possible question in research chapter 2

Nick Huntington-Klein

• What Is a Research Question?
• Why Start with a Question?
• Where Do Research Questions Come From?
• How Do You Know if You’ve Got a Good One?

Chapter 2 - Research Questions

2.1 What Is a Research Question? Copy link

Coming up with a question is easy. Just ask any five-year-old and they can provide you with dozens. Coming up with a good research question is much harder.

What’s the difference? The difference, at least in the case of quantitative empirical research, is that a research question is a question that can be answered , and for which having that answer will improve your understanding of how the world works .

Those are both a little abstract. Let’s take them one at a time.

What does it mean to have a question that can be answered ? It means that it’s possible for there to be some set of evidence in the world that, if you found that evidence, your question would have a believable answer. So for example, “what is the best James Bond movie?” can’t really be answered. 12 12 Although, having seen exactly two James Bond movies myself, I certainly hope it’s not either of them. No matter what evidence you find, “best” is ambiguous enough that you can’t even imagine the evidence that would settle the question for you. You could get every person on Earth to agree it’s Moonraker and that still wouldn’t necessarily settle the question.

On the other hand, “which era of Bond movies had the highest ticket sales?” can definitely be answered. You look at the ticket sales and see when they were highest. Evidence can tell you the answer to this question.

So we have a question that can be answered. But does it improve our understanding of how the world works? What this means is that the research question, once answered, should tell you about something broader than itself. It should inform theory in some way. Theory doesn’t have to be something as important as the theory of gravity or the theory of evolution. It could even be something as generic as “bread costs more today than last year because bread prices have been generally increasing over time.” Theory just means that there’s a why or a because lurking around somewhere. Even hydrogen is a theory - it says that we see material like water behaving a certain way because there’s a kind of atom that behaves in certain ways and has a certain structure.

Take germ theory, for example. Germ theory says that microorganisms like bacteria and viruses can cause disease. This explains why we have diseases, and also why disease can spread from one person to another. We don’t call it “a theory” because we’re uncertain about whether it’s true. 13 13 Theories that are almost certainly correct, like germ theory, aren’t any more or less a theory than theories that are almost certainly incorrect, like the theory that the Egyptians were able to build the pyramids because they had help from aliens. We call it a theory because it tells us why .

A good research question takes us from theory to hypothesis , where a hypothesis is a specific statement about what we will observe in the world, like “people who wash their hands will get sick less often.” That is, a research question should be something that, if you answer it, helps improve your why explanation. Great research questions often come from the theory themselves - the line of thinking being “if this is my explanation of how the world works, then what should I observe in the world? Do I observe it?”

This is easy to miss! Let’s keep working with germ theory as an example. We might ponder germ theory for a while and think “Hey, I wonder how small the smallest microorganism is.” That’s a research question that we could answer with the right evidence, and it’s related to germ theory, and would be kind of neat to know. However, learning the answer to this question wouldn’t really tell us anything new about why we have diseases or why disease can spread from one person to another. 14 14 Or at least I don’t think so… I’m not a biologist. Maybe it helps us understand some other theory better. That would make our small-microorganism question a better research question for that other theory than for germ theory. 15 15 And even if it doesn’t help us understand any theory or broaden our understanding, it might still be worth pursuing just because it’s kinda neat. Nothing wrong with curiosity for its own sake.

So does asking “which era of Bond movies had the highest ticket sales?” improve our understanding of how the world works? Maybe, for the right theory. Maybe we have a theory that says that action movies were generally at their most popular in the 1980s. Asking about Bond sales over time might tell us a little more information about whether that theory is an accurate explanation of ticket sales.

Let’s walk through an example, starting with our theory. Let’s say we have a theory that your curiosity as an adult is harmed by exposure to passive entertainment like TV and movies. Regardless of whether this is actually true or not, it still qualifies as a theory - it explains why we might see certain levels of curiosity in adults.

A natural research question here is “does watching a lot of TV as a child dull your curiosity as an adult?”

Let’s check our two conditions for research questions. Could we answer this question? Yes! The data necessary to answer this question might be hard to come by, but we can at least conceive of it existing. If we randomized a bunch of kids to watch different amounts of TV, and then followed them to adulthood and measured their curiosity, that would be some pretty convincing evidence on our research question. 16 16 Of course, in the real world, very few research questions are ever answered conclusively. Even after this experiment we’d wonder if the results would be the same in a different decade, or country, or if we chose different lengths of TV watching. But even if we couldn’t answer the question conclusively , we’d still be be providing evidence that unambiguously informs our answer to the question.

Second, does this research question tell us about how the world works? Yes! If we answered this question, that would pretty clearly inform our theory. If we answered our research question with “no, watching a lot of TV as a child does not dull your curiosity as an adult,” then it would be a pretty hard sell to explain adult curiosity by saying it’s because of passive entertainment. The research question does help us figure out if the theory is any good.

A good test for whether a research question informs theory is to imagine that you find an unexpected result, and then wonder whether it would make you change your understanding of the world. Let’s say that instead of answering the research question “does watching a lot of TV as a child dull your curiosity as an adult?” we use the research question “do kids who watch lots of Sesame Street tend to have lower levels of curiosity later?” We do our research and find that, actually, kids who watch Sesame Street have higher levels of curiosity! Uh oh. With this new information, do we have to change our theory? Well, we hem and we haw, and we think about how fond we were of that original theory. And we explain away the Sesame Street result by noting that Sesame Street might be different from most kinds of TV, and also that we just looked at which kids did watch Sesame Street, not whether Sesame Street is actually responsible for their curiosity - maybe kids who are more curious in the first place choose Sesame Street.

This ability to see a bad result and still hold on to the original theory tells us that the research question wasn’t very good, at least not for this theory. 17 17 This means if the Sesame Street study had turned out in favor of our theory we shouldn’t have increased our confidence either. A really good research question, once answered, should be hard to explain away just because it’s inconvenient.

So there we have it - “does watching a lot of TV as a child dull your curiosity as an adult?” is a good research question that could be answered with the right data, and would inform our understanding of the world if answered. Granted, the process of actually answering that question is another hurdle. 18 18 Good luck running that big experiment. But at the very least we know that the question itself is good, even if the answer is elusive.

2.2 Why Start with a Question? Copy link

This sounds hard. Why bother? We have a bunch of data at our fingertips. In fact, we’re awash in it. There’s data everywhere. So why not skip the hard part of deriving a research question from a theory and instead just see what sorts of patterns are in the data?

Well, you could. In fact, a lot of people do. This is called “data mining,” and there are people who do that very thing, and manage to do it quite well. They go to the data, look for patterns, and report back. You find a lot of this in the field of data science, but data mining can be done any time you have some data. Just look at the data, see what’s in there, and work backwards.

So, sounds good, right? Well, data mining is well and good, but it turns out to be very good at some things, and very bad at others.

The kinds of things that data mining is good at are in finding patterns and in making predictions under stability . 19 19 What do I mean by “under stability”? I mean that the process giving us the data doesn’t change. If I roll a six-sided die a thousand times, data mining would be great at predicting that the probability of a 1 is \(1/6\) . But if I then switch to a twenty-sided die, that data mining prediction will be bad. It will still predict a \(1/6\) chance of a 1 until it gets a lot more data. Probability theory, on the other hand, will properly predict the switch to a \(1/20\) chance immediately. The kinds of things that data mining is less good at are in improving our understanding , or in other words helping improve theory. It also has a tendency to find false positives if you aren’t careful.

Finding patterns and making predictions are very valuable. And we probably do want to rely on some sort of data mining for these tasks. After all, there’s no way we can really theorize about every possible pattern that could be in the data and think to check it. Doing something that just asks what we see rather than why is the right angle to take there. Plus, sometimes seeing patterns in data can give us ideas for research questions that we can examine further in other data sources.

It’s also probably the best angle to take when we don’t care about why! If I don’t care why the stock market goes up or down, and I just want to predict if it will or not so I know whether to buy or sell, then data mining may well be the way to go.

But outside of those realms?

Why does data mining have difficulty helping theory? There are a few main reasons.

One of the reasons is that data mining, by definition, focuses on what’s in the data, not why it’s in the data. In other words, it’s fantastic at revealing correlations - patterns in the data of how variables we’ve observed have varied together in the past - but the correlations it uncovers may have little to do with causality, or an understanding of why those variables move together.

To introduce an example that will pop up a few times in this book, someone using data mining to try to understand ice cream sales may well notice that the proportion of people who wear shorts is a fantastic predictor of ice cream sales. But shorts-wearing isn’t why people buy ice cream. They buy ice cream and wear shorts because it’s hot. But to a data miner, the shorts/ice cream connection is pretty compelling! After all, shorts can be a great way of predicting ice cream eating, even if there’s no “why” there.

However, if what we’re really interested in isn’t predicting ice cream but explaining why people eat ice cream, it’s pretty tempting at that point to try to invent a story to justify why shorts might actually be the reason people eat ice cream. In the case of ice cream and shorts we can tell that’s ridiculous, but it’s a lot harder when we don’t actually know what’s ridiculous and what’s not ahead of time.

For example, we’d love to know what causes children to act aggressively. That sounds really important! A data mining exercise here might look through all of the things kids do or are exposed to, and check whether any of them are associated with higher levels of aggression. Maybe kids who play a lot of video games are more likely to be aggressive. So … are the video games responsible? Maybe, maybe not. 20 20 Probably not, if more careful research in this area is to be believed. Data mining is well-equipped to find the relationship but poorly-equipped to tell us why that relationship is there. Hopefully someone doesn’t get worried and ban all the video games before the researcher can carefully explain the distinction.

Another reason is that, because it’s so focused on the data, data mining doesn’t really deal in abstraction . For example, take a look at a chair. How do you know it’s a chair? Well, it’s probably got some legs, maybe a back, definitely a flat-ish seating area, and it’s clearly designed for sitting in. This is our “chair theory” - we theorize that there are these objects called chairs that have certain chair-like properties, united in the ability to sit on them some distance off the ground. The chair you’re looking at now is one example of chair theory. 21 21 Were Plato not already dead I’m sure this paragraph would kill him.

But what’s actually in the data? There’s no “chair” in the data. There’s just a flat bit and some straight-up-and-down bits underneath the flat bit. Data mining would be great at noticing that it sees a lot of flat bits on top of straight-up-and-down bits, but it would not be good at developing “chair theory” for us because it would miss why we keep seeing that arrangement - because it allows us to sit on it. A data miner would never guess that the four-legged chair has anything to do with, say, a bean-bag chair, which has no straight-up-and-down bits at all.

False positives are another reason why data mining can be dangerous. Take the video games and aggression example. Okay, sure, maybe the video games aren’t why the kids are aggressive, but we still found the relationship. Surely there’s something there.

Well, maybe, and maybe not. Data mining means looking in the data to see what’s there. And there’s a lot of stuff to look at! If you check, say, a hundred variables and see if they’re related to aggression, something is going to pop up as looking related, just by random chance. That random relationship is unlikely to pop up again if you tried another sample. It’s only in the sample you have by random chance, which is what makes it a “false positive.”

That’s one major danger in proceeding in your work without starting with a solid research question. Without a disciplined research question, there’s no reason not to just check everything! Something is going to pop up as related by random chance if you check enough stuff. It takes a really well-behaved researcher to not pretend that’s exactly what they’d been looking for from the start, and to fill in some reason why the 100th relationship they checked makes perfect sense and supports some theory.

There are ways of avoiding false positives while doing data mining - this is something they worry about a lot in data science and have a lot of tools for. 22 22 Some examples are “cross-validation” and “training and testing sets.” If this interests you, read more about data science. You’ll get some data science in this book but not much. But if you’re just sort of trawling through a data set to see what you see, you’re likely to end up with a whole lot of false positives mixed in with the real positives. You’ll have no way of telling one from another.

That said, data mining isn’t all bad. There’s no way we could possibly think up every interesting theory to test. Plenty of theories come from looking at the data in the first place, noticing a pattern, and wondering why the pattern is showing up, or whether the pattern is even real.

The drug Viagra, for example, was initially being tested as a blood pressure medication. The researchers testing it out to see if it worked to lower blood pressure happened to notice, uh, its other effects.

They’ve done data mining there - instead of coming to the data with a theory, they noticed an interesting pattern in the data.

Of course, the responsible thing to do at that point is to not just take the pattern as given. That’s where the real problem of data mining is. Instead, they took the interesting pattern they noticed and looked to see if it held up in other data - if it replicated - before being sure that the pattern they noticed was real and explained how the drug worked.

Data mining isn’t bad. It’s just bad as a final step if you’re trying to explain the world. It can still work as a source of ideas. And heck, maybe it can help you earn a hojillion dollars like Viagra did, too.

2.3 Where Do Research Questions Come From? Copy link

Research questions can come from lots of places. Mostly, curiosity. We want to know how the world works, and that naturally leads to questions!

There are two steps in this process: thinking about theory, and coming up with a research question. Either one can come first.

Perhaps it begins with theory: “I think this is how the world works” or “I wonder if this is how the world works” - that’s your theory. This could be anything from “I think people make the decisions they do because they follow incentives” to “I think plants survive without eating because they collect energy from the sun” to “I think CD sales are down because people stream music now instead.”

With the theory in place, the process continues with our hypothesis: “if this is how the world works, what would I expect to see in the world?” Our above theories might lead to the research questions “will students work harder in school if you pay them for good grades?” or “will plants die if you store them in a dark room?” or “are CDs more popular now in areas with bad Internet connections?” These research questions tell us a hypothesis to test such that the result of that test tells us something about the theory .

The question might come first. “Will students work harder in school if you pay them for good grades?” we might ask. Then, we might wonder why we came up with such an idea in the first place. Probably because we think students respond to incentives. Or we might wonder, if we answered the question, what sense we might make of it, leading us back to our theory. If you can’t figure out why you would ask the question, it may not be a great research question. Or at least you’d have a hard time getting anyone to care about the answer once you had it.

Let’s be honest, sometimes research questions also come from opportunity .

Have a neat data set? Think about what data is available to you and whether any related research questions or theories come to mind. 23 23 Try to do this after understanding what is in the data but before actually analyzing the data, unless your goal is data mining.

Or, perhaps you’ve learned about something unusual or interesting that has happened in the world. Maybe you’ve learned that a few school districts have decided to try paying their students for good grades. When you hear about something like this, you might ask “what research questions would this allow me to answer?” and from there you have a research question, and from there a theory!

2.4 How Do You Know if You’ve Got a Good One? Copy link

You’ve followed the process. You have a research question in mind. You know it can be answered with data, and you’re pretty sure that if you get the answer to it, it will help you learn how the world works.

But is it really a good one? Just a few things to check before you get too far into the process:

• Consider Potential Results. A good way to double-check the relationship between your research question and your theory is to consider the potential answers you might get. Then, imagine what kind of sense you’d make of that result, or what conclusion you would draw. Let’s say you find that students do tend to work harder in school when they’re paid for good grades. What would this tell us about how students respond to incentives? Or let’s say you find that students don’t work harder when they’re paid. What would that tell us about how students respond to incentives? If you can’t say something interesting about your potential results, that probably means your research question and your theory aren’t as closely linked as you think! Let’s say we do find that kids who happen to play video games are more aggressive. Can we take that result and claim that video games are a cause of aggression? Not really, for the reasons we’ve discussed previously. So maybe that research question really isn’t linked to that theory very well.
• Consider Feasibility. A research question should be a question that can be answered using the right data, if the right data is available. But is the right data available? If answering your research question is possible but requires following millions of people repeatedly for decades, or trying to measure something that’s really hard to measure accurately, like trying to get people to remember what they had for lunch three years ago, or getting access to the private finances of thousands of unwilling people, then that research question might not be feasible. While sometimes you can get around these problems with a clever design, you might want to consider going back to the drawing board.
• Consider Scale. What kind of resources and time can you dedicate to answering the research question? Given a lifetime of effort and considerable resources, you might be able to tackle massive questions like “What causes some countries to become rich and others poor?” Given the confines of, say, a term paper, you could take some wild swings at that question, but you’re likely to do a much more thorough job answering questions with a lot less complexity.
• Consider Design. A research question can be great on its own, but it can only be so interesting without an answer. So, an important part of evaluating whether you have a workable research question is figuring out if there’s a reasonable research design you can use to answer it. Figuring out whether you do have a reasonable research design is the topic of the rest of this book.
• Keep It Simple! Answering any research question can be difficult. Don’t make it even harder on yourself by biting off more than you can chew! A common mistake is to bundle a bunch of research questions into one. “What are the determinants of social mobility?” I.e., how someone can move from one social class to another throughout their lifetime. There are many determinants of social mobility. You’re unlikely to answer that question well. Instead try “Is birth location a determinant of social mobility?” For another example, how about the question “How was the medium of painting affected by the Italian renaissance?” In a million ways! You’ll get lost and do a poor job on a bunch of minor pieces instead of getting at the whole. Instead maybe “What similar characteristics are there among the countries that adopted the use of perspective in painting most quickly?”

So, consider feasibility, scale, and design. Keep it simple, and think about whether the results you might likely see would tell you anything interesting about the world. After all, learning something interesting and new about the world is our goal!

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17 Thesis Defense Questions and How to Answer Them

A thesis defense gives you the chance to show off your thesis work and demonstrate your expertise in your field of study. During this one- to two-hour discussion with the members of your thesis committee, you'll have some control over how you present your research, but your committee will ask you some prodding questions to test your knowledge and preparedness. They will all have read your thesis beforehand, so their questions will relate to your study, topic, methods, data sample, and other aspects.

A good defense requires mastery of the thesis itself, so before you consider the questions you might face,

1. What is your topic, and why did you choose it?

Give a quick summary in just a few sentences on what you've researched. You could certainly go on for hours about your work, but make sure you prepare a way to give a very brief overview of your thesis. Then, give a quick background on your process for choosing this topic.

2. How does your topic contribute to the existing literature? How is it important?

Many researchers identify a need in the field and choose a topic to bridge the gaps that previous literature has failed to cover. For example, previous studies might not have included a certain population, region, or circumstance. Talk about how your thesis enhances the general understanding of the topic to extend the reach beyond what others have found, and then give examples of why the world needs that increased understanding. For instance, a thesis on romaine lettuce crops in desert climates might bring much-needed knowledge to a region that might not have been represented in previous work.

3. What are the key findings of your study?

When reporting your main results, make sure you have a handle on how detailed your committee wants you to be. Give yourself several options by preparing 1) a very general, quick summary of your findings that takes a minute or less, 2) a more detailed rundown of what your study revealed that is 3-5 minutes long, and 3) a 10- to 15-minute synopsis that delves into your results in detail. With each of these responses prepared, you can gauge which one is most appropriate in the moment, based on what your committee asks you and what has already been requested.

4. What type of background research did you do for your study?

Here you'll describe what you did while you were deciding what to study. This usually includes a literary review to determine what previous researchers have already introduced to the field. You also likely had to look into whether your study was going to be possible and what you would need in order to collect the needed data. Did you need info from databases that require permissions or fees?

5. What was your hypothesis, and how did you form it?

Describe the expected results you had for your study and whether your hypothesis came from previous research experience, long-held expectations, or cultural myths.

6. What limitations did you face when writing your text?

It's inevitable — researchers will face roadblocks or limiting factors during their work. This could be a limited population you had access to, like if you had a great method of surveying university students, but you didn't have a way to reach out to other people who weren't attending that school.

7. Why did you choose your particular method for your study?

Different research methods are more fitting to specific studies than others (e.g., qualitative vs. quantitative ), and knowing this, you applied a method that would present your findings most effectively. What factors led you to choose your method?

8. Who formed the sample group of your study, and why did you choose this population?

Many factors go into the selection of a participant group. Perhaps you were motivated to survey women over 50 who experience burnout in the workplace. Did you take extra measures to target this population? Or perhaps you found a sample group that responded more readily to your request for participation, and after hitting dead ends for months, convenience is what shaped your study population. Make sure to present your reasoning in an honest but favorable way.

9. What obstacles or limitations did you encounter while working with your sample?

Outline the process of pursuing respondents for your study and the difficulties you faced in collecting enough quality data for your thesis. Perhaps the decisions you made took shape based on the participants you ended up interviewing.

10. Was there something specific you were expecting to find during your analysis?

Expectations are natural when you set out to explore a topic, especially one you've been dancing around throughout your academic career. This question can refer to your hypotheses , but it can also touch on your personal feelings and expectations about this topic. What did you believe you would find when you dove deeper into the subject? Was that what you actually found, or were you surprised by your results?

11. What did you learn from your study?

Your response to this question can include not only the basic findings of your work (if you haven't covered this already) but also some personal surprises you might have found that veered away from your expectations. Sometimes these details are not included in the thesis, so these details can add some spice to your defense.

12. What are the recommendations from your study?

With connection to the reasons you chose the topic, your results can address the problems your work is solving. Give specifics on how policymakers, professionals in the field, etc., can improve their service with the knowledge your thesis provides.

13. If given the chance, what would you do differently?

Your response to this one can include the limitations you encountered or dead ends you hit that wasted time and funding. Try not to dwell too long on the annoyances of your study, and consider an area of curiosity; for example, discuss an area that piqued your interest during your exploration that would have been exciting to pursue but didn't directly benefit your outlined study.

14. How did you relate your study to the existing theories in the literature?

Your paper likely ties your ideas into those of other researchers, so this could be an easy one to answer. Point out how similar your work is to some and how it contrasts other works of research; both contribute greatly to the overall body of research.

15. What is the future scope of this study?

This one is pretty easy, since most theses include recommendations for future research within the text. That means you already have this one covered, and since you read over your thesis before your defense, it's already fresh in your mind.

16. What do you plan to do professionally after you complete your study?

This is a question directed more to you and your future professional plans. This might align with the research you performed, and if so, you can direct your question back to your research, maybe mentioning the personal motivations you have for pursuing study of that subject.

17. Do you have any questions?

Although your thesis defense feels like an interrogation, and you're the one in the spotlight, it provides an ideal opportunity to gather input from your committee, if you want it. Possible questions you could ask are: What were your impressions when reading my thesis? Do you believe I missed any important steps or details when conducting my work? Where do you see this work going in the future?

Bonus tip: What if you get asked a question to which you don't know the answer? You can spend weeks preparing to defend your thesis, but you might still be caught off guard when you don't know exactly what's coming. You can be ready for this situation by preparing a general strategy. It's okay to admit that your thesis doesn't offer the answers to everything – your committee won't reasonably expect it to do so. What you can do to sound (and feel!) confident and knowledgeable is to refer to a work of literature you have encountered in your research and draw on that work to give an answer. For example, you could respond, "My thesis doesn't directly address your question, but my study of Dr. Leifsen's work provided some interesting insights on that subject…." By preparing a way to address curveball questions, you can maintain your cool and create the impression that you truly are an expert in your field.

After you're done answering the questions your committee presents to you, they will either approve your thesis or suggest changes you should make to your paper. Regardless of the outcome, your confidence in addressing the questions presented to you will communicate to your thesis committee members that you know your stuff. Preparation can ease a lot of anxiety surrounding this event, so use these possible questions to make sure you can present your thesis feeling relaxed, prepared, and confident.

Header image by Kasto .

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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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Doing Research in the Real World

Student resources, chapter 2: theoretical perspectives and research methodologies.

• Checklist for Using Theory

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2.4 Developing a Research Question

Once the research topic has been decided, the next step is to formulate the research question(s) (Figure 2.1). The research question highlights the issue that has to be investigated and directs the methodology. It also results in the development of a testable and appropriate hypothesis. It is thought that high-quality research results from well-crafted research questions. 9 Coming up with good research questions is something that most novice researchers find challenging. A valid tool that is useful in formulating a good research question is the PICOT framework which is commonly used in quantitative research. 6 To use this framework, you need to consider the population of interest (P), the intervention (I) under investigation, the control or comparison group (C), the outcome (O), and the time frame (T) during which the study will take place (see Figure 2.5). 6

In addition, the framework is valuable when developing and stating a research hypothesis driven by the research question. 6 A hypothesis is a foundation and logical construct between a research problem and its solution, and it expresses a possible answer to a research question. 10 The research question serves as the basis for the development of the research hypothesis, which is then summarised in a way that establishes the basis for testing, statistical analysis, and, ultimately, the significance of the study (30). A good hypothesis is usually founded on a good research question. There are two types of research hypotheses – conceptual research hypothesis and operational research hypothesis. 10 The conceptual research hypothesis is a broad or general statement about a research problem, while the operational hypothesis is a more specific statement that provides a detailed description of how the variables in the study will be measured, and predicts how they will be related to one another. 10 Thus, the operational hypothesis is a testable statement that is derived from the conceptual hypothesis. The example below in Figure 2.6 and Box 2.1 shows how the PICOT framework is used to develop a research question and hypotheses.

Characteristics of a good research question

A good research question has the following qualities: it is Feasible, Interesting, Novel, Ethical, and Relevant (FINER). 11

Feasible: b eing feasible indicates that it is practical for the investigator to carry out. 11 This refers to the distribution of appropriate resources (including a budget), sufficient research design, readily available skills and information, and an acceptable time limit. 12   Participants must be realistically recruitable and accurately representative of the relevant population if they are to be chosen for the study. 12 The research question should be connected to the available observations, phenomena, indicators, or variables. 13

Interesting: t he presentation of the research questions and the story that may be told about the research field through investigation and analysis are two important aspects of what makes research intriguing. 12 It is also a good idea to check if the research question is intriguing to others. 11 If research does not address a question of interest, it will have no influence or impact. 11 While a research study may be interesting, other contextual factors, such as the way the issue is presented, the context of the subject, the target audience, and the reader’s expectations, may impact whether the findings will be considered intriguing or not. 14

Novel: n ovelty and relevance are the most important criteria for any research project’s success. 12 Good research adds fresh knowledge. 11 A study that essentially restates what has already been proven is not worth the time, money, and effort and is not likely to be published. 11 Novelty can range in breadth from moderately unique to extremely novel, and it may involve, among other things, contesting preexisting theories, positing new ones, or both. 12 Novelty also has to do with pointing out specific gaps in the field’s current ideas or methods for looking at phenomena or with the extension of theories or results to new subfields. 12 By carefully reading the literature and consulting with specialists who are familiar with ongoing unpublished research, one can assess the uniqueness of a proposed study. 11

Ethical: r esearch must be ethical; thus, research questions must not result in unethical conduct. 11   The research should be conducted in a way that reduces the risk of harm to participants, protects their privacy and confidentiality, and gives them the option to withdraw from the study. 12 It is important to obtain approval from the institutional review board before beginning research. 11 Further details on ethical considerations are presented in chapter 6.

Relevant: r elevant topics are those that are of priority at a given time in the research community and have an impact on upcoming research directions. 12 Relevant questions have a clear target audience in the community, and the answers to their inquiries have an impact on ongoing research projects. 12 Thus, it is crucial to consider the potential outcomes and how each one might increase scientific understanding, impact clinical recommendations and public health policies, or serve as a direction for future study. 11

Use the Padlet below to develop a research question of interest to you.

• Think of a research topic that you are passionate about.
• Do you know if this problem has been researched before? If yes, what are the gaps?
• Articulate on paper a research gap/problem (from this topic) that you would like to investigate.
• Phrase the problem as a research question and post it on the Padlet below.

Reflections: Is your research question feasible, interesting, novel, ethical and relevant?

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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2 Selection of the Research Question

• Published: March 2018
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Selecting a research question is the first step of any research project. This chapter discusses how to formulate a specific research question from a variety of scientific interests. The reader will learn that a good research question needs to consider several aspects, such as feasibility, innovation, and significance, and that merging all these aspects into one research question may be challenging. This chapter reviews the importance of generating a strong research question using the PICOT format: population (P), intervention (I), control (C), outcomes (O), and time (T). This chapter also discusses the selection of appropriate outcome variables—surrogates or clinical endpoints, based on the types of questions or study phase. The final goal of this chapter is to refine the researcher’s general idea into the process of shaping a strong research question that will be feasible, important, ethical, and answerable.

The difficulty in most scientific work lies in framing the questions rather than in finding the answers. —Arthur E. Boycott (Pathologist, 1877–1938)

The grand aim of all science is to cover the greatest number of empirical facts by logical deduction from the smallest number of hypotheses or axioms. —Albert Einstein (Physicist, 1879–1955)

Introduction

The previous chapter provided the reader with an overview of the history of clinical research, followed by an introduction to fundamental concepts of clinical research and clinical trials. It is important to be aware of and to learn lessons from the mistakes of past and current research in order to be prepared to conduct your own research. As you will soon learn, developing your research project is an evolutionary process, and research itself is a continuously changing and evolving field.

Careful conceptual design and planning are crucial for conducting a reproducible, compelling, and ethically responsible research study. In this chapter, we will discuss what should be the first step of any research project, that is, how to develop your own research question. The basic process is to select a topic of interest, identify a research problem within this area of interest, formulate a research question, and finally state the overall research objectives (i.e., the specific aims that define what you want to accomplish).

You will learn how to define your research question, starting from broad interests and then narrowing these down to your primary research question. We will address the key elements you will need to define for your research question: the study population, the intervention ( x , independent variable[s]), and the outcome(s) ( y , dependent variable[s]). Later chapters in this volume will discuss popular study designs and elements such as covariates, confounders, and effect modifiers (interaction) that will help you to further delineate your research question and your data analysis plan.

Although this chapter is not a grant-writing tutorial, most of what you will learn here has very important implications for writing a grant proposal. In fact, the most important part of a grant proposal is the “specific aims” page, where you state your research question, hypotheses, and objectives.

How to Select a Research Question

What is a research question.

A research question is an inquiry about an unanswered scientific problem. The purpose of your research project is to find the answer to this particular research question. Defining a research question can be the most difficult task during the design of your study. Nevertheless, it is fundamental to start with the research question, as it is strongly associated with the study design and predetermines all the subsequent steps in the planning and analysis of the research study.

What Is the Importance of a Research Question?

Defining the research question is instrumental for the success of your study. It determines the study population, outcome, intervention, and statistical analysis of the research study, and therefore the scope of the entire project.

A novice researcher will often jump to methodology and design, eager to collect data and analyze them. It is always tempting to try out a new or “fancy” method (e.g., “Let’s test this new proteomic biomarker in a pilot study!” or “With this Luminex assay we can test 20 cytokines simultaneously in our patient serum!”), but this mistake all too often makes the research project a “fishing expedition,” with the unfortunate outcome that a researcher has invested hours of work and has obtained reams of data, only to find herself at an impasse, and never figuring out what to do with all the information collected. Although it is not wrong to plan an exploratory study (or a hypothesis-generating study), such study has a high risk of not yielding any useful information; thus all the effort to have the study performed will be lost. When planning an exploratory or pilot study (with no defined research question), the investigator must understand the goals and the risks (for additional discussion on pilot studies, see Lancaster et al. 2002 ).

It is important to first establish a concept for your research. You must have a preset idea or a working hypothesis in order to be able to understand the data you will generate. Otherwise, you will not be able to differentiate whether your data were obtained by chance, by mistake, or if they actually reflect a true finding. Also, have in mind ahead of time how you would like to present your study at a conference, in a manuscript, or in a grant proposal. You should be able to present your research to your audience in a well-designed manner that reflects a logical approach and appropriate reasoning.

A good research question leads to useful findings that may have a significant impact on clinical practice and health care, regardless of whether the results are positive or negative. It also gives rise to the next generation of research questions. Therefore, taking enough time to develop the research question is essential.

Where Do Research Questions Come From?

How do we find research questions? As a clinical research scientist, your motivation to conduct a study might be driven by a perceived knowledge gap, the urge to deepen your understanding in a certain phenomenon, or perhaps to clarify contradictory existing findings. Maybe your bench research implies that your findings warrant translation into a study involving patients in a clinical setting. Maybe your clinical work experience gives you the impression that a new intervention would be more effective for your patients compared to standard treatment. For example, your results could lead you to ask, “Does this drug really prolong life in patients with breast cancer?” or, “Does this procedure really decrease pain in patients with chronic arthritis?”

Once you have identified a problem in the area you want to study, you can refine your idea into a research question by gaining a firm grasp of “what is known” and “what is unknown.” To better understand the research problem, you should learn as much as you can about the background pertaining to the topic of interest and specify the gap between current understanding and unsolved problems. As an early step, you should consult the literature, using tools such as MEDLINE or EMBASE, to gauge the current level of knowledge relevant to your potential research question. This is essential in order to avoid spending unnecessary time and effort on questions that have already been solved by other investigators. Meta-analyses and systematic reviews are especially useful to understand the combined level of evidence from a large number of studies and to obtain an overview of clinical trials associated with your questions. You should also pay attention to unpublished results and the progress of important studies whose results are not yet published. It is important to realize that there likely are negative results produced but never published. You can inspect funnel plots obtained from meta-analyses or generated from your own research (see Chapter 14 in this volume for more details) to estimate if there has been publication bias toward positive studies. Also, be aware that clinical trials with aims similar to those of your study might still be ongoing. To find this information, you can check the public registration of trials using sites such as clinicaltrials.gov.

How to Develop the Research Question: Narrow Down the Question

Once you have selected your research topic, you need to develop it into a more specific question. The first step in refining a research question is to narrow down a broad research topic into a specific description ( narrow research question ) that covers the four points of importance, feasibility, answerability , and ethicality.

Importance: Interesting, Novel, and Relevant

Your research can be descriptive, exploratory, or experimental. The purpose of your research can be for diagnostic or treatment purposes, or to discover or elucidate a certain mechanism. The point you will always have to consider when making a plan for your study, however, is how to justify your research proposal. Does your research question have scientific relevance? Can you answer the “so what” question? You need to describe the importance of your research question with careful consideration of the following elements:

The disease (condition or problem) : Novelty, unmet need, or urgency are important. What is the prevalence of this disease/condition? Is there a pressing need for further discoveries regarding this topic because of well-established negative prognoses (e.g., HIV, pancreatic cancer, or Alzheimer’s disease)? Are existing treatment options limited, too complex or costly, or otherwise not satisfactory (e.g., limb replacement, face transplantation)? Does the research topic reflect a major problem in terms of health policy, medical, social, and/or economic aspects (e.g., smoking, hypertension, or obesity)?

The intervention : Is it a new drug, procedure, technology, or medical device (e.g., stem-cell derived pacemaker or artificial heart)? Does it concern an existing drug approved by the Food and Drug Administration (FDA) for a different indication (e.g., is Rituximab, a drug normally indicated for malignant lymphoma, effective for systemic lupus erythematosus or rheumatoid arthritis)? Is there new evidence for application of an existing intervention in a different population (e.g., is Palivizumab also effective in immunodeficiency infants, not only in premature infants to prevent respiratory syncytial virus)? Have recent findings supported the testing of a new intervention in a particular condition (e.g., is a β-blocker effective in preventing cardiovascular events in patients with chronic renal failure)? Even a research question regarding a standard of care intervention can be valuable if in the end it can improve the effectiveness of clinical practice.

Feasibility

In short, be realistic: novel research tends to jump right away into very ambitious projects. You should carefully prove the feasibility of your research idea to prevent wasting precious resources such as time and money:

Patients : Can you recruit the required number of subjects? Do you think your recruitment goal is realistic? Rare diseases such as Pompe or Fabry’s disease will pose a challenge in obtaining a sufficient sample size. Even common diseases, depending on your inclusion criteria and regimen of intervention, may be difficult to recruit. Does your hospital have enough patients? If not, you may have to consider a multi-center study. What about protocol adherence and dropouts ? Do you expect significant deviations from the protocol? Do you need to adjust your sample size accordingly?

Technical expertise : Are there any established measurements or diagnostic tools for your study? Can the outcome be measured? Is there any established diagnostic tool? Do we have any standard techniques for using the device (e.g., guidelines for echocardiographic diagnosis for congenital heart disease)? Is there a defined optimal dose? Can you operate the device, or can the skill be learned appropriately (e.g., training manual for transcutaneous atrial valve replacement)? A pilot study or small preliminary study can be helpful at this stage to help answer these preliminary questions.

Time : Do you have the required time to recruit your patients? Is it possible to follow up with patients for the entire time of the proposed study period (e.g., can you follow preterm infant development at 3, 6, and 9 years of age)? When do you need to have your results in order to apply for your next grant?

Funding : Does you budget allow for the scope of your study? Are there any research grants you can apply for? Do the funding groups’ interests align with those of your study? How realistic are your chances of obtaining the required funding? If there are available funds, how do you apply for the grant?

Team : How about your research environment? Do your mentors and colleagues share your interests? What kind of specialists do you need to invite for your research? Do you have the staff to support your project (technicians, nurses, administrators, etc.)?

Answerability

New knowledge can only originate from questions that are answerable. A broad research problem is still a theoretical idea, and even if it is important and feasible, it still needs to be further specified. You should carefully investigate your research idea and consider the following:

Precisely define what is known or not known and identify what area your research will address. The research question should demonstrate an understanding of the biology, physiology , and epidemiology relevant to your research topic. For example, you may want to investigate the prevalence and incidence of stroke after catheterization and its prognosis before you begin research on the efficacy of a new anticoagulant for patients who received catheter procedures. Again, you may need to conduct a literature review in order to clarify what is already known. Conducting surveys ( interviews or questionnaires ) initially could also be useful to understand the current status of your issues (e.g., how many patients a year are diagnosed with stroke after catherization in your hospital? What kind of anticoagulant is already being used for the patients? How old are the patients? How about the duration of cauterization techniques? etc.).

The standard treatment should be well known before testing a new treatment. Are there any established treatments in your research field? Could your new treatment potentially replace the standard treatment or be complementary to the current treatment of choice? Guidelines can be helpful for discussion (e.g., American College of Cardiology/American Heart Association guidelines for anticoagulant therapy). Without knowing the current practice, your new treatment may never find its clinical relevance.

We also need information about clinical issues for diagnostic tests and interventions . Are you familiar with the diagnoses and treatment of this disease (e.g., computerized tomography or magnetic resonance imaging to rule out stroke after catherization)? Do you know the current guidelines ?

Ethical Aspects

Ethical issues should be discussed before conducting research. Is the subject of your research a controversial topic? The possible ethical issues will often depend largely on whether the study population is considered vulnerable (e.g., children, pregnant women, etc.; see Chapter 1 ) [ 1 ]. You must always determine the possible risks and benefits of your study intervention [ 1 ].

Finally, you may want to ask for expert opinions about whether your research question is answerable and relevant (no matter how strong your personal feelings may be about the relevance). To this end, a presentation of your idea or preliminary results at a study meeting early on in the project development can help refine your question.

How to Build the Research Question

The next step of formulating a narrow research question is to focus on the primary interest ( primary question ): What is the most critical question for your research problem? You will define this primary question by addressing the key elements using the useful acronym PICOT (population, intervention, control, outcome, and time), while keeping in mind the importance, feasibility, answerability, and ethicality. Although PICOT is a useful framework, it does not cover all types of studies, especially some observational studies, for instance those investigating predictors of response (E [exposure] instead of I [intervention] is used for observational studies). But for an experimental study (e.g., a clinical trial), the PICOT framework is extremely useful to guide formulation of the research question.

Building the Research Question: PICOT

P (population or patient).

What is the target population of your research? The target population is the population of interest from which you want to draw conclusions and inferences. Do you want to study mice or rabbits? Adults or children? Nurses or doctors? What are the characteristics of the study subjects, and what are the given problems that should be considered? You may want to consider the pathophysiology (acute or chronic?) and the severity of the disease (severe end stage or early stage?), as well as factors such as geographical background and socioeconomic status.

Once you decide on the target population, you may select a sample as the study population for your study. The study population is a subset of the target population under investigation. However, it is important to remember that the study population is not always a perfect representation of the target population, even when sampled at random. Thus, defining the study population by the inclusion and exclusion criteria is a critical step (see Chapter 3 ).

Since only in rare cases will you be able to study every patient of interest, you will have to identify and select whom from the target population you want to study. This is referred to as the study sample. To do this requires choosing a method of selection or recruitment (see Chapter 7 ).

A specific study sample defined by restricted criteria will have a reduced number of covariates and will be more homogeneous, therefore increasing the chance of higher internal validity for your study. This also typically allows for the study to be smaller and potentially less expensive. In contrast, a restricted population might make it more difficult to recruit a sufficient number of subjects. On the other hand, recruitment can be easier if you define a broad population, which also increases the generalizability of your study results. However, a broad population can make the study larger and more expensive [ 2 ].

I (Intervention)

The I of the acronym usually refers to “intervention.” However, a more general and therefore preferable term would be “independent variable.” The independent variable is the explanatory variable of primary interest, also declared as x in the statistical analysis. The independent variable can be an intervention (e.g., a drug or a specific drug dose), a prognostic factor, or a diagnostic test. I can also be the exposure in an observational study. In an experimental study, I is referred to as the fixed variable (controlled by the investigator), whereas in an observational study, I refers to an exposure that occurs outside of the experimenter’s control.

The independent variable precedes the outcome in time or in its causal path, and thus it “drives” the outcome in a cause-effect relationship.

C (Control)

What comparison or control is being considered? This is an important component when comparing the efficacy of two interventions. The new treatment should be superior to the placebo , when there is no standard treatment available. Placebo is a simulated treatment that has no pharmaceutical effects and is used to mask the recipients to potential expectation biases associated with participating in clinical trials. On the other hand, active controls could be used when an established treatment exists and the efficacy of the new intervention should be examined at least within the context of non-inferiority to the standard treatment. Also the control could be baseline in a one-group study.

O (Outcomes)

O is the dependent variable, or the outcome variable of primary interest; in the statistical analysis, it is also referred to as y . The outcome of interest is a random variable and can be a clinical (e.g., death) or a surrogate endpoint (e.g., hormone level, bone density, antibody titer). Selection of the primary outcome depends on several considerations: What can you measure in a timely and efficient manner? Which measurement will be relevant to understand the effectiveness of the new intervention? What is routinely accepted and established within the clinical community? We will discuss the outcome variable in more detail later in the chapter.

Time is sometimes added as another criterion and often refers to the follow-up time necessary to assess the outcome or the time necessary to recruit the study sample. Rather than viewing time as a separate aspect, it is usually best to consider time in context with the other PICOT criteria.

What Is the Primary Interest in your Research?

Once you have selected your study population, as well as the dependent and independent variables, you are ready to formulate your primary research question, the major specific aim, and a hypothesis. Even if you have several different ideas regarding your research problem, you still need to clearly define what the most important question of your research is. This is called your primary question . A research project may also contain additional secondary questions.

The primary question is the most relevant question of your research that should be driven by the hypothesis. Usually only one primary question should be defined at the beginning of the study, and it must be stated explicitly upfront [ 3 ]. This question is relevant for your sample size calculation (and in turn, for the power of your study—see Chapter 11 ).

The specific aim is a statement of what you are proposing to do in your research project.

The primary hypothesis states your anticipated results by describing how the independent variable will affect the dependent variable. Your hypothesis cannot be just speculation, but rather it must be grounded on the research you have performed and must have a reasonable chance of being proven true.

We can define more than one question for a study, but aside from the primary question, all others associated with your research are treated as secondary questions. Secondary questions may help to clarify the primary question and may add some information to the research study. What potential problems do we encounter with secondary questions? Usually, they are not sufficiently powered to be answered because the sample size is determined based on th e primary question. Also, type I errors (i.e., false positives) may occur due to multiple comparisons if not adjusted for by the proper statistical analysis. Therefore, findings from secondary questions should be considered exploratory and hypothesis generating in nature, with new confirmatory studies needed to further support the results.

An ancillary study is a sub-study built into the main study design. Previous evidence may convince you of the need to test a hypothesis within a sub-group ancillary to the main population of interest (e.g., females, smokers). While this kind of study enables you to perform a detailed analysis of the subpopulation, there are limitations on the generalizability of an ancillary study since the population is usually more restricted (see Further Readings, Examples of Ancillary Studies).

It is important to understand thoroughly the study variables when formulating the study question. Here we will discuss some of the important concepts regarding the variables, which will be discussed in more detail in Chapter 8 .

We have already learned that the dependent variable is the outcome, and the independent variable is the intervention. For study design purposes, it is important to also discuss how the outcome variables are measured. A good measurement requires reliability (precision), validity (“trueness”), and responsiveness to change. Reliability refers to how consistent the measurement is if it is repeated. Validity of a measurement refers to the degree to which it measures what it is supposed to measure. Responsiveness of a measurement means that it can detect differences that are proportional to the change of what is being measured with clinical meaningfulness and statistical significance.

Covariates are independent variables of secondary interest that may influence the relationship between the independent and dependent variables. Age, race, and gender are well-known examples. Since covariates can affect the study results, it is critical to control or adjust for them. Covariates can be controlled for by both planning (inclusion and exclusion criteria, placebo and blinding, sampling and randomization, etc.) and analytical methods (e.g., covariate adjustment [see Chapter 13 ], and propensity scores [see Chapter 17 ]).

Continuous (ratio and interval scale), discrete, ordinal, nominal (categorical, binary) variables: Continuous data represent all numbers (including fractions of numbers, floating point data) and are the common type of raw data. Discrete data are full numbers (i.e., integer data type; e.g., number of hospitalizations). Ordinal data are ordered categories (e.g., mild, moderate, severe). Nominal data can be either categorical (e.g., race) or dichotomous/binary (e.g., gender). Compared to other variables, continuous variables have more power, which is the ability of the study to detect an effect (e.g., differences between study groups) when it is truly present, but they don’t always reflect clinical meaningfulness and therefore make interpretation more difficult. Ordinal and nominal data may better reflect the clinical significance (e.g., dead or alive, relapse or no relapse, stage 1 = localized carcinoma, etc.). However, ordinal and categorical data typically have less power, and important information may be lost (e.g., if an IQ less than 70 is categorized as developmental delay in infants, IQs of 50, 58, and 69 will all fall into the same category, while an IQ of 70 or more is considered to be normal development, although the difference is just 1 point). This approach is called categorization of continuous data, where a certain clinically meaningful threshold is set to make it easier to quickly assess study results. It is important to note that some authors differentiate between continuous and discrete variables by defining the former as having a quantitative characteristic and the latter as having a qualitative characteristic. This is a somewhat problematic classification, especially when it comes to ordinal data.

Single and multiple variables : Having a single variable is simpler, as it is easier for clinical interpretation. Multiple valuables are efficient because we can evaluate many variables within a single trial, but these can be difficult to disentangle and interpret. Composite endpoints are combined multiple variables and are also sometimes used. Because each clinical outcome may separately require a long duration and a large sample size, combining many possible outcomes increases overall efficiency and enables one to reduce sample size requirements and to capture the overall impact of therapeutic interventions. Common examples include MACE (major adverse cardiac events) and TVF (target vessel failure: myocardial infarction in target vessel, target vessel reconstruction, cardiac death, etc.). Interpretation of the results has to proceed with caution, however (see section on case-specific questions) [ 9 ].

Surrogate variables (endpoints) and clinical variables (endpoints): Clinical variables directly assess the effect of therapeutic interventions on patient function and survival, which is the ultimate goal of a clinical trial. Clinical variables may include mortality, events (e.g., myocardial infarction, stroke), and occurrence of disease (e.g., HIV). A clinical endpoint is the most definitive outcome to assess the efficacy of an intervention. Thus, clinical endpoints are preferably used in clinical research. However, it is not always feasible to use clinical outcomes in trials. The evaluation of clinical outcomes presents some methodological problems since they require long-term follow-up (with problems of adherence, dropouts, competing risks, requiring larger sample sizes) and can make a trial more costly. At the same time, the clinical endpoint may be difficult to observe. For this reason, clinical scientists often use alternative outcomes to substitute for the clinical outcomes. So-called surrogate endpoints are a more practical measure to reflect the benefit of a new treatment. Surrogate endpoints (e.g., cholesterol levels, blood sugar, blood pressure, viral load) are defined based on the understanding of the mechanism of a disease that suggests a clear relationship between a marker and a clinical outcome [ 8 ]. Also, a biological rationale provided by epidemiological data, other clinical trials, or animal data should be previously demonstrated. A surrogate is frequently a continuous variable that can be measured early and repeatedly and therefore requires shorter follow-up time, smaller sample size, and reduced costs for conducting a trial. Surrogate endpoints are often used to accelerate the process of new drug development and early stages of development, such as in phase 2 [ 10 ]. As a word of caution, too much reliance on surrogate endpoints alone can be misleading if the results are not interpreted with regard to validation, measurability, and reproducibility (see Further Reading) [ 4 ].

How to Express a Research Question

Once a narrow research question is defined, you should clearly specify a hypothesis in the study protocol. A hypothesis is a statement about the expected results that predicts the effect of the independent on the dependent variable. A research hypothesis is essential to frame the experimental and statistical plan (statistics will be discussed in Unit II of this volume) and is also important to support the aim of the study in a scientific manuscript.

Types of Research Questions

To refine the research question and form the research hypothesis, we will discuss three types of research questions that investigate group differences, correlations, or descriptive measures. This classification is particularly important in discussing which statistical analysis is appropriate for your research question [ 5 ].

Basic/complex difference (group comparison) questions : Samples split into groups by levels associated with the independent variable are compared by considering whether there is a difference in the dependent variable. If you have only one independent variable, the question is classified as a basic difference question (e.g., drug A will reduce time to primary closure in a 5-mm punch biopsy vs. placebo) and you would rely on a t-test or one-way analysis of variance (ANOVA) for the analysis. If you have two or more independent variables (e.g., drugs A and B led to a 15-mg/dl reduction in LDL cholesterol versus placebo, but there was no reduction with only drug A), this then becomes a complex difference question and is analyzed by other statistical methods, such as a factorial ANOVA.

Basic/complex associational (relational/correlation) questions : The independent variable is correlated with the dependent variable. If there is only one dependent variable and one independent variable (e.g., is there a relationship between weight and natriuretic peptide levels?), it is called a basic associational question , and in this situation, a correlation analysis is used. If there is more than one independent variable associated with one dependent variable (e.g., smoking and drinking alcohol are associated with lung cancer), it is called a complex associational question , and multiple regression is used for statistical analysis.

Basic/complex descriptive question : The data are described and summarized using measures of central tendency (means, median, and mode), variability, and percentage (prevalence, frequency). If there is only one variable, it is called a basic descriptive question (e.g., how much MRSA isolates occur after the 15th day of hospitalization?); for more than one variable, a classification of basic/complex descriptive question is used.

Where Should You State Your Research Question?

Finally, where should you state your hypothesis? You may be writing for a research grant, research protocol, or manuscript. Usually, research questions should be stated in the introduction, immediately following the justification (“so what”) section. Research questions should be clearly stated in the form of a hypothesis, such as “We hypothesize that in this particular population (P), the new intervention (I) will improve the outcome (O) more than the standard of care (C).”

A Research Question Should Be Developed over Time

It is important that the investigator spend a good amount of time developing his or her study question. During this process, everything we discussed in this chapter needs to be reviewed and the research question then needs to be refined as this process takes place. A good planning, starting with the research question, is one of the key components for a study’s success.

Related Topics for Choosing the Research Question

Selecting the appropriate control in surgical studies or other challenging situations.

Let’s think about various situations. Can we use placebo (sham) or another procedure as a control in a surgical trial? What exactly can be considered a placebo in surgical studies? How do we control for a placebo effect in surgical procedures?

Placebos can be used for the control group in clinical studies in comparison to a new agent if no standard of care is available. In order to fully assess the placebo effect in the control arm, participants have to be blinded. The control group could either have no surgery at all or undergo a “sham” procedure, but both options might be unethical depending on the given patient population [ 6 ]. In surgical studies, the control group usually receives the “traditional” procedure. In all cases, blinding might be very challenging and even impossible on certain levels (e.g., the surgeon performing the procedure). What about acupuncture? What would you consider a good control? What about cosmetic procedures?

Using Adverse Events as the Primary Research Question

Important questions concerning adverse effects can be answered in a clinical trial. However, as the typical clinical trial is performed in a controlled setting, the information regarding adverse effects is not always generalizable to the real-world setting. Thus, the clinical translation of the results needs careful consideration when carrying out a safety-focused study. The adverse reports from phase 4 (post-marketing marketing surveillance) are considered more generalizable information in drug development, although minimum safety data from phase 1 are required to proceed to subsequent study phases.

Also, it might not be easy to formulate a specific research question regarding adverse effects, as they might not be fully known in the early stage of drug development. This will also make it difficult to power the study properly (e.g., how many patients do we need to examine to show the statistically meaningful difference?).

When the Research Question Leads to Other Research Questions

Medical history is filled with interesting stories about research questions. And sometimes, it is not the intended hypothesis to be proven that yields a big discovery. For example, Sildenafil (Viagra) was initially developed by Pfizer for the treatment of cardiovascular conditions. Although clinical trials showed Sildenafil to have only little effect on the primary outcomes, it was quickly realized that an unexpected but marked “side effect” occurred in men. Careful investigation of clinical and pharmacological data generated the new research question, “Can Sildenafil improve erectile dysfunction?” This question was then answered in clinical trials with nearly 5,000 patients, which led to Sildenafil’s FDA approval in 1998 as the first oral treatment for male erectile dysfunction [ 7 ]. The investigator must be attentive to novel hypothesis that can be learned from a negative study.

Case Study: Finding the Research Question

Dr. L. Heart is a scientist working on cardiovascular diseases in a large, busy emergency room of a tertiary hospital specialized in acute coronarian syndromes. While searching PUBMED, she found an interesting article on a new drug—which animal studies have demonstrated to be a powerful anti-thrombotic agent—showing its safety in healthy volunteers. She then feels that it would be the right time to perform a phase II trial, testing this new drug in patients presenting myocardial infarction (MI). She sees this as her big career breakthrough. However, when Dr. Heart starts writing a study proposal for the internal review board (ethics committee), she asks herself, “What is my research question?” 1

Defining the research question is, perhaps, the most important part of the planning of a research study. That is because the wrong question will eventually lead to a poor study design and therefore all the results will be useless; on the other hand, choosing an elegant, simple question will probably lead to a good study that will be meaningful to the scientific community, even if the results are negative. In fact, the best research question is one that, regardless of the results (negative or positive), produces interesting findings. In addition, a study should be designed with only one main question in mind.

However, choosing the most appropriate question is not always easy, as such a question might not be feasible to be answered. For instance, when researching acute MI, the most important question would be whether or not a new drug decreases mortality. However, for economic and ethical reasons, such an approach can only be considered when previous studies have already suggested that the new drug is a potential candidate. Therefore, the investigator needs to deal with the important issue of feasibility versus clinical relevance. Dr. Heart soon realized that her task would not be an easy one, and also that this task may take some time; she kept thinking about one of the citations in an article she recently read: “One-third of a trial’s time between the germ of your idea and its publication in the New England Journal of Medicine should be spent fighting about the research question.” 2

“So What?” Test for the Research Question

Dr. Heart knows that an important test for the research question is to ask, “So what?” In other words, does the research question address an important issue? She knows, for example, that the main agency funding in the United States, the NIH (National Institutes of Health), considers significance and innovation as important factors to fund grant applications. Dr. Heart also remembers something that her mentor used to tell her at the beginning of her career: “A house built on a weak foundation will not stand.” She knows that even if she has the most refined design and uses the optimal statistical tests, her research will be of very little interest or utility if it does not advance the field. But regarding this point, she is confident that her research will have a significant impact in the field.

Next Step for the Research Question: How to Measure the Efficacy of the Intervention

Dr. L. Heart is in a privileged position. She works in a busy hospital that receives a significant amount of acute cardiovascular patients. She also has received huge departmental support for her research, meaning that she can run a wide range of blood exams to measure specific biological markers related to death in myocardial infarction. Finally, she has a PhD student who is a psychologist working with quality of life post-MI. Therefore, she asks herself whether she should rely on biological markers, on the assessment of quality of life, or if she should go to a more robust outcome to prove the efficacy of the new drug. She knows that this is one of the most critical decisions she has to make. It was a Friday afternoon. She had just packed up her laptop and the articles she was reading, knowing that she will have to make a decision by the end of the weekend.

Dr. Heart is facing a common problem: What outcome should be used in a research study? This needs to be defined for the research question. She knows that there are several options. For instance, the outcome might be mortality, new MI, days admitted to the emergency room, quality of life, specific effect of disease such as angina, a laboratory measure (cholesterol levels), or the cost of the intervention. Also, she might use continuous or categorical outcomes. For instance, if she is measuring angina, she might measure the number of days with angina (continuous outcome) or dichotomize the number of angina days in two categories (less than 100 days with angina vs. more or equal to 100 days with angina). She then lays out her options:

Use of clinical outcomes (such as mortality or new myocardial infarction) : She knows that by using this outcome, her results would be easily accepted by her colleagues; however, using these outcomes will increase the trial duration and costs.

Use of surrogates (for instance, laboratorial measurements) : One attractive alternative for her is to use some biomarkers or radiological exams (such as a catheterism). She knows a colleague in the infectious disease field who only uses CD4 for HIV trials as the main outcome. This would increase the trial feasibility. However, she is concerned that her biomarkers might not really represent disease progression.

Use of quality of life scales : This might be an intermediate solution for her. However, she is still concerned with the interpretation of the results if she decides to use quality of life scales.

More on the Response Variable: Categorical or Continuous?

Even before making the final decision, Dr. Heart needs to decide whether she will use a continuous or categorical variable. She wishes now that she knew the basic concepts of statistics. However, she calls a colleague, who explains to her the main issue of categorical versus continuous outcomes—in summary, the issue is the trade-off of power versus clinical significance.

A categorical outcome usually has two categories (e.g., a yes/no answer), while a continuous outcome can express any value. A categorical approach might be more robust than a continuous one, and it also has more clinical significance, but it also decreases the power of the study due to the use of less information. 3 She is now at the crossroad of feasibility versus clinical significance.

Choosing the Study Population

Now that Dr. Heart has gone through the difficult decision of finding the best outcome measure, she needs to define the target population—that is, in which patients is she going to test the new drug? Her first idea is to select only patients who have a high probability of dying—for instance, males who smoke, are older than 75 years, with insulin-dependent diabetes and hypercholesterolemia. “Then,” she thinks, “it will be easier to prove that the new drug is useful regardless of the population I study. But does that really sound like a good idea?”

The next step is to define the target population. Dr. Heart is inclined to restrict the study population, as she knows that this drug might be effective to a particular population of patients and therefore this increases her chances of getting a good result. In addition, she does remember from her statistical courses that this would imply a smaller variability and therefore she would gain power (power is an important currency in research, as it makes the study more efficient, decreasing costs and time to complete the study). On the other hand, she is concerned that she might put all her efforts in one basket—this is a risky approach, as this specific population might not respond, and she knows that broadening the population also has some advantages, for instance, the results would be more generalizable and it would be easier to recruit patients. But this would also increase the costs of the study.

But How about Other Ideas?

After a weekend of reflection, Dr. Heart called the staff for a team meeting and proudly explained the scenario and stated her initial thoughts. The staff was very eager to start a new study, and they made several suggestions: “We should also use echocardiography to assess the outcome!”; “Why don’t we perform a genotypic analysis on these patients?”; “We need to follow them until one year after discharge.” She started to become anxious again. What should she do with these additional suggestions? They all seem to be good ideas.

When designing a clinical trial, researchers expose a number of subjects to a new intervention. Therefore, they want to extract as much data as possible from studies. On the other hand, it might not be possible to ask all of the questions, since this will increase the study’s duration, costs, and personnel. Also, researchers should be aware that all the other outcomes assessed will be exploratory (i.e., their usefulness remains in suggesting possible associations and future studies) because studies are designed to answer a primary question only—and, as a principle of statistics, there is a 5% probability of observing a positive result just by chance (if you perform 20 tests, for instance, one of them will be positive just by chance!). But Dr. Heart knows that she can test additional hypotheses as secondary questions. She knows that there is another issue to go through: the issue of primary versus secondary questions.

Defining Her Hypothesis

After going through this long process, Dr. Heart is getting close to her research question. But now she needs to define the study hypotheses. In other words, what is her educated guess regarding the study outcome?

An important step when formulating a research question is to define the hypothesis of the study. This is important in terms of designing the analysis plan, as well as estimating the study sample size. Usually, researchers come up with study hypotheses after reviewing the literature and preliminary data. Dr. Heart can choose between a simple and a complex hypothesis. In the first case, her hypothesis would only have one dependent variable (i.e., the response variable) and one independent variable (e.g., the intervention). Complex hypotheses have more than one independent and/or dependent variable and might not be easy to use in planning the data analysis.

By the end of the day, Dr. Heart was overwhelmed with the first steps to put this study together. Although she is confident that this study might be her breakthrough and she needs to get her tenure track position at the institution where she works, she also knows she has only one chance and must be very careful at this stage. After wrestling with her thoughts, she finished her espresso and walked back to her office, confident that she knew what to do.

Case Discussion

Dr. Heart is a busy and ambitious clinical scientist and wants to establish herself within the academic ranks of her hospital. She has some background in statistics but seems to be quite inexperienced in conducting clinical research. She is looking for an idea to write up a research proposal and rightly conducts a literature research in her field of expertise, cardiovascular diseases. She finds an interesting article about a compound that has been demonstrated to be effective in an animal model and safe in healthy volunteers (results of a phase I trial). She now plans to conduct a phase II trial, but struggles to come up with a study design. The most vexing problem for her is formulating the research question.

Dr. Heart then reviews and debates aspects that have to be considered when delineating a research question. The main points she ponders include the following: determining the outcome with regard to feasibility (mainly concerning the time of follow-up when using a clinical outcome) versus clinical relevance (when using a surrogate outcome) and with regard to the data type to be used for the outcome (categorical vs. continuous); the importance of the research proposal (the need for a new anticoagulant drug); whether to use a narrow versus broad study population; whether to include only a primary or also secondary questions; and whether to use a basic versus complex hypothesis. Important aspects that Dr. Heart has not considered include the following: whether to test versus a control (although not mandatory in a phase II trial, it deserves consideration since she is investigating the effects of an anticoagulant and therefore adverse events should be expected, thus justifying the inclusion of a control arm) or to test several dosages (to observe a dose-response effect); logistics; the budget; and the overall scope of her project.

All these aspects are important and need careful consideration, but you have to wonder how this will help Dr. Heart come up with a compelling research question. Rather than assessing each aspect separately and making decisions based on advantages and disadvantages, it is recommended to start from a broad research interest and then develop and further specify the idea into a specific research question.

While Dr. Heart should be applauded for her ambition, she should also try to balance the level of risk of her research given her level of experience.

Finally, we should also question Dr. Heart’s motives for conducting this study. What is her agenda?

Case Questions for Reflection

What are the main challenges faced by Dr. Heart?

Should she be really concerned with the study question?

Which variable response should she choose? Justify your choice to your colleagues (remember, there is no right or wrong).

How should she select the study population?

Should her study have secondary questions?

Finally, try to create a study question for Dr. Heart in the PICOT format, depending on your previous selections.

Further Reading

Haynes B , et al., Clinical epidemiology: how to do clinical practice research forming research questions; part 1. Performing clinical research , 3rd ed. Haynes B , Sackett DL , Guyatt GH , and Tugwell P ; 2006 : 3–14

Portney LG , Watkins MP. Foundations of clinical research: applications to practice . 3rd ed. Pearson; 2008 : 121–139.

Google Scholar

Google Preview

Surrogate Outcomes

D’Agostino RB. Debate: The slippery slope of surrogate outcomes. Curr Contr Trials Cardiovasc Med . 2000 ; 1: 76–78. 10.1186/CVM-1-2-076

Echt DS , Liebson PR , Mitchell LB , et.al., Mortality and morbidity in patients receiving encainide, flecainide, or placebo: The Cardiac Arrhythmia Suppression Trial. N Eng J Med . 1991 ; 324: 781–788. 10.1056/NEJM199103213241201

Web of Science

Feng M , Balter JM , Normolle D , et al. Characterization of pancreatic tumor motion using Cine- MRI: surrogates for tumor position should be used with caution. Int J Radiat Oncol Biol Phys . 2009 July 1; 74(3): 884–891. 10.1016/j.ijrobp.2009.02.003

Katz R. Biomarkers and surrogate markers: an FDA perspective. NeuroRx . 2004 April; 1(2): 189–195. 10.1602/neurorx.1.2.189

Lonn E. The use of surrogate endpoints in clinical trials: focus on clinical trials in cardiovascular diseases. Pharmacoepidemiol Drug Safety . 2001 ; 10: 497–508. 10.1002/pds.654

Composite Endpoint

Cordoba G , Schwartz L , Woloshin S , et al. Definition, reporting, and interpretation of composite outcomes in clinical trials: systematic review. BMJ . 2010 ; 341: c3920. 10.1136/bmj.c3920

Kip KE , Hollabaugh K , Marroquin OC , et al. The problem with composite end points in cardiovascular studies. The story of major adverse cardiac events and percutaneous coronary intervention. JACC . 2008 ; 51(7): 701–707. 10.1016/j.jacc.2007.10.034

Examples of Ancillary Studies

Krishnan JA , Bender BG , Wamboldt FS , et al. Adherence to inhaled corticosteroids: an ancillary study of the Childhood Asthma Management Program clinical trial. J Allergy Clin Immunol. 2012 ; 129 (1): 112–118. 10.1016/j.jaci.2011.10.030

Udelson JE , Pearte CA , Kimmelstiel CD , et al. The Occluded Artery Trial (OAT) Viability Ancillary Study (OAT-NUC): influence of infarct zone viability on left ventricular remodeling after percutaneous coronary intervention versus optimal medical therapy alone. Am Heart J . 2011 Mar; 161(3): 611–621. 10.1016/j.ahj.2010.11.020

Controls, Sham/Placebo

Finnissa DG , Kaptchukb TJ , Millerc F et.al., Placebo effects: biological, clinical and ethical advances. Lancet . 2010 February 20; 375(9715): 686–695. 10.1016/S0140-6736(09)61706-2

Macklin R. The ethical problems with sham surgery in clinical research. N Engl J Med . 1999 Sep 23; 341(13): 992–996. 10.1056/NEJM199909233411312

Pilot Studies

Lancaster GA , Dodd S , Williamson PR. Design and analysis of pilot studies: recommendations for good practice. J Eval Clin Pract . 2002 ; 10(2): 307–312. 10.1111/j..2002.384.doc.x

1. The Belmont Report. Office of the Secretary. Ethical principles and guidelines for the protection of human subjects of research . The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. Washington, DC: U.S. Government Printing Office, 1979 .

2. Ferguson L. External validity, generalizability, and knowledge utilization. J of Nursing Scholarship . 2004 ; 36:1, 16–22. 10.1111/j.1547-5069.2004.04006.x

3. CONSORT statement, http://www.consort-statement.org/consort-statement/ ]

4. Echt DS , Liebson PR , Mitchell LB , et al. Mortality and morbidity in patients receiving encainide, flecainide, or placebo: The Cardiac Arrhythmia Suppression Trial. N Engl J Med . 1991 ; 324: 781–788. 10.1056/NEJM199103213241201

5. Morgan GA , Harmon RJ. Clinician’s guide to research methods and statistics: research question and hypotheses. J Am Acad Child Adolesc Psychiatry . 2000 ; 39(2): 261–263. 10.1097/00004583-200002000-00028

6. Macklin R. The ethical problems with sham surgery in clinical research. N Engl J Med . 1999 Sep 23; 341(13): 992–996. 10.1056/NEJM199909233411312

7. Campbell SF. Science, art and drug discovery: a personal perspective. Clin Sci (Lond) . 2000 Oct; 99(4): 255–260.

8. Lonn E. The use of surrogate endpoints in clinical trials: focus on clinical trial in cardiovascular disease. Pharmacoepidemiol Drug Safety . 2001 ; 10: 497–508. 10.1002/pds.654

9. Kip KE , Hollabaugh K , Marroquin OC , et al. The problem with composite end points in cardiovascular studies: the story of major adverse cardiac events and percutaneous coronary intervention. JACC . 2008 ; 51(7): 701–707 10.1016/j.jacc.2007.10.034

10. Katz R. Biomarkers and surrogate markers: an FDA perspective. NeuroRx . 2004 April; 1(2): 189–195. 10.1602/neurorx.1.2.189

Dr. André Brunoni and Professor Felipe Fregni prepared this case. Course cases are developed solely as the basis for class discussion. The situation in this case is fictional. Cases are not intended to serve as endorsements or sources of primary data. All rights reserved to the authors of this case.

Riva JJ , Malik KM , Burnie SJ , Endicott AR , Busse JW . What is your research question? An introduction to the PICOT format for clinicians. J Can Chiropr Assoc . 2012 Sep; 56(3):167–71 .

These concepts will be discussed in details in Unit II of this volume.

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The A-Z of the PhD Trajectory pp 75–87 Cite as

Formulating Your Research Question

• Eva O. L. Lantsoght 2 , 3  
• First Online: 26 May 2018

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Part of the book series: Springer Texts in Education ((SPTE))

In this chapter, the research question is studied. We focus on how to find a research question that is specific enough, so that you are not tempted to explore paths that are only tangentially related to your research question. The literature review identifies gaps in the current knowledge, and you will learn how to frame a research question within these gaps. We then explore how to subdivide the research question into subquestions. These subquestions become the chapters of your dissertation. We also look at creative thinking, a skill necessary to think out of the box to formulate your research question. This chapter discusses how to convince your supervisor of your research question. It can happen that your supervisor already has an idea of the direction in which your research should be going, but if you can provide technically sound arguments based on your literature review why this approach is not ideal, and why you propose a different road, you should be able to have the freedom to explore your proposed option. Once you have outlined your research question, it is necessary to turn the question and subquestions into practical actions. These practical actions link back to the planning skills you learned in Chap. 3 .

• Research question
• Research methods
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Time blocks of 25 minutes during which you concentrate on one single task. You can find more information about the Pomodoro technique in the glossary of Part II.

I often work with noise-cancelling headphones.

The course is sweet and short, and runs frequently. I highly recommend it!

Refer to Chap. 4 for examples on how I use mindmaps to structure documents, such as a literature review report.

Further Reading and References

Kara, H. (2015). Creative research methods in the Social Sciences: A practical guide . Bristol: Policy Press.

Google Scholar  

Kara, H. (2015). How to choose your research question. PhD Talk . http://phdtalk.blogspot.com/2015/07/how-to-choose-your-research-question.html

Lantsoght, E. (2012). The creative process: The importance of questions. PhD Talk . http://phdtalk.blogspot.nl/2012/11/the-creative-process-importance-of.html

Feynman, R. P., Leighton, R., & Hutchings, E. (1997). “Surely you’re joking, Mr. Feynman!”: Adventures of a curious character (1st pbk ed.). New York: W.W. Norton.

Lantsoght, E. (2012). The creative process: The creative habit. PhD Talk . http://phdtalk.blogspot.nl/2012/11/the-creative-process-creative-habit.html

Rose, C. (2016). 15 minute history . http://15minutehistory.org /

Oakley, B. (2014). A mind for numbers: How to excel at Math and Science (even if you flunked algebra) . New York: TarcherPerigree.

Lantsoght E (2011). Book review: Starting research: An introduction to academic research and dissertation writing – Roy Preece. PhD Talk . http://phdtalk.blogspot.nl/2011/07/book-review-starting-research.html .

Preece, R. (2000). Starting research: An introduction to academic research and dissertation writing . London: Continuum.

Lantsoght, E. (2014). An example outline diagram for structuring your dissertation. PhD Talk . http://phdtalk.blogspot.nl/2014/08/an-example-outline-diagram-for.html

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A Multivariate Analysis of Crash and Naturalistic Driving Data in Relation to Highway Factors (2013)

Chapter: chapter 2 - research questions.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

5Research Questions For decades crashes have been studied as discrete events focusing on the circumstances of the crash event. This type of analysis is exemplified in the review of Appendix A, and has been used to identify the characteristics of highway features associated with higher crash experience; other factors such as traffic volumes, driver characteristics, land use, and the envi- ronmental conditions were also needed to explain or describe crash events. Furthermore, cross-sectional analyses of crash events did not address circumstances leading to a crash. Advances in vehicle instrumentation technology have made it possible to collect longitudinal naturalistic data about the vehicle, driver, and roadway, accumulating information about events preceding a crash, if a crash occurs. However, crashes are rare events, and there are conditions in which a crash, while likely, does not occur. Thus a crash can be considered as a high-probability outcome, given a set of conditions, and validated crash surrogates could be used to identify these con- ditions. Assuming that no driver intentionally crashes, it fol- lows that crashes occur when there is loss of situational control leading to a damaging impact, and hence that surrogates are related in some way to disturbances of the driving control function. Also, it should be possible to identify the disturbance of control from NDD. The research team also expects that crashes are related to crash surrogates in an objective way that the team seeks to determine. “Control” is defined here as the effectiveness of tactical and operational aspects of the driving task (i.e., acquiring and tracking reference information for speed and steering adjust- ment). “Disturbed control” is any interruption or delay in the process of perception (seeing lane boundary or other relevant features that determine the required path), recognition (what are the relevant objects that are relevant to speed and steering control?), judgment/decision (of required steering, throttle pedal, or brake pedal) or action (apply corrections) in the driving task. Disturbed control is not expected to be the same as poor lane keeping. It is quite common in NDD to see lane excursions, such as “cutting a curve” or use of the shoulder, in which the driver appears fully aware of the action and is simply not tracking within the lane markings. One might argue that these excursions still represent poor control (i.e., they do not conform to the transportation researcher’s expectations), but if the driver decides to manipulate the reference conditions used for steering control (essentially a tactical decision to, for example, cut the curve) and follows that action with accuracy and predictability, then at least at the operational level, the con- trol loop is effective. The risk of such behavior clearly depends on the skill and awareness of the driver. Because the number of factors associated with vehicle crashes increase significantly if more than one vehicle is involved, this research examines only the single-vehicle road departure crashes (i.e., crashes involving only one vehicle in which the first harmful event occurs off the road- way). Thus, the research team tentatively expects that crash surrogates are related in some way to the disturbance of the control function of the driving task, and that it is possible to identify various types of disturbance of control from naturalistic driving data. The research team also expects that crashes are related to crash surrogates. These general considerations are now formalized as research hypotheses as follows. Research Hypotheses The research hypotheses are as follows: 1. Single-vehicle road departure crashes occur only under conditions of disturbed control. 2. Naturalistic driving data contain measurable episodes of disturbed control. 3. Crash surrogates exist and are based on a combination of objective measures of disturbed control (from onboard sensors), highway geometric factors, and off-highway factors (environmental factors). 4. Crash surrogates can be related to actual crashes. C H a p t e R 2

63. Are other driving control metrics necessary (in addition to vehicle kinematic measures) to identify disturbed control? Relating Driving Performance to Geometric Features and Road Departure Crash Frequencies 4. Are there measures of driving control performance in existing field operational test (FOT) data that depend on highway factors in a way that is consistent with single- vehicle road departure crash frequencies? 5. Are there specific highway features that are associated with single-vehicle road departure crashes and specific driving control performance measures? (Possible candi- dates are isolated horizontal curves, sharp horizontal curves, sequences of horizontal curves, and combinations of horizontal and vertical curves.) 6. Can roadside factors (e.g., locations of poles, trees, bridge abutments, and side slopes) be coupled to naturalistic driving data? 7. Does the coupling of roadside factors to naturalistic driving data improve correlation with actual crashes? 8. Can general descriptors of roadside environments be used in this coupling (e.g., tree density and proportion of side slope steeper than 4 to 1), or do we have to be more specific about location of roadside obstacles? Statistics 9. What statistical tests are available to determine if the measures of driving control performance in naturalistic data and single-vehicle crashes depend on geometric fea- tures in a consistent way? 10. Can satisfactory crash risk predictions be made on the basis of vehicle/driver/highway information available from nat- uralistic driving (e.g., via extreme value theory), or do additional roadside and environmental factors need to be introduced? Driver Factors 11. Is the pattern of driving control performance different for the same driver when distracted versus not distracted (e.g., on a cell phone or not on a cell phone)? 12. Can various driver states (e.g., drowsy, aggressive, dis- tracted, engaged) be identified from naturalistic driving data? 13. Can driving control performance for various states be categorized more simply (i.e., good and bad, or risky and nonrisky)? 14. Is there a difference in the driving control performance of good and bad drivers (or risky and nonrisky drivers) at locations with geometric features associated with high single-vehicle crash frequency? When conditions leading to single-vehicle crashes are con- sidered, the research team expects that in many cases the dis- turbance to the control function might be measurable over an extended period of time; for example, a drowsy driving surro- gate would only emerge as significant over time as lane-control dysfunction was found to be persistent, compared to what might be seen in a short period of distraction. The key seems to be that surrogacy is an indicator of extremes in a uniform process that includes crashes at the limits, and therefore a road departure surrogate includes the crucial element that it mea- sures how the driving control loop is disturbed and is not sim- ply being manipulated by the driver. While this is a useful guiding principle for the definition of surrogates, it is a matter for analysis and verification of how well such surrogates are matched to actual crash data. The intention has been to focus on those questions most directly related to the hypotheses of disturbed control, surro- gacy, and relationships between data. With this focus, research questions can be posed at many levels from broad general ques- tions down to very specific direct technical questions. The research team focused on three levels: the first level was a restate- ment of the research hypotheses, the second level was specific questions of safety research, and the third level was data quality and validation. Broad Research Questions The research questions are summarized as follows: 1. Do single-vehicle road departure crashes occur only under conditions of disturbed control? 2. Do naturalistic driving data contain measurable episodes of disturbed control? 3. Do objective measures of disturbed control from naturalis- tic driving data, together with highway geometric factors, off-highway factors, and environmental factors, satisfy crite- ria for crash surrogate (i.e., are they related to actual crashes)? Specific Safety Research Questions Specific research questions are broken down into four subtypes: measuring disturbed control, relating driving performance to geometric features and road departure crash frequencies, statis- tics, and driver factors. Measuring Disturbed Control 1. What measures exist in naturalistic driving data that directly measure disturbed control? 2. Are vehicle kinematic measures sufficient to identify dis- turbed control for risk measures in single-vehicle road departure crashes?

7data, and what are the levels of accuracy in those measures? 2. What spatially referenced crash and highway data exist in the regions where the driving took place, and what gaps exist in the data? 3. Can the analysis of data in southeastern Michigan be applied or recreated in another region (e.g., Virginia)? Data Quality and Validation A number of lower-level research questions are related to crosschecking and data quality: 1. What kinematic measures of driving control perfor- mance are available in the available naturalistic driving

TRB’s second Strategic Highway Research Program (SHRP 2) Report S2-S01C-RW-1: A Multivariate Analysis of Crash and Naturalistic Driving Data in Relation to Highway Factors explores analysis methods capable of associating crash risk with quantitative metrics (crash surrogates) available from naturalistic driving data.

Errata: The foreword originally contained incorrect information about the project. The text has been corrected in the online version of the report. (August 2013)

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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.

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Asking the Right Question: Specifying Your Study Question

Annie l. raich.

1 Spectrum Research, Inc., Tacoma, Washington, United States

Andrea C. Skelly

Introduction.

The most important step in conducting a high-quality research study is to create a study question that will provide the guidance for the planning, analysis, and reporting of your study. The process of generating a novel, answerable study question seems like it should be simple at first blush. Perhaps your keen interest in a particular topic sparks an idea for a study that starts the creative process of hypothesizing and wondering “what if.” It is a wonderful experience to witness or be caught up in the joys of such a process. Finding inspiration for a study may, however, be a challenge, and the study idea emerges, instead, with time after thoughtful consideration of a topic. In either scenario, in order for you to design and execute your study, honing your idea and hypothesis into questions that can be realistically studied is required, adding a level of complexity to what at first seemed simple.

Creating the final study question is a formal and iterative process: You create an initial study question by answering questions, defining parameters, getting feedback from colleagues, and conducting a limited literature search. Then you refine your question and define major aspects of your study by using a Patients, Intervention, Comparison, and Outcomes (PICO) table for treatment and diagnostic studies, or a Patients, Prognostic factors, and Outcomes (PPO) table for prognostic studies. By taking the time to complete these steps, you will have a good structure for your research study and will be able to proceed to the next part, a literature review.

What Is a Study Question?

A study question reflects an uncertainty that you want to try to resolve, perhaps an uncertainty about the effectiveness of an intervention or how well an intervention works in a specific patient population. It is the basis for your research study and presents the idea or ideas that are to be examined in your study. Everything included in your study must relate to your study question(s) and study objective. It gives information about the patients to be studied, interventions to be compared, and primary outcomes to focus on.

Your general study question can come from several places. 1 You or others in your field might have observed a pattern of positive or poor outcomes or problems regarding a current treatment. Recent advances or technologies might spawn questions about their safety or applicability to different patient populations. Technologies in other fields might have potential for use in your clinical field. Or, you think that other treatments might perform better than what is currently practiced. Unfortunately, high quality, novel, and answerable study questions do not usually just appear. The inspiration for the study may emerge with time as you confer with colleagues, listen to lectures at professional meetings, or even as you critically appraise literature on a given topic. Frequently, study ideas build on previous research and are honed by working collaboratively with mentors and colleagues. Once you have a general idea of what you would like to research, the process of crafting your study begins with carefully forming and focusing an answerable question.

The Process of Creating an Effective Study Question

Step 1: draft a preliminary study question.

The first step is to draft a simple clinical question you would like to answer or a hypothesis you would like to explore. What do you think the answer to that clinical question might be? Why do you think it may be important to evaluate this question?

Step 2: Focus Your Study Question

Now you can start the process of focusing your question. The following is an example of creating a preliminary study question. Suppose you are interested in several treatments for cervical myelopathy. Table 1 shows an example of progressing from a broad study question to one that is more focused. Note that in creating a more focused study question, we have been more specific on aspects of the diagnostic condition (myelopathy due to spondylosis) and the patient population (adults).

Step 3: Complete a PICO or PPO Table

Add specifications to your study question using a PICO or PPO table to further refine it. While the more focused study question above is an improvement, there are some additional questions you should ask:

• What types of patients and pathologies do you want to study or exclude from the study?
• What variations of the treatments or interventions do you want to consider or exclude from the study?
• What specific outcomes or complications are the most important to measure and evaluate?

The PICO/PPO system provides a framework for further refinement based on these questions. A PICO/PPO table will help you to consider what should be included in your study and what should not be included. Your final PICO/PPO table is an aid to further refine your study question, define inclusion and exclusion criteria, highlight the interventions and outcomes you will measure, and provide the groundwork for a focused literature search. Note that a PICO table is used for treatment studies and a PPO table is used for prognostic studies. A PICO table is used as the example in this article.

Consider the following issues when creating your PICO table:

• Patients : Consider factors related to the condition, demographics (e.g., age, gender), behaviors (e.g., smoking), medical history (e.g., previous treatment, medications, general health factors, comorbidities), factors associated with treatment selection (e.g., severity or location of condition), and other factors that might be relevant to treatment selection or outcomes. For most studies, it is important to define a fairly homogeneous patient population, especially if there are any factors that might influence the outcome other than the intervention you are evaluating. For example, note that in the PICO table below, we are including patients with spondylosis and excluding patients with OPLL. If the condition itself (spondylosis or OPLL) can influence the outcome, it is better to restrict the study population to one condition. However, keep in mind that a restricted study population can limit bias in your study yet will also limit the generalizability of your findings to a patient population in a clinical setting.
• Intervention : Make sure you specify variations of the procedures (e.g., approach, number of levels, use of specific devices, grafting) as being included or excluded. If there are variations of the procedure that could influence results, think carefully about their inclusion.
• Comparison : Specify the alternative treatment to which the intervention is compared. Again, are there variations that should be excluded?
• Outcomes : Be specific and aim for the most important outcomes. They can be patient-reported (e.g., pain, function, quality of life) or clinical outcomes (e.g., nonunion, complications, reoperation, death). List the primary outcome of interest first; this outcome provides the focus for your study, the data collection, and the sample-size estimate. Then list secondary outcomes that might provide valuable contributions to your overall study results.

Table 2 is an example of a PICO table for your study question, “What is the comparative effectiveness following laminoplasty versus laminectomy and fusion for adults with myelopathy due to spondylosis in the cervical spine?”

Step 4: Refine Study Question and Conduct Preliminary Literature Search

Now you can use your completed PICO table to refine your study question and to conduct a quick preliminary literature search. It is important to find out what is currently known and not known about your research topic, what has already been published on this topic, and what gaps exist that your research can fill, whether it be a type of intervention that has not been studied, a particular group of patients who have not previously been included in studies, or an outcome that has not been measured but is important to patients. This initial literature search helps you hone your study question further and may help you determine if it is realistic to answer in a single, focused study. The PICO framework is also helpful for getting feedback from potential co-investigators/colleagues to further refine your study question.

Step 5: Consider Additional Questions

By this point, you should have not only a solid study question, but at least a preliminary idea of how you might approach answering it, and there are some additional questions to consider for another round of refinement.

To be more specific in your study focus, consider these additional questions:

• What might constitute a clinically meaningful improvement?
• What time frame will be important? Are you looking at outcomes that are short-term or long-term to evaluate the effects?
• Is there a specific hypothesis that you would like to test?

After considering the questions above, you can refine your study question further ( Table 3 ).

Step 6: Perform a More Complete Literature Search

Now that you have created a clear, focused, answerable study question and a PICO or PPO table as the framework for your study, you can proceed to a more complete literature search. It is important to solidify your understanding of what is known about your research topic, what gaps in knowledge need to be filled, and what is the best study design to answer your study question. The AO SMART Handbook for Spine Clinical Research 2 is a good reference for planning your research study, including formulating your study question, conducting a literature search, and selecting an appropriate study design. Additionally, you can use a previously published EBSJ article as an aid to literature search. 3

Conclusions

• Great study ideas take time to formulate. Familiarity with the strengths and limitations of the current literature, participation in professional meetings and collegial exchanges are probably the best breeding grounds for generating great, new study ideas. It may take time for the input from such sources to coalesce into an inspired thought that ignites the creative process.
• Your study begins with developing a researchable study question, which is an iterative and deliberate process. You might have to go through multiple iterations in the process of refining your study question into something that is novel and answerable.
• The PICO/PPO framework is invaluable for helping you refine your study question, setting the stage for both preliminary and more complete literature searches, and for laying the groundwork for your study.
• The more focused your study question is, the higher the likelihood that you will be able to find a meaningful answer to it.
• The more thought and effort you put into the initial planning of your research study, especially the creation of a focused, answerable study question and PICO/PPO framework, the higher quality your research study is likely to be and more likely you are to find an answer to that question!

This article was funded by AOSpine.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Module 3 Chapter 1: From Research Questions to Research Approaches

The approaches that social work investigators adopt in their research studies are directly related to the nature of the research questions being addressed.In Module 2 you learned about exploratory, descriptive, and explanatory research questions. Let’s consider different approaches to finding answers to each type of question.

In this chapter we build on what was learned in Module 2 about research questions, examining how investigators’ approaches to research are determined by the nature of those questions. The approaches we explore are all systematic, scientific approaches, and when properly conducted and reported, they all contribute empirical evidence to build knowledge.  In this chapter you will read about:

• qualitative research approaches for understanding diverse populations, social problems, and social phenomena,
• quantitative research approaches for understanding diverse populations, social problems, and social phenomena,
• mixed methods research approaches for understanding diverse populations, social problems, and social phenomena.

Overview of Qualitative Approaches

Questions of a descriptive or exploratory nature are often asked and addressed through  qualitative research . The specific aim in these studies is to understand diverse populations, social work problems, or social phenomena as they naturally occur, situated in their natural environments, providing rich, in-depth, participant-centered descriptions of the phenomena being studied. Qualitative research approaches have been described as “humanistic” in aiming to study the world from the perspective of those who are experiencing it themselves; this also contributes to a social justice commitment in that the approaches give “voice” to the individuals who are experiencing the phenomena of interest (Denzen & Lincoln, 2011).  As such, qualitative research approaches are also credited with being sensitive and responsive to diversity—embracing feminist, ethnic, class, critical race, queer, and ability/disability theory and lenses.

In qualitative research, the investigator is engaged as an observer and interpreter, being acutely aware of the subjectivity of the resulting observations and interpretations.

“At this level, qualitative research involves an interpretive, naturalistic approach to the world” (Denzin & Lincoln, 2011, p. 3)

Because the data are rich and deep, a lot of information is collected by involving relatively few participants; otherwise, the investigator would be overwhelmed by a tremendous volume of information to collect, sift through, process, interpret, and analyze. Thus, a single qualitative study has a relatively low level of generalizability  to the population as a whole because of its methodology, but that is not the aim or goal of this approach.

In addition, because the aim is to develop understanding of the participating individuals’ lived experiences, the investigator in a qualitative study seldom imposes structure with standardized measurement tools. The investigator may not even start with preconceived theory and hypotheses. Instead, the methodologies involve a great deal of open-ended triggers, questions, or stimuli to be interpreted by the persons providing insight:

“Qualitative research’s express purpose is to produce descriptive data in an individual’s own written or spoken words and/or observable behavior” (Holosko, 2006, p. 12).

Furthermore, investigators often become a part of the qualitative research process: they maintain awareness of their own influences on the data being collected and on the impact of their own experiences and processes in interpreting the data provided by participants. In some qualitative methodologies, the investigator actually enters into/becomes immersed in the events or phenomena being studied, to both live and observe the experiences first-hand.

Qualitative data and interpretations are recognized as being subjective in nature—that is the purpose—rather than assuming objectivity. Qualitative research is based on experientially derived data and is interpretive, meaning it is “concerned with understanding the meaning of human experience from the subject’s own frame of reference” (Holosko, 2006, p. 13). In this approach, conclusions about the nature of reality are specific to each individual study participant, following his or her own interpretation of that reality. These approaches are considered to flow from an inductive reasoning process where specific themes or patterns are derived from general data (Creswell & Poth, 2018).

Several purposes of qualitative approaches in social work include:

• describing and exploring the nature of phenomena, events, or relationships at any system level (individual to global)
• generating theory
• initially test ideas or assumptions (in theory or about practices)
• evaluate participants’ lived experiences with practices, programs, policies, or participation in a research study, particularly with diverse participants
• explore “fit” of quantitative research conclusions with participants’ lived experiences, particularly with diverse participants
• inform the development of clinical or research assessment/measurement tools, particularly with diverse participants.

Overview of Quantitative Approaches

Questions of the exploratory, descriptive, or explanatory type are often asked and addressed through quantitative research  approaches, particularly questions that have a numeric component. Exploratory and descriptive quantitative studies rely on objective measures for data collection which is a major difference from qualitative studies which are aimed at understanding subjective perspectives and experiences. Explanatory quantitative studies often begin with theory and hypotheses, and proceed to empirically test the hypotheses that investigators generated. By their quantitative (numeric) nature, statistical hypothesis testing is possible in many types of quantitative studies.

Quantitative research studies utilize methodologies that enhance generalizability of results to the greatest extent possible—individual differences are de-emphasized, similarities across individuals are emphasized. These studies can be quite large in terms of participant numbers, and the study samples need to be developed in such a manner as to support generalization to the larger populations of interest.

The process is generally described as following a deductive logical system where specific data points are combined to lead to developing a generalizable conclusion. The philosophical roots (epistemology) underlying quantitative approaches is positivism, involving the seeking of empirical “facts or causes of social phenomena based on experimentallyderived evidence and/or valid observations” (Holosko, 2006, p. 13). The empirical orientation is objective in that investigators attempt to be detached from the collection and interpretation of data in order to minimize their own influences and biases. Furthermore, investigators utilize objective measurement tools to the greatest extent possible in the process of collecting quantitative study data.

Several purposes of quantitative approaches in social work include:

• describing and exploring the dimensions of diverse populations, phenomena, events, or relationships at any system level (individual to global)—how much, how many, how large, how often, etc. (including epidemiology questions and methods)
• testing theory (including etiology questions)
• experimentally determining the existence of relationships between factors that might influence phenomena or relationships at any system level (including epidemiology and etiology questions)
• testing causal pathways between factors that might influence phenomena or relationships at any system level (including etiology questions)
• evaluate quantifiable outcomes of practices, programs, or policies
• assess the reliability and validity of clinical or research assessment/measurement tools.

Overview of Mixed-Method Approaches

Important dimensions distinguish between qualitative and quantitative approaches. First, qualitative approaches rely on “insider” perspectives, whereas quantitative approaches are directed by “outsiders” in the role of investigator (Padgett, 2008). Second, qualitative results are presented holistically, whereas quantitative approaches present results in terms of specific variables dissected from the whole for close examination; qualitative studies emphasize the context of individuals’ experiences, whereas quantitative studies tend to decontextualize the phenomena under study (Padgett, 2008). Third, quantitative research approaches tend to follow a positivist philosophy, seeking objectivity and representation of what actually exists; qualitative research approaches follow from a post-positivist philosophy, recognizing that observation is always shaped by the observer, therefore is always subjective in nature and this should be acknowledged and embraced. In post-positivist qualitative research traditions, realities are perceived as being socially constructed, whereas in positivist quantitative research, a single reality exists, waiting to be discovered or understood. The quantitative perspective on reality has a long tradition in the physical and natural sciences (physics, chemistry, anatomy, physiology, astronomy, and others). The social construction perspective has a strong hold in social science and understanding social phenomena. But what if an investigator’s questions are relevant to both qualitative and quantitative approaches?

Given the fundamental philosophical and practical differences, some scholars argue that there can be no mixing of the approaches, that the underlying paradigms are too different. However, mixed-methods research  has also been described as a new paradigm (since the 1980s) for social science:

“Like the mythology of the phoenix, mixed methods research has arisen out of the ashes of the paradigm wars to become the third methodological movement. The fields of applied social science and evaluation are among those which have shown the greatest popularity and uptake of mixed methods research designs” (Cameron & Miller, 2007, p. 3). 

Mixed-methods research approaches are used to address in a single study the acknowledged limitations of both quantitative and qualitative approaches. Mixed methods research combines elements of both qualitative and quantitative approaches for the purpose of achieving both depth and breadth of understanding, along with corroboration of results (Johnson, Onwuegbuzie, & Turner, 2007, p. 123). One mixed-methods strategy is related to the concept of  triangulation : understanding an event or phenomenon from the use of varied data sources and methods all applied to understanding the same phenomenon (Denzin & Lincoln, 2011; see Figure 1-1).

Figure 1-1. Depiction of triangulation as synthesis of different data sources

For example, in a survey research study of student debt load experienced by social work doctoral students, the investigators gathered quantitative data concerning demographics, dollar amounts of debt and resources, and other numeric data from students and programs (Begun & Carter, 2017). In addition, they collected qualitative data about the experience of incurring and managing debt load, how debt shaped students’ career path decisions, practices around mentoring doctoral students about student debt load, and ideas for addressing the problem. Triangulation came into play in two ways: first, collecting data from students and programs about the topics, and second, a sub-sample of the original surveyed participants engaged in qualitative interviews concerning the “fit” or validity of conclusions drawn from the prior qualitative and quantitative data.

Three different types of mixed methods approaches are used:

• Convergent designs involve the simultaneous collection of both qualitative and quantitative data, followed by analysis of both data sets, and merging the two sets of results in a comparative manner.
• Explanatory sequential designs use quantitative methods first, and then apply qualitative methods to help explain and further interpret the quantitative results.
• Exploratory sequential designs first explore a problem or phenomenon through qualitative methods, especially if the topic is previously unknown or the population is understudied and unfamiliar. These qualitative findings are then used to build the quantitative phase of a project (Creswell, 2014, p. 6).

Mixed methods approaches are useful in developing and testing new research or clinical measurement tools. For example, this is done in an exploratory sequential process whereby detail-rich qualitative data inform the creation of a quantitative instrument. The quantitative instrument is then tested in both quantitative and qualitative ways to confirm that it is adequate for its intended use. This iterative process is depicted in Figure 1-2.

Figure 1-2. Iterative qualitative and quantitative process of instrument development

One example of how this mixed-methods approach was utilized was in development of the Safe-At-Home instrument for assessing individuals’ subjective readiness to change their intimate partner violence behavior (Begun et al., 2003; 2008). The transtheoretical model of behavior change (TMBC) underlies the instrument’s development: identifying stages in readiness to change one’s behavior and matching these stages to the most appropriate type of intervention strategy (Begun et al., 2001). The first step in developing the intimate partner violence Safe-At-Home instrument for assessing readiness to change was to qualitatively generate a list of statements that could be used in a quantitative rating scale. Providers of treatment services to individuals arrested for domestic or relationship violence were engaged in mutual teaching/learning with the investigators concerning the TMBC as it might relate to the perpetration of intimate partner violence. They independently generated lists of the kinds of statements they heard from individuals in their treatment programs, statements they believed were demonstrative of what they understood as the different stages in the change process. The investigators then worked with them to reduce the amassed list of statements into stage-representative categories, eliminating duplicates and ambiguous statements, and retaining the original words and phrases they heard to the greatest extent possible. The second phase was both quantitative and qualitative in nature: testing the instrument with a small sample of men engaged in batters’ treatment programs and interviewing the men about the experience of using the instrument. Based on the results and their feedback, the instrument was revised. This process was followed through several iterations. The next phases were quantitative: determining the psychometric characteristics of the instrument and using it to quantitatively evaluate batterer treatment programs—the extent to which individuals were helped to move forward in stages of the change cycle.

Interactive Excel Workbook Activities

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Chapter Summary

In this chapter you were introduced to three general approaches for moving from research question to research method. You were provided with a brief overview of the philosophical underpinnings and uses of qualitative, quantitative, and mixed-methods approaches. Next, you are provided with more detailed descriptions of qualitative and quantitative traditions and their associated methodologies.

Take a moment to complete the following activity.

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Chapter 2 IdentIfyIng a ReseaRch PRoblem and QuestIon, and seaRchIng Relevant lIteRatuRe

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Exercises for ‘Introduction to The New Statistics’

Chapter 1 asking and answering research questions.

If you prefer to use for the exercises your phone or tablet, scan the associated QR code .

1.1 Interactive Video

1.1.1 research questions, 1.1.2 meta-analysis, 1.1.3 open science, 1.2 glossary, 1.2.1 accordion, 1.2.2 dialog cards, 1.2.3 drag words, 1.2.4 memory game, 1.2.5 fill in the blanks, 1.2.6 flash cards, 1.3 poll intuitions, 1.4 research process, 1.4.1 drag & drop, 1.4.2 form wizard, 1.4.3 interactive video, 1.4.4 order of steps, 1.4.4.1 research process (summary), 1.4.4.2 estimation plan (image sequencing).

Integrating this exercise into this books destroys the internal linking. It seems to me a bug of the H5P image sequenceing content type.

1.4.4.3 Estimation Plan (Summary)

1.4.4.4 research & estimation (learnr), 1.5 meta-analysis, 1.5.1 explore forest plot (image hotspots), 1.5.2 point estimates in forest plot (find multiple hotspots), 1.6 reporting (accordion), 1.7 quizzes and assessment, 1.7.1 quiz 1 (question set), 1.7.2 quiz 2 (question set), 1.7.3 take-home messages (summary), 1.7.4 assessment (question set).