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Case Study – Methods, Examples and Guide

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A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Qualitative Data Collection: What it is + Methods to do it

Qualitative data collection is vital in qualitative research. It helps researchers understand individuals’ attitudes, beliefs, and behaviors in a specific context.

Several methods are used to collect qualitative data, including interviews, surveys, focus groups, and observations. Understanding the various methods used for gathering qualitative data is essential for successful qualitative research.

In this post, we will discuss qualitative data and its collection methods of it.

Content Index

What is Qualitative Data?

What is qualitative data collection, what is the need for qualitative data collection, effective qualitative data collection methods, qualitative data analysis, advantages of qualitative data collection.

Qualitative data is defined as data that approximates and characterizes. It can be observed and recorded.

This data type is non-numerical in nature. This type of data is collected through methods of observations, one-to-one interviews, conducting focus groups, and similar methods.

Qualitative data in statistics is also known as categorical data – data that can be arranged categorically based on the attributes and properties of a thing or a phenomenon.

It’s pretty easy to understand the difference between qualitative and quantitative data. Qualitative data does not include numbers in its definition of traits, whereas quantitative research data is all about numbers.

• The cake is orange, blue, and black in color (qualitative).
• Females have brown, black, blonde, and red hair (qualitative).

Qualitative data collection is gathering non-numerical information, such as words, images, and observations, to understand individuals’ attitudes, behaviors, beliefs, and motivations in a specific context. It is an approach used in qualitative research. It seeks to understand social phenomena through in-depth exploration and analysis of people’s perspectives, experiences, and narratives. In statistical analysis , distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

The data collected through qualitative methods are often subjective, open-ended, and unstructured and can provide a rich and nuanced understanding of complex social phenomena.

Qualitative research is a type of study carried out with a qualitative approach to understand the exploratory reasons and to assay how and why a specific program or phenomenon operates in the way it is working. A researcher can access numerous qualitative data collection methods that he/she feels are relevant.

LEARN ABOUT: Best Data Collection Tools

Qualitative data collection methods serve the primary purpose of collecting textual data for research and analysis , like the thematic analysis. The collected research data is used to examine:

• Knowledge around a specific issue or a program, experience of people.
• Meaning and relationships.
• Social norms and contextual or cultural practices demean people or impact a cause.

The qualitative data is textual or non-numerical. It covers mostly the images, videos, texts, and written or spoken words by the people. You can opt for any digital data collection methods , like structured or semi-structured surveys, or settle for the traditional approach comprising individual interviews, group discussions, etc.

Data at hand leads to a smooth process ensuring all the decisions made are for the business’s betterment. You will be able to make informed decisions only if you have relevant data.

Well! With quality data, you will improve the quality of decision-making. But you will also enhance the quality of the results expected from any endeavor.

Qualitative data collection methods are exploratory. Those are usually more focused on gaining insights and understanding the underlying reasons by digging deeper.

Although quantitative data cannot be quantified, measuring it or analyzing qualitative data might become an issue. Due to the lack of measurability, collection methods of qualitative data are primarily unstructured or structured in rare cases – that too to some extent.

Let’s explore the most common methods used for the collection of qualitative data:

Individual interview

It is one of the most trusted, widely used, and familiar qualitative data collection methods primarily because of its approach. An individual or face-to-face interview is a direct conversation between two people with a specific structure and purpose.

The interview questionnaire is designed in the manner to elicit the interviewee’s knowledge or perspective related to a topic, program, or issue.

At times, depending on the interviewer’s approach, the conversation can be unstructured or informal but focused on understanding the individual’s beliefs, values, understandings, feelings, experiences, and perspectives on an issue.

More often, the interviewer chooses to ask open-ended questions in individual interviews. If the interviewee selects answers from a set of given options, it becomes a structured, fixed response or a biased discussion.

The individual interview is an ideal qualitative data collection method. Particularly when the researchers want highly personalized information from the participants. The individual interview is a notable method if the interviewer decides to probe further and ask follow-up questions to gain more insights.

Qualitative surveys

To develop an informed hypothesis, many researchers use qualitative research surveys for data collection or to collect a piece of detailed information about a product or an issue. If you want to create questionnaires for collecting textual or qualitative data, then ask more open-ended questions .

LEARN ABOUT: Research Process Steps

To answer such qualitative research questions , the respondent has to write his/her opinion or perspective concerning a specific topic or issue. Unlike other collection methods, online surveys have a wider reach. People can provide you with quality data that is highly credible and valuable.

Paper surveys

Online surveys, focus group discussions.

Focus group discussions can also be considered a type of interview, but it is conducted in a group discussion setting. Usually, the focus group consists of 8 – 10 people (the size may vary depending on the researcher’s requirement). The researchers ensure appropriate space is given to the participants to discuss a topic or issue in a context. The participants are allowed to either agree or disagree with each other’s comments. 

With a focused group discussion, researchers know how a particular group of participants perceives the topic. Researchers analyze what participants think of an issue, the range of opinions expressed, and the ideas discussed. The data is collected by noting down the variations or inconsistencies (if any exist) in the participants, especially in terms of belief, experiences, and practice. 

The participants of focused group discussions are selected based on the topic or issues for which the researcher wants actionable insights. For example, if the research is about the recovery of college students from drug addiction. The participants have to be college students studying and recovering from drug addiction.

Other parameters such as age, qualification, financial background, social presence, and demographics are also considered, but not primarily, as the group needs diverse participants. Frequently, the qualitative data collected through focused group discussion is more descriptive and highly detailed.

Record keeping

This method uses reliable documents and other sources of information that already exist as the data source. This information can help with the new study. It’s a lot like going to the library. There, you can look through books and other sources to find information that can be used in your research.

Case studies

In this method, data is collected by looking at case studies in detail. This method’s flexibility is shown by the fact that it can be used to analyze both simple and complicated topics. This method’s strength is how well it draws conclusions from a mix of one or more qualitative data collection methods.

Observations

Observation is one of the traditional methods of qualitative data collection. It is used by researchers to gather descriptive analysis data by observing people and their behavior at events or in their natural settings. In this method, the researcher is completely immersed in watching people by taking a participatory stance to take down notes.

There are two main types of observation:

• Covert: In this method, the observer is concealed without letting anyone know that they are being observed. For example, a researcher studying the rituals of a wedding in nomadic tribes must join them as a guest and quietly see everything. 
• Overt: In this method, everyone is aware that they are being watched. For example, A researcher or an observer wants to study the wedding rituals of a nomadic tribe. To proceed with the research, the observer or researcher can reveal why he is attending the marriage and even use a video camera to shoot everything around him. 

Observation is a useful method of qualitative data collection, especially when you want to study the ongoing process, situation, or reactions on a specific issue related to the people being observed.

When you want to understand people’s behavior or their way of interaction in a particular community or demographic, you can rely on the observation data. Remember, if you fail to get quality data through surveys, qualitative interviews , or group discussions, rely on observation.

It is the best and most trusted collection method of qualitative data to generate qualitative data as it requires equal to no effort from the participants.

LEARN ABOUT: Behavioral Research

You invested time and money acquiring your data, so analyze it. It’s necessary to avoid being in the dark after all your hard work. Qualitative data analysis starts with knowing its two basic techniques, but there are no rules.

• Deductive Approach: The deductive data analysis uses a researcher-defined structure to analyze qualitative data. This method is quick and easy when a researcher knows what the sample population will say.
• Inductive Approach: The inductive technique has no structure or framework. When a researcher knows little about the event, an inductive approach is applied.

Whether you want to analyze qualitative data from a one-on-one interview or a survey, these simple steps will ensure a comprehensive qualitative data analysis.

Step 1: Arrange your Data

After collecting all the data, it is mostly unstructured and sometimes unclear. Arranging your data is the first stage in qualitative data analysis. So, researchers must transcribe data before analyzing it.

Step 2: Organize all your Data

After transforming and arranging your data, the next step is to organize it. One of the best ways to organize the data is to think back to your research goals and then organize the data based on the research questions you asked.

Step 3: Set a Code to the Data Collected

Setting up appropriate codes for the collected data gets you one step closer. Coding is one of the most effective methods for compressing a massive amount of data. It allows you to derive theories from relevant research findings.

Step 4: Validate your Data

Qualitative data analysis success requires data validation. Data validation should be done throughout the research process, not just once. There are two sides to validating data:

• The accuracy of your research design or methods.
• Reliability—how well the approaches deliver accurate data.

Step 5: Concluding the Analysis Process

Finally, conclude your data in a presentable report. The report should describe your research methods, their pros and cons, and research limitations. Your report should include findings, inferences, and future research.

QuestionPro is a comprehensive online survey software that offers a variety of qualitative data analysis tools to help businesses and researchers in making sense of their data. Users can use many different qualitative analysis methods to learn more about their data.

Users of QuestionPro can see their data in different charts and graphs, which makes it easier to spot patterns and trends. It can help researchers and businesses learn more about their target audience, which can lead to better decisions and better results.

LEARN ABOUT: Steps in Qualitative Research

Qualitative data collection has several advantages, including:

• In-depth understanding: It provides in-depth information about attitudes and behaviors, leading to a deeper understanding of the research.
• Flexibility: The methods allow researchers to modify questions or change direction if new information emerges.
• Contextualization: Qualitative research data is in context, which helps to provide a deep understanding of the experiences and perspectives of individuals.
• Rich data: It often produces rich, detailed, and nuanced information that cannot capture through numerical data.
• Engagement: The methods, such as interviews and focus groups, involve active meetings with participants, leading to a deeper understanding.
• Multiple perspectives: This can provide various views and a rich array of voices, adding depth and complexity.
• Realistic setting: It often occurs in realistic settings, providing more authentic experiences and behaviors.

LEARN ABOUT: 12 Best Tools for Researchers

Qualitative research is one of the best methods for identifying the behavior and patterns governing social conditions, issues, or topics. It spans a step ahead of quantitative data as it fails to explain the reasons and rationale behind a phenomenon, but qualitative data quickly does. 

Qualitative research is one of the best tools to identify behaviors and patterns governing social conditions. It goes a step beyond quantitative data by providing the reasons and rationale behind a phenomenon that cannot be explored quantitatively.

With QuestionPro, you can use it for qualitative data collection through various methods. Using Our robust suite correctly, you can enhance the quality and integrity of the collected data.

LEARN MORE         FREE TRIAL

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Methodology

• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

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

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Qualitative Study Design and Data Collection

• First Online: 10 February 2022

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While the prior chapter set the stage for an understanding of the nature of qualitative evaluation, this chapter will offer strategies for planning a study and making decisions about how to gather data. The process is depicted as an iterative looping through steps beginning with idea generation to dissemination of results. It is critical that strategies for rigor be incorporated throughout the process. This chapter outlines methods for data collection utilizing interviews, focus groups, observation, and naturally occurring data, and then it also describes combinations often used together, which constitute toolkits of complementary techniques.

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This is of course a major point of departure between qualitative methods and their quantitative counterparts. In quantitative work, investigators rarely acknowledge bias, and if they do, they may be disqualified from participating in the study.

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Charles P. Friedman

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Jeremy C. Wyatt

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Answers to Self-Tests

Self-test 15.1.

Which of the strategies to ensure study rigor is primarily employed in the qualitative study scenarios below:

Data from interviews about the usability of a resource are analyzed thematically. The evaluation study team looks to see if and how similar themes have arisen in earlier meetings of the team.

Audit trail

A member of the study team, who has recently participated in another study of a similar kind of resource, becomes concerned that that person’s views about the current study are being shaped by that previous experience. That person sits with another member of the study team to share that person’s concerns and put them in perspective.

Reflexivity

At a “town hall” meeting called to present the results of a qualitative study, the sponsor of the study raises deep and serious questions about the validity of the findings. The study team returns to notes from their team meetings to review how and based on what data they came to this conclusion.

Member checking

During an evaluation project team meeting, one of the study team members finds themselves deeply repelled by off-color comments made by one of the project staff. The team member makes a note of this personal response as part of field notes.

After interviewing 10 patients participating in a study, a study team member perceives that they are hearing the same points raised by all interviewees. The team member requests a study team meeting to consider reducing the total number of interviews from 20, as previously planned, to 12.

Data saturation

A study team member “corners” a participant in a system development effort following a meeting and asks for the participant’s impressions on what transpired in the meeting.

Self-Test 15.2

Label each of the following interview scenarios, conducted as part of a qualitative study, as representing the fully structured, semi-structured or unstructured approach.

A study team member “corners” a participant in a system development project following a meeting and asks for that person’s impressions on what transpired in the meeting.

A study team member schedules time with a patient who is using an information resource to acquire specific information about the patient’s medical history.

Likely fully structured, though it could generate discussion, in which case it could veer towards semi-structured.

A study team member works with partners on the study team to develop a set of questions to be asked to all interviewees. Each question is to be followed up with the question: “Why do you think this is the case?”. At the end of the interview, subjects will be asked: “What else would you like to tell us to shed light on these matters?”

Semi-structured

An interview begins with the statement: “In general, what has been your experience using this EHR?” The remaining questions depend on how the interviewee answers this opening question.

Unstructured

A set of specific questions are read verbatim from an interview guide. No other questions are asked. The interviewees’ responses are recorded.

Fully structured

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Friedman, C.P., Wyatt, J.C., Ash, J.S. (2022). Qualitative Study Design and Data Collection. In: Evaluation Methods in Biomedical and Health Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-86453-8_15

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8 Essential Qualitative Data Collection Methods

Qualitative data methods allow you to deep dive into the mindset of your audience to discover areas for growth, development, and improvement. 

British mathematician and marketing mastermind Clive Humby once famously stated that “Data is the new oil.”  He has a point. Without data, nonprofit organizations are left second-guessing what their clients and supporters think, how their brand compares to others in the market, whether their messaging is on-point, how their campaigns are performing, where improvements can be made, and how overall results can be optimized. 

There are two primary data collection methodologies: qualitative data collection and quantitative data collection. At UpMetrics, we believe that relying on quantitative, static data is no longer an option to drive effective impact. In the nonprofit sector, where financial gain is not the sole purpose of your organization’s existence. In this guide, we’ll focus on qualitative data collection methods and how they can help you gather, analyze, and collate information that can help drive your organization forward. 

What is Qualitative Data? 

Data collection in qualitative research focuses on gathering contextual information. Unlike quantitative data, which focuses primarily on numbers to establish ‘how many’ or ‘how much,’ qualitative data collection tools allow you to assess the ‘why’s’ and ‘how’s’ behind those statistics. This is vital for nonprofits as it enables organizations to determine:

• Existing knowledge surrounding a particular issue.
• How social norms and cultural practices impact a cause.
• What kind of experiences and interactions people have with your brand.
• Trends in the way people change their opinions.
• Whether meaningful relationships are being established between all parties.

In short, qualitative data collection methods collect perceptual and descriptive information that helps you understand the reasoning and motivation behind particular reactions and behaviors. For that reason, qualitative data methods are usually non-numerical and center around spoken and written words rather than data extrapolated from a spreadsheet or report. 

Qualitative vs. Quantitative Data 

Quantitative and qualitative data represent both sides of the same coin. There will always be some degree of debate over the importance of quantitative vs. qualitative research, data, and collection. However, successful organizations should strive to achieve a balance between the two. 

Organizations can track their performance by collecting quantitative data based on metrics including dollars raised, membership growth, number of people served, overhead costs, etc. This is all essential information to have. However, the data lacks value without the additional details provided by qualitative research because it doesn’t tell you anything about how your target audience thinks, feels, and acts. 

Qualitative data collection is particularly relevant in the nonprofit sector as the relationships people have with the causes they support are fundamentally personal and cannot be expressed numerically. Qualitative data methods allow you to deep dive into the mindset of your audience to discover areas for growth, development, and improvement. 

8 Types of Qualitative Data Collection Methods  

As we have firmly established the need for qualitative data, it’s time to answer the next big question: how to collect qualitative data. 

Here is a list of the most common qualitative data collection methods. You don’t need to use them all in your quest for gathering information. However, a foundational understanding of each will help you refine your research strategy and select the methods that are likely to provide the highest quality business intelligence for your organization. 

1. Interviews

One-on-one interviews are one of the most commonly used data collection methods in qualitative research because they allow you to collect highly personalized information directly from the source. Interviews explore participants' opinions, motivations, beliefs, and experiences and are particularly beneficial in gathering data on sensitive topics because respondents are more likely to open up in a one-on-one setting than in a group environment. 

Interviews can be conducted in person or by online video call. Typically, they are separated into three main categories:

• Structured Interviews - Structured interviews consist of predetermined (and usually closed) questions with little or no variation between interviewees. There is generally no scope for elaboration or follow-up questions, making them better suited to researching specific topics. 
• Unstructured Interviews – Conversely, unstructured interviews have little to no organization or preconceived topics and include predominantly open questions. As a result, the discussion will flow in completely different directions for each participant and can be very time-consuming. For this reason, unstructured interviews are generally only used when little is known about the subject area or when in-depth responses are required on a particular subject.
• Semi-Structured Interviews – A combination of the two interviews mentioned above, semi-structured interviews comprise several scripted questions but allow both interviewers and interviewees the opportunity to diverge and elaborate so more in-depth reasoning can be explored. 

While each approach has its merits, semi-structured interviews are typically favored as a way to uncover detailed information in a timely manner while highlighting areas that may not have been considered relevant in previous research efforts. Whichever type of interview you utilize, participants must be fully briefed on the format, purpose, and what you hope to achieve. With that in mind, here are a few tips to follow: 

• Give them an idea of how long the interview will last
• If you plan to record the conversation, ask permission beforehand
• Provide the opportunity to ask questions before you begin and again at the end. 

2. Focus Groups

Focus groups share much in common with less structured interviews, the key difference being that the goal is to collect data from several participants simultaneously. Focus groups are effective in gathering information based on collective views and are one of the most popular data collection instruments in qualitative research when a series of one-on-one interviews proves too time-consuming or difficult to schedule. 

Focus groups are most helpful in gathering data from a specific group of people, such as donors or clients from a particular demographic. The discussion should be focused on a specific topic and carefully guided and moderated by the researcher to determine participant views and the reasoning behind them. 

Feedback in a group setting often provides richer data than one-on-one interviews, as participants are generally more open to sharing when others are sharing too. Plus, input from one participant may spark insight from another that would not have come to light otherwise. However, here are a couple of potential downsides:

• If participants are uneasy with each other, they may not be at ease openly discussing their feelings or opinions.
• If the topic is not of interest or does not focus on something participants are willing to discuss, data will lack value. 

The size of the group should be carefully considered. Research suggests over-recruiting to avoid risking cancellation, even if that means moderators have to manage more participants than anticipated. The optimum group size is generally between six and eight for all participants to be granted ample opportunity to speak. However, focus groups can still be successful with as few as three or as many as fourteen participants. 

3. Observation

Observation is one of the ultimate data collection tools in qualitative research for gathering information through subjective methods. A technique used frequently by modern-day marketers, qualitative observation is also favored by psychologists, sociologists, behavior specialists, and product developers. 

The primary purpose is to gather information that cannot be measured or easily quantified. It involves virtually no cognitive input from the participants themselves. Researchers simply observe subjects and their reactions during the course of their regular routines and take detailed field notes from which to draw information. 

Observational techniques vary in terms of contact with participants. Some qualitative observations involve the complete immersion of the researcher over a period of time. For example, attending the same church, clinic, society meetings, or volunteer organizations as the participants. Under these circumstances, researchers will likely witness the most natural responses rather than relying on behaviors elicited in a simulated environment. Depending on the study and intended purpose, they may or may not choose to identify themselves as a researcher during the process. 

Regardless of whether you take a covert or overt approach, remember that because each researcher is as unique as every participant, they will have their own inherent biases. Therefore, observational studies are prone to a high degree of subjectivity. For example, one researcher’s notes on the behavior of donors at a society event may vary wildly from the next. So, each qualitative observational study is unique in its own right. 

4. Open-Ended Surveys and Questionnaires

Open-ended surveys and questionnaires allow organizations to collect views and opinions from respondents without meeting in person. They can be sent electronically and are considered one of the most cost-effective qualitative data collection tools. Unlike closed question surveys and questionnaires that limit responses, open-ended questions allow participants to provide lengthy and in-depth answers from which you can extrapolate large amounts of data. 

The findings of open-ended surveys and questionnaires can be challenging to analyze because there are no uniform answers. A popular approach is to record sentiments as positive, negative, and neutral and further dissect the data from there. To gather the best business intelligence, carefully consider the presentation and length of your survey or questionnaire. Here is a list of essential considerations:

• Number of questions : Too many can feel intimidating, and you’ll experience low response rates. Too few can feel like it’s not worth the effort. Plus, the data you collect will have limited actionability. The consensus on how many questions to include varies depending on which sources you consult. However, 5-10 is a good benchmark for shorter surveys that take around 10 minutes and 15-20 for longer surveys that take approximately 20 minutes to complete. 
• Personalization: Your response rate will be higher if you greet patients by name and demonstrate a historical knowledge of their interactions with your brand. 
• Visual elements : Recipients can be easily turned off by poorly designed questionnaires. Besides, it’s a good idea to customize your survey template to include brand assets like colors, logos, and fonts to increase brand loyalty and recognition.
• Reminders : Sending survey reminders is the best way to improve your response rate. You don’t want to hassle respondents too soon, nor do you want to wait too long. Sending a follow-up at around the 3-7 mark is usually the most effective. 
• Building a feedback loop : Adding a tick-box requesting permission for further follow-ups is a proven way to elicit more in-depth feedback. Plus, it gives respondents a voice and makes their opinion feel valued.

5. Case Studies

Case studies are often a preferred method of qualitative research data collection for organizations looking to generate incredibly detailed and in-depth information on a specific topic. Case studies are usually a deep dive into one specific case or a small number of related cases. As a result, they work well for organizations that operate in niche markets.

Case studies typically involve several qualitative data collection methods, including interviews, focus groups, surveys, and observation. The idea is to cast a wide net to obtain a rich picture comprising multiple views and responses. When conducted correctly, case studies can generate vast bodies of data that can be used to improve processes at every client and donor touchpoint. 

The best way to demonstrate the purpose and value of a case study is with an example: A Longitudinal Qualitative Case Study of Change in Nonprofits – Suggesting A New Approach to the Management of Change . 

The researchers established that while change management had already been widely researched in commercial and for-profit settings, little reference had been made to the unique challenges in the nonprofit sector. The case study examined change and change management at a single nonprofit hospital from the viewpoint of all those who witnessed and experienced it. To gain a holistic view of the entire process, research included interviews with employees at every level, from nursing staff to CEOs, to identify the direct and indirect impacts of change. Results were collated based on detailed responses to questions about preparing for change, experiencing change, and reflecting on change.

6. Text Analysis

Text analysis has long been used in political and social science spheres to gain a deeper understanding of behaviors and motivations by gathering insights from human-written texts. By analyzing the flow of text and word choices, relationships between other texts written by the same participant can be identified so that researchers can draw conclusions about the mindset of their target audience. Though technically a qualitative data collection method, the process can involve some quantitative elements, as often, computer systems are used to scan, extract, and categorize information to identify patterns, sentiments, and other actionable information. 

You might be wondering how to collect written information from your research subjects. There are many different options, and approaches can be overt or covert. 

Examples include:

• Investigating how often certain cause-related words and phrases are used in client and donor social media posts.
• Asking participants to keep a journal or diary.
• Analyzing existing interview transcripts and survey responses.

By conducting a detailed analysis, you can connect elements of written text to specific issues, causes, and cultural perspectives, allowing you to draw empirical conclusions about personal views, behaviors, and social relations. With small studies focusing on participants' subjective experience on a specific theme or topic, diaries and journals can be particularly effective in building an understanding of underlying thought processes and beliefs. 

7. Audio and Video Recordings

Similarly to how data is collected from a person’s writing, you can draw valuable conclusions by observing someone’s speech patterns, intonation, and body language when you watch or listen to them interact in a particular environment or within specific surroundings. 

Video and audio recordings are helpful in circumstances where researchers predict better results by having participants be in the moment rather than having them think about what to write down or how to formulate an answer to an email survey. 

You can collect audio and video materials for analysis from multiple sources, including:

• Previously filmed records of events
• Interview recordings
• Video diaries

Utilizing audio and video footage allows researchers to revisit key themes, and it's possible to use the same analytical sources in multiple studies – providing that the scope of the original recording is comprehensive enough to cover the intended theme in adequate depth. 

It can be challenging to present the results of audio and video analysis in a quantifiable form that helps you gauge campaign and market performance. However, results can be used to effectively design concept maps that extrapolate central themes that arise consistently. Concept Mapping offers organizations a visual representation of thought patterns and how ideas link together between different demographics. This data can prove invaluable in identifying areas for improvement and change across entire projects and organizational processes. 

8. Hybrid Methodologies

It is often possible to utilize data collection methods in qualitative research that provide quantitative facts and figures. So if you’re struggling to settle on an approach, a hybrid methodology may be a good starting point. For instance, a survey format that asks closed and open questions can collect and collate quantitative and qualitative data. 

A Net Promoter Score (NPS) survey is a great example. The primary goal of an NPS survey is to collect quantitative ratings of various factors on a score of 1-10. However, they also utilize open-ended follow-up questions to collect qualitative data that helps identify insights into the trends, thought processes, reasoning, and behaviors behind the initial scoring. 

Collect and Collate Actionable Data with UpMetrics

Most nonprofits believe data is strategically important. It has been statistically proven that organizations with advanced data insights achieve their missions more efficiently. Yet, studies show that despite 90% of organizations collecting data, only 5% believe internal decision-making is data-driven. At UpMetrics, we’re here to help you change that. 

UpMetrics specializes in bringing technology and humanity together to serve social good. Our unique  social impact software  combines quantitative and qualitative data collection methods and analysis techniques, enabling social impact organizations to gain insights, drive action, and inspire change. By reporting and analyzing quantitative and qualitative data in one intuitive platform, your impact organization gains the understanding it needs to identify the drivers of positive outcomes, achieve transparency, and increase knowledge sharing across stakeholders.

Contact us today  to learn more about our  nonprofit impact measurement  solutions and discover the power of a partnership with UpMetrics. 

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• Published: 27 May 2020

How to use and assess qualitative research methods

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
• Wolfgang Wick 1 , 2 &
• Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 , 8 , 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 , 32 , 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

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Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

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Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z

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Data Collection Methods

Qualitative data-collection methods

5 Essential qualitative data collection methods

One-on-one interviews, open-ended surveys and questionnaires, focus groups, observation, case studies.

Data collection is an important tool for understanding the behavior and motivations of your audience. It helps you gather intel on the kinds of products, services, and initiatives they’d like to see. Good data also makes it easier for you to identify ways to improve the experience they have with your organization at every touchpoint.

There are two main kinds of data collection — qualitative data collection and quantitative data collection . We’re going to focus on qualitative data-collection methods here.

Learn how to gather data the right way – check out free tips on data collection from Jotform.

What’s the difference between qualitative and quantitative data?

Quantitative data is numerical data that can be ranked, categorized, and measured — like the number of customers who spent over $50 last year, the gross or net profit for a specific time period, or the number of subscribers who converted into buyers from an email marketing campaign.

This kind of data answers closed-ended questions, such as “how many” or “how much,” and you can collect it through surveys, polls , and questionnaires.

Qualitative data is descriptive rather than numerical, and it looks for context — it’s about people’s perceptions. You gather it to understand the reasons and motivations that drive certain behavior. For example, qualitative data can reveal people’s feelings and opinions about your organization, and you can use it to determine why customers buy your products (or don’t) .

Quantitative data can tell you about your market share, the demographics of your customers, and how often they buy your products or use your services. Basically, qualitative data can give you the story behind the story. One way to gather this data is through open-ended surveys and questionnaires , but let’s look at a few more.

Face-to-face interviews are one of the most common qualitative data-collection methods; they’re a great approach when you need to gather highly personalized information.

Informal, conversational interviews are ideal for asking open-ended questions , which allow you to gain rich, detailed context and gather in-depth insights into participants’ opinions, experiences, and behavior. And because the interview format provides an opportunity to ask follow-up questions, you’ll be able to fill in any information gaps and gather additional insights.

You can also apply a mixed method by gathering information beforehand using one of Jotform’s interview forms .

Open-ended surveys and questionnaires allow participants to answer freely at length, rather than choosing from a set number of responses, so you get more nuanced and detailed answers. For example, you might ask an open-ended question like “Why don’t you eat ABC brand pizza?”

You would then provide space for people to answer narratively with their opinion or perspective, rather than simply giving them a specific selection of responses to choose from like “I’m a vegan,” “It’s too expensive,” or “I don’t like pizza.”

And because surveys and questionnaires can be executed online, they’re easier to distribute, allow for wider reach, and give you the ability to collect more qualitative data, more easily.

You can easily create a survey using Jotform’s free online survey maker . Simply add your questions, set up conditional logic, and share your custom survey online to start collecting responses instantly.

Focus groups are similar to interviews, except that you conduct them in a group format. You might use a focus group when one-on-one interviews are too difficult or time-consuming to schedule. A focus group will typically consist of eight to 10 people.

Focus group participants are chosen based on the topic the researcher wants to gather insights about. For example, a focus group might be formed to provide feedback on a new marketing campaign from a number of demographically similar people in your target market or to allow people to share their views on a new product.

Qualitative data collected through focus groups can be highly detailed and descriptive, providing the ideal opportunity to gather nuanced information in a short period of time.

Observation is a traditional qualitative data-collection method in which researchers observe subjects in the course of their regular routines, take detailed field notes, and/or record subjects via video or audio. This method is useful when you want to gain a more refined understanding of participants in their natural environment.

The two main types of observation are covert and overt.

As the name suggests, covert observation is when researchers are concealed, and participants don’t know they’re being observed. For example, researchers may embed themselves in public places like stores or restaurants, or in online chat rooms, in order to gather data in a natural setting.

Overt observation is when participants are aware they’re being studied. This method allows researchers to ask questions and record conversations.

Use Jotform’s free, customizable online observation form to get started.

In the case study method, you analyze a combination of multiple qualitative data sources to draw inferences and come to conclusions.Case studies are typically structured to explore a problem, a solution, and the impact of the solution.

If you’re in the marketing space, you’ve likely seen case studies used to highlight the impact of a company’s service within a specific target market. For example, an advertising agency might use a case study to showcase how it helped a healthcare client improve the customer service experience across digital channels, or how it helped a technology client launch a campaign for a new laptop.

The case study would include the situation before and after the agency’s intervention, demonstrating the successful results of the agency’s work.

When should you use qualitative vs quantitative data-collection methods?

To get the best results, you need to rely on both quantitative and qualitative data-collection methods. You’ll get deeper insights when you use a combination of the two.

Qualitative research offers context and nuance, and it can help you develop a fuller understanding of the complexities of human behavior related to your organization’s products or services. It’s data wrapped in a rich contextual story.

That said, qualitative data research can take a lot of time to collect and analyze, and it can sometimes result in biased conclusions. That’s why you should pair it with quantitative data collection to get the best, most accurate information.

For businesses and organizations that want to improve their marketing and outreach game, these qualitative data research methods can help:

• Determine obstacles to purchasing your company’s products or using your company’s services.
• Gain insights into how clients and customers perceive your brand.
• Identify areas for improvement across the entire brand discovery and buying cycle.
• Pinpoint where your messaging may be muddled or confusing.
• Discover in-demand product features to implement.
• Understand how your brand compares to others in the market.
• Gauge marketing campaign performance.
• Identify how your website , apps, and other online assets are performing.

Jotform is a great resource for creating the kind of open-ended surveys and questionnaires that you need for qualitative data collection. And you can use Jotform for quantitative data collection as well. Whichever data-collection method you choose, Jotform can help with a wide range of surveys and questionnaires .

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4 Comments:

More than a year ago

Clearly explained and strait to the point.Thank you

simple and clear English, easy to understand. Thank you.

I have learnt the difference between the qualitative and quantitative data and also tools of collecting such data. Simple clear language used. Thanks a lot.

THANKS TO JOTFORM FOR SUCH A HIGHLY INFORMATIVE ARTICLE, I HAVE LEARNT ALOT FROM IT. I NOW HAVE ADEQUATE ANSWERS FOR ME TO TACKLE MY ASSIGNMENTS ON QUALITATIVE AND QUANTITAVE DATA.

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• Volume 21, Issue 3
• Data collection in qualitative research
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• David Barrett 1 ,
• http://orcid.org/0000-0003-1130-5603 Alison Twycross 2
• 1 Faculty of Health Sciences , University of Hull , Hull , UK
• 2 School of Health and Social Care , London South Bank University , London , UK
• Correspondence to Dr David Barrett, Faculty of Health Sciences, University of Hull, Hull HU6 7RX, UK; D.I.Barrett{at}hull.ac.uk

https://doi.org/10.1136/eb-2018-102939

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Qualitative research methods allow us to better understand the experiences of patients and carers; they allow us to explore how decisions are made and provide us with a detailed insight into how interventions may alter care. To develop such insights, qualitative research requires data which are holistic, rich and nuanced, allowing themes and findings to emerge through careful analysis. This article provides an overview of the core approaches to data collection in qualitative research, exploring their strengths, weaknesses and challenges.

Collecting data through interviews with participants is a characteristic of many qualitative studies. Interviews give the most direct and straightforward approach to gathering detailed and rich data regarding a particular phenomenon. The type of interview used to collect data can be tailored to the research question, the characteristics of participants and the preferred approach of the researcher. Interviews are most often carried out face-to-face, though the use of telephone interviews to overcome geographical barriers to participant recruitment is becoming more prevalent. 1

A common approach in qualitative research is the semistructured interview, where core elements of the phenomenon being studied are explicitly asked about by the interviewer. A well-designed semistructured interview should ensure data are captured in key areas while still allowing flexibility for participants to bring their own personality and perspective to the discussion. Finally, interviews can be much more rigidly structured to provide greater control for the researcher, essentially becoming questionnaires where responses are verbal rather than written.

Deciding where to place an interview design on this ‘structural spectrum’ will depend on the question to be answered and the skills of the researcher. A very structured approach is easy to administer and analyse but may not allow the participant to express themselves fully. At the other end of the spectrum, an open approach allows for freedom and flexibility, but requires the researcher to walk an investigative tightrope that maintains the focus of an interview without forcing participants into particular areas of discussion.

Example of an interview schedule 3

What do you think is the most effective way of assessing a child’s pain?

Have you come across any issues that make it difficult to assess a child’s pain?

What pain-relieving interventions do you find most useful and why?

When managing pain in children what is your overall aim?

Whose responsibility is pain management?

What involvement do you think parents should have in their child’s pain management?

What involvement do children have in their pain management?

Is there anything that currently stops you managing pain as well as you would like?

What would help you manage pain better?

Interviews present several challenges to researchers. Most interviews are recorded and will need transcribing before analysing. This can be extremely time-consuming, with 1 hour of interview requiring 5–6 hours to transcribe. 4 The analysis itself is also time-consuming, requiring transcriptions to be pored over word-for-word and line-by-line. Interviews also present the problem of bias the researcher needs to take care to avoid leading questions or providing non-verbal signals that might influence the responses of participants.

Focus groups

The focus group is a method of data collection in which a moderator/facilitator (usually a coresearcher) speaks with a group of 6–12 participants about issues related to the research question. As an approach, the focus group offers qualitative researchers an efficient method of gathering the views of many participants at one time. Also, the fact that many people are discussing the same issue together can result in an enhanced level of debate, with the moderator often able to step back and let the focus group enter into a free-flowing discussion. 5 This provides an opportunity to gather rich data from a specific population about a particular area of interest, such as barriers perceived by student nurses when trying to communicate with patients with cancer. 6

From a participant perspective, the focus group may provide a more relaxing environment than a one-to-one interview; they will not need to be involved with every part of the discussion and may feel more comfortable expressing views when they are shared by others in the group. Focus groups also allow participants to ‘bounce’ ideas off each other which sometimes results in different perspectives emerging from the discussion. However, focus groups are not without their difficulties. As with interviews, focus groups provide a vast amount of data to be transcribed and analysed, with discussions often lasting 1–2 hours. Moderators also need to be highly skilled to ensure that the discussion can flow while remaining focused and that all participants are encouraged to speak, while ensuring that no individuals dominate the discussion. 7

Observation

Participant and non-participant observation are powerful tools for collecting qualitative data, as they give nurse researchers an opportunity to capture a wide array of information—such as verbal and non-verbal communication, actions (eg, techniques of providing care) and environmental factors—within a care setting. Another advantage of observation is that the researcher gains a first-hand picture of what actually happens in clinical practice. 8 If the researcher is adopting a qualitative approach to observation they will normally record field notes . Field notes can take many forms, such as a chronological log of what is happening in the setting, a description of what has been observed, a record of conversations with participants or an expanded account of impressions from the fieldwork. 9 10

As with other qualitative data collection techniques, observation provides an enormous amount of data to be captured and analysed—one approach to helping with collection and analysis is to digitally record observations to allow for repeated viewing. 11 Observation also provides the researcher with some unique methodological and ethical challenges. Methodologically, the act of being observed may change the behaviour of the participant (often referred to as the ‘Hawthorne effect’), impacting on the value of findings. However, most researchers report a process of habitation taking place where, after a relatively short period of time, those being observed revert to their normal behaviour. Ethically, the researcher will need to consider when and how they should intervene if they view poor practice that could put patients at risk.

The three core approaches to data collection in qualitative research—interviews, focus groups and observation—provide researchers with rich and deep insights. All methods require skill on the part of the researcher, and all produce a large amount of raw data. However, with careful and systematic analysis 12 the data yielded with these methods will allow researchers to develop a detailed understanding of patient experiences and the work of nurses.

• Twycross AM ,
• Williams AM ,
• Huang MC , et al
• Onwuegbuzie AJ ,
• Dickinson WB ,
• Leech NL , et al
• Twycross A ,
• Emerson RM ,
• Meriläinen M ,
• Ala-Kokko T

Competing interests None declared.

Patient consent Not required.

Provenance and peer review Commissioned; internally peer reviewed.

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Qualitative Data Collection and Analysis Methods – SOE

Qualitative data collection methods in each design or approach.

The School of Education approves five approaches or designs within qualitative methodology: basic qualitative, case study, ethnography, grounded theory, and phenomenology.  Each of these designs uses its own kind of data sources.  Table 1 outlines the main primary and secondary sources of data in each design. 

• Primary sources are data from actual participants.
• Secondary data sources are from others.
• The researcher’s notes describing observations of participants or behaviors in their natural environments.  This is the more common usage, and is most common in ethnographic studies.
• The researcher’s notes to self about themes noticed while collecting data, possibly important points in the data, ideas to come back to, and so on.
• Another related term is memos , although memos in grounded theory tend to be brief or extended essays charting the development of theory, rather than simple notes.  Strictly speaking, these notes or memos are not data in themselves, but point to data in another source.

Table 1. The Fit of Method and the Type of Data

Data Collection in Ethnography

Typically, ethnographers collect data while in the field. Their data collection methods can include

• Participant observation.
• Naturalistic observation.
• Writing field notes.
• Conducting semi-structured or structured interviews (sometimes audiotaped or videotaped).
• Reviewing documents, records, photographs, videotapes, maps, and sociograms.
• Any accessible and dependable source of information about the behaviors, interactions, customs, values, beliefs, attitudes, and practices of the members of that culture can be a source of data.

It is worth remembering that the time-world of cultural groups is longer than it is for individual persons, and so:

• Data collection may need to cover a longer time in order to capture the true flavor of the culture.
• Field research methods need to adapt to the demands of the field; ethnography allows for flexibility in the design of its methods to accommodate the challenges of the field.

However, for both of these reasons—the longer time-world of the culture or group and the occasional need to change data collection methods to meet challenges in the field—Institutional Review Board (IRB) complications can be introduced and must be addressed, further lengthening the time of the ethnographic study.

Data Collection in Case Studies

Case studies always include multiple sources of information because the case includes multiple kinds of issues. For example, a case study of a training program would obtain and analyze information about:

• The participants.
• The nature of the organizational issues calling for the training.
• The kinds of training provided.
• The outcomes of the program.
• The background and training of the staff, and so on.

In addition to multiple information sources, every case study provides an in-depth description of the contexts of the case:

• Its setting (for example, the kind of organizational structure and environment in which the training takes place).
• Its contexts (social contexts, political contexts, affiliations affecting outcomes, and so on).

The setting and context are an intrinsic part of the case.

Consequently, because cases contain many kinds of information and contexts, case studies use many different methods of data collection. These can include the full range of qualitative methods such as:

• Open-ended surveys.
• Interviews.
• Field observations. Reviews of documents, records, and other materials.
• Evaluation of audiovisual materials.
• Descriptions of contexts and collateral materials; and so on.

A well-designed case study does not rely on a single method and source of data because any true case (bounded system) will have many characteristics and it is not known ahead of time which characteristics are important. Determining that is the work of the case study.

Data Collection in Grounded Theory

The dominant methods of data collection in grounded theory research are:

• Interviews (usually audiotaped).
• Participant and nonparticipant observations.
• Conversations.
• Recorded diaries.
• Field notes.
• Descriptions of comparative instances.
• Personal narratives of experiences.

The participants in a grounded theory study often will be interviewed more than once and asked to reflect on and refine the preliminary conclusions drawn by the researcher. Grounded theory designs use the constant comparative data analysis technique that requires simultaneously interviewing, analyzing, and constantly comparing the data.  As a result of the constant comparison of interview data, the participants in a grounded theory study often will be interviewed more than once and asked to reflect on and refine the preliminary conclusions drawn by the researcher.

Grounded theorists will develop substantive theories through:

• Employing a theoretical sampling process that might lead to interviewing new rounds of participants until theoretical saturation is reached.
• Reinterviewing participants about them, asking for their feedback.

Data Collection in Phenomenology

There are two descriptive levels of the empirical phenomenological model that arise from the data collected:

• Level 1: The original data are comprised of naïve descriptions obtained from participants through open-ended questions and dialogue. Naïve means simply, “in their own words, without reflection.”
• Level 2: The researcher describes the structures of the experiences based on reflective analysis and interpretation of the research participant’s account or story.

To collect data for these levels of analysis, the primary tool is the in-depth personal interview.

• Interviews typically are open (meaning, no forced answers), with three main kinds of questions:
• An opening or initial question .  Usually this is only pre-written question, designed carefully to inquire into the participant’s lived (everyday) experience of the phenomenon under investigation.
• Follow-up questions are asked to tease out deeper or more detailed elaborations of the earlier answers or to clarify unclear statements or ask about non-verbal gestures.
• Guiding questions are asked to help the respondents return to the topic of the interview when they stray or digress.
• The goal of the opening question (and all other questions) is to allow the respondent the maximum freedom to respond from within his or her lived (everyday, non-reflective) experience.

Because the objective is to collect data that are profoundly descriptive (rich in detail) and introspective, these interviews often can be lengthy, sometimes lasting as long as an hour or more.

Sometimes other sources of data are used in phenomenological studies, when those sources are equivalent in some way to the in-depth interview. For example:

• In a study of the lived experience of teacher creativity, poems or other writings by the participants (or other people) about creative experiences might be collected in the same way as the in-depth interviews.
• Audiovisual materials having a direct bearing on the lived experience of creativity might be included as data (for example, art work or other illustrations of creativity).

Although other less personal data sources (such as letters, official documents, and news accounts) are seldom used as direct information about the lived experience, the researcher may find in a particular case that these are useful either in illuminating the participant’s story itself or in creating a rich and textured background description of the contexts and settings in which the participant experienced the phenomenon.

Data Collection in Basic Qualitative Inquiry

Data collection in this approach typically uses data collection methods that elicit people’s verbal descriptions and interpretations of their experiences and the meaning they ascribe to those experiences.  Basic qualitative research can also focus on understanding a process; for example, a study that examines innovative teaching strategies and practices used by general education teachers in large inclusive classrooms. Basic qualitative researchers will use semi-structure interviews as their main data collection tool.  When appropriate, observations and artifacts might be includes.

Merriam (2009) describes a basic interpretive qualitative research study as having philosophically been derived from constructionism, phenomenology, and symbolic interaction and is used by researchers who are "interested in “(1) how people interpret their experiences, (2) how they construct their worlds, and (3) what meaning they attribute to their experiences. The overall purpose is to understand how people make sense of their lives and their experiences" (p. 23).

This concludes the discussion of qualitative data collection methods.  Please review the Presentation on “Quantitative Data Analysis Methods," if you have not done so already.

(For a more thorough discussion of data collection, see the guide Qualitative Research Approaches in Psychology and Human Services .)

Consider this quotation from Charmaz (2006), “Simply thinking through how to word open-ended questions averts forcing responses into narrow categories” (p. 18).

Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis . Thousand Oaks, CA: SAGE. ISBN: 9780761973522.

Merriam, S. B. (2009). Qualitative research: A guide to design and implementation . San Francisco, CA: Jossey-Bass.

Doc. reference: phd_t3_soe_u04s3_h03_qualcoll.html

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Research Methods: Qualitative

What is qualitative research, about this guide, introduction.

• Qualitative Research Approaches
• Key Resources
• Finding Qualitative Studies

 The purpose of this guide is to provide a starting point for learning about qualitative research. In this guide, you'll find:

• Resources on diverse types of qualitative research.
• An overview of resources for data, methods, coding & analysis
• Popular qualitative software options
• Information on how to find qualitative studies

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data.

Qualitative researchers use multiple systems of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory, and phenomenology.

Watch the following video to learn more about Qualitative Research:

(Video best viewed in Edge and Chrome browsers, or click here to view in the Sage Research Methods Database )

Qualitative Approaches

The case study approach is useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context.

Ethnography

Ethnographies are an in-depth, holistic type of research used to capture cultural practices, beliefs, traditions, and so on. Here, the researcher observes and interviews members of a culture an ethnic group, a clique, members of a religion, etc. and then analyzes their findings.

Grounded Theory

Researchers will create and test a hypothesis using qualitative data. Often, researchers use grounded theory to understand decision-making, problem-solving, and other types of behavior.

Narrative Research

Researchers use this type of framework to understand different aspects of the human experience and how their subjects assign meaning to their experiences. Researchers use interviews to collect data from a small group of subjects, then discuss those results in the form of a narrative or story.

Phenomenology

This type of research attempts to understand the lived experiences of a group and/or how members of that group find meaning in their experiences. Researchers use interviews, observation, and other qualitative methods to collect data.

Watch the video "Choosing among the Five Qualitative Approaches" from Sage Research Methods database for more on these qualitative approaches:

Note: Video is best viewed using Chrome, Edge, or Safari browsers.

Poth, C. (Academic). (2023). Choosing among five qualitative approaches [Video]. Sage Research Methods. doi.org/10.4135/9781529629866

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Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
• Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
• Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
• Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
• Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

• Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

• Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
• Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

• Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

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• Published: 18 July 2024

Insights on the contribution of doctoral research findings from a school in a South African University towards policy formulation

• Florence Upenyu Damba   ORCID: orcid.org/0000-0002-4668-9574 1 ,
• Ntombifikile Gloria Mtshali 1 &
• Moses John Chimbari 1 , 2  

Humanities and Social Sciences Communications volume  11 , Article number:  930 ( 2024 ) Cite this article

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• Health humanities
• Social policy

Translation of health research findings into policy contributes to improvement of health systems. Generally, in sub-Saharan Africa policymakers rarely use research evidence and hence policies are often not informed by research evidence. Unless published or in the case of commissioned research, doctoral health research is often not used for health policy formulation. This paper analysed the potential and utilization of doctoral research from the School of Nursing and Public Health by KwaZulu-Natal Health Department of Health. The study adopted a mixed methods approach that combined elements of qualitative and quantitative research aspects. Qualitative data was collected through content analysis of 29 theses produced in the School of Nursing and Public Health, University of KwaZulu-Natal between 2014 and 2021 and interviews held with four Department of Health personnel as policymakers. When researchers could not get information on how research questions were formulated from content analysis, they checked the student questionnaire for answers. Quantitative data was collected from 79 participants through structured questionnaires. Participants included 47 PhD graduates, 11 final year PhD students and 21 PhD supervisors. Data from content analysis and interviews was analyzed thematically while data from questionnaires was analyzed quantitatively. Eleven (52%) PhD supervisors reported that findings from 22 studies were being considered for policy development and adoption while some had resulted in policy guidelines and frameworks that can be used to formulate policies. Factors such as failure to involve the Department of Health during the formulation of research questions, inappropriate packaging of research findings, policymakers not aware of the availability of research findings, lack of commitment to the dissemination of research results by students and poor demand for research evidence by policymakers hindered the translation of PhD research findings into policy. From the 29 theses reviewed, sixteen (28%) of PhD respondents highlighted that they involved the Department of Health to formulate research questions while forty-two (72%) did not. The theses review also revealed limited identifiable information related to policy formulation. The study confirms the use of PhD research findings for policy formulation. Additionally, it highlights the factors that hinder utilization of PhD work by policymakers. Further research to understand the perspectives of policymakers on factors that contribute to utilization of PhD work as well as how the findings have contributed to policy formulation is recommended since there was not sufficient data collected from policymakers due to Covid-19 restrictions.

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In 2013, World Health Organization (WHO) reported that for 20 years there had been an unprecedented effort to use evidence in policy and decision making for health systems. Globally, it has been acknowledged that translation of health research into policy and practice is vital for enhancing the performance of health systems, promoting service delivery, and improving health outcomes (Barratt et al. 2017 ; Langlois et al. 2016 ; El-Jardali et al. 2014 ). However, evidence indicates that whilst there are numerous promising research findings, they are underutilized and often take a long time to be translated into health policy (Walugembe et al. 2015 ; El-Jardali et al. 2014 ). Research generated by universities can be used to influence national health policies to improve service delivery and outcomes (Pariyo et al. 2011 ; Nankinga et al. 2011 ). Studies that document the pathway of students’ research generally show that a substantial proportion of this work ends up on the shelves and are often underutilized (Caan and Cole 2012 , Bullen and Reeve 2011 ). Translation of research findings into policy can be facilitated through numerous ways. Researchers should strive to disseminate their research findings through appropriate methods for targeted policymakers. Examples of these include news media, social media, policy briefs, one-on-one meetings, policymakers’ workshops, and seminars. Researchers should also involve policymakers and other stakeholders in the earlier stages of the research particularly during the identification of key research questions (Uzochukwu et al. 2016 ).

The KwaZulu-Natal Provincial Health Research and Ethics Committee (KZN-PHREC) in South Africa sets research priority questions for the province and communicates them to the leaders of all academic institutions and research organizations. The priority research questions are also posted on the KwaZulu-Natal Department of Health (KZN-DOH) website to encourage researchers to address the questions through research projects (KZN-DOH webpage). Despite the growing knowledge of the factors influencing utilization of health research into policy, we are not aware of research that has specifically examined how doctoral research generated from the University of KwaZulu-Natal (UKZN) School of Nursing and Public Health (SNPH) through doctoral studies has contributed to existing policies or influenced formulation of guidelines and policies in South Africa. Understanding what facilitates utilization of doctoral research findings for policy formulation is critical to ensure that research conducted by doctoral students does not go to waste. We therefore conducted this study to establish if the knowledge generated from doctoral studies at UKZN, SNPH has contributed to existing DOH policies or formulation of DOH guidelines and policies and analyze the factors that may hinder or promote knowledge uptake by policymakers.

The study was conducted in KwaZulu-Natal (KZN) province and data was collected at UKZN in the College of Health Sciences (CHS), School of Nursing and Public Health (SNPH). The school has an average enrollment of 44 PhD students per year with a throughput of 32 students per year. The school has an average total of 54 PhD supervisors distributed across nine disciplines namely, Behavioral Medicine, Biostatistics & Bioethics and Medical Law, Family Medicine, Nursing, Public Health Medicine, Rural Health, Telemedicine, Traditional Medicine and Occupational and Environmental Health. The SNPH works closely with the KZN-DOH to provide skilled staff and inform research.

Study design

A case study design applying the mixed methods approach was used in this study. The SNPH in the CHS was treated as the case. The case study design was adopted because it allows the researcher to investigate a topic in its real-life context (Crowe et al. 2011 ). Mixed methods research was conducted to get an in-depth and comprehensive understanding of the research questions and complex phenomena that required the use of both qualitative and quantitative methods (Dawadi et al. 2021 ). The use of mixed methods research enabled researchers to answer the research questions with sufficient depth and breadth allowing them to develop more effective and refined conclusions based on complementarity of the different approaches (Dawadi et al. 2021 ). The mixed method approach also allowed triangulation to enrich and strengthen research results through use of different methods of data collection and analysis (Molina-Azorin 2016 ). A convergent mixed method approach was applied in the study (Tariq and Woodman 2013 ). Qualitative and quantitative data was collected concurrently, and the two data sets were analyzed separately and compared, contrasted, and combined at interpretation stage (Creswell and Clark 2017 ). Equal priority was given to both data sets considering the equal importance of both types of data in answering the posed research questions (Dawadi 2019 ). The three research questions that the study responded to were “How have PhD theses produced in the SNPH, UKZN between 2014 and 2021 contributed to existing policies or influenced policy formulation?”, “What factors contribute to utilization of doctoral research findings in the SNPH, UKZN by policymakers?” and “What factors influence utilization of doctoral research findings by DOH?”. The three research questions had the common goal of establishing if doctoral research findings from the SNPH contributed to existing policies or influenced policy formulation.

Study population and sampling

Non-probability purposive sampling was adopted to select the sample for both the qualitative and quantitative aspects of the study. We used our judgment in selecting individuals or items that possessed the required qualities and were able to provide the required data to respond to the questions of interest (Hibberts et al. 2012 ; Baker et al. 2013 ; Creswell 2014a ). Sampled items for the qualitative aspect included PhD theses and key DOH personnel at provincial level who were members of the research committee involved in granting permission to researchers conducting research in the DOH facilities. Sampled individuals for the quantitative aspect of the study included PhD theses, PhD graduates, PhD final year students, and PhD supervisors. The sample size for the two data sets was calculated using the Cochran formula below:

The targeted population included 81 PhD theses, 81 PhD graduates, 48 PhD final year students and 48 PhD supervisors. Out of the 81 theses that were marked and passed only fifty-one were available according to library records. There were however only thirteen theses available in the library repository as the data base was still being developed. Fourteen graduates whose thesis could not be accessed from the library agreed to share their soft copies. An additional twenty- four hard copies of the theses were obtained from the Postgraduate office. Out of the fifty-one theses that were accessed only twenty-nine met the criteria. We considered the 7-year timeframe we used to be reasonable because quality data was available for that period than earlier times and the study period coincided with the time the College adopted the thesis by publications format for presenting thesis which seems favorable for policy processes. We characterized a thesis as “policy related” if it highlighted the development of a framework, a model, guidelines, policy briefs, and if the study highlighted potential for the findings to be translated to policy. We excluded studies that were conducted outside South Africa. Of the eighty-one PhD graduates that were expected to participate only forty-seven (58%) participated. Eleven out of forty-eight (23%) PhD final year students participated. Due to Covid-19, it was not clear if they were still registered or not, so follow-up was difficult. Twenty-one (44%) out of forty-eight PhD supervisors participated making the overall response rate 45%.

Summary of sample selection

We selected all those who met the study’s criteria for eligibility as summarized. Theses produced between 2014 and 2021 based on studies conducted in South Africa, PhD graduates who graduated between 2014 and 2021, PhD final year students who were in the data collection and analysis, thesis write up, thesis submission and awaiting results, PhD supervisors who have supervised PhDs to graduation, and research committee members of the DOH were included in the study. Theses not based on studies conducted in South Africa, PhDs that graduated before 2014, PhD final year students in the proposal development stage, PhD supervisors who have not supervised PhDs to graduation, and those not in the DOH research committee were excluded.

Data collection methods and process

We adopted a mixed methods approach previously used and demonstrated to produce good results (Munce et al. 2021 ; Dawadi 2019 ; Mckim 2017 ). We used a combination of three data collection tools: content analysis, questionnaire, and interviews. The aim of combining the three tools was to manage two research questions and obtain stronger evidence for conclusions by merging research findings (Creswell 2014b ; Greene et al. 1989 ). The data collection tools are described in detail below:

Thesis content analysis

We conducted content analysis of doctoral theses produced between 2014 and 2021 in the SNPH, UKZN. Content analysis allowed us to analyze the data qualitatively and at the same time quantify it by measuring the frequency of different categories and themes (Grbich 2012 ). Content analysis was also conducted to confirm responses to the questionnaires. Twenty-nine PhD theses were analyzed to determine their implications on policy. A thesis by publication is submitted in the form of a series of already published, accepted or under review journal articles. A traditional thesis is a comprehensive piece of research in a book form.

We used a data extraction form to collect information from hard and soft copies of theses. The extraction form captured information on the discipline, research questions identification process, research findings dissemination methods and framework/model/guidelines/policy brief development and contribution of study to policy. The researchers checked the methods section of the theses under review for clues on how the research question formulation process was conducted and from the way thesis are written, the 29 studies had no indication of how the process was carried out. Since we had used mixed methods, we were able to get the information on how the process was conducted from the student questionnaire. Under each of the categories, we extracted information and presented it in a form of questions as indicated in the supplementary file attached:

Qualitative data was also collected through in-depth interviews conducted with four key DOH personnel at provincial level using a structured interview guide which included open-ended questions that were informed by literature review and the objectives of the study (Vaismoradi et al. 2013 ). The DOH personnel were members of the research committee who were responsible for granting permission to the researchers to conduct research. The researcher who conducted the interviews acquired interviewing skills through workshops and consultations with experienced qualitative researchers. The researcher was trained on interview and transcribing skills. Interviews provided detailed and rich data regarding phenomenon under study (Barrett and Twycross 2018 ) which was confirmed by questionnaire data (Harris and Brown 2010 ). Quotations that best illustrated the factors affecting translation of doctoral research into policy were used.

An interview guide with questions focusing on DOH’s expectations from doctoral students and the barriers, and facilitators of utilization of doctoral research findings by DOH was used to solicit for responses from participants. An interview guide allowed the researcher to control the line of questioning (Creswell 2014b ). Participants were contacted through email and telephone. The interviews lasted 40 minutes. Three of the interviews were done through zoom and one was conducted face to face. All four interviews were recorded with permission from the interviewees. Notes were taken to back up the audio recordings in case there were interruptions and, in the event, that the researcher forgot to switch on the recorder.

Questionnaire

Quantitative data was collected through a questionnaire using KoboCollect software. Participants were contacted through email. Two questionnaires were used for data collection. One questionnaire was administered to 47 PhD graduates and 11 PhD final year students. The other one was administered to 21 PhD supervisors. Completing questionnaires took about 40 min to an hour. PhD and PhD final year students’ questionnaire consisted of 50 questions. The supervisors’ questionnaire comprised of 30 questions. Data was fed on Microsoft Excel and cleaned before analysis.

Analysis of qualitative and quantitative data

We used qualitative content analysis to analyze data obtained through review of theses. Qualitative content analysis enables a purposeful interpretation of the data as well as the context in determining meaning which provides a good description of the material (Schreier 2014 ). Content analysis facilitated the categorization of data into themes, thus allowing the information to be analyzed appropriately. We categorized the content of the theses from raw data without a theory-based categorization matrix (Elo et al. 2014 ).

Recorded interviews were transcribed verbatim in a Microsoft word document by the researcher and a research assistant and imported into NVivo 12 to manage coding of the data. The files from which the data came from were given a unique identifier. Transcripts were read over and over as recommended by Erlingsson and Brysiewicz in order to familiarize with the data and get the sense of the text as a whole (Erlingsson and Brysiewicz 2017 ). The scripts were closely examined to identify common themes such as topics and ideas that came up repeatedly. The text was divided into meaning units keeping the research aim and question clearly in focus. The meaning units were then condensed further while keeping the meaning intact. Codes were developed using open coding. The exercise was repeated until the researchers were satisfied with the outcome. Codes that appeared to deal with the same issue were assigned to categories and themes. Quantitative data were analyzed using IBM- SPSS version 27 and summarized as percentages. Data from interviews was analyzed thematically using NVivo 12 software.

Rigour/quality/validity and reliability

Triangulation of qualitative and quantitative methods was used to enhance validity through the convergence of information from different sources (Molina-Azorin 2016 ; Nancy Carter et al. 2014 ; Zohrabi 2013 ; Creswell and Clark 2017 ; Rolfe 2006 ). In-depth interview was pilot tested on DOH personnel who did not take part in the study to check the validity of the tool. Prior to administering the questionnaires, a pilot study was conducted among PhD graduates, PhD final year students and PhD supervisors before being used as final documents, after which they were refined and some questions were rephrased before distribution to participants to ensure validity of the tool (Creswell and Hirose 2019 ; Thomas 2010 ; Ehrenberg and Sniezek 1989 ). The pilot test was used to improve precision, reliability, validity of data, identify problems/omissions, and assess time spent to complete the survey. The interview guide was also pilot tested to ascertain if participants interpreted the meaning of the questions as intended. The research instruments were reviewed by experts in the field of research and unclear questions were revised based on the reviewers’ comments (Zohrabi 2013 ).

Integration of qualitative and quantitative findings

The two data types were handled and analyzed separately and compared and contrasted for corroboration purposes (Tariq and Woodman 2013 ; O’cathain et al. 2010 ). Integration of the two data sets was done during interpretation of the findings (Chaumba 2013 ). The intention of integration was to develop results and interpretations that expand understanding, are comprehensive and validated and confirmed (Creswell and Clark 2017 ). The researchers listed the findings from each component of the study and considered where the findings agreed (convergence), offered complementary information on the same issue (complementarity), or appeared to contradict each other (discrepancy or dissonance) (Farmer et al. 2006 ).

Ethical considerations

The study was approved by the Biomedical Research Ethics Committee (BREC/00001384/2020) and the Kwa-Zulu-Natal Provincial Department of Health (KZ-202008-030). All research was performed in accordance with the ethical standards of institutional research committee applicable when human participants are involved. Written informed consent was obtained from all participants in the study.

Demographics of participants

Table 1 shows the demographics of the sources of data.

Eleven (52%) PhD supervisors reported that findings from 22 studies were being considered for policy development and adoption while some had resulted in policy guidelines and frameworks that can be used to formulate policies. Table 2 below indicates the studies produced between 2014 and 2021 that are being considered for policy development and adoption.

Emerging themes

Two major themes emerged during interviews with DOH personnel and content analysis of PhD theses:

Involvement of DOH in the formulation of research questions

DOH priority research questions

Meetings with DOH

Dissemination methods used to communicate research findings to DOH including policy briefs, journal articles, National Health Research Database (NHRD), conference presentations, research reports, media, copies of theses, presentation at DOH annual health research days and stakeholder meetings.

Findings from the two data sets (qualitative and quantitative) were integrated and are discussed below:

Theme 1: Involvement of DOH in the formulation of research questions

The findings revealed that DOH is somewhat involved in the formulation of research questions. Sixteen (28%) of the PhD respondents highlighted that they involved DOH while forty-two (72%) did not involve DOH. Interview data indicated that DOH publishes a list of priority research questions on its website and sends it to senior management of research and academic organizations in KwaZulu-Natal including the SNPH with the hope that researchers will engage with it, for example, some of the participants stated that:

“We have a list of priority research questions that we have published on our website, and we have also sent to the senior management of research and academic organisations in KwaZulu-Natal. We developed these priority research questions with our district managers, program managers and facility managers. We hope that researchers who are looking for topics will engage with them, and we hope that we have advertised them well enough for them to know about them”. Respondent 1, DOH

“The department of Health has a research agenda that is published on its communication platforms”. Respondent 4, DOH

According to data sources, in most cases students do not respond to priority research questions. In some instances, they conduct studies that are part of their supervisors’ bigger projects. For example, 2 (10%) supervisors mentioned that their commissioned projects involve PhD students. PhD research work is not commissioned by DOH as confirmed by one of the participants from DOH,

“We do not really commission research because we cannot pay for it. When we need research to be done, we usually do it in partnership with institutions or if possible, we just do it ourselves”. Respondent 2, DOH .

Theme 2: Research findings dissemination

Only twenty-two (38%) students confirmed that they sent their findings to DOH while thirty-six (62%) did not share their research findings with DOH despite the condition in the DOH gatekeeper permission that the report should be submitted to the DOH. This was also supported by the qualitative data indicating that although part of DOH approval letter instructs students to share their research findings with them, only a small fraction of the students send their research results on completion of their studies.

“The expectation is that as part of dissemination of research findings, the researcher should then come back and share their findings with the Department of Health and table their recommendations because when we do research we want to come up with recommendations at the end. Unfortunately, this is not really monitored or done”. Respondent 2, DOH .

However, it was reported that it is difficult for DOH to monitor the feedback of research findings since there are many projects approved every year in KZN. For example, one of the participants said,

“ Part of our letter of approval states that students are required to send their research findings to DOH on completion of their studies. Beyond that we don’t really do anything and it’s quite difficult to monitor because there are hundreds of projects approved every year in KZN so to follow up will take a lot of time. There is need to systematize it so that when the researcher is done, we ask them to send us their findings. We cannot really do it on an individual basis, and we haven’t got a system in place yet to automate it. We have been discussing various options, but we have not really hit on one that we think is going to improve everything”. Respondent 1, DOH .

Analysis of theses showed that research findings were disseminated to DOH and stakeholders through various methods; peer-reviewed journal articles, copies of theses, conference presentations, community/stakeholder meetings and policy briefs. This corroborates with what was highlighted by PhD graduates and final years in their responses for the questionnaire study. Table 3 below shows responses from the questionnaire on the methods that were used by students to disseminate research findings to DOH.

Five (24%) supervisors stated that their students used policy briefs to disseminate their findings to DOH while seventeen (81%) supervisors said that students used peer-reviewed publications. Sixteen (76%) supervisors reported that students used stakeholder feedback meetings, two (10%) supervisors said they used the media and one (5%) supervisor stated that the students used X. According to PhD graduates and final years questionnaire data, conference presentations were used more than the other methods to disseminate research findings to DOH. Dissemination to NHRD and use of media were the least used methods of dissemination with 1 (2%) participant each.

Thirty-one (53%) PhD students stated that they had attended the KZN-DOH annual research day and twenty-three (40%) had presented on such research days. Twenty-seven (47%) attended the research days to listen to other researchers’ presentations. The dissemination of research findings through the KZN-DOH annual research day was corroborated by a DOH respondent:

“The department holds a research day annually and only a few researchers get the opportunity to present their research findings. There is poor attendance of policymakers who have the decision-making powers at the event hence the research findings and recommendations will not be translated into policy. Respondent 3, DOH .

Apart from KZN-DOH annual research day, the students also presented at other national and international conferences. 76% of participants presented their work at least at four local scientific conferences while 72% presented their work at least at five international conferences. Policymakers suggested dissemination strategies that are potentially useful to translate research into policy such as setting aside a specific day for DOH employees to meet and read an article by a student from SNPH, UKZN that has policy implications or for students from SNPH, UKZN to present their research findings to relevant employees in the department who may consider them for policy formulation. One of the participants stated that,

“It can be sessions at work where you can come up with one article a Friday once a month and engage in research that has been done and choose whatever works for you to improve practice or even in policy development. DOH needs to allow students who have done research an opportunity to present their studies to relevant employees in the department who might take the recommendations seriously and use them to improve and inform our own practice and develop informed policies from them”. Respondent 4, DOH

Policy briefs produced or policy contributions by students

Only two students produced policy briefs with one student producing two policy briefs and the other one producing one. This was confirmed by data obtained from the PhD graduates and final year students’ questionnaire. Only two (3%) students responded that they produced policy briefs. Only one (5%) supervisor indicated that their students have produced policy briefs. Respondents from DOH were not aware of any research conducted by students in the SNPH during the period 2014 to 2021 that has been used in programs, either for guidelines or policy formulation.

“I cannot name any recent or specific research that was done in the SNPH between 2014 and 2021 that was used in programs either for guidelines or policy formulation. Research that I remember that was conducted at UKZN and translated into policy very quickly was research conducted during the early years of HIV which was used in creating policy around HIV and infant nutrition”. Respondent 1, DOH

“I do not want to lie to you… none. whatsoever. I have not heard of any research study conducted by a student actively being converted into influencing our policies or guidelines”. Respondent 4, DOH

“I don’t know of any specific research from the school that was used for policy formulation”. Respondent 3, DOH

Regarding feedback on research results to DOH, supervisors expressed varied degrees of compliance. They were asked to state their responses on a 5- point Likert scale: Never, Rarely, Sometimes, Often and Always. Table 4 shows frequency of feedback of research results to DOH by PhD supervisors.

The gap between research and policy and practice is still very wide in low and middle- income countries such as South Africa (Uzochukwu et al. 2016 ). The failure to take-up high- quality research evidence by policymakers is a persistent problem. Academics and policymakers have different incentives (Nutley et al. 2007 ), rules, obligations, values and interests (Newman et al. 2016 ). We analysed the contribution of doctoral theses to the formulation of health policies in KwaZulu-Natal, South Africa. Fifty-two percent (52%) of PhD supervisors who participated in the study reported that 22 studies conducted between 2014 and 2021 in the SNPH, UKZN were being considered for policy development and adoption. Some of the studies resulted in the development of policy guidelines and frameworks that can guide the formulation of policies. According to the information obtained from PhD supervisors’ questionnaire, the studies were successful in reaching policymakers because where supervisors thought there was policy relevancy arising from PhD work, they ensured that they engaged with policymaking entities such as the Department of Health and the Department of Environment, Forestry and Fisheries at provincial and local government level as well as at national level for research findings to be translated into policy. Some studies were also successful in reaching policymakers because supervisors had meetings with policymakers to highlight problem areas and possible solutions. Some supervisors revealed that studies were successful in reaching policymakers because the students embedded their work within their larger projects through a learning collaborative that was established within KZN-DOH which facilitates evidence-based learning. However, none of the 22 studies were included in the 29 theses analyzed by the researchers.

Although we found some evidence of utilization of doctoral research findings for policy formulation, the research was not utilized to its fullest potential by policymakers (Nutley et al. 2007 ). Two major themes of the factors that contribute to utilization of PhD work emerged from the study; involvement of DOH in the formulation of research questions and dissemination methods used to communicate research findings to DOH. The factors were the same across the two data sets (qualitative and quantitative) hence they were merged.

In contrast, DOH personnel reported that they were not aware of any PhD research from the SNPH that has influenced policy formulation. Perhaps, the challenge leading to this disparity is that the provincial officials interviewed may not have been fully aware of research conducted in all the districts and municipalities. Students and supervisors may be disseminating findings to the district and municipalities. It could also be a problem of deficiencies of the reporting systems in cascading information upwards. It is sensible that students report their findings to officials who are closer to their research sites. Furthermore, these findings would be relevant to that municipality or district where research is being conducted. Hence, they disseminate their findings to the closest office. On the other hand, provincial officials are swamped with work and may not be fully aware of research conducted in all the eleven districts.

Results of our study showed that policymakers were not aware of the availability of doctoral research findings due to lack of meaningful discussion of available research findings between researchers and policymakers, their suitability to policy- related problems and identification of other policy related areas requiring research attention (Uzochukwu et al. 2016 ). DOH was also not aware of the availability of research findings because they were not involved in the formulation of research questions for the projects. The results revealed that forty-two (72%) students did not engage with DOH/ policymakers during the formulation of research questions for their projects. It is acknowledged that engagement of stakeholders during formulation of research questions for projects ensures that appropriate research questions are pursued as well as informing policymakers of the availability of research findings (Edwards et al. 2019 , Oliver and Cairney 2019 ). This finding is in line with studies conducted in Ghana and Tanzania (Kok et al. 2017 , Wolffers and Adjei 1999 ). WHO stresses the value of closer collaboration between research organizations and the policymakers they seek to influence, so that evidence creation is better aligned with policy priorities (Organization 2016 ). DOH was also not aware of the evidence from research they did not commission. We established that DOH does not commission research due to lack of funding. Policymakers are likely to translate research that they have commissioned because they would have defined what gap needs to be informed by pending evidence (Mapulanga et al. 2020 ).

The poor demand for research evidence on research projects approved by DOH was also reported as a barrier. This may reflect DOH’s perception of the value of doctoral research evidence or their prioritization of research for decision-making (Ezenwaka et al. 2020 ). Part of DOH approval letter states that students are required to send their research findings to DOH on completion of their studies. However, according to the data obtained from the students’ questionnaire only twenty-two (38%) students sent feedback to DOH when they completed their studies. This was supported by supervisors who reported that students hardly give feedback to institutions that give them permission to carry out their studies. Although DOH do not have an automated system to monitor projects that have been completed out of the hundreds of projects they approve in the province per year, they do not have to rely on students who have completed their studies for feedback. They can use other strategies such as journal clubs to access research results. A participant from DOH suggested that as DOH they can form journal clubs where they can meet once a month and read an article by a student from the SNPH. Another participant suggested that students from SNPH can be asked to come and share their research findings with relevant people in the department. The study also revealed that 76.1% supervisors sent feedback of students’ research findings to DOH.

The methods through which research findings were communicated by the students could have also influenced demand and research uptake by DOH (Uzochukwu et al. 2016 ). It was interesting to note that PhD research findings were disseminated at scientific conferences and in scientific journals more than at policy forums or workshops (Edwards et al. 2019 ; Mcvay et al. 2016 ). 76% of the students reported that they presented their work at least at four local scientific conferences whilst 72% presented their research findings at least at five international conferences. Often, policymakers are not present at these conferences. This finding shows that PhD students prefer to communicate their research results through scientific conferences and peer-reviewed journals more than active engagement with policymakers. These results are consistent with the findings of (Ndlovu et al. 2016 ) that academics prioritize scholarly communication and prefer academic journals and conferences as communication platforms. In the SNPH, this could be attributed to a culture where publishing in peer-reviewed journals is rewarded and carries considerable prestige and power. Institutional priorities such as number of journal articles published, number of conferences attended and number of grants attracted limit researchers commitment to responding to policy issues facing policymakers (Ha et al. 2022 ; Gordon and Bartley 2015 ).

Scientific journals, with their assortment of articles may contain nothing of interest to a policymaker whose needs are very specific (Glied et al. 2018 ). It has also been argued that some policymakers might not have the skills and resources to access research evidence or time to source for evidence from scientific journals (Hyder et al. 2011 ). Most policymakers have responsibilities and priorities that may prevent them from spending a lot of time reading or reviewing the materials provided to them in detail (Brownson et al. 2018 ). Presenting research findings in less complex formats such as policy briefs that use simple language, has been shown to improve health research transfer in policymaking (Newman 2014 ). It was not the case with this study where only three policy briefs were produced. The study revealed that only 3.4% of the students who participated in the survey produced policy briefs. This supports findings of a survey of researchers in the Eastern Mediterranean Region that showed that only 15% produced policy briefs (El-Jardali et al. 2012 ). The low production of policy briefs may be attributed to researchers’ lack of policy briefs writing skills.

DOH acknowledges the strategic role of knowledge translation in attaining national health goals, as evidenced by the creation of KZN-DOH annual research day which is a one-day evidence-to-policy workshop aimed at getting feedback on research that they have approved. The main perceived benefit of the platform is to provide a non-academic space (Fernández-Peña et al. 2008 ) recommended for researchers to disseminate research findings to policymakers who can translate research into policies and adoption of interventions to public health settings (Proctor and Chambers 2017 ; Tinkle et al. 2013 ). The other benefit of the forum is that it is a platform where researchers and policymakers discuss health policy implications of research findings pertaining to policy and practice (Parkhurst 2017 ). This finding is also supported by a study conducted in Nigeria which found that the Nigerian research days that were organized by the Department of Family Health, Federal Ministry of Health of Nigeria had fostered a platform to discuss policies on maternal and child health by allowing dialog among various stakeholders, including researchers and policymakers (Johnson et al. 2020 ). The combined use of policy briefs, policy dialogs and meetings with policymakers have been proposed to enhance knowledge translation as the strategies are deemed to be likely familiar to both researchers and policymakers (Edwards et al. 2019 ).

Whilst the KZN-DOH annual research day is ideal for dissemination of research findings; our study reveals that DOH is not utilizing the platform to its fullest potential. Only 40% of the students presented their research findings at the KZN-DOH annual research day. Only a few students got the opportunity to present their research findings since it is a one-day event. The event may be extended to two or three days or may be conducted on a quarterly basis to allow for more research results to be disseminated to DOH. It was also reported that attendance by policymakers who have the decision-making powers at the event was poor hence the likelihood of the research findings and recommendations being translated is low. Our study also found that although researchers are encouraged to address priority research questions posted on DOH website, projects did not respond to these questions. A previous study highlighted that sharing research priority lists is important in research question identification and rewarding such engagement would incentivize postgraduate students to demonstrate how they engaged policymakers at various levels (Obuku et al. 2021 ). When research does not respond to priority research questions raised, it minimizes the likelihood of research findings being used in policy and practice.

Only thirteen PhD theses produced in the SNPH between 2014 and 2021 were accessed from the institutional repository. The SNPH has an average enrollment of 44 students per year and a throughput of 32 students per year. Given the number of PhD students expected to have graduated during the period under study, the theses in the institutional repository reflected very low levels of content deposit. The finding confirms Harnad’s position that most universities’ institutional repositories are 85% empty with deposit levels sitting at 15% or below (Harnad 2011 ). Some supervisors highlighted that they held meetings with policymakers to highlight problem areas and possible solutions.

Implications for future research and policy

This paper has identified the gaps that exist in the process of translating doctoral research findings into policy. This has opened an opportunity to explore possible solutions to address the gaps. Our findings are unique to the field in that they are postulated by authorities and participants who have an active role in both the development and use of research findings with the department of Health in KwaZulu-Natal. The department on its own is a typical case study that can be portrayed as an example of how such challenges in translating research manifest and how they can be solved. Results of this study contributed to the development of a framework that guides both students and policymakers on the processes necessary for consideration of doctoral research findings in policy formulation.

Strengths and limitations

This is the first study that has attempted to highlight the extent to which doctoral research from the SNPH at UKZN has contributed to existing policies or influenced formulation of guidelines and policies of DOH, South Africa. Data were collected using three different sources (document review, questionnaires, and interviews) which allowed cross-checking of findings. Nonetheless, we encountered some shortcomings, particularly with regards to access to PhD theses produced between 2014 and 2021. The library repository was not up to date and had a limited number of theses produced during the period under study; therefore, the document review did not include all the theses that were produced between 2014 and 2021. The only way to obtain all the theses produced during the period under study was to get them from the graduates themselves. However, some of them were reluctant to share their theses for personal reasons. We could have missed some important information pertaining to our study. Another limitation is that the study was carried out in a single school in one college of UKZN, yet the university has four colleges with 19 different schools. For this reason, our findings may not be generalized for UKZN. Some of the reviewed theses were too recent not allowing enough time for research findings to be utilized for policy. The other limitation was that several PhD supervisors who were approached to participate in the study could not participate due to various reasons such as busy schedules and not having had supervised PhD students to completion. It was a requirement for PhD supervisors to have had supervised students to completion. Due to lockdown restrictions the email was the only means of recruiting participants for the study. However, people have a tendency of not responding to emails even after reminded on several occasions. Since the online questionnaire was the only option for collecting data, the response rate was very low.

This study has identified the gaps that exist in the process of translating doctoral research findings into policy. The findings from this study indicated that some studies were being considered for policy development and adoption while some had resulted in policy guidelines and frameworks that can guide the uptake of PhD work. The study revealed that DOH was not aware of the availability of doctoral research findings which could be attributed to the format in which the research results were disseminated and the fact that students did not involve policymakers in the formulation of research questions for their projects. Research results were communicated through scientific conferences and peer-reviewed journals more than active engagement with policymakers. Findings from this study contributed to the development of a framework that guides both students and policymakers on the processes necessary for consideration of doctoral research findings in policy formulation.

Data availability

The data involved in this study are from in-depth interviews and questionnaire surveys, and because the original data involves personal information, it cannot be fully disclosed due to identifiability issues. De-identifiable datasets generated and analyzed during the study will be made available from the corresponding author on reasonable request.

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We would like to thank the participants who participated in this study and the university for library resources.

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FUD, NGM and MJC conceptualized, designed, and drafted the manuscript. FUD collected data and prepared data for qualitative and quantitative analyses. FUD, NGM and MJC conducted the analysis. NGM and MJC guided the design, reviewed all the draft manuscripts, and provided comments and critical revisions to the manuscript. All authors read and approved the final manuscript.

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Damba, F.U., Mtshali, N.G. & Chimbari, M.J. Insights on the contribution of doctoral research findings from a school in a South African University towards policy formulation. Humanit Soc Sci Commun 11 , 930 (2024). https://doi.org/10.1057/s41599-024-03439-x

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Doctoral Dissertations and Projects

Push and pull factors that influence law enforcement officer turnover: a qualitative case study.

Andrew Hunter Kim , Liberty University Follow

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Abiodun Oguntuase

Turnover, unfolding model, law enforcement officers, law enforcement

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Kim, Andrew Hunter, "Push and Pull Factors That Influence Law Enforcement Officer Turnover: A Qualitative Case Study" (2024). Doctoral Dissertations and Projects . 5806. https://digitalcommons.liberty.edu/doctoral/5806

A high frequency of voluntary turnover within a law enforcement organization is problematic due to the potential for an increase in expenditures, a reduction in public safety levels, and an increase in liability. However, research has indicated explicitly that criminal justice professions have a high turnover rate, with many organizations having an annual turnover rate of 10 percent or higher. Investigating employee turnover rates and potential solutions to increase employee retention are significant areas of focus that can reduce expenditures, maintain or improve public safety levels by keeping experienced employees, and create a better working environment. A high employee turnover rate can lead to negative impacts on organizational morale. This qualitative case study investigated the most significant contributor to turnover intentions among law enforcement officers employed by the City of La Palma. Prior research has indicated that salary, benefits, lack of career opportunities, poor organizational support, under-recognition, poor organizational morale, and poor supervision may impact turnover rates within law enforcement organizations. The theory that guided this study was Lee and Mitchell's Unfolding Model Theory of Voluntary Employee Turnover (Lee & Mitchell, 1994). This qualitative multiple case study utilized semi-structured interviews, participant observations, and document analysis as the data collection tools. The sample of this study were current or former law enforcement officers from the City of La Palma recruited via snowball sampling. The findings were five themes: salary and benefits, poor morale, political climate and public support, leadership and management, and limited opportunities. Additionally, it was found that all former employees followed the same path as defined by Lee and Mitchell's Unfolding Molding Theory of Voluntary Employee Turnover when they conducted a job search and then quit the organization. Additional findings and implications will be discussed at length within this study.

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What is quantitative data? How to collect, understand, and analyze it

A comprehensive guide to quantitative data, how it differs from qualitative data, and why it's a valuable tool for solving problems.

• Key takeaways
• What is quantitative data?
• Examples of quantitative data
• Difference between quantitative and qualitative data
• Characteristics of quantitative data
• Types of quantitative data
• When should I use quantitative or qualitative research?
• Pros and cons of quantitative data
• Collection methods

Quantitative data analysis tools

Data is all around us, and every day it becomes increasingly important. Different types of data define more and more of our interactions with the world around us—from using the internet to buying a car, to the algorithms behind news feeds we see, and much more. 

One of the most common and well-known categories of data is quantitative data or data that can be expressed in numbers or numerical values. 

This guide takes a deep look at what quantitative data is , what it can be used for, how it’s collected, its advantages and disadvantages, and more. 

Key takeaways: 

Quantitative data is data that can be counted or measured in numerical values.

The two main types of quantitative data are discrete data and continuous data.

Height in feet, age in years, and weight in pounds are examples of quantitative data. 

Qualitative data is descriptive data that is not expressed numerically. 

Both quantitative research and qualitative research are often conducted through surveys and questionnaires. 

What is quantitative data? 

Quantitative data is information that can be counted or measured—or, in other words, quantified—and given a numerical value.

Quantitative data is used when a researcher needs to quantify a problem, and answers questions like “what,” “how many,” and “how often.” This type of data is frequently used in math calculations, algorithms, or statistical analysis. 

In product management, UX design , or software engineering, quantitative data can be the rate of product adoption (a percentage), conversions (a number), or page load speed (a unit of time), or other metrics. In the context of shopping, quantitative data could be how many customers bought a certain item. Regarding vehicles, quantitative data might be how much horsepower a car has. 

What are examples of quantitative data? 

Quantitative data is anything that can be counted in definite units and numbers . So, among many, many other things, some examples of quantitative data include: 

Revenue in dollars

Weight in kilograms or pounds

Age in months or years

Distance in miles or kilometers

Time in days or weeks

Experiment results

Website conversion rates

Website page load speed

What is the difference between quantitative and qualitative data? 

There are many differences between qualitative and quantitative data —each represents very different data sets and are used in different situations. Often, too, they’re used together to provide more comprehensive insights.

As we’ve described, quantitative data relates to numbers ; it can be definitively counted or measured.  Qualitative data, on the other hand, is descriptive data that are expressed in words or visuals. So, where quantitative data is used for statistical analysis, qualitative data is categorized according to themes. 

Examples of qualitative vs. quantitative data

As mentioned above, examples of quantitative data include distance in miles or age in years. 

Qualitative data , however, is expressed by describing or labeling certain attributes, such as “chocolate milk,” “blue eyes,” and “red flowers.” In these examples, the adjectives chocolate, blue, and red are qualitative data because they tell us something about the objects that cannot be quantified. 

Further reading: The differences between categorical and quantitative Data and examples of qualitative data

Characteristics of quantitative data 

Quantitative data is made up of numerical values has numerical properties, and can easily undergo math operations like addition and subtraction. The nature of quantitative data means that its validity can be verified and evaluated using math techniques. 

Specific types of quantitative data

All quantitative data can be measured numerically, as shown above. But these data types can be broken down into more specific categories, too.

There are two types of quantitative data: discrete and continuous . Continuous data can be further divided into interval data and ratio data . 

Discrete data

In reference to quantitative data, discrete data is information that can only take certain fixed values. While discrete data doesn’t have to be represented by whole numbers, there are limitations to how it can be expressed. 

Examples of discrete data:

The number of players on a team

The number of employees at a company

The number of items eggs broken when you drop the carton

The number of outs a hitter makes in a baseball game

The number of right and wrong questions on a test

A website's bounce rate (percentages can be no less than 0 or greater than 100)

Discrete data is typically most appropriately visualized with a tally chart, pie chart, or bar graph, as shown below.

Continuous data 

Continuous data , on the other hand, can take any value and varies over time. This type of data can be infinitely and meaningfully broken down into smaller and smaller parts. 

Examples of continuous data:

Website traffic

Water temperature

The time it takes to complete a task

Because continuous data changes over time, its insights are best expressed with a line graph or grouped into categories, as shown below.

Continuous data can be further broken down into two categories: interval data and ratio data. 

Interval data

Interval data is information that can be measured along a continuum, where there is equal, meaningful distance between each point on a scale. Interval data is always expressed in numbers where the distance between two points is standardized and equal. These numbers can also be called integers. 

Examples of interval data include temperature since it can move below and above 0.

Ratio data has all the properties of interval data, but unlike interval data, ratio data also has a true zero. For example, weight in grams is a type of ratio data because it is measured along a continuous scale with equal space between each value, and the scale starts at 0.0.

Other examples of ratio data are weight, length, height, and concentration. 

Interval data vs. ratio data

Ratio data gets its name because the ratio of two measurements can be interpreted meaningfully, whereas two measurements cannot be directly compared with intervals.

For example, something that weighs six pounds is twice as heavy as something that weighs three pounds. However, this rule does not apply to interval data, which has no zero value. An SAT score of 700, for instance, is not twice as good as an SAT score of 350, because the scale does not begin at zero.

Similarly, 40º is not twice as hot as 20º. Saying uses 0º as a reference point to compare the two temperatures, which is incorrect.

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When should I use quantitative or qualitative research? 

Quantitative and qualitative research can both yield valuable findings, but it’s important to choose which type of data to collect based on the nature and objectives of your research. 

When to use quantitative research

Quantitative research is likely most appropriate if the thing you are trying to study or measure can be counted and expressed in numbers. For example, quantitative methods are used to calculate a city’s demographics—how many people live there, their ages, their ethnicities, their incomes, and so on. 

When to use qualitative research

Qualitative data is defined as non-numerical data such as language, text, video, audio recordings, and photographs. This data can be collected through qualitative methods and research such as interviews, survey questions, observations, focus groups, or diary accounts. 

Conducting qualitative research involves collecting, analyzing, and interpreting qualitative non-numerical data (like color, flavor, or some other describable aspect). Methods of qualitative analysis include thematic analysis, coding, and content analysis.

If the thing you want to understand is subjective or measured along a scale, you will need to conduct qualitative research and qualitative analysis.

To use our city example from above, determining why a city's population is happy or unhappy—something you would need to ask them to describe—requires qualitative data. 

In short: The goal of qualitative research is to understand how individuals perceive their own social realities. It's commonly used in fields like psychology, social sciences and sociology, educational research, anthropology, political science, and more. 

In some instances, like when trying to understand why users are abandoning your website, it’s helpful to assess both quantitative and qualitative data . Understanding what users are doing on your website—as well as why they’re doing it (or how they feel when they’re doing it)—gives you the information you need to make your website’s experience better. 

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What are the pros and cons of quantitative data? 

Quantitative data is most helpful when trying to understand something that can be counted and expressed in numbers. 

Pros of quantitative data: 

Quantitative data is less susceptible to selection bias than qualitative data.

It can be tested and checked, and anyone can replicate both an experiment and its results.

Quantitative data is relatively quick and easy to collect. 

Cons of quantitative data: 

Quantitative data typically lacks context. In other words, it tells you what something is but not why it is.

Conclusions drawn from quantitative research are only applicable to the particular case studied, and any generalized conclusions are only hypotheses.

How do you collect quantitative data? 

There are many ways to collect quantitative data , with common methods including surveys and questionnaires. These can generate both quantitative data and qualitative data, depending on the questions asked. 

Once the data is collected and analyzed, it can be used to examine patterns, make predictions about the future, and draw inferences. 

For example, a survey of 100 consumers about where they plan to shop during the holidays might show that 45 of them plan to shop online, while the other 55 plan to shop in stores. 

Questionnaires and surveys 

Surveys and questionnaires are commonly used in quantitative research and qualitative research because they are both effective and relatively easy to create and distribute. With a wide array of simple-to-use tools, conducting surveys online is a quick and convenient research method. 

These research types are useful for gathering in-depth feedback from users and customers, particularly for finding out how people feel about a certain product, service, or experience. For example, many e-commerce companies send post-purchase surveys to find out how a customer felt about the transaction — and if any areas could be improved. 

Another common way to collect quantitative data is through a consumer survey, which retailers and other businesses can use to get customer feedback, understand intent, and predict shopper behavior . 

Open-source online datasets 

There are many public datasets online that are free to access and analyze. In some instances, rather than conducting original research through the methods mentioned above, researchers analyze and interpret this previously collected data in the way that suits their own research project. Examples of public datasets include: 

The Bureau of Labor Statistics Data

The Census Bureau Data

World Bank Open Data

The CIA World Factbook  

Experiments

An experiment is another common method that usually involves a  control group  and an  experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

Controlled experiments,  A/B tests ,  blind experiments , and many others fall under this category.

With large data pools, a survey of each individual person or data point may be infeasible. In this instance, sampling is used to conduct quantitative research. Sampling is the process of selecting a representative sample of data , which can save time and resources. There are two types of sampling : random sampling (also known as probability sampling) and non-random sampling (also known as non-probability sampling). 

Probability sampling allows for the randomization of the sample selection, meaning that each sample has the same probability of being selected for survey as any other sample. 

In non-random sampling, each sample unit does not have the same probability of being included in the sample. This type of sampling relies on factors other than random chance to select sample units, such as the researcher’s own subjective judgment. Non-random sampling is most commonly used in qualitative research. 

Typically, data analysts and data scientists use a variety of special tools to gather and analyze quantitative data from different sources. 

For example, many web analysts and marketing professionals use Google Analytics (pictured below) to gather data about their website’s traffic and performance. This tool can reveal how many visitors come to your site in a day or week, the length of an average session, where traffic comes from, and more. In this example, the goal of this quantitative analysis is to understand and optimize your site’s performance. 

Google Analytics is just one example of the many quantitative analytics tools available for different research professionals. 

Other quantitative data tools include…

Microsoft Excel

Microsoft Power BI

Apache Spark

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Frequently asked questions about quantitative data

Is quantitative data objective.

Quantitative researchers do everything they can to ensure data’s objectivity by eliminating bias in the collection and analysis process. However, there are factors that can cause quantitative data to be biased.

For example, selection bias can occur when certain individuals are more likely to be selected for study than others. Other types of bias include reporting bias , attrition bias , recall bias , observer bias , and others. 

Who uses quantitative data?

Quantitative research is used in many fields of study, including psychology, digital experience intelligence , economics, demography, marketing, political science, sociology, epidemiology, gender studies, health, and human development. Quantitative research is used less commonly in fields such as history and anthropology. 

Many people who are seeking advanced degrees in a scientific field use quantitative research as part of their studies.

What is quantitative data in statistics?

Statistics is a branch of mathematics that is commonly used in quantitative research. To conduct quantitative research with statistical methods, a researcher would collect data based on a hypothesis, and then that data is manipulated and studied as part of hypothesis testing, proving the accuracy or reliability of the hypothesis.

Is quantitative data better than qualitative data?

It depends on the researcher’s goal. If the researcher wants to measure something—for example, to understand “how many” or “how often,”—quantitative data is appropriate. However, if a researcher wants to learn the reason behind something—to understand “why” something is—qualitative research methods will better answer these questions.

Further reading: Qualitative vs. quantitative data — what's the difference?

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Qualitative and quantitative data differ on what they emphasize—qualitative focuses on meaning, and quantitative emphasizes statistical analysis.

Categorical & quantitative variables both provide vital info about a data set. But each is important for different reasons and has its own pros/cons.

Quantitative data is used for calculations or obtaining numerical results. Learn about the different types of quantitative data uses cases and more.

Discover how just-in-time data, explained by Lane Greer, enhances customer insights and decision-making beyond real-time analytics.

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One of the most common types of data collection methods in qualitative research

one of the most common types of data collection methods in qualitative research.

What are one of the most common types of data collection methods in qualitative research?

Answer: One of the most common types of data collection methods in qualitative research is interviewing . This method is widely utilized due to its flexibility and depth in gathering detailed, rich qualitative data. Here’s an in-depth look at interviewing as a qualitative data collection method:

1. Types of Interviews

Structured Interviews : These involve a pre-determined set of questions which are asked in the same order to every participant. While they ensure consistency, they may limit the depth of responses.

Semi-Structured Interviews : This is a more flexible approach where the researcher has a guide of questions or themes, but allows for deviation based on the interviewee’s responses. This method balances consistency and depth.

Unstructured Interviews : These are the most flexible type and resemble a conversation more than a formal interview. There are no pre-set questions, allowing the exploration of topics in great depth.

2. Advantages of Interviews

Depth of Information : Interviews allow participants to provide comprehensive and nuanced responses, leading to rich qualitative data.

Flexibility : Researchers can adapt their questions and approach based on the direction of the conversation, leading to unexpected insights.

Clarification : Interviews provide the opportunity to clarify ambiguous responses on the spot, ensuring that the data collected is accurate.

3. Conducting Effective Interviews

Choosing Participants : Selecting participants who are knowledgeable or experienced in the research topic enhances the quality of data.

Developing a Guide : For semi-structured interviews, prepare a flexible interview guide with key questions and topics.

Building Rapport : Establish a comfortable environment for the interviewees to speak openly and honestly.

Active Listening : Pay close attention to the responses, showing genuine interest, and asking follow-up questions as needed.

Recording and Transcribing : Ensure that the interviews are recorded (with consent) and accurately transcribed for data analysis.

4. Ethical Considerations

Informed Consent : Participants should be fully informed about the purpose of the study and provide consent.

Confidentiality : Ensure the privacy of participants by anonymizing data.

Sensitivity : Approach sensitive topics with care and respect, ensuring participants feel safe.

Final Answer:

In qualitative research, interviews serve as one of the most common and effective data collection methods, offering flexibility, depth, and the ability to gather rich, detailed data from participants.

Please example

Please provide an example of a common data collection method in qualitative research

Answer: Certainly! An excellent example of a common data collection method in qualitative research is semi-structured interviews . Let’s walk through an example to illustrate how this method works in a real-world research scenario.

Example: Semi-Structured Interview

Research Topic: The impact of remote work on employee productivity and well-being.

Objective: To understand how the shift to remote work has affected employees’ productivity and mental health.

Steps and Example:

Preparing the Interview Guide:

• The researcher develops a set of open-ended questions and themes to explore during the interview. Although the questions are pre-determined, the interviewer can probe further based on responses.

Example Questions:

• Can you describe your daily routine while working from home?
• How has remote work affected your productivity?
• What challenges have you faced while working remotely?
• How do you balance work and personal life when working from home?
• In what ways has remote work impacted your mental health?

Selecting Participants:

• Participants are selected based on specific criteria such as having at least six months of remote work experience.

Example Participant Criteria:

• Employees from various industries who have transitioned to remote work due to the COVID-19 pandemic.

Conducting the Interviews:

• The interviewer sets up a one-on-one session with each participant, ideally in a quiet and comfortable environment. Interviews can also be conducted via video calls.

Interview Session:

• Interviewer: “Can you describe your daily routine while working from home?”
• Participant: “Generally, I start my day by checking emails and setting my priorities. I take breaks when needed, but it’s sometimes hard to switch off and maintain a strict work schedule.”
• Interviewer: “What challenges have you faced while working remotely?”
• Participant: “The biggest challenge is staying motivated and avoiding distractions. Also, not having direct interaction with colleagues sometimes makes me feel isolated.”

Recording and Transcribing:

• With the participant’s consent, the interview is recorded to capture every detail. Later, the audio recording is transcribed verbatim for analysis.

Example Transcript Excerpt:

• Interviewer: “In what ways has remote work impacted your mental health?”
• Participant: “Initially, it was very stressful to adapt to the new environment. Over time, I found ways to manage, but I still miss the sense of camaraderie in the office.”

Analyzing the Data:

• The researcher reads through the transcripts, identifies recurring themes, and codes the data for patterns.

Example Analysis:

• Key themes might include “work-life balance,” “productivity challenges,” “mental well-being,” and “social isolation.”

Semi-structured interviews involve a focused yet flexible approach that allows researchers to delve deeply into the personal experiences of participants. This method is highly effective for understanding complex phenomena like the impact of remote work on employees.

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Lumivero’s team-based solutions allow you to:, need help choosing qda software, what is nvivo, what can i do with nvivo, who is nvivo for, how much does nvivo cost.

It's easy to buy student, individual and small group licenses (student license limited to one per account, individual and small group licenses up to nine) online.

To purchase ten or more NVivo licenses for your team or organization, Contact Us to reach our sales team or one of our international NVivo partners.

Are there free qualitative data analysis tutorials?

How do i upgrade nvivo.

For individual and small groups (less than 9 individuals), take advantage of the most recent updates and software enhancements by purchasing the latest version here .  For larger groups and institutional/enterprise users, subscription options at volume rates are offered that secure the latest features as they are released. To purchase ten or more NVivo licenses for your team or organization, Contact Us to reach our sales team or one of our international NVivo partners. Lumivero's Support team is committed to your success using our software and actively supports the two previous versions of the most current version.

Get Started with NVivo Qualitative Data Analysis Software (QDA) Today

Begin your journey towards deeper insights and more robust results. NVivo provides better research collaboration, deeper integration, and is easier to use than ever.

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