a good qualitative research question

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How to write qualitative research questions.

11 min read Here’s how to write effective qualitative research questions for your projects, and why getting it right matters so much.

What is qualitative research?

Qualitative research is a blanket term covering a wide range of research methods and theoretical framing approaches. The unifying factor in all these types of qualitative study is that they deal with data that cannot be counted. Typically this means things like people’s stories, feelings, opinions and emotions , and the meanings they ascribe to their experiences.

Qualitative study is one of two main categories of research, the other being quantitative research. Quantitative research deals with numerical data – that which can be counted and quantified, and which is mostly concerned with trends and patterns in large-scale datasets.

What are research questions?

Research questions are questions you are trying to answer with your research. To put it another way, your research question is the reason for your study, and the beginning point for your research design. There is normally only one research question per study, although if your project is very complex, you may have multiple research questions that are closely linked to one central question.

A good qualitative research question sums up your research objective. It’s a way of expressing the central question of your research, identifying your particular topic and the central issue you are examining.

Research questions are quite different from survey questions, questions used in focus groups or interview questions. A long list of questions is used in these types of study, as opposed to one central question. Additionally, interview or survey questions are asked of participants, whereas research questions are only for the researcher to maintain a clear understanding of the research design.

Research questions are used in both qualitative and quantitative research , although what makes a good research question might vary between the two.

In fact, the type of research questions you are asking can help you decide whether you need to take a quantitative or qualitative approach to your research project.

Discover the fundamentals of qualitative research

Quantitative vs. qualitative research questions

Writing research questions is very important in both qualitative and quantitative research, but the research questions that perform best in the two types of studies are quite different.

Quantitative research questions

Quantitative research questions usually relate to quantities, similarities and differences.

It might reflect the researchers’ interest in determining whether relationships between variables exist, and if so whether they are statistically significant. Or it may focus on establishing differences between things through comparison, and using statistical analysis to determine whether those differences are meaningful or due to chance.

• How much? This kind of research question is one of the simplest. It focuses on quantifying something. For example:

How many Yoruba speakers are there in the state of Maine?

• What is the connection?

This type of quantitative research question examines how one variable affects another.

For example:

How does a low level of sunlight affect the mood scores (1-10) of Antarctic explorers during winter?

• What is the difference? Quantitative research questions in this category identify two categories and measure the difference between them using numerical data.

Do white cats stay cooler than tabby cats in hot weather?

If your research question fits into one of the above categories, you’re probably going to be doing a quantitative study.

Qualitative research questions

Qualitative research questions focus on exploring phenomena, meanings and experiences.

Unlike quantitative research, qualitative research isn’t about finding causal relationships between variables. So although qualitative research questions might touch on topics that involve one variable influencing another, or looking at the difference between things, finding and quantifying those relationships isn’t the primary objective.

In fact, you as a qualitative researcher might end up studying a very similar topic to your colleague who is doing a quantitative study, but your areas of focus will be quite different. Your research methods will also be different – they might include focus groups, ethnography studies, and other kinds of qualitative study.

A few example qualitative research questions:

• What is it like being an Antarctic explorer during winter?
• What are the experiences of Yoruba speakers in the USA?
• How do white cat owners describe their pets?

Qualitative research question types

Marshall and Rossman (1989) identified 4 qualitative research question types, each with its own typical research strategy and methods.

• Exploratory questions

Exploratory questions are used when relatively little is known about the research topic. The process researchers follow when pursuing exploratory questions might involve interviewing participants, holding focus groups, or diving deep with a case study.

• Explanatory questions

With explanatory questions, the research topic is approached with a view to understanding the causes that lie behind phenomena. However, unlike a quantitative project, the focus of explanatory questions is on qualitative analysis of multiple interconnected factors that have influenced a particular group or area, rather than a provable causal link between dependent and independent variables.

• Descriptive questions

As the name suggests, descriptive questions aim to document and record what is happening. In answering descriptive questions , researchers might interact directly with participants with surveys or interviews, as well as using observational studies and ethnography studies that collect data on how participants interact with their wider environment.

• Predictive questions

Predictive questions start from the phenomena of interest and investigate what ramifications it might have in the future. Answering predictive questions may involve looking back as well as forward, with content analysis, questionnaires and studies of non-verbal communication (kinesics).

Why are good qualitative research questions important?

We know research questions are very important. But what makes them so essential? (And is that question a qualitative or quantitative one?)

Getting your qualitative research questions right has a number of benefits.

• It defines your qualitative research project Qualitative research questions definitively nail down the research population, the thing you’re examining, and what the nature of your answer will be.This means you can explain your research project to other people both inside and outside your business or organization. That could be critical when it comes to securing funding for your project, recruiting participants and members of your research team, and ultimately for publishing your results. It can also help you assess right the ethical considerations for your population of study.
• It maintains focus Good qualitative research questions help researchers to stick to the area of focus as they carry out their research. Keeping the research question in mind will help them steer away from tangents during their research or while they are carrying out qualitative research interviews. This holds true whatever the qualitative methods are, whether it’s a focus group, survey, thematic analysis or other type of inquiry.That doesn’t mean the research project can’t morph and change during its execution – sometimes this is acceptable and even welcome – but having a research question helps demarcate the starting point for the research. It can be referred back to if the scope and focus of the project does change.
• It helps make sure your outcomes are achievable

Because qualitative research questions help determine the kind of results you’re going to get, it helps make sure those results are achievable. By formulating good qualitative research questions in advance, you can make sure the things you want to know and the way you’re going to investigate them are grounded in practical reality. Otherwise, you may be at risk of taking on a research project that can’t be satisfactorily completed.

Developing good qualitative research questions

All researchers use research questions to define their parameters, keep their study on track and maintain focus on the research topic. This is especially important with qualitative questions, where there may be exploratory or inductive methods in use that introduce researchers to new and interesting areas of inquiry. Here are some tips for writing good qualitative research questions.

1. Keep it specific

Broader research questions are difficult to act on. They may also be open to interpretation, or leave some parameters undefined.

Strong example: How do Baby Boomers in the USA feel about their gender identity?

Weak example: Do people feel different about gender now?

2. Be original

Look for research questions that haven’t been widely addressed by others already.

Strong example: What are the effects of video calling on women’s experiences of work?

Weak example: Are women given less respect than men at work?

3. Make it research-worthy

Don’t ask a question that can be answered with a ‘yes’ or ‘no’, or with a quick Google search.

Strong example: What do people like and dislike about living in a highly multi-lingual country?

Weak example: What languages are spoken in India?

4. Focus your question

Don’t roll multiple topics or questions into one. Qualitative data may involve multiple topics, but your qualitative questions should be focused.

Strong example: What is the experience of disabled children and their families when using social services?

Weak example: How can we improve social services for children affected by poverty and disability?

4. Focus on your own discipline, not someone else’s

Avoid asking questions that are for the politicians, police or others to address.

Strong example: What does it feel like to be the victim of a hate crime?

Weak example: How can hate crimes be prevented?

5. Ask something researchable

Big questions, questions about hypothetical events or questions that would require vastly more resources than you have access to are not useful starting points for qualitative studies. Qualitative words or subjective ideas that lack definition are also not helpful.

Strong example: How do perceptions of physical beauty vary between today’s youth and their parents’ generation?

Weak example: Which country has the most beautiful people in it?

Related resources

Qualitative research design 12 min read, primary vs secondary research 14 min read, business research methods 12 min read, mixed methods research 17 min read, market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, request demo.

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Qualitative Research Questions: Gain Powerful Insights + 25 Examples

We review the basics of qualitative research questions, including their key components, how to craft them effectively, & 25 example questions.

Einstein was many things—a physicist, a philosopher, and, undoubtedly, a mastermind. He also had an incredible way with words. His quote, "Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted," is particularly poignant when it comes to research. 

Some inquiries call for a quantitative approach, for counting and measuring data in order to arrive at general conclusions. Other investigations, like qualitative research, rely on deep exploration and understanding of individual cases in order to develop a greater understanding of the whole. That’s what we’re going to focus on today.

Qualitative research questions focus on the "how" and "why" of things, rather than the "what". They ask about people's experiences and perceptions , and can be used to explore a wide range of topics.

The following article will discuss the basics of qualitative research questions, including their key components, and how to craft them effectively. You'll also find 25 examples of effective qualitative research questions you can use as inspiration for your own studies.

Let’s get started!

What are qualitative research questions, and when are they used?

When researchers set out to conduct a study on a certain topic, their research is chiefly directed by an overarching question . This question provides focus for the study and helps determine what kind of data will be collected.

By starting with a question, we gain parameters and objectives for our line of research. What are we studying? For what purpose? How will we know when we’ve achieved our goals?

Of course, some of these questions can be described as quantitative in nature. When a research question is quantitative, it usually seeks to measure or calculate something in a systematic way.

For example:

• How many people in our town use the library?
• What is the average income of families in our city?
• How much does the average person weigh?

Other research questions, however—and the ones we will be focusing on in this article—are qualitative in nature. Qualitative research questions are open-ended and seek to explore a given topic in-depth.

According to the Australian & New Zealand Journal of Psychiatry , “Qualitative research aims to address questions concerned with developing an understanding of the meaning and experience dimensions of humans’ lives and social worlds.”

This type of research can be used to gain a better understanding of people’s thoughts, feelings and experiences by “addressing questions beyond ‘what works’, towards ‘what works for whom when, how and why, and focusing on intervention improvement rather than accreditation,” states one paper in Neurological Research and Practice .

Qualitative questions often produce rich data that can help researchers develop hypotheses for further quantitative study.

• What are people’s thoughts on the new library?
• How does it feel to be a first-generation student at our school?
• How do people feel about the changes taking place in our town?

As stated by a paper in Human Reproduction , “...‘qualitative’ methods are used to answer questions about experience, meaning, and perspective, most often from the standpoint of the participant. These data are usually not amenable to counting or measuring.”

Both quantitative and qualitative questions have their uses; in fact, they often complement each other. A well-designed research study will include a mix of both types of questions in order to gain a fuller understanding of the topic at hand.

If you would like to recruit unlimited participants for qualitative research for free and only pay for the interview you conduct, try using Respondent  today. 

Crafting qualitative research questions for powerful insights

Now that we have a basic understanding of what qualitative research questions are and when they are used, let’s take a look at how you can begin crafting your own.

According to a study in the International Journal of Qualitative Studies in Education, there is a certain process researchers should follow when crafting their questions, which we’ll explore in more depth.

1. Beginning the process 

Start with a point of interest or curiosity, and pose a draft question or ‘self-question’. What do you want to know about the topic at hand? What is your specific curiosity? You may find it helpful to begin by writing several questions.

For example, if you’re interested in understanding how your customer base feels about a recent change to your product, you might ask: 

• What made you decide to try the new product?
• How do you feel about the change?
• What do you think of the new design/functionality?
• What benefits do you see in the change?

2. Create one overarching, guiding question 

At this point, narrow down the draft questions into one specific question. “Sometimes, these broader research questions are not stated as questions, but rather as goals for the study.”

As an example of this, you might narrow down these three questions: 

into the following question: 

• What are our customers’ thoughts on the recent change to our product?

3. Theoretical framing 

As you read the relevant literature and apply theory to your research, the question should be altered to achieve better outcomes. Experts agree that pursuing a qualitative line of inquiry should open up the possibility for questioning your original theories and altering the conceptual framework with which the research began.

If we continue with the current example, it’s possible you may uncover new data that informs your research and changes your question. For instance, you may discover that customers’ feelings about the change are not just a reaction to the change itself, but also to how it was implemented. In this case, your question would need to reflect this new information: 

• How did customers react to the process of the change, as well as the change itself?

4. Ethical considerations 

A study in the International Journal of Qualitative Studies in Education stresses that ethics are “a central issue when a researcher proposes to study the lives of others, especially marginalized populations.” Consider how your question or inquiry will affect the people it relates to—their lives and their safety. Shape your question to avoid physical, emotional, or mental upset for the focus group.

In analyzing your question from this perspective, if you feel that it may cause harm, you should consider changing the question or ending your research project. Perhaps you’ve discovered that your question encourages harmful or invasive questioning, in which case you should reformulate it.

5. Writing the question 

The actual process of writing the question comes only after considering the above points. The purpose of crafting your research questions is to delve into what your study is specifically about” Remember that qualitative research questions are not trying to find the cause of an effect, but rather to explore the effect itself.

Your questions should be clear, concise, and understandable to those outside of your field. In addition, they should generate rich data. The questions you choose will also depend on the type of research you are conducting: 

• If you’re doing a phenomenological study, your questions might be open-ended, in order to allow participants to share their experiences in their own words.
• If you’re doing a grounded-theory study, your questions might be focused on generating a list of categories or themes.
• If you’re doing ethnography, your questions might be about understanding the culture you’re studying.

Whenyou have well-written questions, it is much easier to develop your research design and collect data that accurately reflects your inquiry.

In writing your questions, it may help you to refer to this simple flowchart process for constructing questions:

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25 examples of expertly crafted qualitative research questions

It's easy enough to cover the theory of writing a qualitative research question, but sometimes it's best if you can see the process in practice. In this section, we'll list 25 examples of B2B and B2C-related qualitative questions.

Let's begin with five questions. We'll show you the question, explain why it's considered qualitative, and then give you an example of how it can be used in research.

1. What is the customer's perception of our company's brand?

Qualitative research questions are often open-ended and invite respondents to share their thoughts and feelings on a subject. This question is qualitative because it seeks customer feedback on the company's brand. 

This question can be used in research to understand how customers feel about the company's branding, what they like and don't like about it, and whether they would recommend it to others.

2. Why do customers buy our product?

This question is also qualitative because it seeks to understand the customer's motivations for purchasing a product. It can be used in research to identify the reasons  customers buy a certain product, what needs or desires the product fulfills for them, and how they feel about the purchase after using the product.

3. How do our customers interact with our products?

Again, this question is qualitative because it seeks to understand customer behavior. In this case, it can be used in research to see how customers use the product, how they interact with it, and what emotions or thoughts the product evokes in them.

4. What are our customers' biggest frustrations with our products?

By seeking to understand customer frustrations, this question is qualitative and can provide valuable insights. It can be used in research to help identify areas in which the company needs to make improvements with its products.

5. How do our customers feel about our customer service?

Rather than asking why customers like or dislike something, this question asks how they feel. This qualitative question can provide insights into customer satisfaction or dissatisfaction with a company. 

This type of question can be used in research to understand what customers think of the company's customer service and whether they feel it meets their needs.

20 more examples to refer to when writing your question

Now that you’re aware of what makes certain questions qualitative, let's move into 20 more examples of qualitative research questions:

• How do your customers react when updates are made to your app interface?
• How do customers feel when they complete their purchase through your ecommerce site?
• What are your customers' main frustrations with your service?
• How do people feel about the quality of your products compared to those of your competitors?
• What motivates customers to refer their friends and family members to your product or service?
• What are the main benefits your customers receive from using your product or service?
• How do people feel when they finish a purchase on your website?
• What are the main motivations behind customer loyalty to your brand?
• How does your app make people feel emotionally?
• For younger generations using your app, how does it make them feel about themselves?
• What reputation do people associate with your brand?
• How inclusive do people find your app?
• In what ways are your customers' experiences unique to them?
• What are the main areas of improvement your customers would like to see in your product or service?
• How do people feel about their interactions with your tech team?
• What are the top five reasons people use your online marketplace?
• How does using your app make people feel in terms of connectedness?
• What emotions do people experience when they're using your product or service?
• Aside from the features of your product, what else about it attracts customers?
• How does your company culture make people feel?

As you can see, these kinds of questions are completely open-ended. In a way, they allow the research and discoveries made along the way to direct the research. The questions are merely a starting point from which to explore.

This video offers tips on how to write good qualitative research questions, produced by Qualitative Research Expert, Kimberly Baker.

Wrap-up: crafting your own qualitative research questions.

Over the course of this article, we've explored what qualitative research questions are, why they matter, and how they should be written. Hopefully you now have a clear understanding of how to craft your own.

Remember, qualitative research questions should always be designed to explore a certain experience or phenomena in-depth, in order to generate powerful insights. As you write your questions, be sure to keep the following in mind:

• Are you being inclusive of all relevant perspectives?
• Are your questions specific enough to generate clear answers?
• Will your questions allow for an in-depth exploration of the topic at hand?
• Do the questions reflect your research goals and objectives?

If you can answer "yes" to all of the questions above, and you've followed the tips for writing qualitative research questions we shared in this article, then you're well on your way to crafting powerful queries that will yield valuable insights.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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McCombes, S. (2023, October 19). 10 Research Question Examples to Guide your Research Project. Scribbr. Retrieved September 14, 2024, from https://www.scribbr.com/research-process/research-question-examples/

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What’s in a Qualitative Research Question?

Qualitative research questions are driven by the need for the study. Ideally, research questions are formulated as a result of the problem and purpose, which leads to the identification of the methodology. When a qualitative methodology is chosen, research questions should be exploratory and focused on the actual phenomenon under study.

From the Dissertation Center, Chapter 1: Research Question Overview , there are several considerations when forming a qualitative research question. Qualitative research questions should

Below is an example of a qualitative phenomenological design. Note the use of the term “lived experience” in the central research question. This aligns with phenomenological design.

RQ1: “ What are the lived experiences of followers of mid-level managers in the financial services sector regarding their well-being on the job?”

If the researcher wants to focus on aspects of the theory used to support the study or dive deeper into aspects of the central RQ, sub-questions might be used. The following sub-questions could be formulated to seek further insight:

RQ1a.   “How do followers perceive the quality and adequacy of the leader-follower exchanges between themselves and their novice leaders?”

RQ1b.  “Under what conditions do leader-member exchanges affect a follower’s own level of well-being?”

Qualitative research questions also display the desire to explore or describe phenomena. Qualitative research seeks the lived experience, the personal experiences, the understandings, the meanings, and the stories associated with the concepts present in our studies.

We want to ensure our research questions are answerable and that we are not making assumptions about our sample. View the questions below:

How do healthcare providers perceive income inequality when providing care to poor patients?

In Example A, we see that there is no specificity of location or geographic areas. This could lead to findings that are varied, and the researcher may not find a clear pattern. Additionally, the question implies the focus is on “income inequality” when the actual focus is on the provision of care. The term “poor patients” can also be offensive, and most providers will not want to seem insensitive and may perceive income inequality as a challenge (of course!).

How do primary care nurses in outreach clinics describe providing quality care to residents of low-income urban neighborhoods?

In Example B, we see that there is greater specificity in the type of care provider. There is also a shift in language so that the focus is on how the individuals describe what they think about, experience, and navigate providing quality care.

Other Qualitative Research Question Examples

Vague : What are the strategies used by healthcare personnel to assist injured patients?

Try this : What is the experience of emergency room personnel in treating patients with a self-inflicted household injury?

The first question is general and vague. While in the same topic area, the second question is more precise and gives the reader a specific target population and a focus on the phenomenon they would have experienced. This question could be in line with a phenomenological study as we are seeking their experience or a case study as the ER personnel are a bounded entity.

Unclear : How do students experience progressing to college?

Try this : How do first-generation community members describe the aspects of their culture that promote aspiration to postsecondary education?

The first question does not have a focus on what progress is or what students are the focus. The second question provides a specific target population and provides the description to be provided by the participants. This question could be in line with a descriptive study.

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Chapter Outline

• Empirical vs. ethical questions (4 minute read)
• Characteristics of a good research question (4 minute read)
• Quantitative research questions (7 minute read)
• Qualitative research questions (3 minute read)
• Evaluating and updating your research questions (4 minute read)

Content warning: examples in this chapter include references to sexual violence, sexism, substance use disorders, homelessness, domestic violence, the child welfare system, cissexism and heterosexism, and truancy and school discipline.

9.1 Empirical vs. ethical questions

Learning objectives.

Learners will be able to…

• Define empirical questions and provide an example
• Define ethical questions and provide an example

Writing a good research question is an art and a science. It is a science because you have to make sure it is clear, concise, and well-developed. It is an art because often your language needs “wordsmithing” to perfect and clarify the meaning. This is an exciting part of the research process; however, it can also be one of the most stressful.

Creating a good research question begins by identifying a topic you are interested in studying. At this point, you already have a working question. You’ve been applying it to the exercises in each chapter, and after reading more about your topic in the scholarly literature, you’ve probably gone back and revised your working question a few times. We’re going to continue that process in more detail in this chapter. Keep in mind that writing research questions is an iterative process, with revisions happening week after week until you are ready to start your project.

Empirical vs. ethical questions

When it comes to research questions, social science is best equipped to answer empirical questions —those that can be answered by real experience in the real world—as opposed to  ethical questions —questions about which people have moral opinions and that may not be answerable in reference to the real world. While social workers have explicit ethical obligations (e.g., service, social justice), research projects ask empirical questions to help actualize and support the work of upholding those ethical principles.

In order to help you better understand the difference between ethical and empirical questions, let’s consider a topic about which people have moral opinions. How about SpongeBob SquarePants? [1] In early 2005, members of the conservative Christian group Focus on the Family (2005) [2] denounced this seemingly innocuous cartoon character as “morally offensive” because they perceived his character to be one that promotes a “pro-gay agenda.” Focus on the Family supported their claim that SpongeBob is immoral by citing his appearance in a children’s video designed to promote tolerance of all family forms (BBC News, 2005). [3] They also cited SpongeBob’s regular hand-holding with his male sidekick Patrick as further evidence of his immorality.

So, can we now conclude that SpongeBob SquarePants is immoral? Not so fast. While your mother or a newspaper or television reporter may provide an answer, a social science researcher cannot. Questions of morality are ethical, not empirical. Of course, this doesn’t mean that social science researchers cannot study opinions about or social meanings surrounding SpongeBob SquarePants (Carter, 2010). [4] We study humans after all, and as you will discover in the following chapters of this textbook, we are trained to utilize a variety of scientific data-collection techniques to understand patterns of human beliefs and behaviors. Using these techniques, we could find out how many people in the United States find SpongeBob morally reprehensible, but we could never learn, empirically, whether SpongeBob is in fact morally reprehensible.

Let’s consider an example from a recent MSW research class I taught. A student group wanted to research the penalties for sexual assault. Their original research question was: “How can prison sentences for sexual assault be so much lower than the penalty for drug possession?” Outside of the research context, that is a darn good question! It speaks to how the War on Drugs and the patriarchy have distorted the criminal justice system towards policing of drug crimes over gender-based violence.

Unfortunately, it is an ethical question, not an empirical one. To answer that question, you would have to draw on philosophy and morality, answering what it is about human nature and society that allows such unjust outcomes. However, you could not answer that question by gathering data about people in the real world. If I asked people that question, they would likely give me their opinions about drugs, gender-based violence, and the criminal justice system. But I wouldn’t get the real answer about why our society tolerates such an imbalance in punishment.

As the students worked on the project through the semester, they continued to focus on the topic of sexual assault in the criminal justice system. Their research question became more empirical because they read more empirical articles about their topic. One option that they considered was to evaluate intervention programs for perpetrators of sexual assault to see if they reduced the likelihood of committing sexual assault again. Another option they considered was seeing if counties or states with higher than average jail sentences for sexual assault perpetrators had lower rates of re-offense for sexual assault. These projects addressed the ethical question of punishing perpetrators of sexual violence but did so in a way that gathered and analyzed empirical real-world data. Our job as social work researchers is to gather social facts about social work issues, not to judge or determine morality.

Key Takeaways

• Empirical questions are distinct from ethical questions.
• There are usually a number of ethical questions and a number of empirical questions that could be asked about any single topic.
• While social workers may research topics about which people have moral opinions, a researcher’s job is to gather and analyze empirical data.
• Take a look at your working question. Make sure you have an empirical question, not an ethical one. To perform this check, describe how you could find an answer to your question by conducting a study, like a survey or focus group, with real people.

9.2 Characteristics of a good research question

• Identify and explain the key features of a good research question
• Explain why it is important for social workers to be focused and clear with the language they use in their research questions

Now that you’ve made sure your working question is empirical, you need to revise that working question into a formal research question. So, what makes a good research question? First, it is generally written in the form of a question. To say that your research question is “the opioid epidemic” or “animal assisted therapy” or “oppression” would not be correct. You need to frame your topic as a question, not a statement. A good research question is also one that is well-focused. A well-focused question helps you tune out irrelevant information and not try to answer everything about the world all at once. You could be the most eloquent writer in your class, or even in the world, but if the research question about which you are writing is unclear, your work will ultimately lack direction.

In addition to being written in the form of a question and being well-focused, a good research question is one that cannot be answered with a simple yes or no. For example, if your interest is in gender norms, you could ask, “Does gender affect a person’s performance of household tasks?” but you will have nothing left to say once you discover your yes or no answer. Instead, why not ask, about the relationship between gender and household tasks. Alternatively, maybe we are interested in how or to what extent gender affects a person’s contributions to housework in a marriage? By tweaking your question in this small way, you suddenly have a much more fascinating question and more to say as you attempt to answer it.

A good research question should also have more than one plausible answer. In the example above, the student who studied the relationship between gender and household tasks had a specific interest in the impact of gender, but she also knew that preferences might be impacted by other factors. For example, she knew from her own experience that her more traditional and socially conservative friends were more likely to see household tasks as part of the female domain, and were less likely to expect their male partners to contribute to those tasks. Thinking through the possible relationships between gender, culture, and household tasks led that student to realize that there were many plausible answers to her questions about how  gender affects a person’s contribution to household tasks. Because gender doesn’t exist in a vacuum, she wisely felt that she needed to consider other characteristics that work together with gender to shape people’s behaviors, likes, and dislikes. By doing this, the student considered the third feature of a good research question–she thought about relationships between several concepts. While she began with an interest in a single concept—household tasks—by asking herself what other concepts (such as gender or political orientation) might be related to her original interest, she was able to form a question that considered the relationships  among  those concepts.

This student had one final component to consider. Social work research questions must contain a target population. Her study would be very different if she were to conduct it on older adults or immigrants who just arrived in a new country. The target population is the group of people whose needs your study addresses. Maybe the student noticed issues with household tasks as part of her social work practice with first-generation immigrants, and so she made it her target population. Maybe she wants to address the needs of another community. Whatever the case, the target population should be chosen while keeping in mind social work’s responsibility to work on behalf of marginalized and oppressed groups.

In sum, a good research question generally has the following features:

• It is written in the form of a question
• It is clearly written
• It cannot be answered with “yes” or “no”
• It has more than one plausible answer
• It considers relationships among multiple variables
• It is specific and clear about the concepts it addresses
• It includes a target population
• A poorly focused research question can lead to the demise of an otherwise well-executed study.
• Research questions should be clearly worded, consider relationships between multiple variables, have more than one plausible answer, and address the needs of a target population.

Okay, it’s time to write out your first draft of a research question.

• Once you’ve done so, take a look at the checklist in this chapter and see if your research question meets the criteria to be a good one.

Brainstorm whether your research question might be better suited to quantitative or qualitative methods.

• Describe why your question fits better with quantitative or qualitative methods.
• Provide an alternative research question that fits with the other type of research method.

9.3 Quantitative research questions

• Describe how research questions for exploratory, descriptive, and explanatory quantitative questions differ and how to phrase them
• Identify the differences between and provide examples of strong and weak explanatory research questions

Quantitative descriptive questions

The type of research you are conducting will impact the research question that you ask. Probably the easiest questions to think of are quantitative descriptive questions. For example, “What is the average student debt load of MSW students?” is a descriptive question—and an important one. We aren’t trying to build a causal relationship here. We’re simply trying to describe how much debt MSW students carry. Quantitative descriptive questions like this one are helpful in social work practice as part of community scans, in which human service agencies survey the various needs of the community they serve. If the scan reveals that the community requires more services related to housing, child care, or day treatment for people with disabilities, a nonprofit office can use the community scan to create new programs that meet a defined community need.

Quantitative descriptive questions will often ask for percentage, count the number of instances of a phenomenon, or determine an average. Descriptive questions may only include one variable, such as ours about student debt load, or they may include multiple variables. Because these are descriptive questions, our purpose is not to investigate causal relationships between variables. To do that, we need to use a quantitative explanatory question.

Quantitative explanatory questions

Most studies you read in the academic literature will be quantitative and explanatory. Why is that? If you recall from Chapter 2 , explanatory research tries to build nomothetic causal relationships. They are generalizable across space and time, so they are applicable to a wide audience. The editorial board of a journal wants to make sure their content will be useful to as many people as possible, so it’s not surprising that quantitative research dominates the academic literature.

Structurally, quantitative explanatory questions must contain an independent variable and dependent variable. Questions should ask about the relationship between these variables. The standard format I was taught in graduate school for an explanatory quantitative research question is: “What is the relationship between [independent variable] and [dependent variable] for [target population]?” You should play with the wording for your research question, revising that standard format to match what you really want to know about your topic.

Let’s take a look at a few more examples of possible research questions and consider the relative strengths and weaknesses of each. Table 9.1 does just that. While reading the table, keep in mind that I have only noted what I view to be the most relevant strengths and weaknesses of each question. Certainly each question may have additional strengths and weaknesses not noted in the table. Each of these questions is drawn from student projects in my research methods classes and reflects the work of many students on their research question over many weeks.

Making it more specific

A good research question should also be specific and clear about the concepts it addresses. A student investigating gender and household tasks knows what they mean by “household tasks.” You likely also have an impression of what “household tasks” means. But are your definition and the student’s definition the same? A participant in their study may think that managing finances and performing home maintenance are household tasks, but the researcher may be interested in other tasks like childcare or cleaning. The only way to ensure your study stays focused and clear is to be specific about what you mean by a concept. The student in our example could pick a specific household task that was interesting to them or that the literature indicated was important—for example, childcare. Or, the student could have a broader view of household tasks, one that encompasses childcare, food preparation, financial management, home repair, and care for relatives. Any option is probably okay, as long as the researcher is clear on what they mean by “household tasks.” Clarifying these distinctions is important as we look ahead to specifying how your variables will be measured in Chapter 11 .

Table 9.2 contains some “watch words” that indicate you may need to be more specific about the concepts in your research question.

It can be challenging to be this specific in social work research, particularly when you are just starting out your project and still reading the literature. If you’ve only read one or two articles on your topic, it can be hard to know what you are interested in studying. Broad questions like “What are the causes of chronic homelessness, and what can be done to prevent it?” are common at the beginning stages of a research project as working questions. However, moving from working questions to research questions in your research proposal requires that you examine the literature on the topic and refine your question over time to be more specific and clear. Perhaps you want to study the effect of a specific anti-homelessness program that you found in the literature. Maybe there is a particular model to fighting homelessness, like Housing First or transitional housing, that you want to investigate further. You may want to focus on a potential cause of homelessness such as LGBTQ+ discrimination that you find interesting or relevant to your practice. As you can see, the possibilities for making your question more specific are almost infinite.

Quantitative exploratory questions

In exploratory research, the researcher doesn’t quite know the lay of the land yet. If someone is proposing to conduct an exploratory quantitative project, the watch words highlighted in Table 9.2 are not problematic at all. In fact, questions such as “What factors influence the removal of children in child welfare cases?” are good because they will explore a variety of factors or causes. In this question, the independent variable is less clearly written, but the dependent variable, family preservation outcomes, is quite clearly written. The inverse can also be true. If we were to ask, “What outcomes are associated with family preservation services in child welfare?”, we would have a clear independent variable, family preservation services, but an unclear dependent variable, outcomes. Because we are only conducting exploratory research on a topic, we may not have an idea of what concepts may comprise our “outcomes” or “factors.” Only after interacting with our participants will we be able to understand which concepts are important.

Remember that exploratory research is appropriate only when the researcher does not know much about topic because there is very little scholarly research. In our examples above, there is extensive literature on the outcomes in family reunification programs and risk factors for child removal in child welfare. Make sure you’ve done a thorough literature review to ensure there is little relevant research to guide you towards a more explanatory question.

• Descriptive quantitative research questions are helpful for community scans but cannot investigate causal relationships between variables.
• Explanatory quantitative research questions must include an independent and dependent variable.
• Exploratory quantitative research questions should only be considered when there is very little previous research on your topic.
• Identify the type of research you are engaged in (descriptive, explanatory, or exploratory).
• Create a quantitative research question for your project that matches with the type of research you are engaged in.

Preferably, you should be creating an explanatory research question for quantitative research.

9.4 Qualitative research questions

• List the key terms associated with qualitative research questions
• Distinguish between qualitative and quantitative research questions

Qualitative research questions differ from quantitative research questions. Because qualitative research questions seek to explore or describe phenomena, not provide a neat nomothetic explanation, they are often more general and openly worded. They may include only one concept, though many include more than one. Instead of asking how one variable causes changes in another, we are instead trying to understand the experiences ,  understandings , and  meanings that people have about the concepts in our research question. These keywords often make an appearance in qualitative research questions.

Let’s work through an example from our last section. In Table 9.1, a student asked, “What is the relationship between sexual orientation or gender identity and homelessness for late adolescents in foster care?” In this question, it is pretty clear that the student believes that adolescents in foster care who identify as LGBTQ+ may be at greater risk for homelessness. This is a nomothetic causal relationship—LGBTQ+ status causes changes in homelessness.

However, what if the student were less interested in  predicting  homelessness based on LGBTQ+ status and more interested in  understanding  the stories of foster care youth who identify as LGBTQ+ and may be at risk for homelessness? In that case, the researcher would be building an idiographic causal explanation . The youths whom the researcher interviews may share stories of how their foster families, caseworkers, and others treated them. They may share stories about how they thought of their own sexuality or gender identity and how it changed over time. They may have different ideas about what it means to transition out of foster care.

Because qualitative questions usually center on idiographic causal relationships, they look different than quantitative questions. Table 9.3 below takes the final research questions from Table 9.1 and adapts them for qualitative research. The guidelines for research questions previously described in this chapter still apply, but there are some new elements to qualitative research questions that are not present in quantitative questions.

• Qualitative research questions often ask about lived experience, personal experience, understanding, meaning, and stories.
• Qualitative research questions may be more general and less specific.
• Qualitative research questions may also contain only one variable, rather than asking about relationships between multiple variables.

Qualitative research questions have one final feature that distinguishes them from quantitative research questions: they can change over the course of a study. Qualitative research is a reflexive process, one in which the researcher adapts their approach based on what participants say and do. The researcher must constantly evaluate whether their question is important and relevant to the participants. As the researcher gains information from participants, it is normal for the focus of the inquiry to shift.

For example, a qualitative researcher may want to study how a new truancy rule impacts youth at risk of expulsion. However, after interviewing some of the youth in their community, a researcher might find that the rule is actually irrelevant to their behavior and thoughts. Instead, their participants will direct the discussion to their frustration with the school administrators or the lack of job opportunities in the area. This is a natural part of qualitative research, and it is normal for research questions and hypothesis to evolve based on information gleaned from participants.

However, this reflexivity and openness unacceptable in quantitative research for good reasons. Researchers using quantitative methods are testing a hypothesis, and if they could revise that hypothesis to match what they found, they could never be wrong! Indeed, an important component of open science and reproducability is the preregistration of a researcher’s hypotheses and data analysis plan in a central repository that can be verified and replicated by reviewers and other researchers. This interactive graphic from 538 shows how an unscrupulous research could come up with a hypothesis and theoretical explanation  after collecting data by hunting for a combination of factors that results in a statistically significant relationship. This is an excellent example of how the positivist assumptions behind quantitative research and intepretivist assumptions behind qualitative research result in different approaches to social science.

• Qualitative research questions often contain words or phrases like “lived experience,” “personal experience,” “understanding,” “meaning,” and “stories.”
• Qualitative research questions can change and evolve over the course of the study.
• Using the guidance in this chapter, write a qualitative research question. You may want to use some of the keywords mentioned above.

9.5 Evaluating and updating your research questions

• Evaluate the feasibility and importance of your research questions
• Begin to match your research questions to specific designs that determine what the participants in your study will do

Feasibility and importance

As you are getting ready to finalize your research question and move into designing your research study, it is important to check whether your research question is feasible for you to answer and what importance your results will have in the community, among your participants, and in the scientific literature

Key questions to consider when evaluating your question’s feasibility include:

• Do you have access to the data you need?
• Will you be able to get consent from stakeholders, gatekeepers, and others?
• Does your project pose risk to individuals through direct harm, dual relationships, or breaches in confidentiality? (see Chapter 6 for more ethical considerations)
• Are you competent enough to complete the study?
• Do you have the resources and time needed to carry out the project?

Key questions to consider when evaluating the importance of your question include:

• Can we answer your research question simply by looking at the literature on your topic?
• How does your question add something new to the scholarly literature? (raises a new issue, addresses a controversy, studies a new population, etc.)
• How will your target population benefit, once you answer your research question?
• How will the community, social work practice, and the broader social world benefit, once you answer your research question?
• Using the questions above, check whether you think your project is feasible for you to complete, given the constrains that student projects face.
• Realistically, explore the potential impact of your project on the community and in the scientific literature. Make sure your question cannot be answered by simply reading more about your topic.

Matching your research question and study design

This chapter described how to create a good quantitative and qualitative research question. In Parts 3 and 4 of this textbook, we will detail some of the basic designs like surveys and interviews that social scientists use to answer their research questions. But which design should you choose?

As with most things, it all depends on your research question. If your research question involves, for example, testing a new intervention, you will likely want to use an experimental design. On the other hand, if you want to know the lived experience of people in a public housing building, you probably want to use an interview or focus group design.

We will learn more about each one of these designs in the remainder of this textbook. We will also learn about using data that already exists, studying an individual client inside clinical practice, and evaluating programs, which are other examples of designs. Below is a list of designs we will cover in this textbook:

• Surveys: online, phone, mail, in-person
• Experiments: classic, pre-experiments, quasi-experiments
• Interviews: in-person or via phone or videoconference
• Focus groups: in-person or via videoconference
• Content analysis of existing data
• Secondary data analysis of another researcher’s data
• Program evaluation

The design of your research study determines what you and your participants will do. In an experiment, for example, the researcher will introduce a stimulus or treatment to participants and measure their responses. In contrast, a content analysis may not have participants at all, and the researcher may simply read the marketing materials for a corporation or look at a politician’s speeches to conduct the data analysis for the study.

I imagine that a content analysis probably seems easier to accomplish than an experiment. However, as a researcher, you have to choose a research design that makes sense for your question and that is feasible to complete with the resources you have. All research projects require some resources to accomplish. Make sure your design is one you can carry out with the resources (time, money, staff, etc.) that you have.

There are so many different designs that exist in the social science literature that it would be impossible to include them all in this textbook. The purpose of the subsequent chapters is to help you understand the basic designs upon which these more advanced designs are built. As you learn more about research design, you will likely find yourself revising your research question to make sure it fits with the design. At the same time, your research question as it exists now should influence the design you end up choosing. There is no set order in which these should happen. Instead, your research project should be guided by whether you can feasibly carry it out and contribute new and important knowledge to the world.

• Research questions must be feasible and important.
• Research questions must match study design.
• Based on what you know about designs like surveys, experiments, and interviews, describe how you might use one of them to answer your research question.
• You may want to refer back to Chapter 2 which discusses how to get raw data about your topic and the common designs used in student research projects.
• Not familiar with SpongeBob SquarePants? You can learn more about him on Nickelodeon’s site dedicated to all things SpongeBob:  http://www.nick.com/spongebob-squarepants/ ↵
• Focus on the Family. (2005, January 26). Focus on SpongeBob.  Christianity Today . Retrieved from  http://www.christianitytoday.com/ct/2005/januaryweb-only/34.0c.html ↵
• BBC News. (2005, January 20). US right attacks SpongeBob video. Retrieved from:  http://news.bbc.co.uk/2/hi/americas/4190699.stm ↵
• In fact, an MA thesis examines representations of gender and relationships in the cartoon: Carter, A. C. (2010).  Constructing gender and   relationships in “SpongeBob SquarePants”: Who lives in a pineapple under the sea . MA thesis, Department of Communication, University of South Alabama, Mobile, AL. ↵
• Writing from an outline (10 minute read plus an 8 minute video, and then a 15 minute video)
• Writing your literature review (30 minute read)

Content warning: TBA

6.1: Writing from an outline

Learners will be able to...

• Integrate facts from the literature into scholarly writing
• Experiment with different approaches to integrating information that do not involve direct quotations from other authors

Congratulations! By now, you should have discovered, retrieved, evaluated, synthesized, and organized the information you need for your literature review. It’s now time to turn that stack of articles, papers, and notes into a literature review–it’s time to start writing!

The first step in research writing is outlining. In Chapter 4, we reviewed how to build a topical outline using quotations and facts from other authors. Use that outline (or one you write now) as a way to organize your thoughts.

Watch this video from Nicholas Cifuentes-Goodbody on Outlining . As he highlights, outlining is like building a mise en place before a meal--arranging your ingredients in an orderly way so you can create your masterpiece.

From quotations to original writing

Much like combining ingredients on a kitchen countertop, you will need to mix your ingredients together. That means you will not be relying extensively on quotations from other authors in your literature review. In moving from an outline to a literature review, the key intellectual move is relying on your own ideas about the literature, rather than quoting extensively from other sources.

Integrating ideas from other authors

Watch this video from Nicholas Cifuentes-Goodbody on using quotations in academic writing . In the video, he reviews a few different techniques to integrate quotations or ideas from other authors into your writing. All literature reviews use the ideas from other authors, but it's important not to overuse others' words. Your literature review is evaluated by your professor based on how well it shows  you are able to make connections between different facts in the scientific literature. The examples in this section should highlight how to get other people's words out of the way of your own. Use these strategies to diversify your writing and show your readers how your sources contributed to your work.

1. Make a claim without a quote

Claim ( Citation )

Some view cities as the storehouse of culture and creativity, and propose that urbanization is a consequence of the attractiveness of these social benefits ( Mumford, 1961 ).

More information

Oftentimes you do not need to directly quote a source to convey its conclusions or arguments – and some disciplines discourage quoting directly! Rather you can paraphrase the main point of a paper in your own words and provide an in-text citation. A benefit of using this strategy is that you can offer support for a claim without using a whole paragraph to introduce and frame a quote. You should make sure that you fully understand the paper's argument and that you are following university citation guidelines before attempting to paraphrase something from a paper.

2. Make a claim that is supported by two or more sources:

Claim ( Citation 1 ; Citation 2 ).

Reviews of this literature concede difficulty in making direct comparisons of emission levels across different sets of analysis ( Bader and Bleischwitz, 2009 ; Kennedy et al., 2009 ; Ramaswami et al., 2012 ).

Sometimes multiple sources support your claim, or there are two major publications that deserve credit for providing evidence on a topic. This is a perfect time to use multiple citations. You can cite two, three, or more sources in a single sentence!

Make a claim that has been supported in multiple contexts:

Context 1 ( Citation ), Context 2 ( Citation ), Context 3 ( Citation ).

These results are supported by more recent research on transportation energy consumption ( Liddle, 2014 ), electricity consumption in buildings ( Lariviere and Lafrance, 1999 ), and overall urban GHG emissions ( Marcotullio et al., 2013b ).

More information:

Use this citation strategy when you want to show that a body of research has found support for some claim across several different contexts. This can show the robustness of an effect or phenomenon and can give your claim some added validity

3. Quote important or unique terms

Claim " Term " ( Citation ).

The spatial implications of this thinking are manifest in the " concentric ring model " of urban expansion and its variants ( Harris and Ullman, 1945 ).

While block or even whole-sentence citations are rare in most research papers in the science and social science disciplines, there is often a need to quote specific terms or phrases that were first coined by a certain source or that were well-explained in a specific paper.

4. Quoting definitions

Contextualize quote , " important word or phrase ."

Role conflict is defined as "A situation in which contradictory, competing, or incompatible expectations are placed on an individual by two or more roles held at the same time" (Open Sociology Dictionary, 2023); whereas, role strain is defined as "a situation caused by higher-than-expected demands placed on an individual performing a specific role that leads to difficulty or stress" (Open Sociology Dictionary, 2023). In our study, we hypothesize that caregivers who reenter higher education experience role conflict between school work, paid work, and care work. Further, we hypothesize that this conflict is greater in individuals who had experienced role strain in employment or caregiving prior to entering college.

A direct quotation can bring attention to specific language in your source. When someone puts something perfectly, you can use a quotation to convey the identical meaning in your work. Definitions are an excellent example of when to use a quotation. In other cases, there may be quotations from important thinkers, clients or community members, and others whose specific wording is important.

I encourage you to use few, if any, direct quotations in your literature review. Personally, I think most students are scared of looking stupid and would rather use a good quotation than risk not getting it right. If you are a student who considers themselves a strong writer, this may not sound relevant to you. However, I'm willing to bet that there are many of your peers for whom this describes a particular bit of research anxiety.

When using quotations, make sure to only include the parts of the quotation that are necessary. You do not need to use quotation marks for statistics you use. And I encourage you to find ways to put others' statistics in  your sentences.

Why share information from other sources?

Now that you know some different sentence structures using APA citations, let's examine the purpose behind why you are sharing information from another source. Cited evidence can serve a wide range of purposes in academic papers. These examples will give you an idea of the different ways that you can use citations in your paper.

1. Summarize your source

The studies of Newman and Kenworthy ( 1989, 1999 ) demonstrate a negative relationship between population density and transportation fuel use .

You will help your reader understand your points better if you summarize the key points of a study. Describe the strengths or weaknesses a specific source that has been pivotal in your field. Describe the source's specific methodology, theory, or approach. Be sure to still include a citation. If you mention the name of the author in your text, you still need to provide the date of the study in a parenthetical citation.

2. Cite a method

Despite the popularity of the WUP indicators , they have been routinely criticized because the methodology relies on local- and country-specific definitions of bounding urban areas, resulting in of ten incomparable and widely divergent definitions of the population, density thresholds, or administrative/political units designated ( Satterthwaite, 2007 ).

This is an easy way to give credit to a source that has provided some evidence for the validity of a method or questionnaire. Readers can reference your citation if they are interested in knowing more about the method and its standing in the current literature.

3. Compare sources

Some evidence for this scaling relationship suggests that urban areas with larger population sizes have proportionally smaller energy infrastructures than smaller cities ( Bettencourt et al., 2007 ; Fragkias et al., 2013 ). Other evidence suggests that GHG emissions may increase more than proportionally to population size, such that larger cities exhibit proportionally higher energy demand as they grow than do smaller cities ( Marcotullio et al., 2013 ).

This is one of the most important techniques for creating an effective literature review. This allows you and your readers to consider controversies and discrepancies among the current literature, revealing gaps in the literature or points of contention for further study.

The examples in this guide come from:

Marcotullio, P. J., Hughes, S., Sarzynski, A., Pincetl, S., Sanchez Peña, L., Romero-Lankao, P., Runfola, D. and Seto, K. C. (2014), Urbanization and the carbon cycle: Contributions from social science. Earth's Future, 2: 496–514. doi:10.1002/2014EF000257

Avoiding plagiarism

The most difficult thing about avoiding plagiarism is that reading so much of other people's ideas can make them seem like your own after a while. We recommend you work through this interactive activity on determining how and when to cite other authors.

• Research writing requires outlining, which helps you arrange your facts neatly before writing. It's similar to arranging all of your ingredients before you start cooking.
• Eliminate quotations from your writing as much as possible. Your literature review needs to be your analysis of the literature, not just a summary of other people's good ideas.
• Experiment with the prompts in this chapter as you begin to write your research question. 

6.2 Writing your literature review

• Describe the components of a literature review
• Begin to write your literature review
• Identify the purpose of a problem statement
• Apply the components of a formal argument to your topic
• Use elements of formal writing style, including signposting and transitions
• Recognize commons errors in literature reviews

Writing about research is different than other types of writing. Research writing is not like a journal entry or opinion paper. The goal here is not to apply your research question to your life or growth as a practitioner. Research writing is about the provision and interpretation of facts. The tone should be objective and unbiased, and personal experiences and opinions are excluded. Particularly for students who are used to writing case notes, research writing can be a challenge. That's why its important to normalize getting help! If your professor has not built in peer review, consider setting up a peer review group among your peers. You should also reach out to your academic advisor to see if there are writing services on your campus available to graduate students. No one should feel bad for needing help with something they haven't done before, haven't done in a while, or were never taught how to do. 

If you’ve followed the steps in this chapter, you likely have an outline, summary table, and concept map from which you can begin the writing process. But what do you need to include in your literature review? We’ve mentioned it before, but to summarize, a literature review should:

• Introduce the topic and define its key terms.
• Establish the importance of the topic.
• Provide an overview of the important literature related to the concepts found in the research question.
• Identify gaps or controversies in the literature.
• Point out consistent findings across studies.
• Synthesize that which is known about a topic, rather than just provide a summary of the articles you read.
• Discuss possible implications and directions for future research.

Do you have enough facts and sources to accomplish these tasks? It’s a good time to consult your outlines and notes on each article you plan to include in your literature review. You may also want to consult with your professor on what is expected of you. If there is something you are missing, you may want to jump back to section 2.3 where we discussed how to search for literature. While you can always fill in material, there is the danger that you will start writing without really knowing what you are talking about or what you want to say. For example, if you don’t have a solid definition of your key concepts or a sense of how the literature has developed over time, it will be difficult to make coherent scholarly claims about your topic.

There is no magical point at which one is ready to write. As you consider whether you are ready, it may be useful to ask yourself these questions:

• How will my literature review be organized?
• What section headings will I be using?
• How do the various studies relate to each other?
• What contributions do they make to the field?
• Where are the gaps or limitations in existing research?
• And finally, but most importantly, how does my own research fit into what has already been done?

The problem statement

Scholarly works often begin with a problem statement, which serves two functions. First, it establishes why your topic is a social problem worth studying. Second, it pulls your reader into the literature review. Who would want to read about something unimportant?

A problem statement generally answers the following questions, though these are far from exhaustive:

• Why is this an important problem to study?
• How many people are affected by this problem?
• How does this problem impact other social issues relevant to social work?
• Why is your target population an important one to study?

A strong problem statement, like the rest of your literature review, should be filled with empirical results, theory, and arguments based on the extant literature. A research proposal differs significantly from other more reflective essays you’ve likely completed during your social work studies. If your topic were domestic violence in rural Appalachia, I’m sure you could come up with answers to the above questions without looking at a single source. However, the purpose of the literature review is not to test your intuition, personal experience, or empathy. Instead, research methods are about gaining specific and articulable knowledge to inform action. With a problem statement, you can take a “boring” topic like the color of rooms used in an inpatient psychiatric facility, transportation patterns in major cities, or the materials used to manufacture baby bottles, and help others see the topic as you see it—an important part of the social world that impacts social work practice.

The structure of a literature review

In general, the problem statement belongs at the beginning of the literature review. We usually advise students to spend no more than a paragraph or two for a problem statement. For the rest of your literature review, there is no set formula by which it needs to be organized. However, a literature review generally follows the format of any other essay—Introduction, Body, and Conclusion.

The introduction to the literature review contains a statement or statements about the overall topic. At a minimum, the introduction should define or identify the general topic, issue, or area of concern. You might consider presenting historical background, mentioning the results of a seminal study, and providing definitions of important terms. The introduction may also point to overall trends in what has been previously published on the topic or on conflicts in theory, methodology, evidence, conclusions, or gaps in research and scholarship. We also suggest putting in a few sentences that walk the reader through the rest of the literature review. Highlight your main arguments from the body of the literature review and preview your conclusion. An introduction should let the reader know what to expect from the rest of your review.

The body of your literature review is where you demonstrate your synthesis and analysis of the literature. Again, do not just summarize the literature. We would also caution against organizing your literature review by source—that is, one paragraph for source A, one paragraph for source B, etc. That structure will likely provide an adequate summary of the literature you’ve found, but it would give you almost no synthesis of the literature. That approach doesn’t tell your reader how to put those facts together, it doesn't highlight points of agreement or contention, or how each study builds on the work of others. In short, it does not demonstrate critical thinking.

Organize your review by argument

Instead, use your outlines and notes as a guide what you have to say about the important topics you need to cover. Literature reviews are written from the perspective of an expert in that field. After an exhaustive literature review, you should feel as though you are able to make strong claims about what is true—so make them! There is no need to hide behind “I believe” or “I think.” Put your voice out in front, loud and proud! But make sure you have facts and sources that back up your claims.

I’ve used the term “ argument ” here in a specific way. An argument in writing means more than simply disagreeing with what someone else said, as this classic Monty Python sketch demonstrates. Toulman, Rieke, and Janik (1984) identify six elements of an argument:

• Claim: the thesis statement—what you are trying to prove
• Grounds: theoretical or empirical evidence that supports your claim
• Warrant: your reasoning (rule or principle) connecting the claim and its grounds
• Backing: further facts used to support or legitimize the warrant
• Qualifier: acknowledging that the argument may not be true for all cases
• Rebuttal: considering both sides (as cited in Burnette, 2012) [1]

Let’s walk through an example. If I were writing a literature review on a negative income tax, a policy in which people in poverty receive an unconditional cash stipend from the government each month equal to the federal poverty level, I would want to lay out the following:

• Claim: the negative income tax is superior to other forms of anti-poverty assistance.
• Grounds: data comparing negative income tax recipients to people receiving anti-poverty assistance in existing programs, theory supporting a negative income tax, data from evaluations of existing anti-poverty programs, etc.
• Warrant: cash-based programs like the negative income tax are superior to existing anti-poverty programs because they allow the recipient greater self-determination over how to spend their money.
• Backing: data demonstrating the beneficial effects of self-determination on people in poverty.
• Qualifier: the negative income tax does not provide taxpayers and voters with enough control to make sure people in poverty are not wasting financial assistance on frivolous items.
• Rebuttal: policy should be about empowering the oppressed, not protecting the taxpayer, and there are ways of addressing taxpayer spending concerns through policy design.

Like any effective argument, your literature review must have some kind of structure. For example, it might begin by describing a phenomenon in a general way along with several studies that provide some detail, then describing two or more competing theories of the phenomenon, and finally presenting a hypothesis to test one or more of the theories. Or, it might describe one phenomenon, then describe another that seems inconsistent with the first, then propose a theory that resolves the inconsistency, and finally present a hypothesis to test that theory. In applied research, it might describe a phenomenon or theory, then describe how that phenomenon or theory applies to some important real-world situation, and finally, may suggest a way to test whether it does, in fact, apply to that situation.

Use signposts

Another important issue is  signposting . It may not be a term you are familiar with, but you are likely familiar with the concept. Signposting refers to the words used to identify the organization and structure of your literature review to your reader. The most basic form of signposting is using a topic sentence at the beginning of each paragraph. A topic sentence introduces the argument you plan to make in that paragraph. For example, you might start a paragraph stating, “There is strong disagreement in the literature as to whether psychedelic drugs cause people to develop psychotic disorders, or whether psychotic disorders cause people to use psychedelic drugs.” Within that paragraph, your reader would likely assume you will present evidence for both arguments. The concluding sentence of your paragraph should address the topic sentence, discussing how the facts and arguments from the paragraph you've written support a specific conclusion. To continue with our example, I might say, “There is likely a reciprocal effect in which both the use of psychedelic drugs worsens pre-psychotic symptoms and worsening psychosis increases the desire to use psychedelic drugs.”

Signposting also involves using headings and subheadings. Your literature review will use APA formatting, which means you need to follow their rules for bolding, capitalization, italicization, and indentation of headings. Headings help your reader understand the structure of your literature review. They can also help if the reader gets lost and needs to re-orient themselves within the document. We often tell our students to assume we know nothing (they don’t mind) and need to be shown exactly where they are addressing each part of the literature review. It’s like walking a small child around, telling them “First we’ll do this, then we’ll do that, and when we’re done, we’ll know this!”

Another way to use signposting is to open each paragraph with a sentence that links the topic of the paragraph with the one before it. Alternatively, one could end each paragraph with a sentence that links it with the next paragraph. For example, imagine we wanted to link a paragraph about barriers to accessing healthcare with one about the relationship between the patient and physician. We could use a transition sentence like this: “Even if patients overcome these barriers to accessing care, the physician-patient relationship can create new barriers to positive health outcomes.” A transition sentence like this builds a connection between two distinct topics. Transition sentences are also useful within paragraphs. They tell the reader how to consider one piece of information in light of previous information. Even simple transitional words like 'however' and 'similarly' can help demonstrate critical thinking and link each building block of your argument together.

Many beginning researchers have difficulty incorporating transitions into their writing. Let’s look at an example. Instead of beginning a sentence or paragraph by launching into a description of a study, such as “Williams (2004) found that…,” it is better to start by indicating something about why you are describing this particular study. Here are some simple examples:

• Another example of this phenomenon comes from the work of Williams (2004)...
• Williams (2004) offers one explanation of this phenomenon...
• An alternative perspective has been provided by Williams (2004)...

Now that we know to use signposts, the natural question is “What goes on the signposts?” First, it is important to start with an outline of the main points that you want to make, organized in the order you want to make them. The basic structure of your argument should then be apparent from the outline itself. Unfortunately, there is no formula we can give you that will work for everyone, but we can provide some general pointers on structuring your literature review.

The literature review tends to move from general to more specific ideas. You can build a review by identifying areas of consensus and areas of disagreement. You may choose to present historical studies—preferably seminal studies that are of significant importance—and close with the most recent research. Another approach is to start with the most distantly related facts and literature and then report on those most closely related to your research question. You could also compare and contrast valid approaches, features, characteristics, theories – that is, one approach, then a second approach, followed by a third approach.

Here are some additional tips for writing the body of your literature review:

• Start broad and then narrow down to more specific information.
• When appropriate, cite two or more sources for a single point, but avoid long strings of references for a single idea.
• Use quotes sparingly. Quotations for definitions are okay, but reserve quotes for when something is said so well you couldn’t possible phrase it differently. Never use quotes for statistics.
• Paraphrase when you need to relay the specific details within an article
• Include only the aspects of the study that are relevant to your literature review. Don’t insert extra facts about a study just to take up space.
• Avoid reflective, personal writing. It is traditional to avoid using first-person language (I, we, us, etc.).
• Avoid informal language like contractions, idioms, and rhetorical questions.
• Note any sections of your review that lack citations from the literature. Your arguments need to be based in empirical or theoretical facts. Do not approach this like a reflective journal entry.
• Point out consistent findings and emphasize stronger studies over weaker ones.
• Point out important strengths and weaknesses of research studies, as well as contradictions and inconsistent findings.
• Implications and suggestions for further research (where there are gaps in the current literature) should be specific.

The conclusion should summarize your literature review, discuss implications, and create a space for further research needed in this area. Your conclusion, like the rest of your literature review, should make a point. What are the important implications of your literature review? How do they inform the question you are trying to answer?

You should consult with your professor and the course syllabus about the final structure your literature review should take. Here is an example of one possible structure:

• Establish the importance of the topic
• Number and type of people affected
• Seriousness of the impact
• Physical, psychological, economic, social, or spiritual consequences of the problem
• Definitions of key terms
• Supporting evidence
• Common findings across studies, gaps in the literature
• Research question(s) and hypothesis(es)

Editing your literature review

Literature reviews are more than a summary of the publications you find on a topic. As you have seen in this brief introduction, literature reviews represent a very specific type of research, analysis, and writing. We will explore these topics further in upcoming chapters. As you begin your literature review, here are some common errors to avoid:

• Accepting a researcher’s finding as valid without evaluating methodology and data
• Ignoring contrary findings and alternative interpretations
• Using findings that are not clearly related to your own study or using findings that are too general
• Dedicating insufficient time to literature searching
• Reporting statistical results from a single study, rather than synthesizing the results of multiple studies to provide a comprehensive view of the literature on a topic
• Relying too heavily on secondary sources
• Overusing quotations
• Not justifying arguments using specific facts or theories from the literature

For your literature review, remember that your goal is to construct an argument for the importance of your research question. As you start editing your literature review, make sure it is balanced. Accurately report common findings, areas where studies contradict each other, new theories or perspectives, and how studies cause us to reaffirm or challenge our understanding of your topic.

It is acceptable to argue that the balance of the research supports the existence of a phenomenon or is consistent with a theory (and that is usually the best that researchers in social work can hope for), but it is not acceptable to ignore contradictory evidence. A large part of what makes a research question interesting is uncertainty about its answer (University of Minnesota, 2016). [2]

In addition to subjectivity and bias, writer's block can obstruct the completion of your literature review. Often times, writer’s block can stem from confusing the creating and editing parts of the writing process. Many writers often start by simply trying to type out what they want to say, regardless of how good it is. Author Anne Lamott (1995) [3] terms these “shitty first drafts,” and we all write them. They are a natural and important part of the writing process.

Even if you have a detailed outline from which to work, the words are not going to fall into place perfectly the first time you start writing. You should consider turning off the editing and critiquing part of your brain for a while and allow your thoughts to flow. Don’t worry about putting a correctly formatted internal citation (as long as  you know which source you used there) when you first write. Just get the information out. Only after you’ve reached a natural stopping point might you go back and edit your draft for grammar, APA style, organization, flow, and more. Divorcing the writing and editing process can go a long way to addressing writer’s block—as can picking a topic about which you have something to say!

As you are editing, keep in mind these questions adapted from Green (2012): [4]

• Content: Have I clearly stated the main idea or purpose of the paper? Is the thesis or focus clearly presented and appropriate for the reader?
• Organization: How well is it structured? Is the organization spelled out and easy to follow for the reader ?
• Flow: Is there a logical flow from section to section, paragraph to paragraph, sentence to sentence? Are there transitions between and within paragraphs that link ideas together?
• Development: Have I validated the main idea with supporting material? Are supporting data sufficient? Does the conclusion match the introduction?
• Form: Are there any APA style issues, redundancy, problematic wording and terminology (always know the definition of any word you use!), flawed sentence constructions and selection, spelling, and punctuation?

Social workers use the APA style guide to format and structure their literature reviews. Most students know APA style only as it relates to internal and external citations. If you are confused about them, consult this amazing APA style guide from the University of Texas-Arlington library. Your university's library likely has resources they created to help you with APA style, and you can meet with a librarian or your professor to talk about formatting questions you have. Make sure you budget in a few hours at the end of each project to build a correctly formatted references page and check your internal citations. The highest quality online source of information on APA style is the APA style blog, where you can search questions and answers from the

Of course, APA style is about much more than knowing there is a period after "et al." or citing the location a book was published. APA style is also about what the profession considers to be good writing. If you haven't picked up an APA publication manual because you use citation generators, know that I did the same thing when I was in school. Purchasing the APA manual can help you with a common problem we hear about from students. Every professor (and every website about APA style) seems to have their own peculiar idea of "correct" APA style that you can, if needed, demonstrate is not accurate.

• A literature review is not a book report. Do not organize it by article, with one paragraph for each source in your references. Instead, organize it based on the key ideas and arguments.
• The problem statement draws the reader into your topic by highlighting the importance of the topic to social work and to society overall.
• Signposting is an important component of academic writing that helps your reader follow the structure of your argument and of your literature review.
• Transitions demonstrate critical thinking and help guide your reader through your arguments.
• Editing and writing are separate processes.
• Consult with an APA style guide or a librarian to help you format your paper.

Look at your professor's prompt for the literature review component of your research proposal (or if you don't have one, use the example question provided in this section).

• Write 2-3 facts you would use to address each question or component in the prompt.
• Reflect on which questions you have a lot of information about and which you need to gather more information about in order to answer adequately.

Outline the structure of your literature review using your concept map from Section 5.2 as a guide.

• Identify the key arguments you will make and how they are related to each other.
• Reflect on topic sentences and concluding sentences you would use for each argument.
• Human subjects research (19 minute read)
• Specific ethical issues to consider (12 minute read)
• Benefits and harms of research across the ecosystem (7 minute read)
• Being an ethical researcher (8 minute read)

Content warning: examples in this chapter contain references to numerous incidents of unethical medical experimentation (e.g. intentionally injecting diseases into unknowing participants, withholding proven treatments), social experimentation under extreme conditions (e.g. being directed to deliver electric shocks to test obedience), violations of privacy, gender and racial inequality, research with people who are incarcerated or on parole, experimentation on animals, abuse of people with Autism, community interactions with law enforcement, WWII, the Holocaust, and Nazi activities (especially related to research on humans).

With your literature review underway, you are ready to begin thinking in more concrete terms about your research topic. Recall our discussion in Chapter 2 on practical and ethical considerations that emerge as part of the research process. In this chapter, we will expand on the ethical boundaries that social scientists must abide by when conducting human subjects research. As a result of reading this chapter, you should have a better sense of what is possible and ethical for the research project you create.

6.1 Human subjects research

• Understand what we mean by ethical research and why it is important
• Understand some of the egregious ethical violations that have occurred throughout history

While all research comes with its own set of ethical concerns, those associated with research conducted on human subjects vary dramatically from those of research conducted on nonliving entities. The US Department of Health and Human Services (USDHHS) defines a human subject as “a living individual about whom an investigator (whether professional or student) conducting research obtains (1) data through intervention or interaction with the individual, or (2) identifiable private information” (USDHHS, 1993, para. 1). [5] Some researchers prefer the term "participants" to "subjects'" as it acknowledges the agency of people who participate in the study. For our purposes, we will use the two terms interchangeably.

In some states, human subjects also include deceased individuals and human fetal materials. Nonhuman research subjects, on the other hand, are objects or entities that investigators manipulate or analyze in the process of conducting research. Nonhuman research subjects typically include sources such as newspapers, historical documents, pieces of clothing, television shows, buildings, and even garbage (to name just a few), that are analyzed for unobtrusive research projects. Unsurprisingly, research on human subjects is regulated much more heavily than research on nonhuman subjects. This is why many student research projects use data that is publicly available, rather than recruiting their own study participants. However, there are ethical considerations that all researchers must take into account, regardless of their research subject. We’ll discuss those considerations in addition to concerns that are unique to human subject research.

Why do research participants need protection?

First and foremost, we are professionally bound to engage in the ethical practice of research. This chapter discusses ethical research and will show you how to engage in research that is consistent with the NASW Code of Ethics as well as national and international ethical standards all researchers are accountable to. Before we begin, we need to understand the historical occurrences that were the catalyst for the formation of the current ethical standards . This chapter will enable you to view ethics from a micro, mezzo, and macro perspective.

The research process has led to many life-changing discoveries; these have improved life expectancy, improved living conditions, and helped us understand what contributes to certain social problems. That said, not all research has been conducted in respectful, responsible, or humane ways. Unfortunately, some research projects have dramatically marginalized, oppressed, and harmed participants and whole communities.

Would you believe that the following actions have been carried out in the name of research? I realize there was a content warning at the beginning of the chapter, but it is worth mentioning that the list below of research atrocities may be particularly upsetting or triggering.

• intentionally froze healthy body parts of prisoners to see if they could develop a treatment for freezing [6]
• scaled the body parts of prisoners to how best to treat soldiers who had injuries from being exposed to high temperatures [7]
• intentionally infected healthy individuals to see if they could design effective methods of treatment for infections [8]
• gave healthy people TB to see if they could treat it [9]
• attempted to transplant limbs, bones, and muscles to another person to see if this was possible [10]
• castrated and irradiated genitals to see if they could develop a faster method of sterilization [11]
• starved people and only allowed them to drink seawater to see if they could make saline water drinkable [12]
• artificially inseminated women with animal sperm to see what would happen [13]
• gassed living people to document how they would die [14]
• conducted cruel experiments on people and if they did not die, would kill them so they could undergo an autopsy [15]
• refused to treat syphilis in African American men (when treatment was available) because they wanted to track the progression of the illness [16]
• vivisected humans without anesthesia to see how illnesses that researches gave prisoners impacted their bodies [17]
• intentionally tried to infect prisoners with the Bubonic Plague [18]
• intentionally infected prisoners, prostitutes, soldiers, and children with syphilis to study the disease's progression [19]
• performed gynecological experiments on female slaves without anesthesia to investigate new surgical methods [20]

The sad fact is that not only did all of these occur, in many instances, these travesties continued for years until exposed and halted. Additionally, these examples have contributed to the formation of a legacy of distrust toward research. Specifically, many underrepresented groups have a deep distrust of agencies that implement research and are often skeptical of research findings. This has made it difficult for groups to support and have confidence in medical treatments, advances in social service programs, and evidence-informed policy changes. While the aforementioned unethical examples may have ended, this deep and painful wound on the public's trust remains. Consequently, we must be vigilant in our commitment to ethical research.

Many of the situations described may seem like extreme historical cases of misuse of power as researchers. However, ethical problems in research don't just happen in these extreme occurrences. None of us are immune to making unethical choices and the ethical practice of research requires conscientious mindful attention to what we are asking of our research participants. A few examples of less noticeable ethical issues might include: failing to fully explain to someone in advance what their participation might involve because you are in a rush to recruit a large enough sample; or only presenting findings that support your ideas to help secure a grant that is relevant to your research area. Remember, any time research is conducted with human beings, there is the chance that ethical violations may occur that pose social, emotional, and even physical risks for groups, and this is especially true when vulnerable or oppressed groups are involved.

A brief history of unethical social science research

Research on humans hasn’t always been regulated in the way it is today. The earliest documented cases of research using human subjects are of medical vaccination trials (Rothman, 1987). [21] One such case took place in the late 1700s, when scientist Edward Jenner exposed an 8-year-old boy to smallpox in order to identify a vaccine for the devastating disease. Medical research on human subjects continued without much law or policy intervention until the mid-1900s when, at the end of World War II, a number of Nazi doctors and scientists were put on trial for conducting human experimentation during the course of which they tortured and murdered many concentration camp inmates (Faden & Beauchamp, 1986). [22] The trials, conducted in Nuremberg, Germany, resulted in the creation of the Nuremberg Code , a 10-point set of research principles designed to guide doctors and scientists who conduct research on human subjects. Today, the Nuremberg Code guides medical and other research conducted on human subjects, including social scientific research.

Medical scientists are not the only researchers who have conducted questionable research on humans. In the 1960s, psychologist Stanley Milgram (1974) [23] conducted a series of experiments designed to understand obedience to authority in which he tricked subjects into believing they were administering an electric shock to other subjects. In fact, the shocks weren’t real at all, but some, though not many, of Milgram’s research participants experienced extreme emotional distress after the experiment (Ogden, 2008). [24] A reaction of emotional distress is understandable. The realization that one is willing to administer painful shocks to another human being just because someone who looks authoritative has told you to do so might indeed be traumatizing—even if you later learn that the shocks weren’t real.

Around the same time that Milgram conducted his experiments, sociology graduate student Laud Humphreys (1970) [25] was collecting data for his dissertation on the tearoom trade, which was the practice of men engaging in anonymous sexual encounters in public restrooms. Humphreys wished to understand who these men were and why they participated in the trade. To conduct his research, Humphreys offered to serve as a “watch queen,” in a local park restroom where the tearoom trade was known to occur. His role would be to keep an eye out for police while also getting the benefit of being able to watch the sexual encounters. What Humphreys did not do was identify himself as a researcher to his research subjects. Instead, he watched his subjects for several months, getting to know several of them, learning more about the tearoom trade practice and, without the knowledge of his research subjects, jotting down their license plate numbers as they pulled into or out of the parking lot near the restroom.

Sometime after participating as a watch queen, with the help of several insiders who had access to motor vehicle registration information, Humphreys used those license plate numbers to obtain the names and home addresses of his research subjects. Then, disguised as a public health researcher, Humphreys visited his subjects in their homes and interviewed them about their lives and their health. Humphreys’ research dispelled a good number of myths and stereotypes about the tearoom trade and its participants. He learned, for example, that over half of his subjects were married to women and many of them did not identify as gay or bisexual. [26]

Once Humphreys’ work became public, there was some major controversy at his home university (e.g., the chancellor tried to have his degree revoked), among scientists in general, and among members of the public, as it raised public concerns about the purpose and conduct of social science research. In addition, the Washington   Post  journalist Nicholas von Hoffman wrote the following warning about “sociological snoopers”:

We’re so preoccupied with defending our privacy against insurance investigators, dope sleuths, counterespionage men, divorce detectives and credit checkers, that we overlook the social scientists behind the hunting blinds who’re also peeping into what we thought were our most private and secret lives. But they are there, studying us, taking notes, getting to know us, as indifferent as everybody else to the feeling that to be a complete human involves having an aspect of ourselves that’s unknown (von Hoffman, 1970). [27]

In the original version of his report, Humphreys defended the ethics of his actions. In 2008 [28] , years after Humphreys’ death, his book was reprinted with the addition of a retrospect on the ethical implications of his work. In his written reflections on his research and the fallout from it, Humphreys maintained that his tearoom observations constituted ethical research on the grounds that those interactions occurred in public places. But Humphreys added that he would conduct the second part of his research differently. Rather than trace license numbers and interview unwitting tearoom participants in their homes under the guise of public health research, Humphreys instead would spend more time in the field and work to cultivate a pool of informants. Those informants would know that he was a researcher and would be able to fully consent to being interviewed. In the end, Humphreys concluded “there is no reason to believe that any research subjects have suffered because of my efforts, or that the resultant demystification of impersonal sex has harmed society” (Humphreys, 2008, p. 231). [29]

Today, given increasing regulation of social scientific research, chances are slim that a researcher would be allowed to conduct a project similar to Humphreys’. Some argue that Humphreys’ research was deceptive, put his subjects at risk of losing their families and their positions in society, and was therefore unethical (Warwick, 1973; Warwick, 1982). [30] Others suggest that Humphreys’ research “did not violate any premise of either beneficence or the sociological interest in social justice” and that the benefits of Humphreys’ research, namely the dissolution of myths about the tearoom trade specifically and human sexual practice more generally, outweigh the potential risks associated with the work (Lenza, 2004, p. 23). [31] What do you think, and why?

These and other studies (Reverby, 2009) [32] led to increasing public awareness of and concern about research on human subjects. In 1974, the US Congress enacted the National Research Act , which created the National Commission for the Protection of Human Subjects in Biomedical and Behavioral Research. The commission produced  The Belmont Report , a document outlining basic ethical principles for research on human subjects (National Commission for the Protection of Human Subjects in Biomedical and Behavioral Research, 1979). [33] The National Research Act (1974) [34] also required that all institutions receiving federal support establish institutional review boards (IRBs) to protect the rights of human research subjects. Since that time, many organizations that do not receive federal support but where research is conducted have also established review boards to evaluate the ethics of the research that they conduct. IRBs are overseen by the federal Office of Human Research Protections .

The Belmont Report

As mentioned above, The Belmont Report is a federal document that outlines the foundational principles that guide the ethical practice of research in the United States. These ethical principles include: respect for persons, beneficence, and justice. Each of these terms has specific implications as they are applied to the practice of research. These three principles arose as a response to many of the mistreatment and abuses that have been previously discussed and provide important guidance as researchers consider how they will construct and conduct their research studies. As you are crafting your research proposal, makes sure you are mindful of these important ethical guidelines.

Respect for Persons

As social workers, our professional code of ethics requires that we recognize and respect the "inherent dignity and worth of the person." [35] This is very similar to the ethical research principle of r espect for persons . According to this principle, as researchers, we need to treat all research participants with respect, dignity and inherent autonomy. This is reflected by ensuring that participants have self-determination to make informed decisions about their participation in research, that they have a clear understanding of what they will be asked to do and any risks involved, and that their participation is voluntary and can be stopped at any time. Furthermore, for those persons who may have diminished autonomy (e.g. children, people who are incarcerated), extra protections must be built in to these research studies to ensure that respect for persons continues to be demonstrated towards these groups, as they may be especially vulnerable to exploitation and coercion through the research process. A critical tool in establishing respect for persons in your research is the informed consent process, which will be discussed in more detail below.

Beneficence

You may not be familiar with this word yet, but the concept is pretty straightforward. The main idea with beneficence is that the intent of research is to do good. As researchers, to accomplish this, we seek to maximize benefits and minimize risks. Benefits may be something good or advantageous directly received by the research participant, or they may represent a broader good to a wider group of people or the scientific community at large (such as increasing knowledge about the topic or social problem that you are studying). Risks are potential physical, social, or emotional harm that may come about as a response to participation in a study. These risks may be more immediate (e.g. risk of identifying information about a participant being shared, or a participant being upset or triggered by a particular question), or long-term (e.g. some aspect about the person could be shared that could lead to long-term stigmatization). As researchers, we need to think about risk that might be experienced by the individual, but also risks that might be directed towards the community or population(s) the individual may represent. For instance, if our study is specifically focused on surveying single parents, we need to consider how the sharing of our findings might impact this group and how they are perceived. It is a very rare study in which there is no risk to participants. However, a well-designed and ethically sound study will seek to minimize these risks, provide resources to anticipate and address them, and maximize the benefits that are gained through the study.

The final ethical principle we need to cover is justice. While you likely have some idea what justice is, for the purposes of research, justice is the idea that the benefits and the burdens of research are distributed fairly across populations and groups. To help illustrate the concept of justice in research, research in the area of mental health and psychology has historically been critiqued as failing to adequately represent women and people of diverse racial and ethnic groups in their samples (Cundiff, 2012). [36] This has created a body of knowledge that is overly representative of the white male experience, further reinforcing systems of power and privilege. In addition, consider the influence of language as it relates to research justice. If we create studies that only recruit participants fluent in English, which many studies do, we are often failing to satisfy the ethical principle of justice as it applies to people who don't speak English. It is unrealistic to think that we can represent all people in all studies. However, we do need to thoughtfully acknowledge voices that might not be reflected in our samples and attempt to recruit diverse and representative samples whenever possible.

These three principles provide the foundation for the oversight work that is carried out by Institutional Review Boards, our next topic.

Institutional review boards

Institutional review boards, or IRBs, are tasked with ensuring that the rights and welfare of human research subjects will be protected at all institutions, including universities, hospitals, nonprofit research institutions, and other organizations, that receive federal support for research. IRBs typically consist of members from a variety of disciplines, such as sociology, economics, education, social work, and communications (to name a few). Most IRBs also include representatives from the community in which they reside. For example, representatives from nearby prisons, hospitals, or treatment centers might sit on the IRBs of university campuses near them. The diversity of membership helps to ensure that the many and complex ethical issues that may arise from human subjects research will be considered fully and by a knowledgeable and experienced panel. Investigators conducting research on human subjects are required to submit proposals outlining their research plans to IRBs for review and approval prior to beginning their research. Even students who conduct research on human subjects must have their proposed work reviewed and approved by the IRB before beginning any research (though, on some campuses, exceptions are made for student projects that will not be shared outside of the classroom).

The IRB has three levels of review, defined in statute by the USDHHS.

Exempt review is the lowest level of review. Studies that are considered exempt expose participants to the least potential for harm and often involve little participation by human subjects. In social work, exempt studies often examine data that is publicly available or secondary data from another researcher that has been de-identified by the person who collected it.

Expedited review is the middle level of review. Studies considered under expedited review do not have to go before the full IRB board because they expose participants to minimal risk. However, the studies must be thoroughly reviewed by a member of the IRB committee. While there are many types of studies that qualify for expedited review, the most relevant to social workers include the use of existing medical records, recordings (such as interviews) gathered for research purposes, and research on individual group characteristics or behavior.

Finally, the highest level of review is called a  full board review . A full board review will involve multiple members of the IRB evaluating your proposal. When researchers submit a proposal under full board review, the full IRB board will meet, discuss any questions or concerns with the study, invite the researcher to answer questions and defend their proposal, and vote to approve the study or send it back for revision. Full board proposals pose greater than minimal risk to participants. They may also involve the participation of  vulnerable populations , or people who need additional protection from the IRB. Vulnerable populations include prisoners, children, people with cognitive impairments, people with physical disabilities, employees, and students. While some of these populations can fall under expedited review in some cases, they will often require the full IRB to approve their study.

It may surprise you to hear that IRBs are not always popular or appreciated by researchers. Who wouldn’t want to conduct ethical research, you ask? In some cases, the concern is that IRBs are most well-versed in reviewing biomedical and experimental research, neither of which is particularly common within social work. Much social work research, especially qualitative research, is open-ended in nature, a fact that can be problematic for IRBs. The members of IRBs often want to know in advance exactly who will be observed, where, when, and for how long, whether and how they will be approached, exactly what questions they will be asked, and what predictions the researcher has for their findings. Providing this level of detail for a year-long participant observation within an activist group of 200-plus members, for example, would be extraordinarily frustrating for the researcher in the best case and most likely would prove to be impossible. Of course, IRBs do not intend to have researchers avoid studying controversial topics or avoid using certain methodologically sound data collection techniques, but unfortunately, that is sometimes the result. The solution is not to eradicate review boards, which serve a necessary and important function, but instead to help educate IRB members about the variety of social scientific research methods and topics covered by social workers and other social scientists.

What we have provided here is only a short summary of federal regulations and international agreements that provide the boundaries between ethical and unethical research.

Here are a few more detailed guides for continued learning about research ethics and human research protections.

• University of California, San Francisco: Levels of IRB Review
• United States Department of Health and Human Services: The Belmont Report
• NIH, National Institute of Environmental Health Sciences: What is Ethics in Research & Why is it important 
• NIH: Guiding Principles for Ethical Research
• Council on Social Work Education: National Statement on Research Integrity in Social Work
• Butler, I. (2002). A code of ethics for social work and social care research.  British Journal of Social Work ,  32 (2), 239-248
• Research on human subjects presents a unique set of challenges and opportunities when it comes to conducting ethical research.
• Research on human subjects has not always been regulated to the extent that it is today.
• All institutions receiving federal support for research must have an IRB. Organizations that do not receive federal support but where research is conducted also often include IRBs as part of their organizational structure.
• Researchers submit studies for IRB review at one of three different levels, depending on the level of harm the study may cause.
• Recall whether your project will gather data from human subjects and sketch out what the data collection process might look like.
• Identify which level of IRB review is most appropriate for your project.
• For many students, your professors may have existing agreements with your university's IRB that allow students to conduct research projects outside the supervision of the IRB. Make sure that your project falls squarely within those parameters. If you feel you may be outside of such an agreement, consult with your professor to see if you will need to submit your study for IRB review before starting your project.

6.2 Specific ethical issues to consider

• Define informed consent, and describe how it works
• Identify the unique concerns related to the study of vulnerable populations
• Differentiate between anonymity and confidentiality
• Explain the ethical responsibilities of social workers conducting research

As should be clear by now, conducting research on humans presents a number of unique ethical considerations. Human research subjects must be given the opportunity to consent to their participation in research, and be fully informed of the study’s risks, benefits, and purpose. Further, subjects’ identities and the information they share should be protected by researchers. Of course, how consent and identity protection are defined may vary by individual researcher, institution, or academic discipline. In this section, we’ll take a look at a few specific topics that individual researchers must consider before embarking on research with human subjects.

Informed consent

An expectation of voluntary participation is presumed in all social work research projects. In other words, we cannot force anyone to participate in our research without that person’s knowledge or consent. Researchers must therefore design procedures to obtain subjects’ informed consent to participate in their research. This specifically relates back to the ethical principle of respect for persons outlined in The Belmont Report . Informed consent  is defined as a subject’s voluntary agreement to participate in research based on a full understanding of the research and of the possible risks and benefits involved. Although it sounds simple, ensuring that one has actually obtained informed consent is a much more complex process than you might initially presume.

The first requirement is that, in giving their informed consent, subjects may neither waive nor even  appear  to waive any of their legal rights. Subjects also cannot release a researcher, her sponsor, or institution from any legal liability should something go wrong during the course of their participation in the research (USDHHS,2009). [37] Because social work research does not typically involve asking subjects to place themselves at risk of physical harm by, for example, taking untested drugs or consenting to new medical procedures, social work researchers do not often worry about potential liability associated with their research projects. However, their research may involve other types of risks.

For example, what if a social work researcher fails to sufficiently conceal the identity of a subject who admits to participating in a local swinger’s club? In this case, a violation of confidentiality may negatively affect the participant’s social standing, marriage, custody rights, or employment. Social work research may also involve asking about intimately personal topics that may be difficult for participants to discuss, such as trauma or suicide. Participants may re-experience traumatic events and symptoms when they participate in your study. Even if you are careful to fully inform your participants of all risks before they consent to the research process, I’m sure you can empathize with thinking you could bear talking about a difficult topic and then finding it too overwhelming once you start. In cases like these, it is important for a social work researcher to have a plan to provide supports. This may mean providing referrals to counseling supports in the community or even calling the police if the participant is an imminent danger to himself or others.

It is vital that social work researchers explain their mandatory reporting duties in the consent form and ensure participants understand them before they participate. Researchers should also emphasize to participants that they can stop the research process at any time or decide to withdraw from the research study for any reason. Importantly, it is not the job of the social work researcher to act as a clinician to the participant. While a supportive role is certainly appropriate for someone experiencing a mental health crisis, social workers must ethically avoid dual roles. Referring a participant in crisis to other mental health professionals who may be better able to help them is the expectation.

Beyond the legal issues, most IRBs require researchers to share some details about the purpose of the research, possible benefits of participation, and, most importantly, possible risks associated with participating in that research with their subjects. In addition, researchers must describe how they will protect subjects’ identities, how, where, and for how long any data collected will be stored, how findings may be shared, and whom to contact for additional information about the study or about subjects’ rights. All this information is typically shared in an informed consent form that researchers provide to subjects. In some cases, subjects are asked to sign the consent form indicating that they have read it and fully understand its contents. In other cases, subjects are simply provided a copy of the consent form and researchers are responsible for making sure that subjects have read and understand the form before proceeding with any kind of data collection. Your IRB will often provide guidance or even templates for what they expect to see included in an informed consent form. This is a document that they will inspect very closely. Table 6.1 outlines elements to include in your informed consent. While these offer a guideline for you, you should always visit your schools, IRB website to see what guidance they offer. They often provide a template that they prefer researchers to use. Using these templates ensures that you are using the language that the IRB reviewers expect to see and this can also save you time.

One last point to consider when preparing to obtain informed consent is that not all potential research subjects are considered equally competent or legally allowed to consent to participate in research. Subjects from vulnerable populations may be at risk of experiencing undue influence or coercion (USDHHS, 2009). [38] The rules for consent are more stringent for vulnerable populations. For example, minors must have the consent of a legal guardian in order to participate in research. In some cases, the minors themselves are also asked to participate in the consent process by signing special, age-appropriate assent forms designed specifically for them. Prisoners and parolees also qualify as vulnerable populations. Concern about the vulnerability of these subjects comes from the very real possibility that prisoners and parolees could perceive that they will receive some highly desired reward, such as early release, if they participate in research or that there could be punitive consequences if they choose not to participate. When a participant faces undue or excess pressure to participate by either favorable or unfavorable means, this is known as coercion and must be avoided by researchers.

Another potential concern regarding vulnerable populations is that they may be underrepresented or left out of research opportunities, specifically because of concerns about their ability to consent. So, on the one hand, researchers must take extra care to ensure that their procedures for obtaining consent from vulnerable populations are not coercive. The procedures for receiving approval to conduct research with these groups may be more rigorous than that for non-vulnerable populations. On the other hand, researchers must work to avoid excluding members of vulnerable populations from participation simply on the grounds that they are vulnerable or that obtaining their consent may be more complex. While there is no easy solution to this ethical research dilemma, an awareness of the potential concerns associated with research on vulnerable populations is important for identifying whatever solution is most appropriate for a specific case.

Protection of identities

As mentioned earlier, the informed consent process includes the requirement that researchers outline how they will protect the identities of subjects. This aspect of the research process, however, is one of the most commonly misunderstood. Furthermore, failing to protect identities is one of the greatest risks to participants in social work research studies.

In protecting subjects’ identities, researchers typically promise to maintain either the anonymity or confidentiality of their research subjects. These are two distinctly different terms and they are NOT interchangeable. Anonymity is the more stringent of the two and is very hard to guarantee in most research studies. When a researcher promises anonymity to participants, not even the researcher is able to link participants’ data with their identities. Anonymity may be impossible for some social work researchers to promise due to the modes of data collection many social workers employ. Face-to-face interviewing means that subjects will be visible to researchers and will hold a conversation, making anonymity impossible. In other cases, the researcher may have a signed consent form or obtain personal information on a survey and will therefore know the identities of their research participants. In these cases, a researcher should be able to at least promise confidentiality to participants.

Offering  confidentiality means that some identifying information is known at some time by the research team, but only the research team has access to this identifying information and this information will not be linked with their data in any publicly accessible way. Confidentiality in research is quite similar to confidentiality in clinical practice. You know who your clients are, but others do not. You agree to keep their information and identity private. As you can see under the “Risks” section of the consent form in Figure 5.1, sometimes it is not even possible to promise that a subject’s confidentiality will be maintained. This is the case if data are collected in public or in the presence of other research participants in the course of a focus group, for example. Participants who social work researchers deem to be of imminent danger to self or others or those that disclose abuse of children and other vulnerable populations fall under a social worker’s duty to report. Researchers must then violate confidentiality to fulfill their legal obligations.

There are a number of steps that researchers can take to protect the identities of research participants. These include, but are not limited to:

• Collecting data in private spaces
• Not requesting information that will uniquely identify or "out" that person as a participant
• Assigning study identification codes or pseudonyms
• Keeping signed informed consent forms separate from other data provided by the participant
• Making sure that physical data is kept in a locked and secured location, and the virtual data is encrypted or password-protected
• Reporting data in aggregate (only discussing the data collectively, not by individual responses)

Protecting research participants’ identities is not always a simple prospect, especially for those conducting research on stigmatized groups or illegal behaviors. Sociologist Scott DeMuth learned that all too well when conducting his dissertation research on a group of animal rights activists. As a participant observer, DeMuth knew the identities of his research subjects. So when some of his research subjects vandalized facilities and removed animals from several research labs at the University of Iowa, a grand jury called on Mr. DeMuth to reveal the identities of the participants in the raid. When DeMuth refused to do so, he was jailed briefly and then charged with conspiracy to commit animal enterprise terrorism and cause damage to the animal enterprise (Jaschik, 2009). [39]

Publicly, DeMuth’s case raised many of the same questions as Laud Humphreys’ work 40 years earlier. What do social scientists owe the public? Is DeMuth, by protecting his research subjects, harming those whose labs were vandalized? Is he harming the taxpayers who funded those labs? Or is it more important that DeMuth emphasize what he owes his research subjects, who were told their identities would be protected? DeMuth’s case also sparked controversy among academics, some of whom thought that as an academic himself, DeMuth should have been more sympathetic to the plight of the faculty and students who lost years of research as a result of the attack on their labs. Many others stood by DeMuth, arguing that the personal and academic freedom of scholars must be protected whether we support their research topics and subjects or not. DeMuth’s academic adviser even created a new group, Scholars for Academic Justice , to support DeMuth and other academics who face persecution or prosecution as a result of the research they conduct. What do you think? Should DeMuth have revealed the identities of his research subjects? Why or why not?

Discipline-specific considerations

Often times, specific disciplines will provide their own set of guidelines for protecting research subjects and, more generally, for conducting ethical research. For social workers, the National Association of Social Workers (NASW) Code of Ethics section 5.02 describes the responsibilities of social workers in conducting research. Summarized below, these responsibilities are framed as part of a social worker’s responsibility to the profession. As representative of the social work profession, it is your responsibility to conduct and use research in an ethical manner.

A social worker should:

• Monitor and evaluate policies, programs, and practice interventions
• Contribute to the development of knowledge through research
• Keep current with the best available research evidence to inform practice
• Ensure voluntary and fully informed consent of all participants
• Not engage in any deception in the research process
• Allow participants to withdraw from the study at any time
• Provide access to appropriate supportive services for participants
• Protect research participants from harm
• Maintain confidentiality
• Report findings accurately
• Disclose any conflicts of interest
• Researchers must obtain the informed consent of research participants.
• Social workers must take steps to minimize the harms that could arise during the research process.
• If anonymity is promised, individual participants cannot be linked with their data.
• If confidentiality is promised, the identities of research participants cannot be revealed, even if individual participants can be linked with their data.
• The NASW Code of Ethics includes specific responsibilities for social work researchers.
• Talk with your professor to see if an informed consent form is required for your research project. If documentation is required, customize the template provided by your professor or the IRB at your school, using the details of your study. If documentation on consent is not required, for example if consent is given verbally, use the templates as guides to create a guide for what you will say to participants regarding informed consent.
• Identify whether your data will be confidential or anonymous. Describe the measures you will take to protect the identities of individuals in your study. How will you store the data? How will you ensure that no one can identify participants based on what you report in papers and presentations? Be sure to think carefully. People can be identified by characteristics such as age, gender, disability status, location, etc.

6.3 Benefits and harms of research across the ecosystem

• Identify and distinguish between micro-, mezzo-, and macro-level considerations with respect to the ethical conduct of social scientific research

This chapter began with a long list of harmful acts that researchers engaged in while conducting studies on human subjects. Indeed, even the last section on informed consent and protection of confidential information can be seen in light of minimizing harm and maximizing benefits. The benefits of your study should be greater than the harms. But who benefits from your research study, and who might be harmed? The first person who benefits is, most clearly, you as the researcher. You need a project to complete, be it for a grade, a grant, an academic responsibility, etc. However you need to make sure that your benefit does not come at the expense of harming others. Furthermore, research requires resources, including resources from the communities we work with. Part of being good stewards of these resources as social work researchers means that we need to engage in research that benefits the people we serve in meaningful and relevant ways. We need to consider how others are impacted by our research.

Micro-, mezzo-, and macro-level concerns

One useful way to think about the breadth of ethical questions that might arise out of any research project is to think about potential issues from the perspective of different analytical levels that are familiar to us as social workers. In Chapter 1 , you learned about the micro-, mezzo-, and macro-levels of inquiry and how a researcher’s specific point of focus might vary depending on her level of inquiry. Here we’ll apply this ecological framework to a discussion of research ethics. Within most research projects, there are specific questions that arise for researchers at each of these three levels.

At the micro-level, researchers must consider their own conduct and the impact on individual research participants. For example, did Stanley Milgram behave ethically when he allowed research participants to think that they were administering electric shocks to fellow participants? Did Laud Humphreys behave ethically when he deceived his research subjects about his own identity? Were the rights of individuals in these studies protected? How did these participants benefit themselves from the research that was conducted? While not social workers by trade, would the actions of these two researchers hold up against our professional NASW Code of Ethics? The questions posed here are the sort that you will want to ask yourself as a researcher when considering ethics at the micro-level.

At the mezzo-level, researchers should think about their duty to the community. How will the results of your study impact your target population? Ideally, your results will benefit your target population by identifying important areas for social workers to intervene and to better understand the experiences of the communities they serve. However, it is possible that your study may perpetuate negative stereotypes about your target population or damage its reputation. Indigenous people in particular have highlighted how historically social science has furthered marginalization of indigenous peoples (Smith, 2013). [40] Mezzo-level concerns should also address other groups or organizations that are connected to your target population. This may include the human service agencies with whom you've partnered for your study as well as the communities and peoples they serve. If your study reflected negatively on a particular housing project in your area, for example, will community members seek to remove it from their community? Or might it draw increased law enforcement presence that is unwanted by participants or community members? Research is a powerful tool and can be used for many purposes, not all of them altruistic. In addition, research findings can have many implications, intended and unintended. As responsible researchers, we need to do our best to thoughtfully anticipate these consequences.

Finally, at the macro-level, a researcher should consider duty to, and the expectations of, society. Perhaps the most high-profile case involving macro-level questions of research ethics comes from debates over whether to use data gathered by, or cite published studies based on data gathered from, the Nazis in the course of their unethical and horrendous experiments on humans during World War II (Moe, 1984). [41] Some argue that because the data were gathered in such an unquestionably unethical manner, they should never be used. The data, say these people, are neither valid nor reliable and should therefore not be used in any current scientific investigation (Berger, 1990). [42]

On the other hand, some people argue that data themselves are neutral; that “information gathered is independent of the ethics of the methods and that the two are not linked together” (Pozos, 1992, p. 104). [43] Others point out that not using the data could inadvertently strengthen the claims of those who deny that the Holocaust ever happened. In his striking statement in support of publishing the data, medical ethics professor Velvl Greene (1992) says,

Instead of banning the Nazi data or assigning it to some archivist or custodial committee, I maintain that it be exhumed, printed, and disseminated to every medical school in the world along with the details of methodology and the names of the doctors who did it, whether or not they were indicted, acquitted, or hanged.…Let the students and the residents and the young doctors know that this was not ancient history or an episode from a horror movie where the actors get up after filming and prepare for another role. It was real. It happened yesterday (p. 169–170). [44]

While debates about the use of data collected by the Nazis are typically centered on medical scientists’ use of them, there are conceivable circumstances under which these data might be used by social scientists. Perhaps, for example, a social scientist might wish to examine contemporary reactions to the experiments. Or perhaps the data could be used in a study of the sociology of science. What do you think? Should data gathered by the Nazis be used or cited today? What arguments can you make in support of your position, and how would you respond to those who disagree?

Additionally at the macro-level, you must also consider your responsibilities to the profession of social work. When you engage in social work research, you stand on the reputation the profession has built for over a century. Since research is public-facing, meaning that research findings are intended to be shared publicly, you are an ambassador for the profession. How you conduct yourself as a social work researcher has potential implications for how the public perceives both social work and research. As a social worker, you have a responsibility to work towards greater social, environmental, and economic justice and human rights. Your research should reflect this responsibility. Attending to research ethics helps to fulfill your responsibilities to the profession, in addition to your target population.

Table 6.2 summarizes the key questions that researchers might ask themselves about the ethics of their research at each level of inquiry.

• At the micro-level, researchers should consider their own conduct and the rights of individual research participants.
• At the mezzo-level, researchers should consider the expectations of their profession, any organizations that may have funded their research, and the communities affected by their research.
• At the macro-level, researchers should consider their duty to and the expectations of society with respect to social science research.
• Summarize the benefits and harms at the micro-, mezzo-, and macro-level of inquiry. At which level of inquiry is your research project?
• In a few sentences, identify whether the benefits of your study outweigh the potential harms.

6.4 Being an ethical researcher

• Identify why researchers must provide a detailed description of methodology
• Describe what it means to use science in an ethical way

Research ethics has to do with both how research is conducted and how findings from that research are used. In this section, we’ll consider research ethics from both angles.

Doing science the ethical way

As you should now be aware, researchers must consider their own personal ethical principles in addition to following those of their institution, their discipline, and their community. We’ve already considered many of the ways that social workers strive to ensure the ethical practice of research, such as informing and protecting subjects. But the practice of ethical research doesn’t end once subjects have been identified and data have been collected. Social workers must also fully disclose their research procedures and findings. This means being honest about how research subjects were identified and recruited, how exactly data were collected and analyzed, and ultimately, what findings were reached.

If researchers fully disclose how they conducted their research, then those who use their work to build research projects, create social policies, or make treatment decisions can have greater confidence in the work. By sharing how research was conducted, a researcher helps assure readers they have conducted legitimate research and didn’t simply come to whatever conclusions they wanted   to find. A description or presentation of research findings that is not accompanied by information about research methodology is missing relevant information. Sometimes methodological details are left out because there isn’t time or space to share them. This is often the case with news reports of research findings. Other times, there may be a more insidious reason that important information is missing. This may be the case if sharing methodological details would call the legitimacy of a study into question. As researchers, it is our ethical responsibility to fully disclose our research procedures. As consumers of research, it is our ethical responsibility to pay attention to such details. We’ll discuss this more in the next section.

There’s a New Yorker cartoon that depicts a set of filing cabinets that aptly demonstrates what we don’t want to see happen with research. Each filing cabinet drawer in the cartoon is labeled differently. The labels include such headings as, “Our Facts,” “Their Facts,” “Neutral Facts,” “Disputable Facts,” “Absolute Facts,” “Bare Facts,” “Unsubstantiated Facts,” and “Indisputable Facts.” The implication of this cartoon is that one might just choose to open the file drawer of her choice and pick whichever facts one likes best. While this may occur if we use some of the unscientific ways of knowing described in Chapter 1 , it is fortunately not how the discovery of knowledge in social work, or in any other science for that matter, takes place. There actually is a method to this madness we call research. At its best, research reflects a systematic, transparent, informative process.

Honesty in research is facilitated by the scientific principle of replication . Ideally, this means that one scientist could repeat another’s study with relative ease. By replicating a study, we may become more (or less) confident in the original study’s findings. Replication is far more difficult (perhaps impossible) to achieve in the case of many qualitative studies, as our purpose is often a deep understanding of very specific circumstances, rather than the broad, generalizable knowledge we traditionally seek in quantitative studies. Nevertheless, transparency in the research process is an important standard for all social scientific researchers—that we provide as much detail as possible about the processes by which we reach our conclusions. This allows the quality of our research to be evaluated. Along with replication, peer review is another important principle of the scientific process. Peer review involves other knowledgeable researchers in our field of study to evaluate our research and to determine if it is of sufficient quality to share with the public. There are valid critiques of the peer review process: that it is biased towards studies with positive findings, that it may reinforce systemic barriers to oppressed groups accessing and leveraging knowledge, that it is far more subjective and/or unreliable than it claims to be. Despite these critiques, peer review remains a foundational concept for how scientific knowledge is generated.

Full disclosure also includes the need to be honest about a study’s strengths and weaknesses, both with oneself and with others. Being aware of the strengths and weaknesses of your own work can help a researcher make reasonable recommendations about the next steps other researchers might consider taking in their inquiries. Awareness and disclosure of a study’s strengths and weaknesses can also help highlight the theoretical or policy implications of one’s work. In addition, openness about strengths and weaknesses helps those reading the research better evaluate the work and decide for themselves how or whether to rely on its findings. Finally, openness about a study’s sponsors is crucial. How can we effectively evaluate research without knowing who paid the bills? This allows us to assess for potential conflicts of interest that may compromise the integrity of the research.

The standard of replicability, the peer-review process, and openness about a study’s strengths, weaknesses, and funding sources enables those who read the research to evaluate it fairly and completely. Knowledge of funding sources is often raised as an issue in medical research. Understandably, independent studies of new drugs may be more compelling to the Food and Drug Administration (FDA) than studies touting the virtues of a new drug that happen to have been funded by the company who created that drug. But medical researchers aren’t the only ones who need to be honest about their funding. If we know, for example, that a political think tank with ties to a particular party has funded some research, we can take that knowledge into consideration when reviewing the study’s findings and stated policy implications. Lastly, and related to this point, we must consider how, by whom, and for what purpose research may be used.

Using science the ethical way

Science has many uses. By “use” I mean the ways that science is understood and applied (as opposed to the way it is conducted). Some use science to create laws and social policies; others use it to understand themselves and those around them. Some people rely on science to improve their life conditions or those of other people, while still others use it to improve their businesses or other undertakings. In each case, the most ethical way for us to use science is to educate ourselves about the design and purpose of any studies we may wish to use. This helps us to more adequately critique the value of this research, to recognize its strengths and limitations.

As part of my research course, students are asked to critique a research article. I often find in this assignment that students often have very lofty expectations for everything that 'should' be included in the journal article they are reviewing. While I appreciate the high standards, I often give them feedback that it is perhaps unrealistic (even unattainable) for a research study to be perfectly designed and described for public consumption. All research has limitations; this may be a consequence of limited resources, issues related to feasibility, and unanticipated roadblocks or problems as we are carrying out our research. Furthermore, the ways we disseminate or share our research often has restrictions on what and how we can share our findings. This doesn't mean that a study with limitations has no value—every study has limitations! However, as we are reviewing research, we should look for an open discussion about methodology , strengths, and weaknesses of the study that helps us to interpret what took place and in what ways it may be important.

For instance, this can be especially important to think about in terms of a study's sample. It can be challenging to recruit a diverse and representative sample for your study (however, that doesn't mean we shouldn't try!). The next time you are reading research studies that were used to help establish an evidence based practice (EBP), make sure to look at the description of the sample. We cannot assume that what works for one group of people will uniformly work with all groups of people with very different life experiences; however, historically much of our intervention repertoire has been both created by and evaluated on white men. If research studies don't obtain a diverse sample, for whatever reason, we would expect that the authors would identify this as a limitation and an area requiring further study. We need to challenge our profession to provide practices, strategies, models, interventions, and policies that have been evaluated and tested for their efficacy with the diverse range of people that we work with as social workers.

Social scientists who conduct research on behalf of organizations and agencies may face additional ethical questions about the use of their research, particularly when the organization for which a study is conducted controls the final report and the publicity it receives. There is a potential conflict of interest for evaluation researchers who are employees of the agency being evaluated. A similar conflict of interest might exist between independent researchers whose work is being funded by some government agency or private foundation.

So who decides what constitutes ethical conduct or use of research? Perhaps we all do. What qualifies as ethical research may shift over time and across cultures as individual researchers, disciplinary organizations, members of society, and regulatory entities, such as institutional review boards, courts, and lawmakers, all work to define the boundaries between ethical and unethical research.

• Conducting research ethically requires that researchers be ethical not only in their data collection procedures but also in reporting their methods and findings.
• The ethical use of research requires an effort to understand research, an awareness of your own limitations in terms of knowledge and understanding, and the honest application of research findings.
• Think about your research hypothesis at this point. What would happen if your results revealed information that could harm the population you are studying? What are your ethical responsibilities as far as reporting about your research?
• Ultimately, we cannot control how others will use the results of our research. What are the implications of this for how you report on your research?
• Reading the results of empirical studies (16 minute read)
• Annotating empirical journal articles (15 minute read)
• Generalizability and transferability of empirical results (15 minute read)

Content warning: examples in this chapter contain references to domestic violence and details on types of abuse, drug use, poverty, mental health, sexual harassment and details on harassing behaviors, children’s mental health, LGBTQ+ oppression and suicide, obesity, anti-poverty stigma, and psychotic disorders.

5.1 Reading the results of empirical studies

• Describe how statistical significance and confidence intervals demonstrate which results are most important
• Differentiate between qualitative and quantitative results in an empirical journal article

If you recall from section 3.1 , empirical journal articles are those that report the results of quantitative or qualitative data analyzed by the author. They follow a set structure—introduction, methods, results, discussion/conclusions. This section is about reading the most challenging section: results.

I want to normalize not understanding statistics terms and symbols. However, a basic understanding of a results section goes a very long way to understanding the key results in an article. This will take you beyond the two or three sentences in the abstract that summarize the study's results and into the nitty-gritty of what they found for each concept they studied.

Read beyond the abstract

At this point, I have read hundreds of literature reviews written by students. One of the challenges I have noted is that students will report the results as summarized in the abstract, rather than the detailed findings laid out in the results section of the article. This poses a problem when you are writing a literature review because you need to provide specific and clear facts that support your reading of the literature. The abstract may say something like: “we found that poverty is associated with mental health status.” For your literature review, you want the details, not the summary. In the results section of the article, you may find a sentence that states: “children living in households experiencing poverty are three times more likely to have a mental health diagnosis.” This more specific statistical information provides a stronger basis on which to build the arguments in your literature review.

Using the summarized results in an abstract is an understandable mistake to make. The results section often contains figures and tables that may be challenging to understand. Often, without having completed more advanced coursework on statistical or qualitative analysis, some of the terminology, symbols, or diagrams may be difficult to comprehend. This section is all about how to read and interpret the results of an empirical (quantitative or qualitative) journal article. Our discussion here will be basic, and in parts three and four of the textbook, you will learn more about how to interpret results from statistical tests and qualitative data analysis.

Remember, this section only addresses empirical articles. Non-empirical articles (e.g., theoretical articles, literature reviews) don't have results. They cite the analysis of raw data completed by other authors, not the person writing the journal article who is merely summarizing others' work.

Quantitative results

Quantitative articles often contain tables, and scanning them is a good way to begin reading the results. A table usually provides a quick, condensed summary of the report’s key findings. Tables are a concise way to report large amounts of data. Some tables present descriptive information about a researcher’s sample (often the first table in a results section). These tables will likely contain frequencies (N) and percentages (%). For example, if gender happened to be an important variable for the researcher’s analysis, a descriptive table would show how many and what percent of all study participants are of a particular gender. Frequencies or “how many” will probably be listed as N, while the percent symbol (%) might be used to indicate percentages.

In a table presenting a causal relationship, two sets of variables are represented. The independent variable , or cause, and the dependent variable , the effect. We will discuss these further when we review quantitative conceptualization and measurement. Independent variable attributes are typically presented in the table’s columns, while dependent variable attributes are presented in rows. This allows the reader to scan a table’s rows to see how values on the dependent variable change as the independent variable values change (i.e., changes in the dependent variable depend on changes in the independent variable). Tables displaying results of quantitative analysis will also likely include some information about which relationships are significant or not. We will discuss the details of significance and p-values later in this section.

Let’s look at a specific example: Table 5.1. It presents the causal relationship between gender and experiencing harassing behaviors at work. In this example, gender is the independent variable (the cause) and the harassing behaviors listed are the dependent variables (the effects). [46] Therefore, we place gender in the table’s columns and harassing behaviors in the table’s rows.

Reading across the table’s top row, we see that 2.9% of women in the sample reported experiencing subtle or obvious threats to their safety at work, while 4.7% of men in the sample reported the same. We can read across each of the rows of the table in this way. Reading across the bottom row, we see that 9.4% of women in the sample reported experiencing staring or invasion of their personal space at work while just 2.3% of men in the sample reported having the same experience. We’ll discuss  p values later in this section.

While you can certainly scan tables for key results, they are often difficult to understand without reading the text of the article. The article and table were meant to complement each other, and the text should provide information on how the authors interpret their findings. The table is not redundant with the text of the results section. Additionally, the first table in most results sections is a summary of the study's sample, which provides more background information on the study than information about hypotheses and findings. It is also a good idea to look back at the methods section of the article as the data analysis plan the authors outline should walk you through the steps they took to analyze their data which will inform how they report them in the results section.

Statistical significance

The statistics reported in Table 5.1 represent what the researchers found in their sample. The purpose of statistical analysis is usually to generalize from a the small number of people in a study's sample to a larger population of people. Thus, the researchers intend to make causal arguments about harassing behaviors at workplaces beyond those covered in the sample.

Generalizing is key to understanding statistical significance . According to Cassidy and colleagues, (2019) [47] 89% of research methods textbooks in psychology define statistical significance incorrectly. This includes an early draft of this textbook which defined statistical significance as "the likelihood that the relationships we observe could be caused by something other than chance." If you have previously had a research methods class, this might sound familiar to you. It certainly did to me!

But statistical significance is less about "random chance" than more about the null hypothesis . Basically, at the beginning of a study a researcher develops a hypothesis about what they expect to find, usually that there is a statistical relationship between two or more variables . The null hypothesis is the opposite. It is the hypothesis that there is no relationship between the variables in a research study. Researchers then can hopefully reject the null hypothesis because they find a relationship between the variables.

For example, in Table 5.1 researchers were examining whether gender impacts harassment. Of course, researchers assumed that women were more likely to experience harassment than men. The null hypothesis, then, would be that gender has no impact on harassment. Once we conduct the study, our results will hopefully lead us to reject the null hypothesis because we find that gender impacts harassment. We would then generalize from our study's sample to the larger population of people in the workplace.

Statistical significance is calculated using a p-value which is obtained by comparing the statistical results with a hypothetical set of results if the researchers re-ran their study a large number of times. Keeping with our example, imagine we re-ran our study with different men and women from different workplaces hundreds and hundred of times and we assume that the null hypothesis is true that gender has no impact on harassment. If results like ours come up pretty often when the null hypothesis is true, our results probably don't mean much. "The smaller the p-value, the greater the statistical incompatibility with the null hypothesis" (Wasserstein & Lazar, 2016, p. 131). [48] Generally, researchers in the social sciences have used 0.05 as the value at which a result is significant (p is less than 0.05) or not significant (p is greater than 0.05). The p-value 0.05 refers to if 5% of those hypothetical results from re-running our study show the same or more extreme relationships when the null hypothesis is true. Researchers, however, may choose a stricter standard such as 0.01 in which only 1% of those hypothetical results are more extreme or a more lenient standard like 0.1 in which 10% of those hypothetical results are more extreme than what was found in the study.

Let's look back at Table 5.1. Which one of the relationships between gender and harassing behaviors is statistically significant? It's the last one in the table, "staring or invasion of personal space," whose p-value is 0.039 (under the p<0.05 standard to establish statistical significance). Again, this indicates that if we re-ran our study over and over again and gender did not  impact staring/invasion of space (i.e., the null hypothesis was true), only 3.9% of the time would we find similar or more extreme differences between men and women than what we observed in our study. Thus, we conclude that for staring or invasion of space only , there is a statistically significant relationship.

For contrast, let's look at "being pushed, hit, or grabbed" and run through the same analysis to see if it is statistically significant. If we re-ran our study over and over again and the null hypothesis was true, 48% of the time (p=.48) we would find similar or more extreme differences between men and women. That means these results are not statistically significant.

This discussion should also highlight a point we discussed previously: that it is important to read the full results section, rather than simply relying on the summary in the abstract. If the abstract stated that most tests revealed no statistically significant relationships between gender and harassment, you would have missed the detail on which behaviors were and were not associated with gender. Read the full results section! And don't be afraid to ask for help from a professor in understanding what you are reading, as results sections are often not written to be easily understood.

Statistical significance and p-values have been critiqued recently for a number of reasons, including that they are misused and misinterpreted (Wasserstein & Lazar, 2016) [49] , that researchers deliberately manipulate their analyses to have significant results (Head et al., 2015) [50] , and factor into the difficulty scientists have today in reproducing many of the results of previous social science studies (Peng, 2015). [51] For this reason, we share these principles, adapted from those put forth by the American Statistical Association, [52]  for understanding and using p-values in social science:

• P-values provide evidence against a null hypothesis.
• P-values do not indicate whether the results were produced by random chance alone or if the researcher's hypothesis is true, though both are common misconceptions.
• Statistical significance can be detected in minuscule differences that have very little effect on the real world.
• Nuance is needed to interpret scientific findings, as a conclusion does not become true or false when the p-value passes from p=0.051 to p=0.049.
• Real-world decision-making must use more than reported p-values. It's easy to run analyses of large datasets and only report the significant findings.
• Greater confidence can be placed in studies that pre-register their hypotheses and share their data and methods openly with the public.
• "By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis. For example, a p-value near 0.05 taken by itself offers only weak evidence against the null hypothesis. Likewise, a relatively large p-value does not imply evidence in favor of the null hypothesis; many other hypotheses may be equally or more consistent with the observed data" (Wasserstein & Lazar, 2016, p. 132).

Confidence intervals

Because of the limitations of p-values, scientists can use other methods to determine whether their models of the world are true. One common approach is to use a confidence interval , or a range of values in which the true value is likely to be found. Confidence intervals are helpful because, as principal #5 above points out, p-values do not measure the size of an effect (Greenland et al., 2016). [53] Remember, something that has very little impact on the world can be statistically significant, and the values in a confidence interval would be helpful. In our example from Table 5.1, imagine our analysis produced a confidence interval that women are 1.2-3.4x more likely to experience "staring or invasion of personal space" than men. As with p-values, calculation for a confidence interval compares what was found in one study with a hypothetical set of results if we repeated the study over and over again. If we calculated 95% confidence intervals for all of the hypothetical set of hundreds and hundreds of studies, that would be our confidence interval. 

Confidence intervals are pretty intuitive. As of this writing, my wife and are expecting our second child. The doctor told us our due date was December 11th. But the doctor also told us that December 11th was only their best estimate. They were actually 95% sure our baby might be born any time in the 30-day period between November 27th and December 25th. Confidence intervals are often listed with a percentage, like 90% or 95%, and a range of values, such as between November 27th and December 25th. You can read that as: "we are 95% sure your baby will be born between November 27th and December 25th because we've studied hundreds of thousands of fetuses and mothers, and we're 95% sure your baby will be within these two dates."

Notice that we're hedging our bets here by using words like "best estimate." When testing hypotheses, social scientists generally phrase their findings in a tentative way, talking about what results "indicate" or "support," rather than making bold statements about what their results "prove." Social scientists have humility because they understand the limitations of their knowledge. In a literature review, using a single study or fact to "prove" an argument right or wrong is often a signal to the person reading your literature review (usually your professor) that you may not have appreciated the limitations of that study or its place in the broader literature on the topic. Strong arguments in a literature review include multiple facts and ideas that span across multiple studies.

You can learn more about creating tables, reading tables, and tests of statistical significance in a class focused exclusively on statistical analysis. We provide links to many free and openly licensed resources on statistics in Chapter 16 . For now, we hope this brief introduction to reading tables will improve your confidence in reading and understanding the results sections in quantitative empirical articles.

Qualitative results

Quantitative articles will contain a lot of numbers and the results of statistical tests demonstrating associations between those numbers. Qualitative articles, on the other hand, will consist mostly of quotations from participants. For most qualitative articles, the authors want to put their results in the words of their participants, as they are the experts. Articles that lack quotations make it difficult to assess whether the researcher interpreted the data in a trustworthy, unbiased manner. These types of articles may also indicate how often particular themes or ideas came up in the data, potentially reflective of how important they were to participants.

Authors often organize qualitative results by themes and subthemes. For example, see this snippet from the results section in Bonanno and Veselak (2019) [54] discussion parents' attitudes towards child mental health information sources.

Data analysis revealed four themes related to participants’ abilities to access mental health help and information for their children, and parents’ levels of trust in these sources. These themes are: others’ firsthand experiences family and friends with professional experience, protecting privacy, and uncertainty about schools as information sources. Trust emerged as an overarching and unifying concept for all of these themes. Others’ firsthand experiences. Several participants reported seeking information from other parents who had experienced mental health struggles similar to their own children. They often referenced friends or family members who had been or would be good sources of information due to their own personal experiences. The following quote from Adrienne demonstrates the importance of firsthand experience: [I would only feel comfortable sharing concerns or asking for advice] if I knew that they had been in the same situation. (Adrienne) Similarly, Michelle said: And I talked to a friend of mine who has kids who have IEPs in the district to see, kind of, how did she go about it. (Michelle) ... Friends/family with professional experience . Several respondents referred to friends or family members who had professional experience with or knowledge of child mental health and suggested that these individuals would be good sources of information. For example, Hannah said: Well, what happened with me was I have an uncle who’s a psychiatrist. Sometimes if he’s up in (a city to the north), he’s retired, I can call him sometimes and get information. (Hannah) Michelle, who was in nursing school, echoed this sentiment: At this point, [if my child’s behavioral difficulties continued], I would probably call one of my [nursing] professors. That’s what I’ve done in the past when I’ve needed help with certain things...I have a professor who I would probably consider a friend who I would probably talk to first. She has a big adolescent practice. (Michelle) (p. 402-403)

The terms in bold above refer to the key themes (i.e., qualitative results) that were present in the data. Researchers will state the process by which they interpret each theme, providing a definition and usually some quotations from research participants. Researchers will also draw connections between themes, note consensus or conflict over themes, and situate the themes within the study context.

Qualitative results are specific to the time, place, and culture in which they arise, so you will have to use your best judgment to determine whether these results are relevant to your study. For example, students in my class at Radford University in Southwest Virginia may be studying rural populations. Would a study on group homes in a large urban city transfer well to group homes in a rural area?

Maybe. But even if you were using data from a qualitative study in another rural area, are all rural areas the same? How is the client population and sociocultural context in the article similar or different to the one in your study? Qualitative studies have tremendous depth, but researchers must be intentional about drawing conclusions about one context based on a study in another context. To make conclusions about how a study applies in another context, researchers need to examine each component of an empirical journal article--they need to annotate!

• The results section of empirical articles are often the most difficult to understand.
• To understand a quantitative results section, look for results that were statistically significant and examine the confidence interval, if provided.
• To understand a qualitative results section, look for definitions of themes or codes and use the quotations provided to understand the participants’ perspective.

Select a quantitative empirical article related to your topic.

• Write down the results the authors identify as statistically significant in the results section.
• How do the authors interpret their results in the discussion section?
• Do the authors provide enough information in the introduction for you to understand their results?

Select a qualitative empirical article relevant to your topic.

• Write down the key themes the authors identify and how they were defined by the participants.

5.2 Annotating empirical journal articles

• Define annotation and describe how to use it to identify, extract, and reflect on the information you need from an article

Annotation refers to the process of writing notes on an article. There are many ways to do this. The most basic technique is to print out the article and build a binder related to your topic. Raul Pacheco-Vega's excellent blog has a post on his approach to taking physical notes. Honestly, while you are there, browse around that website. It is full of amazing tips for students conducting a literature review and graduate research projects. I see a lot of benefits to the paper, pen, and highlighter approach to annotating articles. Personally though, I prefer to use a computer to write notes on an article because my handwriting is terrible and typing notes allows me search for keywords. For other students, electronic notes work best because they cannot afford to print every article that they will use in their paper. No matter what you use, the point is that you need to write notes when you're reading. Reading is research!

There are a number of free software tools you can use to help you annotate a journal article. Most PDF readers like Adobe Acrobat have a commenting and highlighting feature, though the PDF readers included with internet browsers like Google Chrome, Microsoft Edge, and Safari do not have this feature. The best approach may be to use a citation manager like Zotero. Using a citation manager, you can build a library of articles, save your annotations, and link annotations across PDFs using keywords. They also provide integration with word processing programs to help with citations in a reference list

Of course, I don't follow this advice because I have a system that works well for me. I have a PDF open in one computer window and a Word document open in a window next to it. I type notes and copy quotes, listing the page number for each note I take. It's a bit low-tech, but it does make my notes searchable. This way, when I am looking for a concept or quote, I can simply search my notes using the Find feature in Word and get to the information I need.

Annotation and reviewing literature does not have to be a solo project. If are working in a group, you can use the Hypothes.is web browser extension to annotate articles collaboratively. You can also use Google Docs to collaboratively annotate a shared PDF using the commenting feature and write collaborative notes in a shared document. By sharing your highlights and comments, you can split the work of getting the most out of each article you read and build off one another's ideas.

Common annotations

In this section, we present common annotations people make when reading journal articles. These annotations are adapted from Craig Whippo and Raul Pacheco-Vega . If you are annotating on paper, I suggest using different color highlighters for each type of annotation listed below. If you are annotating electronically, you can use the names below as tags to easily find information later. For example, if you are searching for definitions of key concepts, you can either click on the tag for [definitions] in your PDF reader or thumb through a printed copy of article for whatever color or tag you used to indicate definitions of key terms. Most of all, you want to avoid reading through all of your sources again just to find that one thing you know you read somewhere . Time is a graduate student's most valuable resource, so our goal here is to help you spend your time reading the literature wisely.

Personal reflections

Personal reflections are all about you. What do you think? Are there any areas you are confused about? Any new ideas or reflections come to mind while you're reading? Treat these annotations as a means of capturing your first reflections about an article. Write down any questions or thoughts that come to mind as you read. If you think the author says something inaccurate or unsubstantiated, write that down. If you don't understand something, make a note about it and ask your professor. Don't feel bad! Journal articles are hard to understand sometimes, even for professors. Your goal is to critically read the literature, so write down what you think while reading! Table 4.2 contains some questions that might stimulate your thoughts.

Definitions

Note definitions of key terms for your topic. At minimum, you should include a scholarly definition for the concepts represented in your working question. If your working question asks about the process of leaving a relationship with domestic violence, your research proposal will have to explain how you define domestic violence, as well as how you define "leaving" an abusive relationship. While you may already know what you mean by domestic violence, the person reading your research proposal does not.

Annotating definitions also helps you engage with the scholarly debate around your topic. Definitions are often contested among scholars. Some definitions of domestic violence will be more comprehensive, including things such economic abuse or forcing the victim to problematically use substances. Other definitions will be less comprehensive, covering only physical, verbal, and sexual abuse. Often, how someone defines something conceptually is highly related to how they measure it in their study. Since you will have to do both of these things, find a definition that feels right to you or create your own, noting the ways in which it is similar or different from those in the literature.

Definitions are also an important way of dealing with jargon. Becoming familiar with a new content area involves learning the jargon experts use. For example, in the last paragraph I used the term economic abuse, but that's probably not a term you've heard before. If you were conducting a literature review on domestic violence, you would want to search for keywords like economic abuse if they are relevant to your working question. You will also want to know what they mean so you can use them appropriately in designing your study and writing your literature review.

Theoretical perspective

Noting the theoretical perspective of the article can help you interpret the data in the same manner as the author. For example, articles on supervised injection facilities for people who use intravenous drugs most likely come from a harm reduction perspective, and understanding the theory behind harm reduction is important to make sense of empirical results. Articles should be grounded in a theoretical perspective that helps the author conceptualize and understand the data. As we discussed in Chapter 3 , some journal articles are entirely theoretical and help you understand the theories or conceptual models related to your topic. We will help you determine a theoretical perspective for your project in Chapter 7 . For now, it's a good idea to note what theories authors mention when talking about your topic area. Some articles are better about this than others, and many authors make it a bit challenging to find theory (if mentioned at all). In other articles, it may help to note which social work theories are missing  from the literature. For example, a study's findings might address issues of oppression and discrimination, but the authors may not use critical theory to make sense of what happened.

Background knowledge

It's a good idea to note any relevant information the author relies on for background. When an author cites facts or opinions from others, you are subsequently able to get information from multiple articles simultaneously. For example, if we were looking at this meta-analysis about domestic violence , in the introduction section, the authors provide facts from many other sources. These facts will likely be relevant to your inquiry on domestic violence, as well.

As you are looking at background information, you should also note any subtopics or concepts about which there is controversy or consensus. The author may present one viewpoint and then an opposing viewpoint, something you may do in your literature review as well. Similarly, they may present facts that scholars in the field have come to consensus on and describe the ways in which different sources support these conclusions.

Sources of interest

Note any relevant sources the author cites. If there is any background information you plan to use, note the original source of that information. When you write your literature review, cite the original source of a piece of information you are using, which may not be where you initially read it . Remember that you should read and refer to the primary source . If you are reading Article A and the author cites a fact from Article B, you should note Article B in your annotations and use Article B when you cite the fact in your paper. You should also make sure Article A interpreted Article B correctly and scan Article B for any other useful facts.

Research question/Purpose

Authors should be clear about the purpose of their article. Charitable authors will give you a sentence that starts with something like this:

• "The purpose of this research project was..."
• "Our research question was..."
• "The research project was designed to test the following hypothesis..."

Unfortunately, not all authors are so clear, and you may to hunt around for the research question or hypothesis. Generally, in an empirical article, the research question or hypothesis is at the end of the introduction. In non-empirical articles, the author will likely discuss the purpose of the article in the abstract or introduction.

We will discuss in greater detail how to read the results of empirical articles in Chapter 5 . For now, just know that you should highlight any of the key findings of an article. They will be described very briefly in the abstract, and in much more detail in the article itself. In an empirical article, you should look at both the 'Results' and 'Discussion' sections. For a non-empirical article, the key findings will likely be in the conclusion. You can also find them in the topic or concluding sentences in a paragraph within the body of the article.

How do researchers know something when they see it? Found in the 'Methods' section of empirical articles, the measures section is where researchers spell out the tools, or measures, they used to gather data. For quantitative studies, you will want to get familiar with the questions researchers typically use to measure key variables. For example, to measure domestic violence, researchers often use the Conflict Tactics Scale . The more frequently used and cited a measure is, the more we know about how well it works (or not). Qualitative studies will often provide at least some of the interview or focus group questions they used with research participants. They will also include information about how their inquiry and hypotheses may have evolved over time. Keep in mind however, sometimes important information is cut out of an article during editing. If you need more information, consider reaching out to the author directly. Before you do so, check if the author provided an appendix with the information you need or if the article links to a their data and measures as part open data sharing practices.

Who exactly were the study participants and how were they recruited? In quantitative studies, you will want to pay attention to the sample size. Generally, the larger the sample, the greater the study's explanatory power. Additionally, randomly drawn samples are desirable because they leave any variation up to chance. Samples that are conducted out of convenience can be biased and non-representative of the larger population. In qualitative studies, non-random sampling is appropriate but consider this: how well does what we find for this group of people transfer to the people who will be in your study? For qualitative studies and quantitative studies, look for how well the sample is described and whether there are important characteristics missing from the article that you would need to determine the quality of the sample.

Limitations

Honest authors will include these at the end of each article. But you should also note any additional limitations you find with their work as well.

Your annotations

These are just a few suggested annotations, but you can come up with your own. For example, maybe there are annotations you would use for different assignments or for the problem statement in your research proposal. If you have an argument or idea that keeps coming to mind when you read, consider creating an annotation for it so you can remember which part of each article supports your ideas. Whatever works for you. The goal with annotation is to extract as much information from each article while reading, so you don't have to go back through everything again. It's useless to read an article and forget most of what you read. Annotate!

• Begin your search by reading thorough and cohesive literature reviews. Review articles are great sources of information to get a broad perspective of your topic.
• Don’t read an article just to say you’ve read it. Annotate and take notes so you don’t have to re-read it later.
• Use software or paper-and-pencil approaches to write notes on articles.
• Annotation is best used when closely reading an empirical study highly similar to your research project.
• Select an empirical article highly related to the study you would like to conduct.
• Annotate the article using the aforementioned annotations and create some of your own.
• Create the first draft of a summary table with key information from this empirical study that you would like to compare to other empirical studies you closely read.

5.3 Generalizability and transferability of empirical results

• Define generalizability and transferability.
• Assess the generalizability and transferability to how researchers use the results from empirical research studies to make arguments about what is objectively true.
• Relate both concepts to the hierarchy of evidence and the types of articles in the scholarly literature

Now that you have read an empirical article in detail, it's important to put its results in conversation with the broader literature on your topic. In this chapter we discuss two important concepts-- generalizability and   transferability --and the interrelationship between the two. We also explain how these two properties of empirical data impact your literature review and evidence-based practice.

Generalizability

The figure below provides a common approach to assessing empirical evidence. As you move up the pyramid below, you can be more sure that the data contained in those studies generalizes to all people who experience the issue.

As we reviewed in Chapter 1, objective truth is true for everyone, regardless of context. In other words, objective truths generalize beyond the sample of people from whom data were collected to the larger population of people who experience the issue under examination. You can be much more sure that information from a systematic review or meta-analysis will generalize than something from a case study of a single person, pilot projects, and other studies that do not seek to establish generalizability.

The type of article listed here is also related to the types of research methods the authors used. While we cover many of these approaches in this textbook, some of them (like cohort studies) are somewhat less common in social work. Additionally, there is one important research method, survey design, that does not appear in this diagram. Finally, social work research uses many different types of qualitative research--some of which generates more generalizable data than others.

For a refresher on the different types of evidence available in each type of article, refer back to section 4.1. You'll recall the hierarchy of evidence as described by McNeese & Thyer (2004) [55]

• Systematic reviews and meta-analyses
• Randomized controlled trials
• Quasi-experimental studies
• Case-control and cohort studies
• Pre-experimental (or non-experimental) group studies
• Qualitative studies

Because there is further variation in the types of studies used by social work researchers, I expanded the hierarchy of evidence to cover a greater breadth of research methods in Figure 5.3.

Refined information from multiple sources

The top of the hierarchy represents refined scientific information or meta-research . Meta-research uses the scientific method to analyze and improve the scientific production of knowledge. For example, meta-analyses pull together samples of people from all high-quality studies on a given topic area creating a super-study with far more people than any single researcher could feasibly collect data from. Because scientists (and clinical experts) refine data across multiple studies, these represent the most generalizable research findings.

Of course, not all meta-analyses or systematic reviews are of good quality. As a peer reviewer for a scholarly journal, I have seen poor quality systematic reviews that make methodological mistakes—like not including relevant keywords—that lead to incorrect conclusions. Unfortunately, not all errors are caught in the peer review process, and not all limitations are acknowledged by the authors. Just because you are looking at a systematic review does not mean you are looking at THE OBJECTIVE TRUTH. Nevertheless, you can be pretty sure that results from these studies are generalizable to the population in the study’s research question.

A good way to visualize the process of sampling is by examining the procedure used for systematic reviews and meta-analyses to scientifically search for articles. In Figure 5.4 below, you can see how researchers conducting a systematic review identified a large pool of potentially relevant articles, downloaded and analyzed them for relevance, and in the end, analyzed only 71 articles in their systematic review out of a total of 1,589 potentially relevant articles. Because systematic reviews or meta-analyses are intended to make strong, generalizable conclusions, they often exclude studies that still contain good information.

In the process of selecting articles for a meta-analysis and systematic review, researchers may exclude articles with important information for a number of good reasons. No study is perfect, and all research methods decisions come with limitations--including meta-research. Authors conducting a meta-analysis cannot include a study unless researchers provide data for the authors to include in their meta-analysis, and many empirical journal articles do not make their data available. Additionally, a study’s intervention or measures may be a bit different than what researchers want to make conclusions about. This is a key truth applicable across all articles you read—who or what gets selected for analysis in a research project determines how well the project’s results generalize to everyone.

We will talk about this in future chapters as sampling, and in those chapters, we will learn which sampling approaches are intended to support generalizability and which are used for other purposes. For example, availability or convenience sampling is often used to get quick information while random sampling approaches are intended to support generalizability. It is impossible to know everything about your article right now, but by the end of this course, you will have the information you need to critically examine the generalizability of a sample.

Primary sources (empirical studies)

Because refined sources like systematic reviews exclude good studies, they are only a first step in getting to know a topic area. You will need to examine primary sources--the reports of researchers who conducted empirical studies--to make evidence-based conclusions about your topic. Figure 5.3 describes three different types of data and ranks them vertically based on how well you can be sure the information generalizes.

As we will discuss further in our chapter on causal explanations, a key factor in scientifically assessing what happened first. Researchers conducting intervention studies are causing change by providing therapy, housing, or whatever the intervention is and measuring the outcomes of that intervention after they happen. This is unlike survey researchers, who do not introduce an intervention but ask people to self-report information on a questionnaire. Longitudinal surveys are particularly helpful because they can provide a clearer picture of whether the cause came before the effect in a causal relationship, but because they are expensive and time-consuming to conduct, longitudinal studies are relatively rare in the literature and most surveys measure people at only one point in time. Thus, because researchers cannot tightly control the causal variable (an intervention, an experience of abuse, etc.) we can be somewhat less certain of the conclusions of surveys than experiments. At the same time, because surveys measure people in their naturalistic environment rather than in a laboratory or artificial setting, they may do a better job at reducing the potential for the researcher to influence the data a participant provides. Surveys also provide descriptive information--like the number of people with a diagnosis or risk factor--that experiments cannot provide.

Surveys and experiments are commonly used in social work, and we will describe the methods they use in future chapters. When assessing the generalizability of a given survey or experiment, you are looking at whether the methods used by the researchers improve generalizability (or, at least that those methods are intended to improve generalizability). Specifically, there are sampling, measurement, and design decisions that researchers make that can improve generalizability. And once the study is conducted, whether those methods worked as intended also impact generalizability.

We address sampling, measurement, and design in the coming chapters, and you will need more in-depth knowledge of research methods to assess the generalizability of the results you are reading. In the meantime, Figure 5.3 is organized by design, and this is a good starting point for your inquiry since it only requires you to identify the design in each empirical article--which should be included in the abstract and described in detail in the methods section. For more information on how to conduct sampling, measurement, and design in a way that maximizes generalizability, read Part 2 of this textbook.

When searching for design of a study, look for specific keywords that indicate the researcher used methods that do not generalize well like pilot study, pre-experiment, non-experiment, convenience sample, availability sample, and exploratory study. When researchers are seeking to perform a pilot study, they are optimizing for time, not generalizability. Their results may still be useful to you! But, you should not generalize from their study to all people with the issue under analysis without a lot of caution and additional supporting evidence. Instead, you should see whether the lessons from this study might transfer to the context in which you are researching--our next topic.

Qualitative studies use sampling, measures, and designs that do not try to optimize generalizability. Thus, if the results of a qualitative study indicate 10 out of 50 students who participated in the focus group found the mandatory training on harassment to be unhelpful, does that mean 20% of all college students at this university find it unhelpful? Because focus groups and interviews (and other qualitative methods we will discuss) use qualitative methods, they are not concerned with generalizability. It would not make sense to generalize from focus groups to all people in a population. Instead, focus groups methods optimize for trustworthy and authentic research projects that make sure, for example, all themes and quotes in the researcher's report are traceable to quotes from focus group participants. Instead of providing what is generally true, qualitative research provides a thick description of people's experiences so you can understand them. S ubjective inquiry is less generalizable but provides greater depth in understanding people's feelings, beliefs, and decision-making processes within their context. 

In Figure 5.3, you will note that some qualitative studies are ranked higher than others in terms of generalizability. Meta-syntheses are ranked highest because they are meta-research, pooling together the themes and raw data from multiple qualitative studies into a super-study. A meta-synthesis is the qualitative equivalent of a meta-analysis, which analyzes quantitative data. Because the researchers conducting the meta-syntheses aim to make more broad generalizations across research studies, even though generalizability is not strictly the goal. In a similar way, grounded theory studies (a type of qualitative design) aim to produce a testable hypothesis that could generalize. At the bottom of the hierarchy are individual case studies, which report what happens with a single person, organization, or event. It's best not to think too long about the generalizability of qualitative results. When examining qualitative articles, you should be examining their transferability, our topic for the next subsection.

Transferability

Generalizability asks one question: How well does the sample of people in this study represent everyone with this issue? If you read in a study that 50% of people in the sample experienced depression, does that mean 50% of everyone experiences depression? We previewed future discussions in this textbook that will discuss the specific quantitative research methods used to optimize the generalizability of results. By adhering strictly to best practices in sampling, measurement, and design, researchers can provide you with good evidence for the generalizability of their study's results.

Of course, generalizability is not the only question worth asking. Just because a study's sample represents a broader population does not mean it is helpful for making conclusions about your working question. In assessing a study's transferability, you are making a weaker but compelling argument that the conclusions of one study can be applied to understanding the people in your working question and research project. Generalizable results may be applicable because they are broadly transferable across situations, and you can be confident in that when they follow the best practices in this textbook for improving generalizability. However, there may be aspects of a study that make its results difficult to transfer to your topic area.

When evaluating the transferability of a research result to your working question, consider the sample, measures, and design. That is, how data was collected from individuals, who those individuals are, and what researchers did with them. You may find that the samples in generalizable studies do not talk about the specific ethnic, cultural, or geographic group that is in your working question. Similarly, studies that measure the outcomes of substance use treatment by measuring sobriety may not match your working question on moderation, medication adherence, or substitution as an outcome in substance use treatment. Evaluating the transferability of designs may help you identify whether the methods the authors used would be similar to those you might use if you were to conduct a study gathering and collecting your own raw data.

Assessing transferability is more subjective. You are using your knowledge of your topic area and research methods (which are always improving!) to make a reasonable argument about why a given piece of evidence from a primary source helps you understand something. Look back at Table 5.2, your annotations, and the researchers' sampling, data analysis, results, and design. Using your critical thinking (and the knowledge you can in Part 2 and Part 3 of this textbook) you will need to make a reasonable argument that these results transfer to the people, places, and culture that you are talking about in your working question.

In the final chapter of Part 1, we will discuss how to assemble the facts you have taken from journal articles into a literature review that represents what  you think about the topic.

• Developing your theoretical framework
• Conceptual definitions
• Inductive & deductive reasoning

Nomothetic causal explanations

Content warning: examples in this chapter include references to sexual harassment, domestic violence, gender-based violence, the child welfare system, substance use disorders, neonatal abstinence syndrome, child abuse, racism, and sexism.

11.1 Developing your theoretical framework

• Differentiate between theories that explain specific parts of the social world versus those that are more broad and sweeping in their conclusions
• Identify the theoretical perspectives that are relevant to your project and inform your thinking about it
• Define key concepts in your working question and develop a theoretical framework for how you understand your topic.

Theories provide a way of looking at the world and of understanding human interaction. Paradigms are grounded in big assumptions about the world—what is real, how do we create knowledge—whereas theories describe more specific phenomena. Well, we are still oversimplifying a bit. Some theories try to explain the whole world, while others only try to explain a small part. Some theories can be grouped together based on common ideas but retain their own individual and unique features. Our goal is to help you find a theoretical framework that helps you understand your topic more deeply and answer your working question.

Theories: Big and small

In your human behavior and the social environment (HBSE) class, you were introduced to the major theoretical perspectives that are commonly used in social work. These are what we like to call big-T 'T'heories. When you read about systems theory, you are actually reading a synthesis of decades of distinct, overlapping, and conflicting theories that can be broadly classified within systems theory. For example, within systems theory, some approaches focus more on family systems while others focus on environmental systems, though the core concepts remain similar.

Different theorists define concepts in their own way, and as a result, their theories may explore different relationships with those concepts. For example, Deci and Ryan's (1985) [56] self-determination theory discusses motivation and establishes that it is contingent on meeting one's needs for autonomy, competency, and relatedness. By contrast, ecological self-determination theory, as written by Abery & Stancliffe (1996), [57] argues that self-determination is the amount of control exercised by an individual over aspects of their lives they deem important across the micro, meso, and macro levels. If self-determination were an important concept in your study, you would need to figure out which of the many theories related to self-determination helps you address your working question.

Theories can provide a broad perspective on the key concepts and relationships in the world or more specific and applied concepts and perspectives. Table 7.2 summarizes two commonly used lists of big-T Theoretical perspectives in social work. See if you can locate some of the theories that might inform your project.

Competing theoretical explanations

Within each area of specialization in social work, there are many other theories that aim to explain more specific types of interactions. For example, within the study of sexual harassment, different theories posit different explanations for why harassment occurs.

One theory, first developed by criminologists, is called routine activities theory. It posits that sexual harassment is most likely to occur when a workplace lacks unified groups and when potentially vulnerable targets and motivated offenders are both present (DeCoster, Estes, & Mueller, 1999). [60]

Other theories of sexual harassment, called relational theories, suggest that one's existing relationships are the key to understanding why and how workplace sexual harassment occurs and how people will respond when it does occur (Morgan, 1999). [61] Relational theories focus on the power that different social relationships provide (e.g., married people who have supportive partners at home might be more likely than those who lack support at home to report sexual harassment when it occurs).

Finally, feminist theories of sexual harassment take a different stance. These theories posit that the organization of our current gender system, wherein those who are the most masculine have the most power, best explains the occurrence of workplace sexual harassment (MacKinnon, 1979). [62] As you might imagine, which theory a researcher uses to examine the topic of sexual harassment will shape the questions asked about harassment. It will also shape the explanations the researcher provides for why harassment occurs.

For a graduate student beginning their study of a new topic, it may be intimidating to learn that there are so many theories beyond what you’ve learned in your theory classes. What’s worse is that there is no central database of theories on your topic. However, as you review the literature in your area, you will learn more about the theories scientists have created to explain how your topic works in the real world. There are other good sources for theories, in addition to journal articles. Books often contain works of theoretical and philosophical importance that are beyond the scope of an academic journal. Do a search in your university library for books on your topic, and you are likely to find theorists talking about how to make sense of your topic. You don't necessarily have to agree with the prevailing theories about your topic, but you do need to be aware of them so you can apply theoretical ideas to your project.

Applying big-T theories to your topic

The key to applying theories to your topic is learning the key concepts associated with that theory and the relationships between those concepts, or propositions . Again, your HBSE class should have prepared you with some of the most important concepts from the theoretical perspectives listed in Table 7.2. For example, the conflict perspective sees the world as divided into dominant and oppressed groups who engage in conflict over resources. If you were applying these theoretical ideas to your project, you would need to identify which groups in your project are considered dominant or oppressed groups, and which resources they were struggling over. This is a very general example. Challenge yourself to find small-t theories about your topic that will help you understand it in much greater detail and specificity. If you have chosen a topic that is relevant to your life and future practice, you will be doing valuable work shaping your ideas towards social work practice.

Integrating theory into your project can be easy, or it can take a bit more effort. Some people have a strong and explicit theoretical perspective that they carry with them at all times. For me, you'll probably see my work drawing from exchange and choice, social constructionist, and critical theory. Maybe you have theoretical perspectives you naturally employ, like Afrocentric theory or person-centered practice. If so, that's a great place to start since you might already be using that theory (even subconsciously) to inform your understanding of your topic. But if you aren't aware of whether you are using a theoretical perspective when you think about your topic, try writing a paragraph off the top of your head or talking with a friend explaining what you think about that topic. Try matching it with some of the ideas from the broad theoretical perspectives from Table 7.2. This can ground you as you search for more specific theories. Some studies are designed to test whether theories apply the real world while others are designed to create new theories or variations on existing theories. Consider which feels more appropriate for your project and what you want to know.

Another way to easily identify the theories associated with your topic is to look at the concepts in your working question. Are these concepts commonly found in any of the theoretical perspectives in Table 7.2? Take a look at the Payne and Hutchison texts and see if any of those look like the concepts and relationships in your working question or if any of them match with how you think about your topic. Even if they don't possess the exact same wording, similar theories can help serve as a starting point to finding other theories that can inform your project. Remember, HBSE textbooks will give you not only the broad statements of theories but also sources from specific theorists and sub-theories that might be more applicable to your topic. Skim the references and suggestions for further reading once you find something that applies well.

Choose a theoretical perspective from Hutchison, Payne, or another theory textbook that is relevant to your project. Using their textbooks or other reputable sources, identify :

• At least five important concepts from the theory
• What relationships the theory establishes between these important concepts (e.g., as x increases, the y decreases)
• How you can use this theory to better understand the concepts and variables in your project?

Developing your own theoretical framework

Hutchison's and Payne's frameworks are helpful for surveying the whole body of literature relevant to social work, which is why they are so widely used. They are one framework, or way of thinking, about all of the theories social workers will encounter that are relevant to practice. Social work researchers should delve further and develop a theoretical or conceptual framework of their own based on their reading of the literature. In Chapter 8 , we will develop your theoretical framework further, identifying the cause-and-effect relationships that answer your working question. Developing a theoretical framework is also instructive for revising and clarifying your working question and identifying concepts that serve as keywords for additional literature searching. The greater clarity you have with your theoretical perspective, the easier each subsequent step in the research process will be.

Getting acquainted with the important theoretical concepts in a new area can be challenging. While social work education provides a broad overview of social theory, you will find much greater fulfillment out of reading about the theories related to your topic area. We discussed some strategies for finding theoretical information in Chapter 3 as part of literature searching. To extend that conversation a bit, some strategies for searching for theories in the literature include:

• Consider searching for these keywords in the title or abstract, specifically
• Looking at the references and cited by links within theoretical articles and textbooks
• Looking at books, edited volumes, and textbooks that discuss theory
• Talking with a scholar on your topic, or asking a professor if they can help connect you to someone
• Nice authors are clear about how they use theory to inform their research project, usually in the introduction and discussion section.
• For example, from the broad umbrella of systems theory, you might pick out family systems theory if you want to understand the effectiveness of a family counseling program.

It's important to remember that knowledge arises within disciplines, and that disciplines have different theoretical frameworks for explaining the same topic. While it is certainly important for the social work perspective to be a part of your analysis, social workers benefit from searching across disciplines to come to a more comprehensive understanding of the topic. Reaching across disciplines can provide uncommon insights during conceptualization, and once the study is completed, a multidisciplinary researcher will be able to share results in a way that speaks to a variety of audiences. A study by An and colleagues (2015) [63] uses game theory from the discipline of economics to understand problems in the Temporary Assistance for Needy Families (TANF) program. In order to receive TANF benefits, mothers must cooperate with paternity and child support requirements unless they have "good cause," as in cases of domestic violence, in which providing that information would put the mother at greater risk of violence. Game theory can help us understand how TANF recipients and caseworkers respond to the incentives in their environment, and highlight why the design of the "good cause" waiver program may not achieve its intended outcome of increasing access to benefits for survivors of family abuse.

Of course, there are natural limits on the depth with which student researchers can and should engage in a search for theory about their topic. At minimum, you should be able to draw connections across studies and be able to assess the relative importance of each theory within the literature. Just because you found one article applying your theory (like game theory, in our example above) does not mean it is important or often used in the domestic violence literature. Indeed, it would be much more common in the family violence literature to find psychological theories of trauma, feminist theories of power and control, and similar theoretical perspectives used to inform research projects rather than game theory, which is equally applicable to survivors of family violence as workers and bosses at a corporation. Consider using the Cited By feature to identify articles, books, and other sources of theoretical information that are seminal or well-cited in the literature. Similarly, by using the name of a theory in the keywords of a search query (along with keywords related to your topic), you can get a sense of how often the theory is used in your topic area. You should have a sense of what theories are commonly used to analyze your topic, even if you end up choosing a different one to inform your project.

Theories that are not cited or used as often are still immensely valuable. As we saw before with TANF and "good cause" waivers, using theories from other disciplines can produce uncommon insights and help you make a new contribution to the social work literature. Given the privileged position that the social work curriculum places on theories developed by white men, students may want to explore Afrocentricity as a social work practice theory (Pellebon, 2007) [64] or abolitionist social work (Jacobs et al., 2021) [65] when deciding on a theoretical framework for their research project that addresses concepts of racial justice. Start with your working question, and explain how each theory helps you answer your question. Some explanations are going to feel right, and some concepts will feel more salient to you than others. Keep in mind that this is an iterative process. Your theoretical framework will likely change as you continue to conceptualize your research project, revise your research question, and design your study.

By trying on many different theoretical explanations for your topic area, you can better clarify your own theoretical framework. Some of you may be fortunate enough to find theories that match perfectly with how you think about your topic, are used often in the literature, and are therefore relatively straightforward to apply. However, many of you may find that a combination of theoretical perspectives is most helpful for you to investigate your project. For example, maybe the group counseling program for which you are evaluating client outcomes draws from both motivational interviewing and cognitive behavioral therapy. In order to understand the change happening in the client population, you would need to know each theory separately as well as how they work in tandem with one another. Because theoretical explanations and even the definitions of concepts are debated by scientists, it may be helpful to find a specific social scientist or group of scientists whose perspective on the topic you find matches with your understanding of the topic. Of course, it is also perfectly acceptable to develop your own theoretical framework, though you should be able to articulate how your framework fills a gap within the literature.

If you are adapting theoretical perspectives in your study, it is important to clarify the original authors' definitions of each concept. Jabareen (2009) [66] offers that conceptual frameworks are not merely collections of concepts but, rather, constructs in which each concept plays an integral role. [67] A conceptual framework is a network of linked concepts that together provide a comprehensive understanding of a phenomenon. Each concept in a conceptual framework plays an ontological or epistemological role in the framework, and it is important to assess whether the concepts and relationships in your framework make sense together. As your framework takes shape, you will find yourself integrating and grouping together concepts, thinking about the most important or least important concepts, and how each concept is causally related to others.

Much like paradigm, theory plays a supporting role for the conceptualization of your research project. Recall the ice float from Figure 7.1. Theoretical explanations support the design and methods you use to answer your research question. In student projects that lack a theoretical framework, I often see the biases and errors in reasoning that we discussed in Chapter 1 that get in the way of good social science. That's because theories mark which concepts are important, provide a framework for understanding them, and measure their interrelationships. If you are missing this foundation, you will operate on informal observation, messages from authority, and other forms of unsystematic and unscientific thinking we reviewed in Chapter 1 .

Theory-informed inquiry is incredibly helpful for identifying key concepts and how to measure them in your research project, but there is a risk in aligning research too closely with theory. The theory-ladenness of facts and observations produced by social science research means that we may be making our ideas real through research. This is a potential source of confirmation bias in social science. Moreover, as Tan (2016) [68] demonstrates, social science often proceeds by adopting as true the perspective of Western and Global North countries, and cross-cultural research is often when ethnocentric and biased ideas are most visible . In her example, a researcher from the West studying teacher-centric classrooms in China that rely partially on rote memorization may view them as less advanced than student-centered classrooms developed in a Western country simply because of Western philosophical assumptions about the importance of individualism and self-determination. Developing a clear theoretical framework is a way to guard against biased research, and it will establish a firm foundation on which you will develop the design and methods for your study.

• Just as empirical evidence is important for conceptualizing a research project, so too are the key concepts and relationships identified by social work theory.
• Using theory your theory textbook will provide you with a sense of the broad theoretical perspectives in social work that might be relevant to your project.
• Try to find small-t theories that are more specific to your topic area and relevant to your working question.
• In Chapter 2 , you developed a concept map for your proposal. Take a moment to revisit your concept map now as your theoretical framework is taking shape. Make any updates to the key concepts and relationships in your concept map. . If you need a refresher, we have embedded a short how-to video from the University of Guelph Library (CC-BY-NC-SA 4.0) that we also used in Chapter 2 .

11.2 Conceptual definitions

• Define measurement and conceptualization
• Apply Kaplan’s three categories to determine the complexity of measuring a given variable
• Identify the role previous research and theory play in defining concepts
• Distinguish between unidimensional and multidimensional concepts
• Critically apply reification to how you conceptualize the key variables in your research project

In social science, when we use the term  measurement , we mean the process by which we describe and ascribe meaning to the key facts, concepts, or other phenomena that we are investigating. At its core, measurement is about defining one’s terms in as clear and precise a way as possible. Of course, measurement in social science isn’t quite as simple as using a measuring cup or spoon, but there are some basic tenets on which most social scientists agree when it comes to measurement. We’ll explore those, as well as some of the ways that measurement might vary depending on your unique approach to the study of your topic.

An important point here is that measurement does not require any particular instruments or procedures. What it does require is a systematic procedure for assigning scores, meanings, and descriptions to individuals or objects so that those scores represent the characteristic of interest. You can measure phenomena in many different ways, but you must be sure that how you choose to measure gives you information and data that lets you answer your research question. If you're looking for information about a person's income, but your main points of measurement have to do with the money they have in the bank, you're not really going to find the information you're looking for!

The question of what social scientists measure can be answered by asking yourself what social scientists study. Think about the topics you’ve learned about in other social work classes you’ve taken or the topics you’ve considered investigating yourself. Let’s consider Melissa Milkie and Catharine Warner’s study (2011) [69] of first graders’ mental health. In order to conduct that study, Milkie and Warner needed to have some idea about how they were going to measure mental health. What does mental health mean, exactly? And how do we know when we’re observing someone whose mental health is good and when we see someone whose mental health is compromised? Understanding how measurement works in research methods helps us answer these sorts of questions.

As you might have guessed, social scientists will measure just about anything that they have an interest in investigating. For example, those who are interested in learning something about the correlation between social class and levels of happiness must develop some way to measure both social class and happiness. Those who wish to understand how well immigrants cope in their new locations must measure immigrant status and coping. Those who wish to understand how a person’s gender shapes their workplace experiences must measure gender and workplace experiences (and get more specific about which experiences are under examination). You get the idea. Social scientists can and do measure just about anything you can imagine observing or wanting to study. Of course, some things are easier to observe or measure than others.

Observing your variables

In 1964, philosopher Abraham Kaplan (1964) [70] wrote The   Conduct of Inquiry,  which has since become a classic work in research methodology (Babbie, 2010). [71] In his text, Kaplan describes different categories of things that behavioral scientists observe. One of those categories, which Kaplan called “observational terms,” is probably the simplest to measure in social science. Observational terms are the sorts of things that we can see with the naked eye simply by looking at them. Kaplan roughly defines them as conditions that are easy to identify and verify through direct observation. If, for example, we wanted to know how the conditions of playgrounds differ across different neighborhoods, we could directly observe the variety, amount, and condition of equipment at various playgrounds.

Indirect observables , on the other hand, are less straightforward to assess. In Kaplan's framework, they are conditions that are subtle and complex that we must use existing knowledge and intuition to define. If we conducted a study for which we wished to know a person’s income, we’d probably have to ask them their income, perhaps in an interview or a survey. Thus, we have observed income, even if it has only been observed indirectly. Birthplace might be another indirect observable. We can ask study participants where they were born, but chances are good we won’t have directly observed any of those people being born in the locations they report.

Sometimes the measures that we are interested in are more complex and more abstract than observational terms or indirect observables. Think about some of the concepts you’ve learned about in other social work classes—for example, ethnocentrism. What is ethnocentrism? Well, from completing an introduction to social work class you might know that it has something to do with the way a person judges another’s culture. But how would you  measure  it? Here’s another construct: bureaucracy. We know this term has something to do with organizations and how they operate but measuring such a construct is trickier than measuring something like a person’s income. The theoretical concepts of ethnocentrism and bureaucracy represent ideas whose meanings we have come to agree on. Though we may not be able to observe these abstractions directly, we can observe their components.

Kaplan referred to these more abstract things that behavioral scientists measure as constructs.  Constructs  are “not observational either directly or indirectly” (Kaplan, 1964, p. 55), [72] but they can be defined based on observables. For example, the construct of bureaucracy could be measured by counting the number of supervisors that need to approve routine spending by public administrators. The greater the number of administrators that must sign off on routine matters, the greater the degree of bureaucracy. Similarly, we might be able to ask a person the degree to which they trust people from different cultures around the world and then assess the ethnocentrism inherent in their answers. We can measure constructs like bureaucracy and ethnocentrism by defining them in terms of what we can observe. [73]

The idea of coming up with your own measurement tool might sound pretty intimidating at this point. The good news is that if you find something in the literature that works for you, you can use it (with proper attribution, of course). If there are only pieces of it that you like, you can reuse those pieces (with proper attribution and describing/justifying any changes). You don't always have to start from scratch!

Look at the variables in your research question.

• Classify them as direct observables, indirect observables, or constructs.
• Do you think measuring them will be easy or hard?
• What are your first thoughts about how to measure each variable? No wrong answers here, just write down a thought about each variable.

Measurement starts with conceptualization

In order to measure the concepts in your research question, we first have to understand what we think about them. As an aside, the word concept  has come up quite a bit, and it is important to be sure we have a shared understanding of that term. A  concept is the notion or image that we conjure up when we think of some cluster of related observations or ideas. For example, masculinity is a concept. What do you think of when you hear that word? Presumably, you imagine some set of behaviors and perhaps even a particular style of self-presentation. Of course, we can’t necessarily assume that everyone conjures up the same set of ideas or images when they hear the word  masculinity . While there are many possible ways to define the term and some may be more common or have more support than others, there is no universal definition of masculinity. What counts as masculine may shift over time, from culture to culture, and even from individual to individual (Kimmel, 2008). This is why defining our concepts is so important.\

Not all researchers clearly explain their theoretical or conceptual framework for their study, but they should! Without understanding how a researcher has defined their key concepts, it would be nearly impossible to understand the meaning of that researcher’s findings and conclusions. Back in Chapter 7 , you developed a theoretical framework for your study based on a survey of the theoretical literature in your topic area. If you haven't done that yet, consider flipping back to that section to familiarize yourself with some of the techniques for finding and using theories relevant to your research question. Continuing with our example on masculinity, we would need to survey the literature on theories of masculinity. After a few queries on masculinity, I found a wonderful article by Wong (2010) [74] that analyzed eight years of the journal Psychology of Men & Masculinity and analyzed how often different theories of masculinity were used . Not only can I get a sense of which theories are more accepted and which are more marginal in the social science on masculinity, I am able to identify a range of options from which I can find the theory or theories that will inform my project. 

Identify a specific theory (or more than one theory) and how it helps you understand...

• Your independent variable(s).
• Your dependent variable(s).
• The relationship between your independent and dependent variables.

Rather than completing this exercise from scratch, build from your theoretical or conceptual framework developed in previous chapters.

In quantitative methods, conceptualization involves writing out clear, concise definitions for our key concepts. These are the kind of definitions you are used to, like the ones in a dictionary. A conceptual definition involves defining a concept in terms of other concepts, usually by making reference to how other social scientists and theorists have defined those concepts in the past. Of course, new conceptual definitions are created all the time because our conceptual understanding of the world is always evolving.

Conceptualization is deceptively challenging—spelling out exactly what the concepts in your research question mean to you. Following along with our example, think about what comes to mind when you read the term masculinity. How do you know masculinity when you see it? Does it have something to do with men or with social norms? If so, perhaps we could define masculinity as the social norms that men are expected to follow. That seems like a reasonable start, and at this early stage of conceptualization, brainstorming about the images conjured up by concepts and playing around with possible definitions is appropriate. However, this is just the first step. At this point, you should be beyond brainstorming for your key variables because you have read a good amount of research about them

In addition, we should consult previous research and theory to understand the definitions that other scholars have already given for the concepts we are interested in. This doesn’t mean we must use their definitions, but understanding how concepts have been defined in the past will help us to compare our conceptualizations with how other scholars define and relate concepts. Understanding prior definitions of our key concepts will also help us decide whether we plan to challenge those conceptualizations or rely on them for our own work. Finally, working on conceptualization is likely to help in the process of refining your research question to one that is specific and clear in what it asks. Conceptualization and operationalization (next section) are where "the rubber meets the road," so to speak, and you have to specify what you mean by the question you are asking. As your conceptualization deepens, you will often find that your research question becomes more specific and clear.

If we turn to the literature on masculinity, we will surely come across work by Michael Kimmel , one of the preeminent masculinity scholars in the United States. After consulting Kimmel’s prior work (2000; 2008), [75] we might tweak our initial definition of masculinity. Rather than defining masculinity as “the social norms that men are expected to follow,” perhaps instead we’ll define it as “the social roles, behaviors, and meanings prescribed for men in any given society at any one time” (Kimmel & Aronson, 2004, p. 503). [76] Our revised definition is more precise and complex because it goes beyond addressing one aspect of men’s lives (norms), and addresses three aspects: roles, behaviors, and meanings. It also implies that roles, behaviors, and meanings may vary across societies and over time. Using definitions developed by theorists and scholars is a good idea, though you may find that you want to define things your own way.

As you can see, conceptualization isn’t as simple as applying any random definition that we come up with to a term. Defining our terms may involve some brainstorming at the very beginning. But conceptualization must go beyond that, to engage with or critique existing definitions and conceptualizations in the literature. Once we’ve brainstormed about the images associated with a particular word, we should also consult prior work to understand how others define the term in question. After we’ve identified a clear definition that we’re happy with, we should make sure that every term used in our definition will make sense to others. Are there terms used within our definition that also need to be defined? If so, our conceptualization is not yet complete. Our definition includes the concept of "social roles," so we should have a definition for what those mean and become familiar with role theory to help us with our conceptualization. If we don't know what roles are, how can we study them?

Let's say we do all of that. We have a clear definition of the term masculinity with reference to previous literature and we also have a good understanding of the terms in our conceptual definition...then we're done, right? Not so fast. You’ve likely met more than one man in your life, and you’ve probably noticed that they are not the same, even if they live in the same society during the same historical time period. This could mean there are dimensions of masculinity. In terms of social scientific measurement, concepts can be said to have multiple dimensions  when there are multiple elements that make up a single concept. With respect to the term  masculinity , dimensions could based on gender identity, gender performance, sexual orientation, etc.. In any of these cases, the concept of masculinity would be considered to have multiple dimensions.

While you do not need to spell out every possible dimension of the concepts you wish to measure, it is important to identify whether your concepts are unidimensional (and therefore relatively easy to define and measure) or multidimensional (and therefore require multi-part definitions and measures). In this way, how you conceptualize your variables determines how you will measure them in your study. Unidimensional concepts are those that are expected to have a single underlying dimension. These concepts can be measured using a single measure or test. Examples include simple concepts such as a person’s weight, time spent sleeping, and so forth. 

One frustrating this is that there is no clear demarcation between concepts that are inherently unidimensional or multidimensional. Even something as simple as age could be broken down into multiple dimensions including mental age and chronological age, so where does conceptualization stop? How far down the dimensional rabbit hole do we have to go? Researchers should consider two things. First, how important is this variable in your study? If age is not important in your study (maybe it is a control variable), it seems like a waste of time to do a lot of work drawing from developmental theory to conceptualize this variable. A unidimensional measure from zero to dead is all the detail we need. On the other hand, if we were measuring the impact of age on masculinity, conceptualizing our independent variable (age) as multidimensional may provide a richer understanding of its impact on masculinity. Finally, your conceptualization will lead directly to your operationalization of the variable, and once your operationalization is complete, make sure someone reading your study could follow how your conceptual definitions informed the measures you chose for your variables. 

Write a conceptual definition for your independent and dependent variables.

• Cite and attribute definitions to other scholars, if you use their words.
• Describe how your definitions are informed by your theoretical framework.
• Place your definition in conversation with other theories and conceptual definitions commonly used in the literature.
• Are there multiple dimensions of your variables?
• Are any of these dimensions important for you to measure?

Do researchers actually know what we're talking about?

Conceptualization proceeds differently in qualitative research compared to quantitative research. Since qualitative researchers are interested in the understandings and experiences of their participants, it is less important for them to find one fixed definition for a concept before starting to interview or interact with participants. The researcher’s job is to accurately and completely represent how their participants understand a concept, not to test their own definition of that concept.

If you were conducting qualitative research on masculinity, you would likely consult previous literature like Kimmel’s work mentioned above. From your literature review, you may come up with a  working definition  for the terms you plan to use in your study, which can change over the course of the investigation. However, the definition that matters is the definition that your participants share during data collection. A working definition is merely a place to start, and researchers should take care not to think it is the only or best definition out there.

In qualitative inquiry, your participants are the experts (sound familiar, social workers?) on the concepts that arise during the research study. Your job as the researcher is to accurately and reliably collect and interpret their understanding of the concepts they describe while answering your questions. Conceptualization of concepts is likely to change over the course of qualitative inquiry, as you learn more information from your participants. Indeed, getting participants to comment on, extend, or challenge the definitions and understandings of other participants is a hallmark of qualitative research. This is the opposite of quantitative research, in which definitions must be completely set in stone before the inquiry can begin.

The contrast between qualitative and quantitative conceptualization is instructive for understanding how quantitative methods (and positivist research in general) privilege the knowledge of the researcher over the knowledge of study participants and community members. Positivism holds that the researcher is the "expert," and can define concepts based on their expert knowledge of the scientific literature. This knowledge is in contrast to the lived experience that participants possess from experiencing the topic under examination day-in, day-out. For this reason, it would be wise to remind ourselves not to take our definitions too seriously and be critical about the limitations of our knowledge.

Conceptualization must be open to revisions, even radical revisions, as scientific knowledge progresses. While I’ve suggested consulting prior scholarly definitions of our concepts, you should not assume that prior, scholarly definitions are more real than the definitions we create. Likewise, we should not think that our own made-up definitions are any more real than any other definition. It would also be wrong to assume that just because definitions exist for some concept that the concept itself exists beyond some abstract idea in our heads. Building on the paradigmatic ideas behind interpretivism and the critical paradigm, researchers call the assumption that our abstract concepts exist in some concrete, tangible way is known as reification . It explores the power dynamics behind how we can create reality by how we define it.

Returning again to our example of masculinity. Think about our how our notions of masculinity have developed over the past few decades, and how different and yet so similar they are to patriarchal definitions throughout history. Conceptual definitions become more or less popular based on the power arrangements inside of social science the broader world. Western knowledge systems are privileged, while others are viewed as unscientific and marginal. The historical domination of social science by white men from WEIRD countries meant that definitions of masculinity were imbued their cultural biases and were designed explicitly and implicitly to preserve their power. This has inspired movements for cognitive justice as we seek to use social science to achieve global development.

• Measurement is the process by which we describe and ascribe meaning to the key facts, concepts, or other phenomena that we are investigating.
• Kaplan identified three categories of things that social scientists measure including observational terms, indirect observables, and constructs.
• Some concepts have multiple elements or dimensions.
• Researchers often use measures previously developed and studied by other researchers.
• Conceptualization is a process that involves coming up with clear, concise definitions.
• Conceptual definitions are based on the theoretical framework you are using for your study (and the paradigmatic assumptions underlying those theories).
• Whether your conceptual definitions come from your own ideas or the literature, you should be able to situate them in terms of other commonly used conceptual definitions.
• Researchers should acknowledge the limited explanatory power of their definitions for concepts and how oppression can shape what explanations are considered true or scientific.

Think historically about the variables in your research question.

• How has our conceptual definition of your topic changed over time?
• What scholars or social forces were responsible for this change?

Take a critical look at your conceptual definitions.

• How participants might define terms for themselves differently, in terms of their daily experience?
• On what cultural assumptions are your conceptual definitions based?
• Are your conceptual definitions applicable across all cultures that will be represented in your sample?

11.3 Inductive and deductive reasoning

• Describe inductive and deductive reasoning and provide examples of each
• Identify how inductive and deductive reasoning are complementary

Congratulations! You survived the chapter on theories and paradigms. My experience has been that many students have a difficult time thinking about theories and paradigms because they perceive them as "intangible" and thereby hard to connect to social work research. I even had one student who said she got frustrated just reading the word "philosophy."

Rest assured, you do not need to become a theorist or philosopher to be an effective social worker or researcher. However, you should have a good sense of what theory or theories will be relevant to your project, as well as how this theory, along with your working question, fit within the three broad research paradigms we reviewed. If you don't have a good idea about those at this point, it may be a good opportunity to pause and read more about the theories related to your topic area.

Theories structure and inform social work research. The converse is also true: research can structure and inform theory. The reciprocal relationship between theory and research often becomes evident to students when they consider the relationships between theory and research in inductive and deductive approaches to research. In both cases, theory is crucial. But the relationship between theory and research differs for each approach.

While inductive and deductive approaches to research are quite different, they can also be complementary. Let’s start by looking at each one and how they differ from one another. Then we’ll move on to thinking about how they complement one another.

Inductive reasoning

A researcher using inductive reasoning begins by collecting data that is relevant to their topic of interest. Once a substantial amount of data have been collected, the researcher will then step back from data collection to get a bird’s eye view of their data. At this stage, the researcher looks for patterns in the data, working to develop a theory that could explain those patterns. Thus, when researchers take an inductive approach, they start with a particular set of observations and move to a more general set of propositions about those experiences. In other words, they move from data to theory, or from the specific to the general. Figure 8.1 outlines the steps involved with an inductive approach to research.

There are many good examples of inductive research, but we’ll look at just a few here. One fascinating study in which the researchers took an inductive approach is Katherine Allen, Christine Kaestle, and Abbie Goldberg’s (2011) [77] study of how boys and young men learn about menstruation. To understand this process, Allen and her colleagues analyzed the written narratives of 23 young cisgender men in which the men described how they learned about menstruation, what they thought of it when they first learned about it, and what they think of it now. By looking for patterns across all 23 cisgender men’s narratives, the researchers were able to develop a general theory of how boys and young men learn about this aspect of girls’ and women’s biology. They conclude that sisters play an important role in boys’ early understanding of menstruation, that menstruation makes boys feel somewhat separated from girls, and that as they enter young adulthood and form romantic relationships, young men develop more mature attitudes about menstruation. Note how this study began with the data—men’s narratives of learning about menstruation—and worked to develop a theory.

In another inductive study, Kristin Ferguson and colleagues (Ferguson, Kim, & McCoy, 2011) [78] analyzed empirical data to better understand how to meet the needs of young people who are homeless. The authors analyzed focus group data from 20 youth at a homeless shelter. From these data they developed a set of recommendations for those interested in applied interventions that serve homeless youth. The researchers also developed hypotheses for others who might wish to conduct further investigation of the topic. Though Ferguson and her colleagues did not test their hypotheses, their study ends where most deductive investigations begin: with a theory and a hypothesis derived from that theory. Section 8.4 discusses the use of mixed methods research as a way for researchers to test hypotheses created in a previous component of the same research project.

You will notice from both of these examples that inductive reasoning is most commonly found in studies using qualitative methods, such as focus groups and interviews. Because inductive reasoning involves the creation of a new theory, researchers need very nuanced data on how the key concepts in their working question operate in the real world. Qualitative data is often drawn from lengthy interactions and observations with the individuals and phenomena under examination. For this reason, inductive reasoning is most often associated with qualitative methods, though it is used in both quantitative and qualitative research.

Deductive reasoning

If inductive reasoning is about creating theories from raw data, deductive reasoning is about testing theories using data. Researchers using deductive reasoning take the steps described earlier for inductive research and reverse their order. They start with a compelling social theory, create a hypothesis about how the world should work, collect raw data, and analyze whether their hypothesis was confirmed or not. That is, deductive approaches move from a more general level (theory) to a more specific (data); whereas inductive approaches move from the specific (data) to general (theory).

A deductive approach to research is the one that people typically associate with scientific investigation. Students in English-dominant countries that may be confused by inductive vs. deductive research can rest part of the blame on Sir Arthur Conan Doyle, creator of the Sherlock Holmes character. As Craig Vasey points out in his breezy introduction to logic book chapter , Sherlock Holmes more often used inductive rather than deductive reasoning (despite claiming to use the powers of deduction to solve crimes). By noticing subtle details in how people act, behave, and dress, Holmes finds patterns that others miss. Using those patterns, he creates a theory of how the crime occurred, dramatically revealed to the authorities just in time to arrest the suspect. Indeed, it is these flashes of insight into the patterns of data that make Holmes such a keen inductive reasoner. In social work practice, rather than detective work, inductive reasoning is supported by the intuitions and practice wisdom of social workers, just as Holmes' reasoning is sharpened by his experience as a detective.

So, if deductive reasoning isn't Sherlock Holmes' observation and pattern-finding, how does it work? It starts with what you have already done in Chapters 3 and 4, reading and evaluating what others have done to study your topic. It continued with Chapter 5, discovering what theories already try to explain how the concepts in your working question operate in the real world. Tapping into this foundation of knowledge on their topic, the researcher studies what others have done, reads existing theories of whatever phenomenon they are studying, and then tests hypotheses that emerge from those theories. Figure 8.2 outlines the steps involved with a deductive approach to research.

While not all researchers follow a deductive approach, many do. We’ll now take a look at a couple excellent recent examples of deductive research. 

In a study of US law enforcement responses to hate crimes, Ryan King and colleagues (King, Messner, & Baller, 2009) [79] hypothesized that law enforcement’s response would be less vigorous in areas of the country that had a stronger history of racial violence. The authors developed their hypothesis from prior research and theories on the topic. They tested the hypothesis by analyzing data on states’ lynching histories and hate crime responses. Overall, the authors found support for their hypothesis and illustrated an important application of critical race theory.

In another recent deductive study, Melissa Milkie and Catharine Warner (2011) [80] studied the effects of different classroom environments on first graders’ mental health. Based on prior research and theory, Milkie and Warner hypothesized that negative classroom features, such as a lack of basic supplies and heat, would be associated with emotional and behavioral problems in children. One might associate this research with Maslow's hierarchy of needs or systems theory. The researchers found support for their hypothesis, demonstrating that policymakers should be paying more attention to the mental health outcomes of children’s school experiences, just as they track academic outcomes (American Sociological Association, 2011). [81]

Complementary approaches

While inductive and deductive approaches to research seem quite different, they can actually be rather complementary. In some cases, researchers will plan for their study to include multiple components, one inductive and the other deductive. In other cases, a researcher might begin a study with the plan to conduct either inductive or deductive research, but then discovers along the way that the other approach is needed to help illuminate findings. Here is an example of each such case.

Dr. Amy Blackstone (n.d.), author of Principles of sociological inquiry: Qualitative and quantitative methods , relates a story about her mixed methods research on sexual harassment.

We began the study knowing that we would like to take both a deductive and an inductive approach in our work. We therefore administered a quantitative survey, the responses to which we could analyze in order to test hypotheses, and also conducted qualitative interviews with a number of the survey participants. The survey data were well suited to a deductive approach; we could analyze those data to test hypotheses that were generated based on theories of harassment. The interview data were well suited to an inductive approach; we looked for patterns across the interviews and then tried to make sense of those patterns by theorizing about them. For one paper (Uggen & Blackstone, 2004) [82] , we began with a prominent feminist theory of the sexual harassment of adult women and developed a set of hypotheses outlining how we expected the theory to apply in the case of younger women’s and men’s harassment experiences. We then tested our hypotheses by analyzing the survey data. In general, we found support for the theory that posited that the current gender system, in which heteronormative men wield the most power in the workplace, explained workplace sexual harassment—not just of adult women but of younger women and men as well. In a more recent paper (Blackstone, Houle, & Uggen, 2006), [83] we did not hypothesize about what we might find but instead inductively analyzed interview data, looking for patterns that might tell us something about how or whether workers’ perceptions of harassment change as they age and gain workplace experience. From this analysis, we determined that workers’ perceptions of harassment did indeed shift as they gained experience and that their later definitions of harassment were more stringent than those they held during adolescence. Overall, our desire to understand young workers’ harassment experiences fully—in terms of their objective workplace experiences, their perceptions of those experiences, and their stories of their experiences—led us to adopt both deductive and inductive approaches in the work. (Blackstone, n.d., p. 21) [84]

Researchers may not always set out to employ both approaches in their work but sometimes find that their use of one approach leads them to the other. One such example is described eloquently in Russell Schutt’s  Investigating the Social World (2006). [85] As Schutt describes, researchers Sherman and Berk (1984) [86] conducted an experiment to test two competing theories of the effects of punishment on deterring deviance (in this case, domestic violence).Specifically, Sherman and Berk hypothesized that deterrence   theory (see Williams, 2005 [87] for more information on that theory) would provide a better explanation of the effects of arresting accused batterers than labeling theory . Deterrence theory predicts that arresting an accused spouse batterer will  reduce  future incidents of violence. Conversely, labeling theory predicts that arresting accused spouse batterers will  increase  future incidents (see Policastro & Payne, 2013 [88] for more information on that theory). Figure 8.3 summarizes the two competing theories and the hypotheses Sherman and Berk set out to test.

Research from these follow-up studies were mixed. In some cases, arrest deterred future incidents of violence. In other cases, it did not. This left the researchers with new data that they needed to explain. The researchers therefore took an inductive approach in an effort to make sense of their latest empirical observations. The new studies revealed that arrest seemed to have a deterrent effect for those who were married and employed, but that it led to increased offenses for those who were unmarried and unemployed. Researchers thus turned to control theory, which posits that having some stake in conformity through the social ties provided by marriage and employment, as the better explanation (see Davis et al., 2000 [90] for more information on this theory).

What the original Sherman and Berk study, along with the follow-up studies, show us is that we might start with a deductive approach to research, but then, if confronted by new data we must make sense of, we may move to an inductive approach. We will expand on these possibilities in section 8.4 when we discuss mixed methods research.

Ethical and critical considerations

Deductive and inductive reasoning, just like other components of the research process comes with ethical and cultural considerations for researchers. Specifically, deductive research is limited by existing theory. Because scientific inquiry has been shaped by oppressive forces such as sexism, racism, and colonialism, what is considered theory is largely based in Western, white-male-dominant culture. Thus, researchers doing deductive research may artificially limit themselves to ideas that were derived from this context. Non-Western researchers, international social workers, and practitioners working with non-dominant groups may find deductive reasoning of limited help if theories do not adequately describe other cultures.

While these flaws in deductive research may make inductive reasoning seem more appealing, on closer inspection you'll find similar issues apply. A researcher using inductive reasoning applies their intuition and lived experience when analyzing participant data. They will take note of particular themes, conceptualize their definition, and frame the project using their unique psychology. Since everyone's internal world is shaped by their cultural and environmental context, inductive reasoning conducted by Western researchers may unintentionally reinforcing lines of inquiry that derive from cultural oppression.

Inductive reasoning is also shaped by those invited to provide the data to be analyzed. For example, I recently worked with a student who wanted to understand the impact of child welfare supervision on children born dependent on opiates and methamphetamine. Due to the potential harm that could come from interviewing families and children who are in foster care or under child welfare supervision, the researcher decided to use inductive reasoning and to only interview child welfare workers.

Talking to practitioners is a good idea for feasibility, as they are less vulnerable than clients. However, any theory that emerges out of these observations will be substantially limited, as it would be devoid of the perspectives of parents, children, and other community members who could provide a more comprehensive picture of the impact of child welfare involvement on children. Notice that each of these groups has less power than child welfare workers in the service relationship. Attending to which groups were used to inform the creation of a theory and the power of those groups is an important critical consideration for social work researchers.

As you can see, when researchers apply theory to research they must wrestle with the history and hierarchy around knowledge creation in that area. In deductive studies, the researcher is positioned as the expert, similar to the positivist paradigm presented in Chapter 5. We've discussed a few of the limitations on the knowledge of researchers in this subsection, but the position of the "researcher as expert" is inherently problematic. However, it should also not be taken to an extreme. A researcher who approaches inductive inquiry as a naïve learner is also inherently problematic. Just as competence in social work practice requires a baseline of knowledge prior to entering practice, so does competence in social work research. Because a truly naïve intellectual position is impossible—we all have preexisting ways we view the world and are not fully aware of how they may impact our thoughts—researchers should be well-read in the topic area of their research study but humble enough to know that there is always much more to learn.

• Inductive reasoning begins with a set of empirical observations, seeking patterns in those observations, and then theorizing about those patterns.
• Deductive reasoning begins with a theory, developing hypotheses from that theory, and then collecting and analyzing data to test the truth of those hypotheses.
• Inductive and deductive reasoning can be employed together for a more complete understanding of the research topic.
• Though researchers don’t always set out to use both inductive and deductive reasoning in their work, they sometimes find that new questions arise in the course of an investigation that can best be answered by employing both approaches.
• Identify one theory and how it helps you understand your topic and working question.

I encourage you to find a specific theory from your topic area, rather than relying only on the broad theoretical perspectives like systems theory or the strengths perspective. Those broad theoretical perspectives are okay...but I promise that searching for theories about your topic will help you conceptualize and design your research project.

• Using the theory you identified, describe what you expect the answer to be to your working question.
• Define and provide an example of idiographic causal relationships
• Describe the role of causality in quantitative research as compared to qualitative research
• Identify, define, and describe each of the main criteria for nomothetic causal relationships
• Describe the difference between and provide examples of independent, dependent, and control variables
• Define hypothesis, state a clear hypothesis, and discuss the respective roles of quantitative and qualitative research when it comes to hypotheses

Causality  refers to the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief. In other words, it is about cause and effect. It seems simple, but you may be surprised to learn there is more than one way to explain how one thing causes another. How can that be? How could there be many ways to understand causality?

Think back to our discussion in Section 5.3 on paradigms [insert chapter link plus link to section 1.2]. You’ll remember the positivist paradigm as the one that believes in objectivity. Positivists look for causal explanations that are universally true for everyone, everywhere  because they seek objective truth. Interpretivists, on the other hand, look for causal explanations that are true for individuals or groups in a specific time and place because they seek subjective truths. Remember that for interpretivists, there is not one singular truth that is true for everyone, but many truths created and shared by others.

"Are you trying to generalize or nah?"

One of my favorite classroom moments occurred in the early days of my teaching career. Students were providing peer feedback on their working questions. I overheard one group who was helping someone rephrase their research question. A student asked, “Are you trying to generalize or nah?” Teaching is full of fun moments like that one. Answering that one question can help you understand how to conceptualize and design your research project.

Nomothetic causal explanations are incredibly powerful. They allow scientists to make predictions about what will happen in the future, with a certain margin of error. Moreover, they allow scientists to generalize —that is, make claims about a large population based on a smaller sample of people or items. Generalizing is important. We clearly do not have time to ask everyone their opinion on a topic or test a new intervention on every person. We need a type of causal explanation that helps us predict and estimate truth in all situations.

Generally, nomothetic causal relationships work best for explanatory research projects [INSERT SECTION LINK]. They also tend to use quantitative research: by boiling things down to numbers, one can use the universal language of mathematics to use statistics to explore those relationships. On the other hand, descriptive and exploratory projects often fit better with idiographic causality. These projects do not usually try to generalize, but instead investigate what is true for individuals, small groups, or communities at a specific point in time. You will learn about this type of causality in the next section. Here, we will assume you have an explanatory working question. For example, you may want to know about the risk and protective factors for a specific diagnosis or how a specific therapy impacts client outcomes.

What do nomothetic causal explanations look like?

Nomothetic causal explanations express relationships between variables . The term variable has a scientific definition. This one from Gillespie & Wagner (2018) "a logical grouping of attributes that can be observed and measured and is expected to vary from person to person in a population" (p. 9). [91] More practically, variables are the key concepts in your working question. You know, the things you plan to observe when you actually do your research project, conduct your surveys, complete your interviews, etc. These things have two key properties. First, they vary , as in they do not remain constant. "Age" varies by number. "Gender" varies by category. But they both vary. Second, they have attributes . So the variable "health professions" has attributes or categories, such as social worker, nurse, counselor, etc.

It's also worth reviewing what is  not a variable. Well, things that don't change (or vary) aren't variables. If you planned to do a study on how gender impacts earnings but your study only contained women, that concept would not vary . Instead, it would be a constant . Another common mistake I see in students' explanatory questions is mistaking an attribute for a variable. "Men" is not a variable. "Gender" is a variable. "Virginia" is not a variable. The variable is the "state or territory" in which someone or something is physically located.

When one variable causes another, we have what researchers call independent and dependent variables. For example, in a study investigating the impact of spanking on aggressive behavior, spanking would be the independent variable and aggressive behavior would be the dependent variable. An independent variable is the cause, and a  dependent variable  is the effect. Why are they called that? Dependent variables  depend on independent variables. If all of that gets confusing, just remember the graphical relationship in Figure 8.5.

Write out your working question, as it exists now. As we said previously in the subsection, we assume you have an explanatory research question for learning this section.

• Write out a diagram similar to Figure 8.5.
• Put your independent variable on the left and the dependent variable on the right.
• Can your variables vary?
• Do they have different attributes or categories that vary from person to person?
• How does the theory you identified in section 8.1 help you understand this causal relationship?

If the theory you've identified isn't much help to you or seems unrelated, it's a good indication that you need to read more literature about the theories related to your topic.

For some students, your working question may not be specific enough to list an independent or dependent variable clearly. You may have "risk factors" in place of an independent variable, for example. Or "effects" as a dependent variable. If that applies to your research question, get specific for a minute even if you have to revise this later. Think about which specific risk factors or effects you are interested in. Consider a few options for your independent and dependent variable and create diagrams similar to Figure 8.5.

Finally, you are likely to revisit your working question so you may have to come back to this exercise to clarify the causal relationship you want to investigate.

For a ten-cent word like "nomothetic," these causal relationships should look pretty basic to you. They should look like "x causes y." Indeed, you may be looking at your causal explanation and thinking, "wow, there are so many other things I'm missing in here." In fact, maybe my dependent variable sometimes causes changes in my independent variable! For example, a working question asking about poverty and education might ask how poverty makes it more difficult to graduate college or how high college debt impacts income inequality after graduation. Nomothetic causal relationships are slices of reality. They boil things down to two (or often more) key variables and assert a one-way causal explanation between them. This is by design, as they are trying to generalize across all people to all situations. The more complicated, circular, and often contradictory causal explanations are idiographic, which we will cover in the next section of this chapter.

Developing a hypothesis

A hypothesis   is a statement describing a researcher’s expectation regarding what they anticipate finding. Hypotheses in quantitative research are a nomothetic causal relationship that the researcher expects to determine is true or false. A hypothesis is written to describe the expected relationship between the independent and dependent variables. In other words, write the answer to your working question using your variables. That's your hypothesis! Make sure you haven't introduced new variables into your hypothesis that are not in your research question. If you have, write out your hypothesis as in Figure 8.5.

A good hypothesis should be testable using social science research methods. That is, you can use a social science research project (like a survey or experiment) to test whether it is true or not. A good hypothesis is also  specific about the relationship it explores. For example, a student project that hypothesizes, "families involved with child welfare agencies will benefit from Early Intervention programs," is not specific about what benefits it plans to investigate. For this student, I advised her to take a look at the empirical literature and theory about Early Intervention and see what outcomes are associated with these programs. This way, she could  more clearly state the dependent variable in her hypothesis, perhaps looking at reunification, attachment, or developmental milestone achievement in children and families under child welfare supervision.

Your hypothesis should be an informed prediction based on a theory or model of the social world. For example, you may hypothesize that treating mental health clients with warmth and positive regard is likely to help them achieve their therapeutic goals. That hypothesis would be based on the humanistic practice models of Carl Rogers. Using previous theories to generate hypotheses is an example of deductive research. If Rogers’ theory of unconditional positive regard is accurate, a study comparing clinicians who used it versus those who did not would show more favorable treatment outcomes for clients receiving unconditional positive regard.

Let’s consider a couple of examples. In research on sexual harassment (Uggen & Blackstone, 2004), [92] one might hypothesize, based on feminist theories of sexual harassment, that more females than males will experience specific sexually harassing behaviors. What is the causal relationship being predicted here? Which is the independent and which is the dependent variable? In this case, researchers hypothesized that a person’s sex (independent variable) would predict their likelihood to experience sexual harassment (dependent variable).

Sometimes researchers will hypothesize that a relationship will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the relationship between age and support for legalization of marijuana. Perhaps you’ve taken a sociology class and, based on the theories you’ve read, you hypothesize that age is negatively related to support for marijuana legalization. [93] What have you just hypothesized?

You have hypothesized that as people get older, the likelihood of their supporting marijuana legalization decreases. Thus, as age (your independent variable) moves in one direction (up), support for marijuana legalization (your dependent variable) moves in another direction (down). So, a direct relationship (or positive correlation) involve two variables going in the same direction and an inverse relationship (or negative correlation) involve two variables going in opposite directions. If writing hypotheses feels tricky, it is sometimes helpful to draw them out and depict each of the two hypotheses we have just discussed.

It’s important to note that once a study starts, it is unethical to change your hypothesis to match the data you find. For example, what happens if you conduct a study to test the hypothesis from Figure 8.7 on support for marijuana legalization, but you find no relationship between age and support for legalization? It means that your hypothesis was incorrect, but that’s still valuable information. It would challenge what the existing literature says on your topic, demonstrating that more research needs to be done to figure out the factors that impact support for marijuana legalization. Don’t be embarrassed by negative results, and definitely don’t change your hypothesis to make it appear correct all along!

Criteria for establishing a nomothetic causal relationship

Let’s say you conduct your study and you find evidence that supports your hypothesis, as age increases, support for marijuana legalization decreases. Success! Causal explanation complete, right? Not quite.

You’ve only established one of the criteria for causality. The criteria for causality must include all of the following: covariation, plausibility, temporality, and nonspuriousness. In our example from Figure 8.7, we have established only one criteria—covariation. When variables covary , they vary together. Both age and support for marijuana legalization vary in our study. Our sample contains people of varying ages and varying levels of support for marijuana legalization. If, for example, we only included 16-year-olds in our study, age would be a  constant , not a variable.

Just because there might be some correlation between two variables does not mean that a causal relationship between the two is really plausible. Plausibility means that in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense. It makes sense that people from previous generations would have different attitudes towards marijuana than younger generations. People who grew up in the time of Reefer Madness or the hippies may hold different views than those raised in an era of legalized medicinal and recreational use of marijuana. Plausibility is of course helped by basing your causal explanation in existing theoretical and empirical findings.

Once we’ve established that there is a plausible relationship between the two variables, we also need to establish whether the cause occurred before the effect, the criterion of temporality . A person’s age is a quality that appears long before any opinions on drug policy, so temporally the cause comes before the effect. It wouldn’t make any sense to say that support for marijuana legalization makes a person’s age increase. Even if you could predict someone’s age based on their support for marijuana legalization, you couldn’t say someone’s age was caused by their support for legalization of marijuana.

Finally, scientists must establish nonspuriousness. A spurious relationship is one in which an association between two variables appears to be causal but can in fact be explained by some third variable. This third variable is often called a confound or confounding variable because it clouds and confuses the relationship between your independent and dependent variable, making it difficult to discern the true causal relationship is.

Continuing with our example, we could point to the fact that older adults are less likely to have used marijuana recreationally. Maybe it is actually recreational use of marijuana that leads people to be more open to legalization, not their age. In this case, our confounding variable would be recreational marijuana use. Perhaps the relationship between age and attitudes towards legalization is a spurious relationship that is accounted for by previous use. This is also referred to as the third variable problem , where a seemingly true causal relationship is actually caused by a third variable not in the hypothesis. In this example, the relationship between age and support for legalization could be more about having tried marijuana than the age of the person.

Quantitative researchers are sensitive to the effects of potentially spurious relationships. As a result, they will often measure these third variables in their study, so they can control for their effects in their statistical analysis. These are called  control variables , and they refer to potentially confounding variables whose effects are controlled for mathematically in the data analysis process. Control variables can be a bit confusing, and we will discuss them more in Chapter 10, but think about it as an argument between you, the researcher, and a critic.

Researcher: “The older a person is, the less likely they are to support marijuana legalization.” Critic: “Actually, it’s more about whether a person has used marijuana before. That is what truly determines whether someone supports marijuana legalization.” Researcher: “Well, I measured previous marijuana use in my study and mathematically controlled for its effects in my analysis. Age explains most of the variation in attitudes towards marijuana legalization.”

Let’s consider a few additional, real-world examples of spuriousness. Did you know, for example, that high rates of ice cream sales have been shown to cause drowning? Of course, that’s not really true, but there is a positive relationship between the two. In this case, the third variable that causes both high ice cream sales and increased deaths by drowning is time of year, as the summer season sees increases in both (Babbie, 2010). [94]

Here’s another good one: it is true that as the salaries of Presbyterian ministers in Massachusetts rise, so too does the price of rum in Havana, Cuba. Well, duh, you might be saying to yourself. Everyone knows how much ministers in Massachusetts love their rum, right? Not so fast. Both salaries and rum prices have increased, true, but so has the price of just about everything else (Huff & Geis, 1993). [95]

Finally, research shows that the more firefighters present at a fire, the more damage is done at the scene. What this statement leaves out, of course, is that as the size of a fire increases so too does the amount of damage caused as does the number of firefighters called on to help (Frankfort-Nachmias & Leon-Guerrero, 2011). [96] In each of these examples, it is the presence of a confounding variable that explains the apparent relationship between the two original variables.

In sum, the following criteria must be met for a nomothetic causal relationship:

• The two variables must vary together.
• The relationship must be plausible.
• The cause must precede the effect in time.
• The relationship must be nonspurious (not due to a confounding variable).

The hypothetico-dedutive method

The primary way that researchers in the positivist paradigm use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers choose an existing theory. Then, they make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary.

This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 8.8 shows, this approach meshes nicely with the process of conducting a research project—creating a more detailed model of “theoretically motivated” or “theory-driven” research. Together, they form a model of theoretically motivated research. 

Keep in mind the hypothetico-deductive method is only one way of using social theory to inform social science research. It starts with describing one or more existing theories, deriving a hypothesis from one of those theories, testing your hypothesis in a new study, and finally reevaluating the theory based on the results data analyses. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

But what if your research question is more interpretive? What if it is less about theory-testing and more about theory-building? This is what our next chapters will cover: the process of inductively deriving theory from people's stories and experiences. This process looks different than that depicted in Figure 8.8. It still starts with your research question and answering that question by conducting a research study. But instead of testing a hypothesis you created based on a theory, you will create a theory of your own that explain the data you collected. This format works well for qualitative research questions and for research questions that existing theories do not address.

• In positivist and quantitative studies, the goal is often to understand the more general causes of some phenomenon rather than the idiosyncrasies of one particular instance, as in an idiographic causal relationship.
• Nomothetic causal explanations focus on objectivity, prediction, and generalization.
• Criteria for nomothetic causal relationships require the relationship be plausible and nonspurious; and that the cause must precede the effect in time.
• In a nomothetic causal relationship, the independent variable causes changes in the dependent variable.
• Hypotheses are statements, drawn from theory, which describe a researcher’s expectation about a relationship between two or more variables.
• Write out your working question and hypothesis.
• Defend your hypothesis in a short paragraph, using arguments based on the theory you identified in section 8.1.
• Review the criteria for a nomothetic causal relationship. Critique your short paragraph about your hypothesis using these criteria.
• Are there potentially confounding variables, issues with time order, or other problems you can identify in your reasoning?

Inductive & deductive (deductive focus)

9. Writing your research question Copyright © 2020 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How to Write Qualitative Research Questions: Types & Examples

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

Qualitative research questions focus on depth and quality, exploring the “why and how” behind decisions, without relying on statistical tools.

Unlike quantitative research, which aims to collect tangible, measurable data from a broader demographic, qualitative analysis involves smaller, focused datasets, identifying patterns for insights.

The information collected by qualitative surveys can vary from text to images, demanding a deep understanding of the subject, and therefore, crafting precise qualitative research questions is crucial for success.

In this guide, we’ll discuss how to write effective qualitative research questions, explore various types, and highlight characteristics of good qualitative research questions.

Let’s dive in!

What Are Qualitative Research Questions?

Qualitative questions aim to understand the depth and nuances of a phenomenon, focusing on “why” and “how” rather than quantifiable measures.

They explore subjective experiences, perspectives, and behaviors, often using open-ended inquiries to gather rich, descriptive data.

Unlike quantitative questions, which seek numerical data, qualitative questions try to find out meanings, patterns, and underlying processes within a specific context.

These questions are essential for exploring complex issues, generating hypotheses, and gaining deeper insights into human behavior and phenomena.

Here’s an example of a qualitative research question:

“How do you perceive and navigate organizational culture within a tech startup environment?”

This question asks about the respondent’s subjective interpretations and experiences of organizational culture within a specific context, such as a tech startup.

It seeks to uncover insights into the values, norms, and practices that shape workplace dynamics and employee behaviors, providing qualitative data for analysis and understanding.

When Should We Use Qualitative Research Questions?

Qualitative research questions typically aim to open up conversations, encourage detailed narratives, and foster a deep understanding of the subject matter. Here are some scenarios they are best suited for:

• Exploring Complex Phenomena : When the research topic involves understanding complex processes, behaviors, or interactions that cannot be quantified easily, qualitative questions help delve into these intricate details.
• Understanding Contexts and Cultures : To grasp the nuances of different social contexts, cultures, or subcultures, qualitative research questions allow for an in-depth exploration of these environments and how they influence individuals and groups.
• Exploring Perceptions and Experiences : When the aim is to understand people’s perceptions, experiences, or feelings about a particular subject, qualitative questions facilitate capturing the depth and variety of these perspectives.
• Developing Concepts or Theories : In the early stages of research, where concepts or theories are not yet well-developed, qualitative questions can help generate hypotheses, identify variables, and develop theoretical frameworks based on observations and interpretations.
• Investigating Processes : To understand how processes unfold over time and the factors that influence these processes, qualitative questions are useful for capturing the dynamics and complexities involved.
• Seeking to Understand Change : When researching how individuals or groups experience change, adapt to new circumstances, or make decisions, qualitative research questions can provide insights into the motivations, challenges, and strategies involved.
• Studying Phenomena Not Easily Quantified : For phenomena that are not easily captured through quantitative measures, such as emotions, beliefs, or motivations, qualitative questions can probe these abstract concepts more effectively.
• Addressing Sensitive or Taboo Topics : In studies where topics may be sensitive, controversial, or taboo, qualitative research questions allow for a respectful and empathetic exploration of these subjects, providing space for participants to share their experiences in their own words.

How to Write Qualitative Research Questions?

Read this guide to learn how you can craft well-thought-out qualitative research questions:

1. Begin with Your Research Goals

The first step in formulating qualitative research questions is to have a clear understanding of what you aim to discover or understand through your research. There are two types of qualitative questionnaires or research – Ontological and Epistemological.

Finding out the nature of your research influences all aspects of your research design, including the formulation of research questions.

Subsequently:

• Identify your main objective : Consider the broader context of your study. Are you trying to explore a phenomenon, understand a process, or interpret the meanings behind behaviors? Your main objective should guide the formulation of your questions, ensuring they are aligned with what you seek to achieve.
• Focus on the ‘how’ and ‘why’ : Qualitative research is inherently exploratory and aims to understand the nuances of human behavior and experience. Starting your questions with “how” or “why” encourages a deeper investigation into the motivations, processes, and contexts underlying the subject matter. This approach facilitates an open-ended exploration, allowing participants to provide rich, detailed responses that illuminate their perspectives and experiences.

Take a quick look at the following visual for a better understanding:

So, if you are doing Ontological research, ensure that the questions focus on the “what” aspects of reality (the premise of your research) and opt for the nature of the knowledge for Epistemological research.

2. Choose the Right Structure

The structure of your research questions significantly impacts the depth and quality of data you collect. Opting for an open-ended format allows respondents the flexibility to express themselves freely, providing insights that pre-defined answers might miss.

• Open-ended format : These questions do not constrain respondents to a set of predetermined answers, unlike closed-ended questions. By allowing participants to articulate their thoughts in their own words, you can uncover nuances and complexities in their responses that might otherwise be overlooked.
• Avoid yes/no questions : Yes/no questions tend to limit the depth of responses. While they might be useful for gathering straightforward factual information, they are not conducive to exploring the depths and nuances that qualitative research seeks to uncover. Encouraging participants to elaborate on their experiences and perspectives leads to richer, more informative data.

For example, take a look at some qualitative questions examples shown in the following image:

3. Be Clear and Specific

Clarity and specificity in your questions are crucial to ensure that participants understand what is being asked and that their responses are relevant to your research objectives.

• Use clear language : Use straightforward, understandable language in your questions. Avoid jargon, acronyms, or overly technical terms that might confuse participants or lead to misinterpretation. The goal is to make your questions accessible to everyone involved in your study.
• Be specific : While maintaining the open-ended nature of qualitative questions, it’s important to narrow down your focus to specific aspects of the phenomenon you’re studying. This specificity helps guide participants’ responses and ensures that the data you collect directly relates to your research objectives.

4. Ensure Relevance and Feasibility

Each question should be carefully considered for its relevance to your research goals and its feasibility, given the constraints of your study.

• Relevance : Questions should be crafted to address the core objectives of your research directly. They should probe areas that are essential to understanding the phenomenon under investigation and should align with your theoretical framework or literature review findings.
• Feasibility : Consider the practical aspects of your research, including the time available for data collection and analysis, resources, and access to participants. Questions should be designed to elicit meaningful responses within the constraints of your study, ensuring that you can gather and analyze data effectively.

5. Focus on a Single Concept or Theme per Question

To ensure clarity and depth, each question should concentrate on a single idea or theme. However, if your main qualitative research question is tough to understand or has a complex structure, you can create sub-questions in limited numbers and with a “ladder structure”.

This will help your respondents understand the overall research objective in mind, and your research can be executed in a better manner.

For example, suppose your main question is – “What is the current state of illiteracy in your state?”

Then, you can create the following subquestions: 

“How does illiteracy block progress in your state?”

“How would you best describe the feelings you have about illiteracy in your state?”

For an even better understanding, you can see the various qualitative research question examples in the following image:

Types of Qualitative Research Questions With Examples

Qualitative survey questions primarily focus on a specific group of respondents that are participating in case studies, surveys, ethnography studies, etc., rather than numbers or statistics.

As a result, the questions are mostly open-ended and can be subdivided into the following types as discussed below:

1. Descriptive Questions

Descriptive research questions aim to detail the “what” of a phenomenon, providing a comprehensive overview of the context, individuals, or situations under study. These questions are foundational, helping to establish a baseline understanding of the research topic.

• What are the daily experiences of teachers in urban elementary schools?
• What strategies do small businesses employ to adapt to rapid technological changes?
• How do young adults describe their transition from college to the workforce?
• What are the coping mechanisms of families with members suffering from chronic illnesses?
• How do community leaders perceive the impact of gentrification in their neighborhoods?

2. Interpretive Questions

Interpretive questions seek to understand the “how” and “why” behind a phenomenon, focusing on the meanings people attach to their experiences. These questions delve into the subjective interpretations and perceptions of participants.

• How do survivors of natural disasters interpret their experiences of recovery and rebuilding?
• Why do individuals engage in voluntary work within their communities?
• How do parents interpret and navigate the challenges of remote schooling for their children?
• Why do consumers prefer local products over global brands in certain markets?
• How do artists interpret the influence of digital media on traditional art forms?

3. Comparative Questions

Comparative research questions are designed to explore differences and similarities between groups, settings, or time periods. These questions can help to highlight the impact of specific variables on the phenomenon under study.

• How do the strategies for managing work-life balance compare between remote and office workers?
• What are the differences in consumer behavior towards sustainable products in urban versus rural areas?
• How do parenting styles in single-parent households compare to those in dual-parent households?
• What are the similarities and differences in leadership styles across different cultures?
• How has the perception of online privacy changed among teenagers over the past decade?

4. Process-oriented Questions

These questions focus on understanding the processes or sequences of events over time. They aim to uncover the “how” of a phenomenon, tracing the development, changes, or evolution of specific situations or behaviors.

• How do non-profit organizations develop and implement community outreach programs?
• What is the process of decision-making in high-stakes business environments?
• How do individuals navigate the process of career transition after significant industry changes?
• What are the stages of adaptation for immigrants in a new country?
• How do social movements evolve from inception to national recognition?

5. Evaluative Questions

Evaluative questions aim to assess the effectiveness, value, or impact of a program, policy, or phenomenon. These questions are critical for understanding the outcomes and implications of various initiatives or situations.

• How effective are online therapy sessions compared to in-person sessions in treating anxiety?
• What is the impact of community gardening programs on neighborhood cohesion?
• How do participants evaluate the outcomes of leadership training programs in their professional development?
• What are the perceived benefits and drawbacks of telecommuting for employees and employers?
• How do residents evaluate the effectiveness of local government policies on waste management?

6. One-on-One Questions

The one-on-one questions are asked to a single person and can be thought of as individual interviews that you can conduct online via phone and video chat as well.

The main aim of such questions is to ask your customers or people in the focus group a series of questions about their purchase motivations. These questions might also come with follow-ups, and if your customers respond with some interesting fact or detail, dig deeper and explore the findings as much as you want.

• What makes you happy in regard to [your research topic]?
• If I could make a wish of yours come true, what do you desire the most?
• What do you still find hard to come to terms with?
• Have you bought [your product] before?
• If so, what was your initial motivation behind the purchase?

7. Exploratory Questions

These questions are designed to enhance your understanding of a particular topic. However, while asking exploratory questions, you must ensure that there are no preconceived notions or biases to it. The more transparent and bias-free your questions are, the better and fair results you will get.

• What is the effect of personal smart devices on today’s youth?
• Do you feel that smart devices have positively or negatively impacted you?
• How do your kids spend their weekends?
• What do you do on a typical weekend morning?

8. Predictive Questions

The predictive questions are used for qualitative research that is focused on the future outcomes of an action or a series of actions. So, you will be using past information to predict the reactions of respondents to hypothetical events that might or might not happen in the future.

These questions come in extremely handy for identifying your customers’ current brand expectations, pain points, and purchase motivation.

• Are you more likely to buy a product when a celebrity promotes it?
• Would you ever try a new product because one of your favorite celebs claims that it actually worked for them?
• Would people in your neighborhood enjoy a park with rides and exercise options?
• How often would you go to a park with your kids if it had free rides?

9. Focus Groups

These questions are mostly asked in person to the customer or respondent groups. The in-person nature of these surveys or studies ensures that the group members get a safe and comfortable environment to express their thoughts and feelings about your brand or services.

• How would you describe your ease of using our product?
• How well do you think you were able to do this task before you started using our product?
• What do you like about our promotional campaigns?
• How well do you think our ads convey the meaning?

10. In-Home Videos

Collecting video feedback from customers in their comfortable, natural settings offers a unique perspective. At home, customers are more relaxed and less concerned about their mannerisms, posture, and choice of words when responding.

This approach is partly why Vogue’s 73 Questions Series is highly popular among celebrities and viewers alike. In-home videos provide insights into customers in a relaxed environment, encouraging them to be honest and share genuine experiences.

• What was your first reaction when you used our product for the first time?
• How well do you think our product performed compared to your expectations?
• What was your worst experience with our product?
• What made you switch to our brand?

11. Online Focus Groups

Online focus groups mirror the traditional, in-person format but are conducted virtually, offering a more cost-effective and efficient approach to gathering data. This digital format extends your reach and allows a rapid collection of responses from a broader audience through online platforms.

You can utilize social media and other digital forums to create communities of respondents and initiate meaningful discussions. Once you have them started, you can simply observe the exchange of thoughts and gather massive amounts of interesting insights!

• What do you like best about our product?
• How familiar are you with this particular service or product we offer?
• What are your concerns with our product?
• What changes can we make to make our product better?

Ask the Right Qualitative Research Questions for Meaningful Insights From Your Respondents

Watch: How to Create a Survey Using ProProfs Survey Maker

By now, you might have realized that manually creating a list of qualitative research questions is a daunting task. Keeping numerous considerations in mind, it’s easy to run out of ideas while crafting qualitative survey questions .

However, investing in smart survey tools, like ProProfs Survey Maker, can significantly streamline this process, allowing you to create various types of surveys in minutes.

With this survey tool , you can generate forms, NPS surveys , tests, quizzes, and assessments.

It’s also useful for conducting polls, sidebar surveys, and in-app surveys. Offering over 100 templates and more than 1,000,000 ready-to-use examples of phenomenological research questions, this software simplifies the task immensely.

Equipped with the right tools and the professional tips shared here, you’re well-prepared to conduct thorough research studies and obtain valuable insights that drive impactful results.

Frequently Asked Questions on Q ualitative Research Questions

1. how do you choose qualitative research questions.

To choose qualitative research questions, identify your main research goal, focus on exploring ‘how’ and ‘why’ aspects, ensure questions are open-ended, and align them with your theoretical framework and methodology.

2. Why are good qualitative research questions important?

Good qualitative research questions are important because they guide the research focus, enable the exploration of depth and complexity, and facilitate the gathering of rich, detailed insights into human experiences and behaviors.

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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Chapter 4. Finding a Research Question and Approaches to Qualitative Research

We’ve discussed the research design process in general and ways of knowing favored by qualitative researchers.  In chapter 2, I asked you to think about what interests you in terms of a focus of study, including your motivations and research purpose.  It might be helpful to start this chapter with those short paragraphs you wrote about motivations and purpose in front of you.  We are now going to try to develop those interests into actual research questions (first part of this chapter) and then choose among various “traditions of inquiry” that will be best suited to answering those questions.  You’ve already been introduced to some of this (in chapter 1), but we will go further here.

Developing a Research Question

Research questions are different from general questions people have about the social world.  They are narrowly tailored to fit a very specific issue, complete with context and time boundaries.  Because we are engaged in empirical science and thus use “data” to answer our questions, the questions we ask must be answerable by data.  A question is not the same as stating a problem.  The point of the entire research project is to answer a particular question or set of questions.  The question(s) should be interesting, relevant, practical, and ethical.  Let’s say I am generally interested in the problem of student loan debt.  That’s a good place to start, but we can’t simply ask,

General question: Is student loan debt really a problem today?

How could we possibly answer that question? What data could we use? Isn’t this really an axiological (values-based) question? There are no clues in the question as to what data would be appropriate here to help us get started. Students often begin with these large unanswerable questions. They are not research questions. Instead, we could ask,

Poor research question: How many people have debt?

This is still not a very good research question. Why not? It is answerable, although we would probably want to clarify the context. We could add some context to improve it so that the question now reads,

Mediocre research question: How many people in the US have debt today? And does this amount vary by age and location?

Now we have added some context, so we have a better idea of where to look and who to look at. But this is still a pretty poor or mediocre research question. Why is that? Let’s say we did answer it. What would we really know? Maybe we would find out that student loan debt has increased over time and that young people today have more of it. We probably already know this. We don’t really want to go through a lot of trouble answering a question whose answer we already have. In fact, part of the reason we are even asking this question is that we know (or think) it is a problem. Instead of asking what you already know, ask a question to which you really do not know the answer. I can’t stress this enough, so I will say it again: Ask a question to which you do not already know the answer . The point of research is not to prove or make a point but to find out something unknown. What about student loan debt is still a mystery to you? Reviewing the literature could help (see chapter 9). By reviewing the literature, you can get a good sense of what is still mysterious or unknown about student loan debt, and you won’t be reinventing the wheel when you conduct your research. Let’s say you review the literature, and you are struck by the fact that we still don’t understand the true impact of debt on how people are living their lives. A possible research question might be,

Fair research question: What impact does student debt have on the lives of debtors?

Good start, but we still need some context to help guide the project. It is not nearly specific enough.

Better research question: What impact does student debt have on young adults (ages twenty-five to thirty-five) living in the US today?

Now we’ve added context, but we can still do a little bit better in narrowing our research question so that it is both clear and doable; in other words, we want to frame it in a way that provides a very clear research program:

Optimal research question: How do young adults (ages twenty-five to thirty-five) living in the US today who have taken on $30,000 or more in student debt describe the impact of their debt on their lives in terms of finding/choosing a job, buying a house, getting married, and other major life events?

Now you have a research question that can be answered and a clear plan of how to answer it. You will talk to young adults living in the US today who have high debt loads and ask them to describe the impacts of debt on their lives. That is all now in the research question. Note how different this very specific question is from where we started with the “problem” of student debt.

Take some time practicing turning the following general questions into research questions:

• What can be done about the excessive use of force by police officers?
• Why haven’t societies taken firmer steps to address climate change?
• How do communities react to / deal with the opioid epidemic?
• Who has been the most adversely affected by COVID?
• When did political polarization get so bad?

Hint: Step back from each of the questions and try to articulate a possible underlying motivation, then formulate a research question that is specific and answerable.

It is important to take the time to come up with a research question, even if this research question changes a bit as you conduct your research (yes, research questions can change!). If you don’t have a clear question to start your research, you are likely to get very confused when designing your study because you will not be able to make coherent decisions about things like samples, sites, methods of data collection, and so on. Your research question is your anchor: “If we don’t have a question, we risk the possibility of going out into the field thinking we know what we’ll find and looking only for proof of what we expect to be there. That’s not empirical research (it’s not systematic)” ( Rubin 2021:37 ).

Researcher Note

How do you come up with ideas for what to study?

I study what surprises me. Usually, I come across a statistic that suggests something is common that I thought was rare. I tend to think it’s rare because the theories I read suggest it should be, and there’s not a lot of work in that area that helps me understand how the statistic came to be. So, for example, I learned that it’s common for Americans to marry partners who grew up in a different class than them and that about half of White kids born into the upper-middle class are downwardly mobile. I was so shocked by these facts that they naturally led to research questions. How do people come to marry someone who grew up in a different class? How do White kids born near the top of the class structure fall?

—Jessi Streib, author of The Power of the Past and Privilege Lost

What if you have literally no idea what the research question should be? How do you find a research question? Even if you have an interest in a topic before you get started, you see the problem now: topics and issues are not research questions! A research question doesn’t easily emerge; it takes a lot of time to hone one, as the practice above should demonstrate. In some research designs, the research question doesn’t even get clearly articulated until the end of data collection . More on that later. But you must start somewhere, of course. Start with your chosen discipline. This might seem obvious, but it is often overlooked. There is a reason it is called a discipline. We tend to think of “sociology,” “public health,” and “physics” as so many clusters of courses that are linked together by subject matter, but they are also disciplines in the sense that the study of each focuses the mind in a particular way and for particular ends. For example, in my own field, sociology, there is a loosely shared commitment to social justice and a general “sociological imagination” that enables its practitioners to connect personal experiences to society at large and to historical forces. It is helpful to think of issues and questions that are germane to your discipline. Within that overall field, there may be a particular course or unit of study you found most interesting. Within that course or unit of study, there may be an issue that intrigued you. And finally, within that issue, there may be an aspect or topic that you want to know more about.

When I was pursuing my dissertation research, I was asked often, “Why did you choose to study intimate partner violence among Native American women?” This question is necessary, and each time I answered, it helped shape me into a better researcher. I was interested in intimate partner violence because I am a survivor. I didn’t have intentions to work with a particular population or demographic—that came from my own deep introspection on my role as a researcher. I always questioned my positionality: What privileges do I hold as an academic? How has public health extracted information from institutionally marginalized populations? How can I build bridges between communities using my position, knowledge, and power? Public health as a field would not exist without the contributions of Indigenous people. So I started hanging out with them at community events, making friends, and engaging in self-education. Through these organic relationships built with Native women in the community, I saw that intimate partner violence was a huge issue. This led me to partner with Indigenous organizations to pursue a better understanding of how Native survivors of intimate partner violence seek support.

—Susanna Y. Park, PhD, mixed-methods researcher in public health and author of “How Native Women Seek Support as Survivors of Intimate Partner Violence: A Mixed-Methods Study”

One of the most exciting and satisfying things about doing academic research is that whatever you end up researching can become part of the body of knowledge that we have collectively created. Don’t make the mistake of thinking that you are doing this all on your own from scratch. Without even being aware of it, no matter if you are a first-year undergraduate student or a fourth-year graduate student, you have been trained to think certain questions are interesting. The very fact that you are majoring in a particular field or have signed up for years of graduate study in a program testifies to some level of commitment to a discipline. What we are looking for, ideally, is that your research builds on in some way (as extension, as critique, as lateral move) previous research and so adds to what we, collectively, understand about the social world. It is helpful to keep this in mind, as it may inspire you and also help guide you through the process. The point is, you are not meant to be doing something no one has ever thought of before, even if you are trying to find something that does not exactly duplicate previous research: “You may be trying to be too clever—aiming to come up with a topic unique in the history of the universe, something that will have people swooning with admiration at your originality and intellectual precociousness. Don’t do it. It’s safer…to settle on an ordinary, middle-of-the-road topic that will lend itself to a nicely organized process of project management. That’s the clever way of proceeding.… You can always let your cleverness shine through during the stages of design, analysis, and write-up. Don’t make things more difficult for yourself than you need to do” ( Davies 2007:20 ).

Rubin ( 2021 ) suggests four possible ways to develop a research question (there are many more, of course, but this can get you started). One way is to start with a theory that interests you and then select a topic where you can apply that theory. For example, you took a class on gender and society and learned about the “glass ceiling.” You could develop a study that tests that theory in a setting that has not yet been explored—maybe leadership at the Oregon Country Fair. The second way is to start with a topic that interests you and then go back to the books to find a theory that might explain it. This is arguably more difficult but often much more satisfying. Ask your professors for help—they might have ideas of theories or concepts that could be relevant or at least give you an idea of what books to read. The third way is to be very clever and select a question that already combines the topic and the theory. Rubin gives as one example sentencing disparities in criminology—this is both a topic and a theory or set of theories. You then just have to figure out particulars like setting and sample. I don’t know if I find this third way terribly helpful, but it might help you think through the possibilities. The fourth way involves identifying a puzzle or a problem, which can be either theoretical (something in the literature just doesn’t seem to make sense and you want to tackle addressing it) or empirical (something happened or is happening, and no one really understands why—think, for example, of mass school shootings).

Once you think you have an issue or topic that is worth exploring, you will need to (eventually) turn that into a good research question. A good research question is specific, clear, and feasible .

Specific . How specific a research question needs to be is somewhat related to the disciplinary conventions and whether the study is conceived inductively or deductively. In deductive research, one begins with a specific research question developed from the literature. You then collect data to test the theory or hypotheses accompanying your research question. In inductive research, however, one begins with data collection and analysis and builds theory from there. So naturally, the research question is a bit vaguer. In general, the more closely aligned to the natural sciences (and thus the deductive approach), the more a very tight and specific research question (along with specific, focused hypotheses) is required. This includes disciplines like psychology, geography, public health, environmental science, and marine resources management. The more one moves toward the humanities pole (and the inductive approach), the more looseness is permitted, as there is a general belief that we go into the field to find what is there, not necessarily what we imagine we are looking for (see figure 4.2). Disciplines such as sociology, anthropology, and gender and sexuality studies and some subdisciplines of public policy/public administration are closer to the humanities pole in this sense.

Regardless of discipline and approach, however, it is a good idea for beginning researchers to create a research question as specific as possible, as this will serve as your guide throughout the process. You can tweak it later if needed, but start with something specific enough that you know what it is you are doing and why. It is more difficult to deal with ambiguity when you are starting out than later in your career, when you have a better handle on what you are doing. Being under a time constraint means the more specific the question, the better. Questions should always specify contexts, geographical locations, and time frames. Go back to your practice research questions and make sure that these are included.

Clear . A clear research question doesn’t only need to be intelligible to any reader (which, of course, it should); it needs to clarify any meanings of particular words or concepts (e.g., What is excessive force?). Check all your concepts to see if there are ways you can clarify them further—for example, note that we shifted from impact of debt to impact of high debt load and specified this as beginning at $30,000. Ideally, we would use the literature to help us clarify what a high debt load is or how to define “excessive” force.

Feasible . In order to know if your question is feasible, you are going to have to think a little bit about your entire research design. For example, a question that asks about the real-time impact of COVID restrictions on learning outcomes would require a time machine. You could tweak the question to ask instead about the long-term impacts of COVID restrictions, as measured two years after their end. Or let’s say you are interested in assessing the damage of opioid abuse on small-town communities across the United States. Is it feasible to cover the entire US? You might need a team of researchers to do this if you are planning on on-the-ground observations. Perhaps a case study of one particular community might be best. Then your research question needs to be changed accordingly.

Here are some things to consider in terms of feasibility:

• Is the question too general for what you actually intend to do or examine? (Are you specifying the world when you only have time to explore a sliver of that world?)
• Is the question suitable for the time you have available? (You will need different research questions for a study that can be completed in a term than one where you have one to two years, as in a master’s program, or even three to eight years, as in a doctoral program.)
• Is the focus specific enough that you know where and how to begin?
• What are the costs involved in doing this study, including time? Will you need to travel somewhere, and if so, how will you pay for it?
• Will there be problems with “access”? (More on this in later chapters, but for now, consider how you might actually find people to interview or places to observe and whether gatekeepers exist who might keep you out.)
• Will you need to submit an application proposal for your university’s IRB (institutional review board)? If you are doing any research with live human subjects, you probably need to factor in the time and potential hassle of an IRB review (see chapter 8). If you are under severe time constraints, you might need to consider developing a research question that can be addressed with secondary sources, online content, or historical archives (see chapters 16 and 17).

In addition to these practicalities, you will also want to consider the research question in terms of what is best for you now. Are you engaged in research because you are required to be—jumping a hurdle for a course or for your degree? If so, you really do want to think about your project as training and develop a question that will allow you to practice whatever data collection and analysis techniques you want to develop. For example, if you are a grad student in a public health program who is interested in eventually doing work that requires conducting interviews with patients, develop a research question and research design that is interview based. Focus on the practicality (and practice) of the study more than the theoretical impact or academic contribution, in other words. On the other hand, if you are a PhD candidate who is seeking an academic position in the future, your research question should be pitched in a way to build theoretical knowledge as well (the phrasing is typically “original contribution to scholarship”).

The more time you have to devote to the study and the larger the project, the more important it is to reflect on your own motivations and goals when crafting a research question (remember chapter 2?). By “your own motivations and goals,” I mean what interests you about the social world and what impact you want your research to have, both academically and practically speaking. Many students have secret (or not-so-secret) plans to make the world a better place by helping address climate change, pointing out pressure points to fight inequities, or bringing awareness to an overlooked area of concern. My own work in graduate school was motivated by the last of these three—the not-so-secret goal of my research was to raise awareness about obstacles to success for first-generation and working-class college students. This underlying goal motivated me to complete my dissertation in a timely manner and then to further continue work in this area and see my research get published. I cared enough about the topic that I was not ready to put it away. I am still not ready to put it away. I encourage you to find topics that you can’t put away, ever. That will keep you going whenever things get difficult in the research process, as they inevitably will.

On the other hand, if you are an undergraduate and you really have very little time, some of the best advice I have heard is to find a study you really like and adapt it to a new context. Perhaps you read a study about how students select majors and how this differs by class ( Hurst 2019 ). You can try to replicate the study on a small scale among your classmates. Use the same research question, but revise for your context. You can probably even find the exact questions I  used and ask them in the new sample. Then when you get to the analysis and write-up, you have a comparison study to guide you, and you can say interesting things about the new context and whether the original findings were confirmed (similar) or not. You can even propose reasons why you might have found differences between one and the other.

Another way of thinking about research questions is to explicitly tie them to the type of purpose of your study. Of course, this means being very clear about what your ultimate purpose is! Marshall and Rossman ( 2016 ) break down the purpose of a study into four categories: exploratory, explanatory, descriptive, and emancipatory ( 78 ). Exploratory purpose types include wanting to investigate little-understood phenomena, or identifying or discovering important new categories of meaning, or generating hypotheses for further research. For these, research questions might be fairly loose: What is going on here? How are people interacting on this site? What do people talk about when you ask them about the state of the world? You are almost (but never entirely) starting from scratch. Be careful though—just because a topic is new to you does not mean it is really new. Someone else (or many other someones) may already have done this exploratory research. Part of your job is to find this out (more on this in “What Is a ‘Literature Review’?” in chapter 9). Descriptive purposes (documenting and describing a phenomenon) are similar to exploratory purposes but with a much clearer goal (description). A good research question for a descriptive study would specify the actions, events, beliefs, attitudes, structures, and/or processes that will be described.

Most researchers find that their topic has already been explored and described, so they move to trying to explain a relationship or phenomenon. For these, you will want research questions that capture the relationships of interest. For example, how does gender influence one’s understanding of police brutality (because we already know from the literature that it does, so now we are interested in understanding how and why)? Or what is the relationship between education and climate change denialism? If you find that prior research has already provided a lot of evidence about those relationships as well as explanations for how they work, and you want to move the needle past explanation into action, you might find yourself trying to conduct an emancipatory study. You want to be even more clear in acknowledging past research if you find yourself here. Then create a research question that will allow you to “create opportunities and the will to engage in social action” ( Marshall and Rossman 2016:78 ). Research questions might ask, “How do participants problematize their circumstances and take positive social action?” If we know that some students have come together to fight against student debt, how are they doing this, and with what success? Your purpose would be to help evaluate possibilities for social change and to use your research to make recommendations for more successful emancipatory actions.

Recap: Be specific. Be clear. Be practical. And do what you love.

Choosing an Approach or Tradition

Qualitative researchers may be defined as those who are working with data that is not in numerical form, but there are actually multiple traditions or approaches that fall under this broad category. I find it useful to know a little bit about the history and development of qualitative research to better understand the differences in these approaches. The following chart provides an overview of the six phases of development identified by Denzin and Lincoln ( 2005 ):

Table 4.1. Six Phases of Development

There are other ways one could present the history as well. Feminist theory and methodologies came to the fore in the 1970s and 1980s and had a lot to do with the internal critique of more positivist approaches. Feminists were quite aware that standpoint matters—that the identity of the researcher plays a role in the research, and they were ardent supporters of dismantling unjust power systems and using qualitative methods to help advance this mission. You might note, too, that many of the internal disputes were basically epistemological disputes about how we know what we know and whether one’s social location/position delimits that knowledge. Today, we are in a bountiful world of qualitative research, one that embraces multiple forms of knowing and knowledge. This is good, but it means that you, the student, have more choice when it comes to situating your study and framing your research question, and some will expect you to signal the choices you have made in any research protocols you write or publications and presentations.

Creswell’s ( 1998 ) definition of qualitative research includes the notion of distinct traditions of inquiry: “Qualitative research is an inquiry process of understanding based on distinct methodological traditions of inquiry that explore a social or human problem. The research builds complex,   holistic pictures, analyzes words, reports detailed views of informants , and conducted the study in a natural setting” (15; emphases added). I usually caution my students against taking shelter under one of these approaches, as, practically speaking, there is a lot of mixing of traditions among researchers. And yet it is useful to know something about the various histories and approaches, particularly as you are first starting out. Each tradition tends to favor a particular epistemological perspective (see chapter 3), a way of reasoning (see “ Advanced: Inductive versus Deductive Reasoning ”), and a data-collection technique.

There are anywhere from ten to twenty “traditions of inquiry,” depending on how one draws the boundaries. In my accounting, there are twelve, but three approaches tend to dominate the field.

Ethnography

Ethnography was developed from the discipline of anthropology, as the study of (other) culture(s). From a relatively positivist/objective approach to writing down the “truth” of what is observed during the colonial era (where this “truth” was then often used to help colonial administrators maintain order and exploit people and extract resources more effectively), ethnography was adopted by all kinds of social science researchers to get a better understanding of how groups of people (various subcultures and cultures) live their lives. Today, ethnographers are more likely to be seeking to dismantle power relations than to support them. They often study groups of people that are overlooked and marginalized, and sometimes they do the obverse by demonstrating how truly strange the familiar practices of the dominant group are. Ethnography is also central to organizational studies (e.g., How does this institution actually work?) and studies of education (e.g., What is it like to be a student during the COVID era?).

Ethnographers use methods of participant observation and intensive fieldwork in their studies, often living or working among the group under study for months at a time (and, in some cases, years). I’ve called this “deep ethnography,” and it is the subject of chapter 14. The data ethnographers analyze are copious “field notes” written while in the field, often supplemented by in-depth interviews and many more casual conversations. The final product of ethnographers is a “thick” description of the culture. This makes reading ethnographies enjoyable, as the goal is to write in such a way that the reader feels immersed in the culture.

There are variations on the ethnography, such as the autoethnography , where the researcher uses a systematic and rigorous study of themselves to better understand the culture in which they find themselves. Autoethnography is a relatively new approach, even though it is derived from one of the oldest approaches. One can say that it takes to heart the feminist directive to “make the personal political,” to underscore the connections between personal experiences and larger social and political structures. Introspection becomes the primary data source.

Grounded Theory

Grounded Theory holds a special place in qualitative research for a few reasons, not least of which is that nonqualitative researchers often mistakenly believe that Grounded Theory is the only qualitative research methodology . Sometimes, it is easier for students to explain what they are doing as “Grounded Theory” because it sounds “more scientific” than the alternative descriptions of qualitative research. This is definitely part of its appeal. Grounded Theory is the name given to the systematic inductive approach first developed by Glaser and Strauss in 1967, The Discovery of Grounded Theory: Strategies for Qualitative Research . Too few people actually read Glaser and Strauss’s book. It is both groundbreaking and fairly unremarkable at the same time. As a historical intervention into research methods generally, it is both a sharp critique of positivist methods in the social sciences (theory testing) and a rejection of purely descriptive accounts-building qualitative research. Glaser and Strauss argued for an approach whose goal was to construct (middle-level) theories from recursive data analysis of nonnumerical data (interviews and observations). They advocated a “constant comparative method” in which coding and analysis take place simultaneously and recursively. The demands are fairly strenuous. If done correctly, the result is the development of a new theory about the social world.

So why do I call this “fairly unremarkable”? To some extent, all qualitative research already does what Glaser and Strauss ( 1967 ) recommend, albeit without denoting the processes quite so specifically. As will be seen throughout the rest of this textbook, all qualitative research employs some “constant comparisons” through recursive data analyses. Where Grounded Theory sets itself apart from a significant number of qualitative research projects, however, is in its dedication to inductively building theory. Personally, I think it is important to understand that Glaser and Strauss were rejecting deductive theory testing in sociology when they first wrote their book. They were part of a rising cohort who rejected the positivist mathematical approaches that were taking over sociology journals in the 1950s and 1960s. Here are some of the comments and points they make against this kind of work:

Accurate description and verification are not so crucial when one’s purpose is to generate theory. ( 28 ; further arguing that sampling strategies are different when one is not trying to test a theory or generalize results)

Illuminating perspectives are too often suppressed when the main emphasis is verifying theory. ( 40 )

Testing for statistical significance can obscure from theoretical relevance. ( 201 )

Instead, they argued, sociologists should be building theories about the social world. They are not physicists who spend time testing and refining theories. And they are not journalists who report descriptions. What makes sociologists better than journalists and other professionals is that they develop theory from their work “In their driving efforts to get the facts [research sociologists] tend to forget that the distinctive offering of sociology to our society is sociological theory, not research description” ( 30–31 ).

Grounded Theory’s inductive approach can be off-putting to students who have a general research question in mind and a working hypothesis. The true Grounded Theory approach is often used in exploratory studies where there are no extant theories. After all, the promise of this approach is theory generation, not theory testing. Flying totally free at the start can be terrifying. It can also be a little disingenuous, as there are very few things under the sun that have not been considered before. Barbour ( 2008:197 ) laments that this approach is sometimes used because the researcher is too lazy to read the relevant literature.

To summarize, Glaser and Strauss justified the qualitative research project in a way that gave it standing among the social sciences, especially vis-à-vis quantitative researchers. By distinguishing the constant comparative method from journalism, Glaser and Strauss enabled qualitative research to gain legitimacy.

So what is it exactly, and how does one do it? The following stages provide a succinct and basic overview, differentiating the portions that are similar to/in accordance with qualitative research methods generally and those that are distinct from the Grounded Theory approach:

Step 1. Select a case, sample, and setting (similar—unless you begin with a theory to test!).

Step 2. Begin data collection (similar).

Step 3. Engage data analysis (similar in general but specificity of details somewhat unique to Grounded Theory): (1) emergent coding (initial followed by focused), (2) axial (a priori) coding , (3) theoretical coding , (4) creation of theoretical categories; analysis ends when “theoretical saturation ” has been achieved.

Grounded Theory’s prescriptive (i.e., it has a set of rules) framework can appeal to beginning students, but it is unnecessary to adopt the entire approach in order to make use of some of its suggestions. And if one does not exactly follow the Grounded Theory rulebook, it can mislead others if you tend to call what you are doing Grounded Theory when you are not:

Grounded theory continues to be a misunderstood method, although many researchers purport to use it. Qualitative researchers often claim to conduct grounded theory studies without fully understanding or adopting its distinctive guidelines. They may employ one or two of the strategies or mistake qualitative analysis for grounded theory. Conversely, other researchers employ grounded theory methods in reductionist, mechanistic ways. Neither approach embodies the flexible yet systematic mode of inquiry, directed but open-ended analysis, and imaginative theorizing from empirical data that grounded theory methods can foster. Subsequently, the potential of grounded theory methods for generating middle-range theory has not been fully realized ( Charmaz 2014 ).

Phenomenology

Where Grounded Theory sets itself apart for its inductive systematic approach to data analysis, phenomenologies are distinct for their focus on what is studied—in this case, the meanings of “lived experiences” of a group of persons sharing a particular event or circumstance. There are phenomenologies of being working class ( Charlesworth 2000 ), of the tourist experience ( Cohen 1979 ), of Whiteness ( Ahmed 2007 ). The phenomenon of interest may also be an emotion or circumstance. One can study the phenomenon of “White rage,” for example, or the phenomenon of arranged marriage.

The roots of phenomenology lie in philosophy (Husserl, Heidegger, Merleau-Ponty, Sartre) but have been adapted by sociologists in particular. Phenomenologists explore “how human beings make sense of experience and transform experience into consciousness, both individually and as shared meaning” ( Patton 2002:104 ).

One of the most important aspects of conducting a good phenomenological study is getting the sample exactly right so that each person can speak to the phenomenon in question. Because the researcher is interested in the meanings of an experience, in-depth interviews are the preferred method of data collection. Observations are not nearly as helpful here because people may do a great number of things without meaning to or without being conscious of their implications. This is important to note because phenomenologists are studying not “the reality” of what happens at all but an articulated understanding of a lived experience. When reading a phenomenological study, it is important to keep this straight—too often I have heard students critique a study because the interviewer didn’t actually see how people’s behavior might conflict with what they say (which is, at heart, an epistemological issue!).

In addition to the “big three,” there are many other approaches; some are variations, and some are distinct approaches in their own right. Case studies focus explicitly on context and dynamic interactions over time and can be accomplished with quantitative or qualitative methods or a mixture of both (for this reason, I am not considering it as one of the big three qualitative methods, even though it is a very common approach). Whatever methods are used, a contextualized deep understanding of the case (or cases) is central.

Critical inquiry is a loose collection of techniques held together by a core argument that understanding issues of power should be the focus of much social science research or, to put this another way, that it is impossible to understand society (its people and institutions) without paying attention to the ways that power relations and power dynamics inform and deform those people and institutions. This attention to power dynamics includes how research is conducted too. All research fundamentally involves issues of power. For this reason, many critical inquiry traditions include a place for collaboration between researcher and researched. Examples include (1) critical narrative analysis, which seeks to describe the meaning of experience for marginalized or oppressed persons or groups through storytelling; (2) participatory action research, which requires collaboration between the researcher and the research subjects or community of interest; and (3) critical race analysis, a methodological application of Critical Race Theory (CRT), which posits that racial oppression is endemic (if not always throughout time and place, at least now and here).

Do you follow a particular tradition of inquiry? Why?

Shawn Wilson’s book, Research Is Ceremony: Indigenous Research Methods , is my holy grail. It really flipped my understanding of research and relationships. Rather than thinking linearly and approaching research in a more canonical sense, Wilson shook my world view by drawing me into a pattern of inquiry that emphasized transparency and relational accountability. The Indigenous research paradigm is applicable in all research settings, and I follow it because it pushes me to constantly evaluate my position as a knowledge seeker and knowledge sharer.

Autoethnography takes the researcher as the subject. This is one approach that is difficult to explain to more quantitatively minded researchers, as it seems to violate many of the norms of “scientific research” as understood by them. First, the sample size is quite small—the n is 1, the researcher. Two, the researcher is not a neutral observer—indeed, the subjectivity of the researcher is the main strength of this approach. Autoethnographies can be extremely powerful for their depth of understanding and reflexivity, but they need to be conducted in their own version of rigor to stand up to scrutiny by skeptics. If you are skeptical, read one of the excellent published examples out there—I bet you will be impressed with what you take away. As they say, the proof is in the pudding on this approach.

Advanced: Inductive versus Deductive Reasoning

There has been a great deal of ink shed in the discussion of inductive versus deductive approaches, not all of it very instructive. Although there is a huge conceptual difference between them, in practical terms, most researchers cycle between the two, even within the same research project. The simplest way to explain the difference between the two is that we are using deductive reasoning when we test an existing theory (move from general to particular), and we are using inductive reasoning when we are generating theory (move from particular to general). Figure 4.2 provides a schematic of the deductive approach. From the literature, we select a theory about the impact of student loan debt: student loan debt will delay homeownership among young adults. We then formulate a hypothesis based on this theory: adults in their thirties with high debt loads will be less likely to own homes than their peers who do not have high debt loads. We then collect data to test the hypothesis and analyze the results. We find that homeownership is substantially lower among persons of color and those who were the first in their families to graduate from college. Notably, high debt loads did not affect homeownership among White adults whose parents held college degrees. We thus refine the theory to match the new findings: student debt loads delay homeownership among some young adults, thereby increasing inequalities in this generation. We have now contributed new knowledge to our collective corpus.

The inductive approach is contrasted in figure 4.3. Here, we did not begin with a preexisting theory or previous literature but instead began with an observation. Perhaps we were conducting interviews with young adults who held high amounts of debt and stumbled across this observation, struck by how many were renting apartments or small houses. We then noted a pattern—not all the young adults we were talking to were renting; race and class seemed to play a role here. We would then probably expand our study in a way to be able to further test this developing theory, ensuring that we were not seeing anomalous patterns. Once we were confident about our observations and analyses, we would then develop a theory, coming to the same place as our deductive approach, but in reverse.

A third form of reasoning, abductive (sometimes referred to as probabilistic reasoning) was developed in the late nineteenth century by American philosopher Charles Sanders Peirce. I have included some articles for further reading for those interested.

Among social scientists, the deductive approach is often relaxed so that a research question is set based on the existing literature rather than creating a hypothesis or set of hypotheses to test. Some journals still require researchers to articulate hypotheses, however. If you have in mind a publication, it is probably a good idea to take a look at how most articles are organized and whether specific hypotheses statements are included.

Table 4.2. Twelve Approaches. Adapted from Patton 2002:132-133.

Further Readings

The following readings have been examples of various approaches or traditions of inquiry:

Ahmed, Sara. 2007. “A Phenomenology of Whiteness.” Feminist Theory 8(2):149–168.

Charlesworth, Simon. 2000. A Phenomenology of Working-Class Experience . Cambridge: Cambridge University Press.*

Clandinin, D. Jean, and F. Michael Connelly. 2000. Narrative Inquiry: Experience and Story in Qualitative Research . San Francisco: Jossey-Bass.

Cohen, E. 1979. “A Phenomenology of Tourist Experiences.” Sociology 13(2):179–201.

Cooke, Bill, and Uma Kothari, eds. 2001. Participation: The New Tyranny? London: Zed Books. A critique of participatory action.

Corbin, Juliet, and Anselm Strauss. 2008. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory . 3rd ed. Thousand Oaks, CA: SAGE.

Crabtree, B. F., and W. L. Miller, eds. 1999. Doing Qualitative Research: Multiple Strategies . Thousand Oaks, CA: SAGE.

Creswell, John W. 1997. Qualitative Inquiry and Research Design: Choosing among Five Approaches. Thousand Oaks, CA: SAGE.

Glaser, Barney G., and Anselm Strauss. 1967. The Discovery of Grounded Theory: Strategies for Qualitative Research . New York: Aldine.

Gobo, Giampetro, and Andrea Molle. 2008. Doing Ethnography . Thousand Oaks, CA: SAGE.

Hancock, Dawson B., and Bob Algozzine. 2016. Doing Case Study Research: A Practical Guide for Beginning Research . 3rd ed. New York: Teachers College Press.

Harding, Sandra. 1987. Feminism and Methodology . Bloomington: Indiana University Press.

Husserl, Edmund. (1913) 2017. Ideas: Introduction to Pure Phenomenology . Eastford, CT: Martino Fine Books.

Rose, Gillian. 2012. Visual Methodologies . 3rd ed. London: SAGE.

Van der Riet, M. 2009. “Participatory Research and the Philosophy of Social Science: Beyond the Moral Imperative.” Qualitative Inquiry 14(4):546–565.

Van Manen, Max. 1990. Researching Lived Experience: Human Science for an Action Sensitive Pedagogy . Albany: State University of New York.

Wortham, Stanton. 2001. Narratives in Action: A Strategy for Research and Analysis . New York: Teachers College Press.

Inductive, Deductive, and Abductive Reasoning and Nomothetic Science in General

Aliseda, Atocha. 2003. “Mathematical Reasoning vs. Abductive Reasoning: A Structural Approach.” Synthese 134(1/2):25–44.

Bonk, Thomas. 1997. “Newtonian Gravity, Quantum Discontinuity and the Determination of Theory by Evidence.” Synthese 112(1):53–73. A (natural) scientific discussion of inductive reasoning.

Bonnell, Victoria E. 1980. “The Uses of Theory, Concepts and Comparison in Historical Sociology.” C omparative Studies in Society and History 22(2):156–173.

Crane, Mark, and Michael C. Newman. 1996. “Scientific Method in Environmental Toxicology.” Environmental Reviews 4(2):112–122.

Huang, Philip C. C., and Yuan Gao. 2015. “Should Social Science and Jurisprudence Imitate Natural Science?” Modern China 41(2):131–167.

Mingers, J. 2012. “Abduction: The Missing Link between Deduction and Induction. A Comment on Ormerod’s ‘Rational Inference: Deductive, Inductive and Probabilistic Thinking.’” Journal of the Operational Research Society 63(6):860–861.

Ormerod, Richard J. 2010. “Rational Inference: Deductive, Inductive and Probabilistic Thinking.” Journal of the Operational Research Society 61(8):1207–1223.

Perry, Charner P. 1927. “Inductive vs. Deductive Method in Social Science Research.” Southwestern Political and Social Science Quarterly 8(1):66–74.

Plutynski, Anya. 2011. “Four Problems of Abduction: A Brief History.” HOPOS: The Journal of the International Society for the History of Philosophy of Science 1(2):227–248.

Thompson, Bruce, and Gloria M. Borrello. 1992. “Different Views of Love: Deductive and Inductive Lines of Inquiry.” Current Directions in Psychological Science 1(5):154–156.

Tracy, Sarah J. 2012. “The Toxic and Mythical Combination of a Deductive Writing Logic for Inductive Qualitative Research.” Qualitative Communication Research 1(1):109–141.

A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community.

A person who introduces the researcher to a field site’s culture and population.  Also referred to as guides.  Used in ethnography .

A form of research and a methodological tradition of inquiry in which the researcher uses self-reflection and writing to explore personal experiences and connect this autobiographical story to wider cultural, political, and social meanings and understandings.  “Autoethnography is a research method that uses a researcher's personal experience to describe and critique cultural beliefs, practices, and experiences” ( Adams, Jones, and Ellis 2015 ).

The philosophical framework in which research is conducted; the approach to “research” (what practices this entails, etc.).  Inevitably, one’s epistemological perspective will also guide one’s methodological choices, as in the case of a constructivist who employs a Grounded Theory approach to observations and interviews, or an objectivist who surveys key figures in an organization to find out how that organization is run.  One of the key methodological distinctions in social science research is that between quantitative and qualitative research.

The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages.  See coding frame and  codebook.

A later stage coding process used in Grounded Theory in which data is reassembled around a category, or axis.

A later stage-coding process used in Grounded Theory in which key words or key phrases capture the emergent theory.

The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted.  Achieving saturation is often used as the justification for the final sample size.

A methodological tradition of inquiry that focuses on the meanings held by individuals and/or groups about a particular phenomenon (e.g., a “phenomenology of whiteness” or a “phenomenology of first-generation college students”).  Sometimes this is referred to as understanding “the lived experience” of a particular group or culture.  Interviews form the primary tool of data collection for phenomenological studies.  Derived from the German philosophy of phenomenology (Husserl 1913; 2017).

The number of individuals (or units) included in your sample

A form of reasoning which employs a “top-down” approach to drawing conclusions: it begins with a premise or hypothesis and seeks to verify it (or disconfirm it) with newly collected data.  Inferences are made based on widely accepted facts or premises.  Deduction is idea-first, followed by observations and a conclusion.  This form of reasoning is often used in quantitative research and less often in qualitative research.  Compare to inductive reasoning .  See also abductive reasoning .

A form of reasoning that employs a “bottom-up” approach to drawing conclusions: it begins with the collection of data relevant to a particular question and then seeks to build an argument or theory based on an analysis of that data.  Induction is observation first, followed by an idea that could explain what has been observed.  This form of reasoning is often used in qualitative research and seldom used in qualitative research.  Compare to deductive reasoning .  See also abductive reasoning .

An “interpretivist” form of reasoning in which “most likely” conclusions are drawn, based on inference.  This approach is often used by qualitative researchers who stress the recursive nature of qualitative data analysis.  Compare with deductive reasoning and inductive reasoning .

A form of social science research that generally follows the scientific method as established in the natural sciences.  In contrast to idiographic research , the nomothetic researcher looks for general patterns and “laws” of human behavior and social relationships.  Once discovered, these patterns and laws will be expected to be widely applicable.  Quantitative social science research is nomothetic because it seeks to generalize findings from samples to larger populations.  Most qualitative social science research is also nomothetic, although generalizability is here understood to be theoretical in nature rather than statistical .  Some qualitative researchers, however, espouse the idiographic research paradigm instead.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Introduction

Why are research questions so important?

Research question examples, types of qualitative research questions, writing a good research question, guiding your research through research questions.

• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Research questions

The research question plays a critical role in the research process, as it guides the study design, data collection , analysis , and interpretation of the findings.

A research paper relies on a research question to inform readers of the research topic and the research problem being addressed. Without such a question, your audience may have trouble understanding the rationale for your research project.

People can take for granted the research question as an essential part of a research project. However, explicitly detailing why researchers need a research question can help lend clarity to the research project. Here are some of the key roles that the research question plays in the research process:

Defines the scope and focus of the study

The research question helps to define the scope and focus of the study. It identifies the specific topic or issue that the researcher wants to investigate, and it sets the boundaries for the study. A research question can also help you determine if your study primarily contributes to theory or is more applied in nature. Clinical research and public health research, for example, may be more concerned with research questions that contribute to practice, while a research question focused on cognitive linguistics are aimed at developing theory.

Provides a rationale for the study

The research question provides a rationale for the study by identifying a gap or problem in existing literature or practice that the researcher wants to address. It articulates the purpose and significance of the study, and it explains why the study is important and worth conducting.

Guides the study design

The research question guides the study design by helping the researcher select appropriate research methods , sampling strategies, and data collection tools. It also helps to determine the types of data that need to be collected and the best ways to analyze and interpret the data because the principal aim of the study is to provide an answer to that research question.

Shapes the data analysis and interpretation

The research question shapes the data analysis and interpretation by guiding the selection of appropriate analytical methods and by focusing the interpretation of the findings. It helps to identify which patterns and themes in the data are more relevant and worth digging into, and it guides the development of conclusions and recommendations based on the findings.

Generates new knowledge

The research question is the starting point for generating new knowledge. By answering the research question, the researcher contributes to the body of knowledge in the field and helps to advance the understanding of the topic or issue under investigation.

Overall, the research question is a critical component of the research process, as it guides the study from start to finish and provides a foundation for generating new knowledge.

Supports the thesis statement

The thesis statement or main assertion in any research paper stems from the answers to the research question. As a result, you can think of a focused research question as a preview of what the study aims to present as a new contribution to existing knowledge.

Here area few examples of focused research questions that can help set the stage for explaining different types of research questions in qualitative research . These questions touch upon various fields and subjects, showcasing the versatility and depth of research.

• What factors contribute to the job satisfaction of remote workers in the technology industry?
• How do teachers perceive the implementation of technology in the classroom, and what challenges do they face?
• What coping strategies do refugees use to deal with the challenges of resettlement in a new country?
• How does gentrification impact the sense of community and identity among long-term residents in urban neighborhoods?
• In what ways do social media platforms influence body image and self-esteem among adolescents?
• How do family dynamics and communication patterns affect the management of type 2 diabetes in adult patients?
• What is the role of mentorship in the professional development and career success of early-career academics?
• How do patients with chronic illnesses experience and navigate the healthcare system, and what barriers do they encounter?
• What are the motivations and experiences of volunteers in disaster relief efforts, and how do these experiences impact their future involvement in humanitarian work?
• How do cultural beliefs and values shape the consumer preferences and purchasing behavior of young adults in a globalized market?
• How do individuals whose genetic factors predict a high risk for developing a specific medical condition perceive, cope with, and make lifestyle choices based on this information?

These example research questions highlight the different kinds of inquiries common to qualitative research. They also demonstrate how qualitative research can address a wide range of topics, from understanding the experiences of specific populations to examining the impact of broader social and cultural phenomena.

Also, notice that these types of research questions tend to be geared towards inductive analyses that describe a concept in depth or develop new theory. As such, qualitative research questions tend to ask "what," "why," or "how" types of questions. This contrasts with quantitative research questions that typically aim to verify an existing theory. and tend to ask "when," "how much," and "why" types of questions to nail down causal mechanisms and generalizable findings.

Whatever your research inquiry, turn to ATLAS.ti

Powerful tools to help turn your research question into meaningful analysis, starting with a free trial.

As you can see above, the research questions you ask play a critical role in shaping the direction and depth of your study. These questions are designed to explore, understand, and interpret social phenomena, rather than testing a hypothesis or quantifying data like in quantitative research. In this section, we will discuss the various types of research questions typically found in qualitative research, making it easier for you to craft appropriate questions for your study.

Descriptive questions

Descriptive research questions aim to provide a detailed account of the phenomenon being studied. These questions usually begin with "what" or "how" and seek to understand the nature, characteristics, or functions of a subject. For example, "What are the experiences of first-generation college students?" or "How do small business owners adapt to economic downturns?"

Comparative questions

Comparative questions seek to examine the similarities and differences between two or more groups, cases, or phenomena. These questions often include the words "compare," "contrast," or "differences." For example, "How do parenting practices differ between single-parent and two-parent families?" or "What are the similarities and differences in leadership styles among successful female entrepreneurs?"

Exploratory questions

Exploratory research questions are open-ended and intended to investigate new or understudied areas. These questions aim to identify patterns, relationships, or themes that may warrant further investigation. For example, "How do teenagers use social media to construct their identities?" or "What factors influence the adoption of renewable energy technologies in rural communities?"

Explanatory questions

Explanatory research questions delve deeper into the reasons or explanations behind a particular phenomenon or behavior. They often start with "why" or "how" and aim to uncover underlying motivations, beliefs, or processes. For example, "Why do some employees resist organizational change?" or "How do cultural factors influence decision-making in international business negotiations?"

Evaluative questions

Evaluative questions assess the effectiveness, impact, or outcomes of a particular intervention, program, or policy. They seek to understand the value or significance of an initiative by examining its successes, challenges, or unintended consequences. For example, "How effective is the school's anti-bullying program in reducing incidents of bullying?" or "What are the long-term impacts of a community-based health promotion campaign on residents' well-being?"

Interpretive questions

Interpretive questions focus on understanding how individuals or groups make sense of their experiences, actions, or social contexts. These questions often involve the analysis of language, symbols, or narratives to uncover the meanings and perspectives that shape human behavior. For example, "How do cancer survivors make sense of their illness journey?" or "What meanings do members of a religious community attach to their rituals and practices?"

There are mainly two overarching ways to think about how to devise a research question. Many studies are built on existing research, but others can be founded on personal experiences or pilot research.

Using the literature review

Within scholarly research, the research question is often built from your literature review . An analysis of the relevant literature reporting previous studies should allow you to identify contextual, theoretical, or methodological gaps that can be addressed in future research.

A compelling research question built on a robust literature review ultimately illustrates to your audience what is novel about your study's objectives.

Conducting pilot research

Researchers may conduct preliminary research or pilot research when they are interested in a particular topic but don't yet have a basis for forming a research question on that topic. A pilot study is a small-scale, preliminary study that is conducted in order to test the feasibility of a research design, methods, and procedures. It can help identify unresolved puzzles that merit further investigation, and pilot studies can draw attention to potential issues or problems that may arise in the full study.

One potential benefit of conducting a pilot study in qualitative research is that it can help the researcher to refine their research question. By collecting and analyzing a small amount of data, the researcher can get a better sense of the phenomenon under investigation and can develop a more focused and refined research question for the full study. The pilot study can also help the researcher to identify key themes, concepts, or variables that should be included in the research question.

In addition to helping to refine the research question, a pilot study can also help the researcher to develop a more effective data collection and analysis plan. The researcher can test different methods for collecting and analyzing data, and can make adjustments based on the results of the pilot study. This can help to ensure that the full study is conducted in the most effective and efficient manner possible.

Overall, conducting a pilot study in qualitative research can be a valuable tool for refining the research question and developing a more effective research design, methods, and procedures. It can help to ensure that the full study is conducted in a rigorous and effective manner, and can increase the likelihood of generating meaningful and useful findings.

When you write a research question for your qualitative study, consider which type of question best aligns with your research objectives and the nature of the phenomenon you are investigating. Remember, qualitative research questions should be open-ended, allowing for a range of perspectives and insights to emerge. As you progress in your research, these questions may evolve or be refined based on the data you collect, helping to guide your analysis and deepen your understanding of the topic.

Use ATLAS.ti for every step of your research project

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Qualitative Research Design: Start

Qualitative Research Design

What is Qualitative research design?

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. 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 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. Qualitative research is the opposite of quantitative research, which involves collecting and analyzing numerical data for statistical analysis. Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Research Paradigms 

• Positivist versus Post-Positivist
• Social Constructivist (this paradigm/ideology mostly birth qualitative studies)

Events Relating to the Qualitative Research and Community Engagement Workshops @ CMU Libraries

CMU Libraries is committed to helping members of our community become data experts. To that end, CMU is offering public facing workshops that discuss Qualitative Research, Coding, and Community Engagement best practices.

The following workshops are a part of a broader series on using data. Please follow the links to register for the events. 

Qualitative Coding

Using Community Data to improve Outcome (Grant Writing)

Survey Design  

Upcoming Event: March 21st, 2024 (12:00pm -1:00 pm)

Community Engagement and Collaboration Event 

Join us for an event to improve, build on and expand the connections between Carnegie Mellon University resources and the Pittsburgh community. CMU resources such as the Libraries and Sustainability Initiative can be leveraged by users not affiliated with the university, but barriers can prevent them from fully engaging.

The conversation features representatives from CMU departments and local organizations about the community engagement efforts currently underway at CMU and opportunities to improve upon them. Speakers will highlight current and ongoing projects and share resources to support future collaboration.

Event Moderators:

Taiwo Lasisi, CLIR Postdoctoral Fellow in Community Data Literacy,  Carnegie Mellon University Libraries

Emma Slayton, Data Curation, Visualization, & GIS Specialist,  Carnegie Mellon University Libraries

Nicky Agate , Associate Dean for Academic Engagement, Carnegie Mellon University Libraries

Chelsea Cohen , The University’s Executive fellow for community engagement, Carnegie Mellon University

Sarah Ceurvorst , Academic Pathways Manager, Program Director, LEAP (Leadership, Excellence, Access, Persistence) Carnegie Mellon University

Julia Poeppibg , Associate Director of Partnership Development, Information Systems, Carnegie Mellon University 

Scott Wolovich , Director of New Sun Rising, Pittsburgh 

Additional workshops and events will be forthcoming. Watch this space for updates. 

Workshop Organizer

Qualitative Research Methods

What are Qualitative Research methods?

Qualitative research adopts numerous methods or techniques including interviews, focus groups, and observation. Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant observers to share the experiences of the subject or non-participant or detached observers.

What constitutes a good research question? Does the question drive research design choices?

According to Doody and Bailey (2014);

 We can only develop a good research question by consulting relevant literature, colleagues, and supervisors experienced in the area of research. (inductive interactions).

Helps to have a directed research aim and objective.

Researchers should not be “ research trendy” and have enough evidence. This is why research objectives are important. It helps to take time, and resources into consideration.

Research questions can be developed from theoretical knowledge, previous research or experience, or a practical need at work (Parahoo 2014). They have numerous roles, such as identifying the importance of the research and providing clarity of purpose for the research, in terms of what the research intends to achieve in the end.

Qualitative Research Questions

What constitutes a good Qualitative research question?

A good qualitative question 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 research gathers participants' experiences, perceptions and behavior.

Examples of good Qualitative Research Questions:

What are people's thoughts on the new library? 

How does it feel to be a first-generation student attending college?

Difference example (between Qualitative and Quantitative research questions):

How many college students signed up for the new semester? (Quan) 

How do college students feel about the new semester? What are their experiences so far? (Qual)

• Qualitative Research Design Workshop Powerpoint

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Corner EJ, Murray EJ, Brett SJ. Qualitative, grounded theory exploration of patients' experience of early mobilisation, rehabilitation and recovery after critical illness. BMJ Open. 2019 Feb 24;9(2):e026348. [ PMC free article: PMC6443050 ] [ PubMed: 30804034 ]

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Soratto J, Pires DEP, Friese S. Thematic content analysis using ATLAS.ti software: Potentialities for researchs in health. Rev Bras Enferm. 2020;73(3):e20190250. [ PubMed: 32321144 ]

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Korstjens I, Moser A. Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. Eur J Gen Pract. 2018 Dec;24(1):120-124. [ PMC free article: PMC8816392 ] [ PubMed: 29202616 ]

Saldaña, J. (2021). The coding manual for qualitative researchers. The coding manual for qualitative researchers, 1-440.

O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014 Sep;89(9):1245-51. [ PubMed: 24979285 ]

Palermo C, King O, Brock T, Brown T, Crampton P, Hall H, Macaulay J, Morphet J, Mundy M, Oliaro L, Paynter S, Williams B, Wright C, E Rees C. Setting priorities for health education research: A mixed methods study. Med Teach. 2019 Sep;41(9):1029-1038. [ PubMed: 31141390 ]

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