematic review and meta-analysis exploring the impact of the COVID-19 pandemic on the prevalence of psychological symptoms among nurses showed that the pooled prevalence of anxiety, depression, and sleep disturbance was 37%, 35% and 43%, respectively [ 7 ]. During the COVID-19 pandemic, the workload of frontline nurses also increased significantly due to multiple factors, such as increased patient requirements and work content, longer work hours, and a shortage of staff and personal protective equipment [ 8 – 9 ]. In addition, nurses also expressed feelings of helplessness and inadequacy because, despite hard work, they were unable to provide dignified and acceptable-quality care [ 10 ]. Therefore, it is necessary to emphasize the significance of support for nurses from governments, policy-makers, and nursing organizations to reduce the negative impacts on nurses’ well-being during and after a pandemic or epidemic [ 11 ]. Otherwise, nurses may feel burnout, leading to turnover [ 12 – 13 ].

Nevertheless, existing studies on frontline nurses’ work experiences have been conducted predominantly in the context of nurses as physically healthy individuals providing health care services to COVID-19 patients. With the rapid spread of the Omicron BA.5.2 and BF.7 variants, it was estimated that most of the Chinese population was infected in December 2023 [ 14 ]. It has been reported that the number of clinic visits due to fever in China peaked on December 23, 2023. Two weeks later, the number of critical hospitalizations for COVID-19 also peaked [ 4 ]. During that period, the challenges faced by nurses in China were unprecedented and vastly different from those of other nurses worldwide. Most nurses were both health care providers and infected patients. The present qualitative study aimed to explore the work experiences of frontline respiratory nurses during the Omicron epidemic, develop better nursing countermeasures and management strategies for managers and promote better support for frontline nurses to provide patients with higher-quality care in possible future outbreaks.

Study design

The present study adopted a qualitative descriptive phenomenological design to conduct in-depth interviews. This design is suitable for providing detailed descriptions of participants’ emotions, opinions, and experiences and interpreting the meaning of their behaviours [ 15 ].

Participants and setting

All participants were recruited from a tertiary hospital in Wuhan, Hubei Province, China. A purposive sampling method was used in the present study. To obtain a wide range of experiences, we considered a diverse range of personal details, including age, sex, education level, marital status, years of nursing experience, professional title, type of employment, and workplace type, during the selection of participants. The sample size was determined based on data saturation [ 16 ].

The inclusion criteria were registered nurses working at the PCCM who provided direct care to COVID-19 patients between December 8, 2022, and January 8, 2023, and those who expressed willingness to participate in the study and share their experience. Nurse managers and nurses working less than two weeks during the abovementioned period were excluded.

Data collection

The data were collected through individual and face-to-face, in-depth interviews from January 9 to 22, 2023.

After a literature review and panel discussion, an interview guide was developed. Two pilot interviews were also conducted to investigate the appropriateness of the interview questions, and the guide remained the same. The data from the pilot interviews were not included in the analysis. All interviews were conducted by one researcher (first author), who completed a thorough and systematic study of qualitative research methods and reviewing skills before the start of the study. The final semistructured interview guide consisted of nine open-ended questions (see Supplementary file 1 ).

The interviewer and the participants had been colleagues for 3–7 years and trusted each other. The interviewer informed the participants about the purpose, voluntariness, anonymity, and confidentiality of the study one day before the interview and scheduled the time of the interview. Interviews were usually conducted on an afternoon when the participants were off duty, or an alternative time was arranged if the participants could not leave work on time. The interviews were conducted in a one-room office to ensure that the environment was quiet and undisturbed so that the participants could express their inner feelings to the interviewer with an open mind. With the participants’ permission, all interviews were audio-recorded using a digital voice recorder. The duration of the interviews varied between 30 and 60 min. Within 24 h of each interview, the audio-recorded data were fully transcribed, and two researchers independently evaluated the data saturation. Any disagreements were resolved through a panel discussion. Behavioural data (laughing, crying, sighing, silence or pausing, etc.) were also recorded during transcription for data analysis. Data saturation was reached at the 10th interview, but an additional interview was also conducted to ensure that no new information emerged. Therefore, a total of 11 respiratory nurses were recruited. None of the nurses dropped out of the study.

Data analysis

Colaizzi’s method was used to analyse the data [ 17 ]. This method involved the following steps: (a) Familiarization: rereading the transcripts verbatim multiple times to become familiar with the data; (b) Identifying significant statements: identifying and extracting meaningful statements relevant to the phenomenon; (c) Formulating meanings: formulating and encoding meanings from important statements; (d) Clustering themes: aggregating the encoded meanings into preliminary themes; (e) Developing an exhaustive description: providing a detailed description of each of the themes generated in step d with the addition of participants’ original statements; (f) Producing the fundamental structure: generating themes to reveal the basic structure of the phenomenon using short and condensed phrases; and (g) Verifying the fundamental structure: presenting the transcripts of the interviews, codes, and themes to the participants for feedback on whether their experience of the phenomenon had been accurately represented. Two independent researchers analysed the data simultaneously.

In this study, Lincoln and Guba’s criteria of credibility, transferability, dependability, and confirmability were utilized to ensure rigor [ 18 ]. The following strategies were implemented to achieve credible study findings: conducting semistructured, in-depth interviews with open-ended questions and field notes; transcribing audio-recorded data word-for-word and independently analysing the raw data by two researchers; and asking participants to provide feedback on the transcripts, codes, and themes. Transferability was established by considering maximum variations in participant characteristics and presenting appropriate participant quotes. To facilitate dependability and confirmability, several meetings were held among the researchers to discuss and identify codes, subthemes, and themes.

Ethical considerations

This study was approved by the research and ethics committees of Renmin Hospital of Wuhan University (Approval NO: WDRY2023-K031). Before the interviews, the details of the study, the expected risks and benefits, and the right to withdraw at any time was verbally explained to all participants, and written informed consent was obtained. After the interviews were transcribed, the participants’ names were deleted instead of their identities (A‒K). To ensure confidentiality and privacy, the text data were stored in a locked cabinet, and the audio data were stored on a password-protected computer.

Participant characteristics

A total of 11 nurses, including 10 females (90.9%) and 1 male (9.1%), were included. The mean age was 32.09 ± 5.45 years (range = 24–43 years), and the mean number of years of nursing experience was 10.36 ± 5.50 years (range = 3–21 years). The sociodemographic data are displayed in Table  1 .

Thematic results

Three major themes emerged: multiple stressors, mixed emotions, and a perceived social support system. Ten subthemes were identified. The findings are described in Fig.  1 .

figure 1

Themes and sub-themes of work experience for respiratory nurses during Omicron outbreak

Theme 1: Multiple Stressors

This theme focused on the workplace stressors experienced by respiratory nurses during the Omicron outbreak. Three subthemes were identified in this theme: intense workload due to high variability in COVID patients; worry about not having enough ability and energy to care for critically ill patients; and fighting for anxious clients, colleagues, and selves.

Intense workload due to high variability in COVID patients

Most participants reported a high level of work pressure, such as a high number of admissions, a high percentage of critical patients, rapid changes in patient conditions, and frequent resuscitations. As one participant said,

“For some time now, the RICU has been particularly busy. Every shift is filled with resuscitation cases and the admission of new critically ill patients, usually those who need to be intubated. We borrowed much equipment from the Equipment Division, such as ventilators and high-flow nasal cannula oxygen therapy devices. We usually have enough equipment in our Department, but now we do not.” (Participant G)

Almost all participants stated that the workload of nursing care associated with COVID-19 had significantly increased, and nurses often had to work overtime to complete their work. As two participants said,

“Almost all newly admitted patients are given nebulizers and oxygen and undergo urgent arterial blood gas analysis. I could not leave work on time almost daily (bitter smile).” (Participant C)
“There are many patients on oral corticosteroids, which is different than usual. I have to talk to the patients about the use and the dosage, tell them when to taper, and talk to the doctor before I give the medication. It all takes time.” (Participant I)

Another participant said the following:

“Except for nursing records, I get things done during working hours. Then, I spend off-duty time writing the records.” (Participant E)

Some participants reported working at an accelerated pace during the work period. One of the participants described their experience as follows:

“Patients ask me questions, and maybe I am fast in my speech and, well, fast enough in my steps.” (Participant D)

Most participants reported returning to work after taking a short break from their infections. However, they were still symptomatic when they returned to work. One participant said the following:

“I had three days of rest and came back to work when my fever was down, and my cough has not gone away yet.” (Participant A)

Worry about not having enough ability and energy to care for critically ill patients

Some of the participants in this study reported significant psychological distress from worrying about not having enough ability and energy to care for critically ill patients. The following excerpts illustrate this subtheme:

“There are many patients on invasive mechanical ventilation, and the biggest worry is accidental extubation. It is nerve-wracking.” (Participant F)
“Some patients are ventilated in the prone position; some are intubated, and some are not. Although the therapeutic efficacy was quite good, at least four colleagues were needed to change the position. It is a big risk at night when we are short-staffed, especially in a resuscitation situation.” (Participant G)
“I was worried about making mistakes. During that time, I had night sweats, did not sleep well, often felt weak and dizzy during the day, and was afraid that I would make a mistake while providing care because of my lack of concentration.” (Participant K)

Fighting for anxious clients, colleagues, and selves

In this study, most participants said that patients and their family members, doctors, other nurses, and themselves were experiencing negative emotions such as anxiety. Some participants expressed this as follows:

“In my communication with patients, I have noticed that many patients are anxious, so I do more explaining than before when I give patients medication. Many patients ask me if their disease is serious…” (Participant I)
“Some patients are transferred to the RICU when their condition deteriorates, and their families have no sight of them and are very anxious every day. There is also much pressure on the doctors.” (Participant G)
“For us young nurses who are faced with so many critically ill patients who experience rapid changes in their conditions, we often have to communicate with doctors, especially senior doctors. If (we are) inexperienced, communication is slightly difficult. Additionally, because everyone has been working for a long time, it is difficult to know whether (the staff) are irritated or can communicate well with their colleagues. Because after a long shift, they may all be experiencing negative emotions.” (Participant F)
“I am not sure if it is because of my illness or because of my work. I often dream about saving patients, probably for both reasons… I hope the hospital will open a free psychiatric and sleep disorder clinic for us.” (Participant K)

Some participants mentioned maintaining a positive mindset through self-regulation and psychological suggestions as a stress management strategy and expressed the hope that managers would pay attention to the psychological states of frontline nurses and provide psychological support. One participant said,

“It is important to keep thinking positively. We are all in the same boat now (laughs). The other thing is to learn some relaxation techniques. Leaders should be aware of the psychological dynamics of nurses on the front line and provide psychological comfort.” (Participant F)

Theme 2: Mixed emotions

This theme focused on mixed emotional states, that is, the co-occurrence of positive and negative emotions in respiratory nurses during the Omicron outbreak. Within this theme, three subthemes were identified: feelings of loss and responsibility, feelings of frustration and achievement, and feelings of nervousness and security.

Feelings of loss and responsibility

Some of the participants in this study expressed a certain sense of loss. This feeling stemmed from nurses caring for patients, uncertain about when they might become infected, and their lack of a role in taking care of family. One of the participants said,

“There could still be a psychological setback. I went through the 2020 pandemic in Wuhan, and then I went to another city (to offer support) and witnessed another outbreak. Previously, we thought about how to protect ourselves while helping others. This time, it is unclear how to protect ourselves while treating others.” (Participant H)

Another participant said,

“My family members were infected. I was working hard and very busy, and I did not have the extra time or energy to care for them. My parents did not live with me, and I wanted to have time to get them some medicine and check on them. During that time, I was worried about their health because the risks for older people were high. I was worried that their health conditions would become more serious, and I was not caring for them.” (Participant I)

The majority of the participants in this study stated that they stayed in their jobs despite experiencing substantial and multiple pressures because of a sense of responsibility. One participant, who was asymptomatic and not sure if he was infected, said the following:

“I think we have to work and stick to the job. First, we have to go to work according to the schedule, which is the most important point, the duty. I cannot stay away from work just because I haven’t been infected. At this most critical point, running away at the first sign of difficulty is impossible. That is certainly not the right thing to do. The main thing is duty because that is one of the most fundamental qualities of an employee.” (Participant F)

Some participants who had symptoms indicated that their intention in returning to work without fully recovering was to allow other nurses to also have breaks. One participant mentioned,

“At the time, I had been off for 3 days. Some of my colleagues were just showing symptoms and had no breaks. I thought I should go to work so those colleagues could have breaks, so I picked myself up and came to work.” (Participant A)

Feelings of frustration and achievement

Some of the participants in this study reported that patient blaming made them feel frustrated. Some participants claimed that their frustration stemmed from not seeing a significant improvement in patient outcomes in the short term. Participants described their experiences as follows:

“When I came back to work after being sick, I had not fully recovered, and occasionally I moved a little slower. Some patients did not understand my situation. I felt despondent at that moment (tears).” (Participant A)
“It is very depressing. Intubated patients are difficult to wean from mechanical ventilation for an extended period, and even less severe patients still have symptoms.” (Participant G)

Most of the participants in this study reported feeling a sense of achievement. The reasons included receiving affirmation from patients or their families; noticing gradual improvement in patient conditions; being helpful to families, friends, or colleagues; and enhancing professional competence. The participants described their experiences as follows:

“Many patients expressed admiration for my hard work and understood the challenges I faced, some even telling me to take a break. Their empathy motivated me to continue making contributions.” (Participant D)
“When the patients were admitted, they were extremely unwell, struggling with speech and reluctant to move. Following treatment, they could eat independently, move about independently, and express gratitude for feeling better. Moments like this bring great happiness to me!” (Participant H)
“During this period, I received more calls from acquaintances for counselling and felt fulfilled. They asked questions, such as if azvudine was effective, and I could advise them on the optimal stage for taking medication. Consequently, I felt that I was valued and was motivated to be a respiratory nurse. We are also confident that the mortality rate in our ward is very low, and many patients have been discharged.” (Participant I)
“This experience can be considered a form of training, helping us develop specialized skills and gain personal insights. If we face a similar emergency in the future, we will possess greater knowledge and skills regarding how to tackle it.” (Participant F)

Feelings of nervousness and security

Some of the participants in this study expressed nervousness due to the fear of being infected and of passing the virus on to their family members. One participant who tested negative for SARS-CoV-2 antibodies described her feelings as follows:

“My workmates falling ill affected me. I did not know what the symptoms would be if I got it. It was that uncertainty. Therefore, going to work caused anxiety at the beginning of the outbreak. It is that feeling of not knowing if you will go down next… It is like there’s no escape.” (Participant H)

Another participant stated the following:

“I am feeling nervous. I am in daily contact with patients who have tested positive, and since I have elderly relatives and young children at home, I am more concerned about bringing the virus back with me. That is why, when I return home from work, I leave my clothes and shoes outside, and the first thing I do upon entering my home is shower. When I returned home, my children used to hug me, but I would say, “Stay back, stay back.” I had to take a shower before I embraced them. Will there be a second or third wave? Can elderly people and children withstand this? Will my health worsen over time?” (Participant B)

Some of the participants expressed that their work in the PCCM made them feel reassured:

“I feel that working in a hospital makes it easier to get help if I become infected. As a respiratory staff member, I feel safe.” (Participant K)
“ It is not really that worrying. I think I was in the PCCM, and if anything happened to me, everyone would save me. I’m in this department, and the backup is strong. ” (Participant C)

Theme 3: Perceived social support systems

The vast majority of participants talked about the social support systems they perceived and how these social support systems impacted them. Within this theme, four subthemes were identified: team cohesion, family support, head nurse leadership, and the impact of social media

Team cohesion

Most participants in this study reported that coworkers helped each other at work, comforted each other psychologically, and were more unified than before the epidemic. The following descriptions represented this subtheme:

“During that time, even though almost everyone was sick and very busy at work, the atmosphere in our department was amiable. Every time you were busy, others would come to help you, and so would I. No one slacked off or hid from work, and everyone worked hard. It was a positive boost because no one was dragging their feet.” (Participant B)
“In such a busy situation, our colleagues are more united. We help each other. It is more cohesive. Busier, but more in touch (smile).” (Participant C)
“After my colleagues got infected, they shared some of their feelings with me. It was not really that uncomfortable, so my mind quickly relaxed. When people’s symptoms subsided, their temperature dropped, or the pain in their bodies eased, you could sense their happiness. I also felt happy when I heard such news. I feel that this kind of happiness is different from usual.” (Participant H)

Family support

Some participants in this study indicated that the health and support of their families strongly supported them in focusing on fighting against the outbreak:

“My family was very supportive (laughs). Everyone was very supportive. They were trying to minimize my burden. Because I did not know if I was infected, but when they were infected, they drank water, took their own medicine, and took their temperature. They wore masks, and they disinfected at home. I think that this was also a kind of support. They did not delay buying food or cooking every day and did not stop cooking or eating just because they were lethargic after the infection. Therefore, I think that is a kind of support (laughs).” (Participant H)
“I think my family… my support system is stable (grin), so I think I would be fine (to work).” (Participant C)

Head nurse leadership

Some of the participants in this study indicated that the head nurses’ leadership had a significant impact on the nurses’ work experiences:

“Rational scheduling and decision-making by the nurse managers is important. Pairing senior nurses with junior nurses during scheduling can avoid several risks. It is also important to try to ensure that everyone gets enough rest while maximizing the potential of the frontline nurses.” (Participant F)
“One day, the on-call shift started. Zhang was on it, and she did not get a moment’s rest until the end of the shift, and neither did we. She came to help us. She helped everyone. Where we were busy, where she was, arranging that shift helped our whole team and individuals a lot.” (Participant B)
“Any shortage of supplies or equipment or emergency, just talk to the head nurse, and it all gets resolved, so it is not so draining to work.” (Participant D)

Impact of social media

In this study, some participants mentioned that social media use impacted their psychological feelings, as follows:

“There are some very positive short videos online. One of our colleagues and some well-known people have shared their personal experiences fighting the outbreak, and it has been helpful to see others actively confronting it.” (Participant H)

Some participants expressed the opposite view:

“It worries me a little bit because the reinfections that are rumoured online can be scary.” (Participant C).

This study describes the challenges faced by respiratory nurses caring for COVID-19 patients during the Omicron outbreak in China from late 2022 to early 2023. Specifically, the findings interpreted these experiences as multiple stressors, mixed emotions, and perceived social support systems.

Like in the study by Al Maqbali M [ 7 ], a significant proportion of participants in our study reported that they had psychological problems such as stress, anxiety, frustration, or sleep disturbance and expressed a need for psychological support. Falatah’s [ 12 ] study showed that nurses’ turnover intentions increased significantly during the COVID-19 pandemic compared with that before the pandemic, and stress, anxiety, and fear of disease were predictors of nurses’ turnover intentions. In contrast to those in other studies, the participants in our study expressed their sense of security, which stemmed from confidence in their own professional background and trust in their colleagues. A previous study emphasized that understanding the psychological needs of frontline nurses and providing them with tailored psychological support can improve their mental health status and promote quality responses to clinical nursing and public health emergencies [ 19 ]. In addition, a cross-sectional correlation study conducted by Hoşgör [ 20 ] revealed that there was a significant positive correlation between nurses’ psychological resilience and job performance during the COVID-19 pandemic. These findings show that adopting strategies to improve the psychological resilience of nurses is helpful for optimizing the efficiency of nursing work and improving the quality of patient care. Therefore, during a public health crisis, nurse managers should assess the mental health status of frontline nurses in a timely manner, understand in depth the sources of pressure experienced by nurses, and establish psychological treatment teams to provide offline or online psychological support in the form of one-on-one or group support to improve the mental resilience and physical health of nurses.

In our study, participants described their sources of perceived social support, such as support from their teams, family members, head nurses, and social media. This social support helped them cope with the challenges during this difficult time and encouraged them to provide nursing care to the best of their ability. The participants had positive expressions and emotions when discussing their perceived social support systems. These findings are consistent with the findings of the Shen study [ 21 ], which revealed that the greater the level of social support, the better the psychological condition of nurses during the COVID-19 pandemic. Therefore, we strongly recommend that hospital managers regularly visit clinics, interact with frontline nurses, praise their vital role in dealing with the outbreak, and take comprehensive measures to increase value awareness, including compensation, honorary certificates, and publicly recognizing nurses’ contributions. In addition, visiting nurses on the frontline will help address difficulties such as shortages of equipment and human resources in the early stages of outbreaks.

Conversely, some participants in our study reported that rumours on social media about the serious consequences of reinfection negatively affected them. This may be related to the fact that most of the study participants were both patients and caregivers at the beginning of the outbreak. This points to the importance of leading public health experts being organized by the executive branch to provide evidence-based information to the public through social media.

Consistent with the findings of previous research [ 22 ], some participants described concerns not only about their own health but also about the health of their family members. This highlights the necessity of extending support for frontline nurses to their family members, including providing medicine and medical counselling. In addition, developing contingency plans to ensure the timeliness and accessibility of social support systems is an issue that managers must address.

The results of this study showed that flexible shift scheduling, active communication, timely resolution of problems, and close working cooperation with nurses played crucial roles in facilitating frontline nurses’ responses to the outbreak. Nursing managers are critical in maximizing the retention of nursing human resources and maintaining productivity and efficiency in health care organizations. Nursing leadership styles strongly influence nurses’ happiness and work environments. Niinihuhta [ 23 ] suggested that nurse leaders should use a supportive and relationship-focused leadership style. Another systematic review conducted by Cummings [ 24 ] provided robust evidence that relational leadership styles, such as transformational and authentic leadership styles, are significantly associated with improved outcomes, including outcomes regarding job satisfaction, employee-work relationships, employee health and well-being, the organizational environment and productivity.

In contrast, leadership focusing only on task completion is insufficient for achieving positive nursing health and workforce outcomes. As revealed in the scoping review conducted by Sihvola [ 25 ], nurse leaders should adopt a relational leadership style and positive communication to support nurse resilience during the COVID-19 pandemic. Furthermore, as an extension of the relational leadership style, inclusive leadership could increase the psychological ownership of nurses and reduce turnover intentions [ 26 ].

Unlike in previous situations, most participants in our study had symptoms, such as coughing or weakness, while caring for their clients. Therefore, as the bellwether of frontline nursing caregivers, head nurses should consider the overall situation of hospital management when public health emergencies occur, pay attention to the needs of frontline nurses, consider nurses’ advice, tolerate nurses’ shortcomings and mistakes, and construct an organizational relationship with clear and transparent communication, updated information, flexible shift arrangements, and mutual trust among colleagues to achieve the common goals of organizations and individuals to defeat the pandemic.

According to the results of the present study, respiratory nurses generally work longer hours in the event of an outbreak. At the beginning of the outbreak, the care workload surged as a large number of patients flooded hospitals. As a result, the amount of time to required complete nursing records increased. Consequently, bedside care was commonly provided to patients during normal business hours, and care notes were commonly completed during off hours. In addition, staff shortages were exacerbated by the infection of most logistics staff, and nurses had to take over delivering meals to patients and transporting medical and living supplies.

To alleviate the acute shortage of nursing staff and improve the quality and efficiency of nursing care, attempts are being made worldwide to apply AI technology to care, including COVID-19 care. Kagiyama [ 27 ] reported that a telemedicine-based self-vital sign examination system could quickly and accurately obtain vital sign information by measuring and uploading COVID-19 patient data without the risk of spreading infections. Mairittha [ 28 ] integrated a spoken conversation system into a smartphone application for care records. They found that the method increased the documentation speed by approximately 58.3% compared to the traditional keyboard-based method. Alderden [ 29 ] explored an AI-based transparent machine learning model that could predict the risk of hospital-acquired pressure injuries in ICU patients with COVID-19. Other studies have shown that nurses already use AI to perform various tasks across multiple patient populations, such as assisting elderly patients or recovering patients with exercise and in pain management, communication, interviewing, and patient education [ 30 ]. Nurses should recognize the need using AI in care. Nurses should increase their awareness of AI development; actively communicate and collaborate with experts in related fields; and advocate for patient and nurse involvement in the design, implementation, and evaluation of all aspects of AI health technology to prepare for possible future public health events.

Limitations

All participants in this study were from a tertiary hospital in Wuhan, China. Therefore, the results of the current study may not be generalizable to other settings. Despite we utilized purposive sampling method to ensure diversity of opinions, the majority of participants were female, which was due to the relatively small proportion of male nurse in China. In addition, although our interviews began one month after the start of the outbreak, they took place for two weeks, which may have influenced the views and expressions of the participants over time.

Respiratory department nurses provided insight into their work experiences during the Omicron outbreak in China from late 2022 to early 2023. Despite experiencing exhaustion, nurses continued to take care of COVID-19 patients with the sense of responsibility of “angels without wings.” Respiratory nurses also experienced a sense of accomplishment from helping patients and a sense of security from their professional backgrounds. The mutual help of team members, support from family members, leadership by head nurses, and influence of social media are essential factors supporting frontline respiratory nurses in the fight against COVID-19. Hospital administrators should pay attention to the pressure and needs of frontline nurses during epidemics, improve psychosocial support systems, optimize the leadership styles of nurse managers, and actively explore the use of AI in the field of clinical nursing to improve nurses’ abilities to respond to public health emergencies.

Implications

The findings of this study reveal the multiple stressors and mixed emotions encountered by frontline respiratory nurses in combating COVID-19, which is helpful for nurse managers to develop comprehensive strategies that mitigate the adverse impact of these stressors and the negative emotions on nurses’ well-being and augment the positive emotions’ influence on nurses’ work engagement. Moreover, the identification of the nurses perceived social support system would assist policy-makers and hospital administrators in formulating more tailored polices to enhance their support for frontline nurses. Additionally, the design and implementation of training programs focusing on respiratory intensive care for nurses and leadership skills for charge nurse, will play a crucial role in effectively responding to extreme pandemic events. Furthermore, the researchers recommend that more qualitative research be carried out in different medical institutions and that more male nurses be included to improve understanding of the phenomenon. It is also suggested that further research be conducted to explore the psychosocial support needs of frontline nurses and ultimately improve their mental and physical health and quality of care for COVID-19 patients.

Data availability

The datasets generated and/or analyzed in this study are not publicly available because the data contain individual participant information, but are available from the corresponding author on reasonable request.

Abbreviations

Department of Pulmonary and Critical Care Medicine

Novel coronavirus disease 2019

Artificial intelligence

World Health Organization

Variants of concern

Chinese Center for Disease Control and Prevention

Respiratory intensive care unit

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Acknowledgements

The authors thank the participants in this study for sharing their experiences.

This study was supported by the Hubei key laboratory opening project of Health Commission of Hubei Province (2022KFH002) and general project of Health Commission of Hubei Province (WJ2021M150).

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Wenzhen Yu, Ying Zhang, Yunyan Xianyu & Dan Cheng

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Contributions

W.Y., Y.Z., and D.C. conceptualized and designed the study. W.Y. collected the data. W.Y. and Y.Z. analyzed and interpreted the data. Y.X. acquired the funding and administered the projects. W.Y. wrote the original draft. W.Y., Y.Z., Y.X., and D.C. reviewed and edited the draft manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Dan Cheng .

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This study was approved by the research and ethics committees of Renmin Hospital of Wuhan University (Approval NO: WDRY2023-K031). Before the interviews, all participants were verbally explained the details of the study, expected risks and benefits, and their right to withdraw at any time, and written informed consents were obtained. All the methods and procedures in this study are in accordance with the Declaration of Helsinki.

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Yu, W., Zhang, Y., Xianyu, Y. et al. Stressors, emotions, and social support systems among respiratory nurses during the Omicron outbreak in China: a qualitative study. BMC Nurs 23 , 188 (2024). https://doi.org/10.1186/s12912-024-01856-6

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Received : 23 November 2023

Accepted : 10 March 2024

Published : 21 March 2024

DOI : https://doi.org/10.1186/s12912-024-01856-6

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dissertation descriptive study

Victoria Kleiner Successfully Defends Dissertation

Join us in congratulating Victoria Kleiner in the Fearns Lab on the successful defense of her dissertation entitled “RNA Synthesis Initiation and Termination by the Non-segmented Negative-strand RNA Virus Polymerase ” on March 14, 2024. Great job, Victoria!

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    Revised on 10 October 2022. Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what, where, when, and how questions, but not why questions. A descriptive research design can use a wide variety of research methods to investigate one or more variables.

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    Qualitative description (QD) is a label used in qualitative research for studies which are descriptive in nature, particularly for examining health care and nursing-related phenomena (Polit & Beck, 2009, 2014).QD is a widely cited research tradition and has been identified as important and appropriate for research questions focused on discovering the who, what, and where of events or ...

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    Microsoft Word - Proposal-QUAL-Morales.doc. A Sample Qualitative Dissertation Proposal. Prepared by. Alejandro Morales. NOTE: This proposal is included in the ancillary materials of Research Design with permission of the author. LANGUAGE BROKERING IN MEXICAN IMMIGRANT FAMILIES LIVING IN.

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    Yang Liu - "Nonparametric regression and density estimation on a network" Dissertation Advisor: David Ruppert and Peter Frazier Initial job placement: Research Analyst - Cubist Systematic Strategies Skyler Seto - "Learning from less : improving and understanding model selection in penalized machine learning problems" Dissertation Advisor: Martin Wells Initial job placement: Machine ...

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    Dissertation & Thesis Outline | Example & Free Templates. Published on June 7, 2022 by Tegan George.Revised on November 21, 2023. A thesis or dissertation outline is one of the most critical early steps in your writing process.It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding the specifics of your dissertation topic and showcasing its relevance to ...

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    This study utilized a descriptive phenomenological method. Between January 9 and 22, 2023, semistructured and individual in-depth interviews were conducted with 11 respiratory nurses at a tertiary hospital in Wuhan, Hubei Province. A purposive sampling method was used to select the participants, and the sample size was determined based on data ...

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    03/21/2024 By Cynthia Murphy. The College of Fine Arts, Humanities, and Social Sciences, School of Education, invites you to attend a doctoral dissertation defense by Cynthia J. Murphy on "Peer Review of Writing in the Remote-Synchronous Classroom: Examining How Teachers Establish Trust and Psychological Safety and the First-Year, Nontraditional Student Experience of Those Variables."

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