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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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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.

• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.

• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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.

How to Develop a Good Research Question? — Types & Examples

Cecilia is living through a tough situation in her research life. Figuring out where to begin, how to start her research study, and how to pose the right question for her research quest, is driving her insane. Well, questions, if not asked correctly, have a tendency to spiral us!

Image Source: https://phdcomics.com/

Questions lead everyone to answers. Research is a quest to find answers. Not the vague questions that Cecilia means to answer, but definitely more focused questions that define your research. Therefore, asking appropriate question becomes an important matter of discussion.

A well begun research process requires a strong research question. It directs the research investigation and provides a clear goal to focus on. Understanding the characteristics of comprising a good research question will generate new ideas and help you discover new methods in research.

In this article, we are aiming to help researchers understand what is a research question and how to write one with examples.

Table of Contents

What Is a Research Question?

A good research question defines your study and helps you seek an answer to your research. Moreover, a clear research question guides the research paper or thesis to define exactly what you want to find out, giving your work its objective. Learning to write a research question is the beginning to any thesis, dissertation , or research paper. Furthermore, the question addresses issues or problems which is answered through analysis and interpretation of data.

Why Is a Research Question Important?

A strong research question guides the design of a study. Moreover, it helps determine the type of research and identify specific objectives. Research questions state the specific issue you are addressing and focus on outcomes of the research for individuals to learn. Therefore, it helps break up the study into easy steps to complete the objectives and answer the initial question.

Types of Research Questions

Research questions can be categorized into different types, depending on the type of research you want to undergo. Furthermore, knowing the type of research will help a researcher determine the best type of research question to use.

1. Qualitative Research Question

Qualitative questions concern broad areas or more specific areas of research. However, unlike quantitative questions, qualitative research questions are adaptable, non-directional and more flexible. Qualitative research question focus on discovering, explaining, elucidating, and exploring.

i. Exploratory Questions

This form of question looks to understand something without influencing the results. The objective of exploratory questions is to learn more about a topic without attributing bias or preconceived notions to it.

Research Question Example: Asking how a chemical is used or perceptions around a certain topic.

ii. Predictive Questions

Predictive research questions are defined as survey questions that automatically predict the best possible response options based on text of the question. Moreover, these questions seek to understand the intent or future outcome surrounding a topic.

Research Question Example: Asking why a consumer behaves in a certain way or chooses a certain option over other.

iii. Interpretive Questions

This type of research question allows the study of people in the natural setting. The questions help understand how a group makes sense of shared experiences with regards to various phenomena. These studies gather feedback on a group’s behavior without affecting the outcome.

Research Question Example: How do you feel about AI assisting publishing process in your research?

2. Quantitative Research Question

Quantitative questions prove or disprove a researcher’s hypothesis through descriptions, comparisons, and relationships. These questions are beneficial when choosing a research topic or when posing follow-up questions that garner more information.

i. Descriptive Questions

It is the most basic type of quantitative research question and it seeks to explain when, where, why, or how something occurred. Moreover, they use data and statistics to describe an event or phenomenon.

Research Question Example: How many generations of genes influence a future generation?

ii. Comparative Questions

Sometimes it’s beneficial to compare one occurrence with another. Therefore, comparative questions are helpful when studying groups with dependent variables.

Example: Do men and women have comparable metabolisms?

iii. Relationship-Based Questions

This type of research question answers influence of one variable on another. Therefore, experimental studies use this type of research questions are majorly.

Example: How is drought condition affect a region’s probability for wildfires.  

How to Write a Good Research Question?

1. Select a Topic

The first step towards writing a good research question is to choose a broad topic of research. You could choose a research topic that interests you, because the complete research will progress further from the research question. Therefore, make sure to choose a topic that you are passionate about, to make your research study more enjoyable.

2. Conduct Preliminary Research

After finalizing the topic, read and know about what research studies are conducted in the field so far. Furthermore, this will help you find articles that talk about the topics that are yet to be explored. You could explore the topics that the earlier research has not studied.

3. Consider Your Audience

The most important aspect of writing a good research question is to find out if there is audience interested to know the answer to the question you are proposing. Moreover, determining your audience will assist you in refining your research question, and focus on aspects that relate to defined groups.

4. Generate Potential Questions

The best way to generate potential questions is to ask open ended questions. Questioning broader topics will allow you to narrow down to specific questions. Identifying the gaps in literature could also give you topics to write the research question. Moreover, you could also challenge the existing assumptions or use personal experiences to redefine issues in research.

5. Review Your Questions

Once you have listed few of your questions, evaluate them to find out if they are effective research questions. Moreover while reviewing, go through the finer details of the question and its probable outcome, and find out if the question meets the research question criteria.

6. Construct Your Research Question

There are two frameworks to construct your research question. The first one being PICOT framework , which stands for:

• Population or problem
• Intervention or indicator being studied
• Comparison group
• Outcome of interest
• Time frame of the study.

The second framework is PEO , which stands for:

• Population being studied
• Exposure to preexisting conditions
• Outcome of interest.

Research Question Examples

• How might the discovery of a genetic basis for alcoholism impact triage processes in medical facilities?
• How do ecological systems respond to chronic anthropological disturbance?
• What are demographic consequences of ecological interactions?
• What roles do fungi play in wildfire recovery?
• How do feedbacks reinforce patterns of genetic divergence on the landscape?
• What educational strategies help encourage safe driving in young adults?
• What makes a grocery store easy for shoppers to navigate?
• What genetic factors predict if someone will develop hypothyroidism?
• Does contemporary evolution along the gradients of global change alter ecosystems function?

How did you write your first research question ? What were the steps you followed to create a strong research question? Do write to us or comment below.

Frequently Asked Questions

Research questions guide the focus and direction of a research study. Here are common types of research questions: 1. Qualitative research question: Qualitative questions concern broad areas or more specific areas of research. However, unlike quantitative questions, qualitative research questions are adaptable, non-directional and more flexible. Different types of qualitative research questions are: i. Exploratory questions ii. Predictive questions iii. Interpretive questions 2. Quantitative Research Question: Quantitative questions prove or disprove a researcher’s hypothesis through descriptions, comparisons, and relationships. These questions are beneficial when choosing a research topic or when posing follow-up questions that garner more information. Different types of quantitative research questions are: i. Descriptive questions ii. Comparative questions iii. Relationship-based questions

Qualitative research questions aim to explore the richness and depth of participants' experiences and perspectives. They should guide your research and allow for in-depth exploration of the phenomenon under investigation. After identifying the research topic and the purpose of your research: • Begin with Broad Inquiry: Start with a general research question that captures the main focus of your study. This question should be open-ended and allow for exploration. • Break Down the Main Question: Identify specific aspects or dimensions related to the main research question that you want to investigate. • Formulate Sub-questions: Create sub-questions that delve deeper into each specific aspect or dimension identified in the previous step. • Ensure Open-endedness: Make sure your research questions are open-ended and allow for varied responses and perspectives. Avoid questions that can be answered with a simple "yes" or "no." Encourage participants to share their experiences, opinions, and perceptions in their own words. • Refine and Review: Review your research questions to ensure they align with your research purpose, topic, and objectives. Seek feedback from your research advisor or peers to refine and improve your research questions.

Developing research questions requires careful consideration of the research topic, objectives, and the type of study you intend to conduct. Here are the steps to help you develop effective research questions: 1. Select a Topic 2. Conduct Preliminary Research 3. Consider Your Audience 4. Generate Potential Questions 5. Review Your Questions 6. Construct Your Research Question Based on PICOT or PEO Framework

There are two frameworks to construct your research question. The first one being PICOT framework, which stands for: • Population or problem • Intervention or indicator being studied • Comparison group • Outcome of interest • Time frame of the study The second framework is PEO, which stands for: • Population being studied • Exposure to preexisting conditions • Outcome of interest

A tad helpful

Had trouble coming up with a good research question for my MSc proposal. This is very much helpful.

This is a well elaborated writing on research questions development. I found it very helpful.

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Literature Searching

Characteristics of a good research question

The first step in a literature search is to construct a well-defined question.  This helps in ensuring a comprehensive and efficient search of the available literature for relevant publications on your topic.  The well-constructed research question provides guidance for determining search terms and search strategy parameters.

A good or well-constructed research question is:

• Original and of interest to the researcher and the outside world
• It is clear and focused: it provides enough specifics that it is easy to understand its purpose and it is narrow enough that it can be answered. If the question is too broad it may not be possible to answer it thoroughly. If it is too narrow you may not find enough resources or information to develop a strong argument or research hypothesis.  
• The question concept is researchable in terms of time and access to a suitable amount of quality research resources.
• It is analytical rather than descriptive.  The research question should allow you to produce an analysis of an issue or problem rather than a simple description of it.  In other words, it is not answerable with a simple “yes” or “no” but requires a synthesis and analysis of ideas and sources.
• The results are potentially important and may change current ideas and/or practice
• And there is the potential to develop further projects with similar themes

The question you ask should be developed for the discipline you are studying. A question appropriate for Physical Therapy, for instance, is different from an appropriate one in Sociology, Political Science or Microbiology .

The well-constructed question provides guidance for determining search terms and search strategy parameters. The process of developing a good question to research involves taking your topic and breaking each aspect of it down into its component parts. 

One well-established way that can be used both for creating research questions and developing strategies is known as PICO(T). The PICO framework was designed primarily for questions that include clinical interventions and comparisons, however other types of questions may also be able to follow its principles.  If the PICO framework does not precisely fit your question, using its principles can help you to think about what you want to explore even if you do not end up with a true PICO question.

References/Additional Resources

Fandino W. (2019). Formulating a good research question: Pearls and pitfalls.   Indian journal of anaesthesia ,  63 (8), 611–616. 

Vandenbroucke, J. P., & Pearce, N. (2018). From ideas to studies: how to get ideas and sharpen them into research questions .  Clinical epidemiology ,  10 , 253–264.

Ratan, S. K., Anand, T., & Ratan, J. (2019). Formulation of Research Question - Stepwise Approach .  Journal of Indian Association of Pediatric Surgeons ,  24 (1), 15–20.

Lipowski, E.E. (2008). Developing great research questions. American Journal of Health-System Pharmacy, 65(17) , 1667–1670.

FINER Criteria

Another set of criteria for developing a research question was proposed by Hulley (2013) and is known as the FINER criteria. 

FINER stands for:

Feasible – Writing a feasible research question means that it CAN be answered under objective aspects like time, scope, resources, expertise, or funding. Good questions must be amenable to the formulation of clear hypotheses.

Interesting – The question or topic should be of interest to the researcher and the outside world. It should have a clinical and/or educational significance – the “so what?” factor. 

Novel – In scientific literature, novelty defines itself by being an answer to an existing gap in knowledge. Filling one of these gaps is highly rewarding for any researcher as it may represent a real difference in peoples’ lives.

Good research leads to new information. An investigation which simply reiterates what is previously proven is not worth the effort and cost. A question doesn’t have to be completely original. It may ask whether an earlier observation could be replicated, whether the results in one population also apply to others, or whether enhanced measurement methods can make clear the relationship between two variables.  

Ethical – In empirical research, ethics is an absolute MUST. Make sure that safety and confidentiality measures are addressed, and according to the necessary IRB protocols.

Relevant – An idea that is considered relevant in the healthcare community has better chances to be discussed upon by a larger number of researchers and recognized experts, leading to innovation and rapid information dissemination.

The results could potentially be important and may change current ideas and/or practice.

Cummings, S.R., Browner, W.S., & Hulley, S.B. (2013). Conceiving the research question and developing the study plan. In: Designing clinical research (Hulley, S. R. Cummings, W. S. Browner, D. Grady, & T. B. Newman, Eds.; Fourth edition.). Wolters Kluwer/Lippincott Williams & Wilkins. Pp. 14-22.    

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Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

39 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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How to Write a Research Question: Types and Examples 

The first step in any research project is framing the research question. It can be considered the core of any systematic investigation as the research outcomes are tied to asking the right questions. Thus, this primary interrogation point sets the pace for your research as it helps collect relevant and insightful information that ultimately influences your work.   

Typically, the research question guides the stages of inquiry, analysis, and reporting. Depending on the use of quantifiable or quantitative data, research questions are broadly categorized into quantitative or qualitative research questions. Both types of research questions can be used independently or together, considering the overall focus and objectives of your research.  

What is a research question?

A research question is a clear, focused, concise, and arguable question on which your research and writing are centered. 1 It states various aspects of the study, including the population and variables to be studied and the problem the study addresses. These questions also set the boundaries of the study, ensuring cohesion. 

Designing the research question is a dynamic process where the researcher can change or refine the research question as they review related literature and develop a framework for the study. Depending on the scale of your research, the study can include single or multiple research questions. 

A good research question has the following features: 

• It is relevant to the chosen field of study. 
• The question posed is arguable and open for debate, requiring synthesizing and analysis of ideas. 
• It is focused and concisely framed. 
• A feasible solution is possible within the given practical constraint and timeframe. 

A poorly formulated research question poses several risks. 1   

• Researchers can adopt an erroneous design. 
• It can create confusion and hinder the thought process, including developing a clear protocol.  
• It can jeopardize publication efforts.  
• It causes difficulty in determining the relevance of the study findings.  
• It causes difficulty in whether the study fulfils the inclusion criteria for systematic review and meta-analysis. This creates challenges in determining whether additional studies or data collection is needed to answer the question.  
• Readers may fail to understand the objective of the study. This reduces the likelihood of the study being cited by others. 

Now that you know “What is a research question?”, let’s look at the different types of research questions. 

Types of research questions

Depending on the type of research to be done, research questions can be classified broadly into quantitative, qualitative, or mixed-methods studies. Knowing the type of research helps determine the best type of research question that reflects the direction and epistemological underpinnings of your research. 

The structure and wording of quantitative 2 and qualitative research 3 questions differ significantly. The quantitative study looks at causal relationships, whereas the qualitative study aims at exploring a phenomenon. 

• Quantitative research questions:  
• Seeks to investigate social, familial, or educational experiences or processes in a particular context and/or location.  
• Answers ‘how,’ ‘what,’ or ‘why’ questions. 
• Investigates connections, relations, or comparisons between independent and dependent variables. 

Quantitative research questions can be further categorized into descriptive, comparative, and relationship, as explained in the Table below. 

• Qualitative research questions  

Qualitative research questions are adaptable, non-directional, and more flexible. It concerns broad areas of research or more specific areas of study to discover, explain, or explore a phenomenon. These are further classified as follows: 

• Mixed-methods studies  

Mixed-methods studies use both quantitative and qualitative research questions to answer your research question. Mixed methods provide a complete picture than standalone quantitative or qualitative research, as it integrates the benefits of both methods. Mixed methods research is often used in multidisciplinary settings and complex situational or societal research, especially in the behavioral, health, and social science fields. 

What makes a good research question

A good research question should be clear and focused to guide your research. It should synthesize multiple sources to present your unique argument, and should ideally be something that you are interested in. But avoid questions that can be answered in a few factual statements. The following are the main attributes of a good research question. 

• Specific: The research question should not be a fishing expedition performed in the hopes that some new information will be found that will benefit the researcher. The central research question should work with your research problem to keep your work focused. If using multiple questions, they should all tie back to the central aim. 
• Measurable: The research question must be answerable using quantitative and/or qualitative data or from scholarly sources to develop your research question. If such data is impossible to access, it is better to rethink your question. 
• Attainable: Ensure you have enough time and resources to do all research required to answer your question. If it seems you will not be able to gain access to the data you need, consider narrowing down your question to be more specific. 
• You have the expertise 
• You have the equipment and resources 
• Realistic: Developing your research question should be based on initial reading about your topic. It should focus on addressing a problem or gap in the existing knowledge in your field or discipline. 
• Based on some sort of rational physics 
• Can be done in a reasonable time frame 
• Timely: The research question should contribute to an existing and current debate in your field or in society at large. It should produce knowledge that future researchers or practitioners can later build on. 
• Novel 
• Based on current technologies. 
• Important to answer current problems or concerns. 
• Lead to new directions. 
• Important: Your question should have some aspect of originality. Incremental research is as important as exploring disruptive technologies. For example, you can focus on a specific location or explore a new angle. 
• Meaningful whether the answer is “Yes” or “No.” Closed-ended, yes/no questions are too simple to work as good research questions. Such questions do not provide enough scope for robust investigation and discussion. A good research question requires original data, synthesis of multiple sources, and original interpretation and argumentation before providing an answer. 

Steps for developing a good research question

The importance of research questions cannot be understated. When drafting a research question, use the following frameworks to guide the components of your question to ease the process. 4  

• Determine the requirements: Before constructing a good research question, set your research requirements. What is the purpose? Is it descriptive, comparative, or explorative research? Determining the research aim will help you choose the most appropriate topic and word your question appropriately. 
• Select a broad research topic: Identify a broader subject area of interest that requires investigation. Techniques such as brainstorming or concept mapping can help identify relevant connections and themes within a broad research topic. For example, how to learn and help students learn. 
• Perform preliminary investigation: Preliminary research is needed to obtain up-to-date and relevant knowledge on your topic. It also helps identify issues currently being discussed from which information gaps can be identified. 
• Narrow your focus: Narrow the scope and focus of your research to a specific niche. This involves focusing on gaps in existing knowledge or recent literature or extending or complementing the findings of existing literature. Another approach involves constructing strong research questions that challenge your views or knowledge of the area of study (Example: Is learning consistent with the existing learning theory and research). 
• Identify the research problem: Once the research question has been framed, one should evaluate it. This is to realize the importance of the research questions and if there is a need for more revising (Example: How do your beliefs on learning theory and research impact your instructional practices). 

How to write a research question

Those struggling to understand how to write a research question, these simple steps can help you simplify the process of writing a research question. 

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 

• Example 2 

References:  

• Thabane, L., Thomas, T., Ye, C., & Paul, J. (2009). Posing the research question: not so simple.  Canadian Journal of Anesthesia/Journal canadien d’anesthésie ,  56 (1), 71-79. 
• Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research.  Nephrology Nursing Journal ,  45 (2), 209-213. 
• Kyngäs, H. (2020). Qualitative research and content analysis.  The application of content analysis in nursing science research , 3-11. 
• Mattick, K., Johnston, J., & de la Croix, A. (2018). How to… write a good research question.  The clinical teacher ,  15 (2), 104-108. 
• Fandino, W. (2019). Formulating a good research question: Pearls and pitfalls.  Indian Journal of Anaesthesia ,  63 (8), 611. 
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: a key to evidence-based decisions.  ACP journal club ,  123 (3), A12-A13 

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Scientific understanding in biomedical research

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Motivated by a recent trend that advocates a reassessment of the aim of medical science and clinical practice, this paper investigates the epistemic aims of biomedical research. Drawing on contemporary discussions in epistemology and the philosophy of science, along with a recent study on scurvy, this paper (1) explores the concept of understanding as the aim of scientific inquiry and (2) establishes a framework that will guide the examination of its forms in biomedical research. Using the case of Tuberculosis (TB), (3) it is argued that grasping a mechanistic explanation is crucial for reaching a threshold of understanding at which we may speak of an objectual, biomedical understanding of TB.

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Avoid common mistakes on your manuscript.

Within just a few years, multiple editorials in prominent medical journals have issued a call to reflect on the aim of clinical medicine and medical science. Focusing on the latter matter, in a 2013 editorial, the editors of The Lancet clarified that they frequently confront not only queries about the rationale behind specific scientific studies but also broader inquiries regarding the overarching aim of medical science, which includes both clinical and medical laboratory research. They contend that the moment has come to rethink our approach to conducting and incentivizing research, and for this, “we need to remind ourselves about the real purpose of research” (The Lancet, 2013 , p. 347; see also Thornton, 2013 ). The authors express concern that a significant portion of the vast sums allocated annually to biomedical research fails to meet its true objectives. This shortfall is attributed not only to deficiencies in research design and methodology but also to a lack of “clinical meaningfulness.” Specifically, they highlight that many research projects pose questions that are not sufficiently aligned with clinical medicine and relevant to the treatment, control, prevention, or prediction of diseases. The authors note that the issues extend beyond merely reducing the potential impact of biomedical research; they suggest a fundamental misunderstanding of the very purpose of biomedical research, implying that such studies may not truly qualify as medical . Footnote 1

This short Lancet piece highlights significant, yet often overlooked, questions concerning the epistemic aim of medical research. This paper will address these questions, positing that medical science is fundamentally engaged in inquiries aimed at achieving what we shall refer to as biomedical understanding . To investigate and clarify what such understanding amounts to, the paper takes inspiration from two sources. On the one hand, it draws on contemporary discussions in philosophy of science and epistemology, which have seen a renewed interest in understanding as a distinct cognitive accomplishment (Grimm, 2021 ; Baumberger et al., 2017 ), as the epistemic aim of scientific inquiry, and the measure of progress (see e.g., Potochnik, 2015 ; De Regt & Dieks, 2005 ; Elgin, 2017 ). Acknowledging that what constitutes proper understanding can depend on the field, as noted by scholars in the field (Strevens, 2010 ; De Regt et al., 2009 ), this paper aims to specifically articulate what understanding entails within medical science. On the other hand, this paper draws on and employs several distinctions from a recent study on scurvy (Varga, 2023 ). However, while that study focused on a noncommunicable condition stemming from severe dietary deficiencies, this paper shifts our focus to Tuberculosis (TB), a multifaceted and emblematic infectious disease often accompanied by stigma (WHO, 2023 ). TB, which is one of the oldest known infectious diseases, is caused by the bacterium Mycobacterium tuberculosis (Kapur et al., 1994 ; Daniel, 2006 ). The bacteria are transmitted when an infected individual coughs, sneezes, or speaks, allowing another person to breathe in the pathogens. Symptoms of TB can include coughing, chest pain, fatigue, fever, and night sweats and although the condition is treatable with antibiotics it remains a major global health concern.

The paper is organized as follows. It (1) explores the idea of understanding as the aim of scientific inquiry and (2) lays down a framework of understanding that will subsequently guide our exploration of its forms in medicine. Using the case of Tuberculosis (TB), (3) it is argued that grasping a mechanistic explanation is crucial for reaching a threshold of understanding at which we may speak of an objectual, biomedical understanding of TB. If evidence can be gathered to support this argument, it would align with the previously mentioned research on a noncommunicable disease (scurvy), suggesting a recurring pattern across various contexts of medical research.

1 The aim of scientific inquiry: constitutive aim and truth

Scientific inquiries can be viewed as extensions of our day-to-day endeavors to gather information albeit executed in a more systematic manner (see e.g., Kelp, 2021 ). They are goal-directed activities, implying that there is some aim that inquiry strives to accomplish. It is quite natural to assume that this description also fits medical science; however, before delving into the question of what constitutes the epistemic aim of medical science, it is crucial to first briefly clarify what medical science refers to. What sets medical science apart and qualifies something as specifically medical science, rather than just science in general?

Medical science, which includes clinical research and laboratory research in medicine, is fundamentally based on the life sciences. Over the past two centuries, it has extensively leveraged discoveries in biology that have identified cellular, genetic, and molecular entities and processes that help explain the development and course of diseases. While some aspects of medical science may not differ essentially from laboratory sciences within contributing disciplines such as biology, biochemistry, and physiology, medical science cannot simply be reduced to the sum of these fields. One reason is that medical research is only deemed properly medical when it has a specific practical orientation—that is, when it is driven by the goal of contributing to clinical medicine, which primarily focuses on the diagnosis, prevention, and treatment of disease. Without this practical focus, research might be more accurately described as biological rather than medical. Take, for example, large-scale laboratory research that aims to chart the functions of specific limbic structures in the brain. Without a practical focus on clinical applications or health outcomes, such research might be more accurately described as neurobiological rather than medical. Of course, this research could potentially yield benefits for clinical medicine in the future, but without a direct and immediate practical orientation, it would not be classified as medical research. Moreover, if we were to classify such research as medical merely based on potential future benefits, the distinction between medical and non-medical research would collapse.

Of course, this practical orientation toward health outcomes is a characteristic that biomedical research shares with related fields such as public health. However, their epistemic aims are directed towards different objectives: biomedical research typically focuses on the biological and physiological aspects of diseases at a molecular or cellular level, aiming to elucidate disease mechanisms and develop new treatments, whereas public health is primarily concerned with improving the health of populations through prevention strategies, health education, surveillance, and improving access to health care. Public health aims encompass a wide array of objectives: ensuring safe environments by controlling hazards in air, water, and food, enhancing host resistance through balanced nutrition and immunization, promoting health-supportive behaviors, and improving equitable access to health and social services (White et al., 2013 ; Munthe, 2008 ). Footnote 2

Although both fields are dedicated to conducting research with the final aim to improve health outcomes, they operate with different priorities and methodologies, each aligned with their distinct epistemic goals. A biomedical researcher might delve into the genetic factors that contribute to the resistance of TB to antibiotics, focusing on molecular and cellular details. In contrast, public health initiatives may not require such knowledge; instead, they often concentrate on understanding societal or other health factors that hinder the implementation of vaccination programs or public campaigns aimed at increasing awareness and prevention of TB.

Having briefly clarified at least some of the aspects that set medical science apart, we can now turn to the question of its aim. As we begin to explore this, it is worth considering how plausible it is to claim that scientific inquiries in medicine are driven by a single aim. This consideration is crucial because the diversity of methods, approaches, and priorities within medical science suggests that its objectives might not be unified under a single overarching aim. In response, it is important to clarify that for the purposes of this paper, we do not assert that medical science is driven by a single aim. Instead, among potentially other aims, our objective is to explore the nature of medical science’s epistemic aim , which also determines what counts as progress at least in this limited sense. Thus, very roughly, if A is the aim of inquiry, then medical science makes progress when A accumulates or increases (for a discussion, see Bird, 2007 ; Varga, 2024 ).

So what is the epistemic aim of scientific inquiry in medicine? According to a plausible suggestion, the aim is simply to discover truths about health and disease and correct past errors (e.g., false beliefs about diseases like scurvy or depression being caused by humoral imbalance) that were based on tradition, cognitive errors, ideologies, or religious dogma. Correspondingly, progress consists in a cumulative acquisition of true beliefs. For example, until the nineteenth century, the prevailing belief was that TB was inherited or caused by environmental factors such as bad air or poor living conditions. But already in 1720, the English physician Benjamin Marten hypothesized that TB and its symptomatic lesions in the lungs are caused by “species of Animalcula or wonderfully minute living Creatures” that can be transmitted “by very frequently conversing so nearly as to draw inpart of the breath he emits from the lungs” (cited in Cambau & Drancourt, 2014 ; Daniel, 2006 ). Supporting this hypothesis, in 1865, the French physician Jean-Antoine Villemin provided experimental evidence that TB could be transmitted. He observed that TB was more prevalent in people living close and in poorly ventilated buildings, and he noted that while TB was common among troops in barracks, it decreased during military campaigns when soldiers were not housed (Daniel et al., 1994 ). Thus, Marten and Villemin unearthed truths regarding TB, rectified previous mistakes, and aided in the ongoing accumulation of accurate beliefs, which constitutes progress.

On its face, the suggestion that the aim of medical research is simply to discover truths is plausible. After all, it is often said that scientific inquiries are in the “truth business” (Pennock, 2019 ; Lipton, 2004 ), and it is difficult to imagine that contemporary medical science would be able to achieve what it does if its claims would not at least roughly correspond to how the world actually is. Nonetheless, the acquisition of true beliefs does not seem sufficient to constitute progress. Footnote 3 Take, for instance, a scenario where Marten and Villemin arrived at the same conclusion through unreliable methods and, coincidentally, the theory they came to accept happened to be true. In that case, Marten and Villemin would have acquired a true belief, but it would not have counted as genuine progress. What would be lacking is suitable justification for holding the relevant belief. In other words, the belief that they would have acquired would not qualify as knowledge .

1.1 Knowledge and understanding

What we learn from these considerations is that progress not only requires that our beliefs and theories be true but that we have attained adequate reasons for forming them. If this is correct, then it seems safe to conclude that the aim of inquiry is not merely truth, but knowledge (achieved by reliable means), which would mean that progress consists in the increase not of true beliefs, but of knowledge. Although this correction marks an improvement, it is necessary to supply some clarifications and caveats.

First, the aim of inquiry cannot simply be the mere accumulation of knowledge. Medical science has an expansive range of questions at its disposal, and it could potentially attain a vast pool of knowledge, but much of this potential knowledge might be trivial or inconsequential, lacking the impact or significance to be deemed progress. Imagine that researchers could come to know everything about some minor and transient symptom (e.g., a slight, transient change in nail coloration or longitudinal nail ridging) observed in a small subset of TB patients that are known not to have bearing on the disease’s diagnosis, progression, or response to treatment. While detailed knowledge of these symptoms might add to the clinical descriptions of TB, the reason this gained knowledge is not considered significant or constitutive of true progress likely stems from its limited impact on key areas of TB research and clinical management. It lacks the potential to advance our understanding of TB (or indeed other medically relevant conditions), uncover new treatment targets, enhance diagnostic methods, or deepen our understanding of disease transmission and resistance mechanisms.

If we accept this line of reasoning, then the aim of inquiry in medicine cannot be simply to amass knowledge, but rather a selective process that prioritizes the acquisition of certain kinds of significant knowledge. Hence, part of the scientific endeavor involves a critical evaluation process to identify which pieces of knowledge are significant and worth pursuing. This selection process is fundamental to progress, ensuring that scientific efforts are directed toward areas of genuine importance and potential impact (Kitcher, 2001 ; Dupré, 2016 ). Identifying and focusing on significant knowledge, therefore, becomes a crucial aspect of the scientific method, guiding researchers in making meaningful advancements rather than merely expanding the repository of human knowledge.

While the aim of inquiry is significant knowledge, the selection process to identify which pieces of knowledge count as significant cannot be extracted from nature and is largely relative to specific interests. As Kitcher ( 2001 , 61) stated regarding scientific inquiry in general, “significant science must be understood in the context of a particular group with particular practical interests and a particular history”. In the context of TB, it is far more plausible to suggest that what constitutes significant knowledge is closely interwoven with practical concerns related to the understanding and treatment of TB.

Having discussed the issue of significance, we are now faced with a final challenge that questions the notion that the goal of inquiry in medical science is best described as the pursuit of knowledge. In recent years, numerous philosophers of science have contended that framing the aim of inquiry in terms of understanding offers significant benefits over viewing progress merely as an accumulation of knowledge. The advantage with comprehending progress in terms of increased understanding is that it avoids the challenges faced by accounts measuring scientific progress in terms of knowledge (see e.g., Elgin, 2007 , 2017 ; De Regt & Dieks, 2005 ; Potochnik, 2017 ). Footnote 4 First, traditional accounts have problems explaining the significance of certain pragmatic virtues (e.g., simplicity) that do not affect the truth of claims, theories, and explanations. In contrast, an account of progress based on the notion of understanding does not face this problem, as these pragmatic virtues clearly affect the ability to understand (Dellsén, 2016 ). Second, traditional accounts of progress as knowledge accumulation have problems explaining abstractions, approximations, and idealizations. For example, in medicine, physiological accounts often offer idealized and simplified descriptions of organs and their functions (Ereshefsky, 2009 ). These provide computational tractability and improve understanding, but they also include aspects that are, strictly taken, inaccurate or false. However, such falsehoods are, as Elgin ( 2017 ) puts it, “felicitous”: although they involve false representations, they also exemplify significant aspects of phenomena in a tractable manner. Several philosophers have argued that science can increase understanding and contribute to progress even if it involves departing from the truth (e.g., Elgin, 2009a , b ; Strevens, 2017 ; Potochnik, 2015 ).

On an account of progress in terms of knowledge, the presence of manifest falsehoods seems incompatible with progress. However, an account of progress in terms of understanding fares better here, since understanding is compatible with a limited number of falsehoods, which are outweighed by practical advantages. Strevens argues that idealized models can provide understanding, but in a somewhat more limited way, showing why some causal factors are difference-makers and others are not (Strevens, 2017 ). Potochnik ( 2017 , 102; 2015 ) holds that while idealizations cannot be true or approximately true, they can be epistemically acceptable. Because such idealizations are rampant in science and they always detract from the truth, truth does not seem to be a good candidate for describing the aim of science. However, given that idealizations can support understanding, it is more adequate to suppose that understanding is what science aims at.

The latter is not susceptible to such worries, because, in contradistinction to knowledge, understanding is only quasi-factive: it can survive false beliefs if they are not absolutely vital to the phenomenon in question. For example, Marten hypothesized that TB was caused by “species of Animalcula or wonderfully minute living Creatures” (Doetsch, 1978 ; Daniel et al., 1994 ). Strictly taken, this is false: TB was not caused by such small creatures, but by the Mycobacterium tuberculosis bacteria, which Marten had no knowledge of. Nevertheless, it is hard to deny that some progress occurred and an increase in the (objectual) understanding of TB had been obtained.

In all, as opposed to truth or knowledge, the epistemic aim of scientific inquiry is best comprehended as understanding. Comprehending progress in terms of increased understanding dovetails more closely with the pragmatic nature of medicine and has the advantage of being resistant to some of the problems that haunt accounts that comprehend progress as knowledge accumulation. If the epistemic goal of inquiry is best framed as seeking understanding, this raises questions about what understanding is in medical research. The following sections will initially delve into theories of understanding, followed by an examination of the specific nature of understanding within the realm of medicine.

2 Forms of understanding

The debates on understanding have focused on three types of understanding: propositional understanding (understanding that something is the case), explanatory understanding (understanding why something is the case), and objectual understanding (understanding a particular topic or subject matter) (see e.g., Kvanvig, 2003 ; Hannon, 2021 ; Grimm, 2021 ). Footnote 5 In what follows, we are going to be focusing on explanatory and objectual understanding, in part because propositional understanding is often largely reducible to propositional knowledge or explanatory understanding. For example, saying “he understands that he needs to come to TB screening” could amount to the attribution of propositional knowledge (“he knows that he needs to come to TB screening”) or to explanatory understanding (“he understands why it is important for him to come to TB screening”). Of course, there are many other examples of how the term “understanding” is used. But many of them are either reducible to claims about knowledge, objectual understanding or explanatory understanding. For example, when we say that a person really understands how x works, then we are attributing to this person some degree of objectual understanding of x.

To illustrate the difference between knowledge and understanding, consider the example of TB. A student of medicine may attend a lecture on infectious diseases and come to know from a reliable source that TB is caused by Mycobacterium tuberculosis. Accepting the testimony from a reliable source and even double checking it in an encyclopedia of infectious diseases, the student gains causal knowledge. But while the student now knows a proposition that picks out the cause of TB, that is not enough for explanatory understanding, which not only requires knowledge of what caused the effect, but also grasping how that cause brings about the effect (Kvanvig, 2003 ; Pritchard, 2010a ), which many take to involves a type of “skill” (see e.g., De Regt, 2017 ). Understanding does not only require the possession of a theory or model, but also the skill or ability to use it to discern the causal relationship involved. One way to comprehend the difference is that unless explanatory understanding about how cause and effect are related is attained, she will be unable to address what-if-things-had-been-different questions or predict the outcomes of potential interventions (Grimm, 2011 ).

For another example, consider an utterly false theory leading to correct results. Charles Locock’s mid-19th century discovery of the anticonvulsant effect of potassium bromide. Locock, a physician working in London, shared the widely accepted theory among his contemporaries of a causal relationship between masturbation, convulsions, and epilepsy (Ban, 2006 ). As bromides were known to reduce the sex drive, Locock reasoned that the ingestion of potassium bromides would control convulsions by reducing the rate of masturbation. His account of the drug’s effectiveness was published in The Lancet in 1857, and subsequent independent studies confirmed potassium bromide’s antiepileptic efficacy, albeit evidently not by reducing masturbation frequency. Through observations and inference to the best explanation, Locock had attained knowledge that potassium bromide reduced convulsions, and such knowledge allowed the introduction of a relatively effective antiepileptic treatment into medical practice.

Still, in an important sense, such causal knowledge does not properly close the inquiry, which would require grasping a correct explanation and attaining understanding of what happens and how cause and effect are related. Locock did not understand why potassium bromide was effective, why it failed to be effective in some people, and so on. This meant that he lacked the ability to improve the efficiency of the intervention, since he was unable to counterfactually anticipate the effects of changes he could have made with respect to the treatment. More precisely, the lack of understanding means that Locock was unable to (i) predict the changes that would occur if the factors cited as explanatory were different and (ii) to draw correct inferences about similar situations under slightly varied conditions.

2.1 Explanatory and objectual understanding

Objectual and explanatory understanding differ in several ways (Kvanvig, 2003 , 2009 ; Hannon, 2019 , 2021 ). Explanatory understanding involves grasping why something is the case (e.g., uncovering the causal mechanisms or reasons behind phenomena) and its scope is less expansive than that of objectual understanding (Hannon, 2021 ). Objectual understanding, usually expressed using the verb “understands”, followed by a noun, as in the phrase “she understands TB”, entails a comprehensive grasp of a particular topic or subject matter, which includes incorporating these causal explanations into a broader context. While explanatory understanding is often necessary, it is not sufficient for objectual understanding, which requires integrating these explanatory insights within a larger framework.

To illustrate the difference, imagine that our student has now acquired knowledge of a vast number of isolated facts about TB, such that her peers would not hesitate to say that she has knowledge about TB. Nonetheless, this would not imply that the student understands TB, which would attribute to the student a more profound penetration of TB, a sort of epistemic acquaintance that is more profound than knowing particular propositions (Kvanvig, 2003 , p. 191; Strevens, 2017 ). Her objectual understanding of TB is gradable and can always become more profound along various dimensions (Bengson, 2017 ).

Often, achieving (full) objectual understanding is the aim of inquiry, and reaching it justifiably concludes the investigation of the topic (Kvanvig, 2013 ). If we think of medical research, objectual understanding seems to better capture the primary aim of inquiry and the conditions under which it can be concluded. To take the example of TB, researchers not only want to understand why it arises or why certain characteristic biochemical reactions occur but also why it leads to the characteristic symptoms, why it has varied effects on individuals, how it relates to other conditions, and so on. Even though single research projects cannot take on such a large task, the ultimate goal seems to go beyond obtaining explanatory understanding of features of TB to systematically understanding TB , which means attaining some level of coherence and completeness in terms of knowledge, as well as in taxonomies and classifications.

A prevalent perspective posits that achieving objectual understanding marks the endpoint of inquiry and legitimately closes the investigation into the subject (Kvanvig, 2013 ; Carter and Gordon 2014). This perspective aligns well with medicine, where an objectual understanding of a condition, rather than just its explanation, is often the ultimate aim. In their pursuit of understanding TB, researchers aim to grasp not just its origins, but also its manifestations, correlations with other conditions, its varied effects on individuals, and the most useful systematic categorization of its characteristic symptoms and signs.

Some argue that objectual understanding is not merely a subset of explanatory understanding, in part because it is possible to achieve objectual understanding of indeterministic systems where explanatory relations do not obtain (Kvanvig, 2009 ). But even if this turns out to be false (see e.g., Khalifa, 2013 , ch. 4), maintaining this distinction conserves the intuition that when we attribute to somebody objectual understanding of a subject matter (as opposed to explanatory understanding), we imply that the agent’s epistemic commitments relevant to the subject matter form a coherent network. Also, the distinction upholds the idea that objectual understanding’s factivity requirement is more lenient, making it less susceptible to peripheral falsehoods compared to explanatory understanding (see e.g., Elgin, 2017 ; Bamberger, Beisbart, & Brun 2017; Kvanvig, 2009 ).

2.2 Grasping explanations and context-dependency

Both explanatory and objectual understanding go beyond mere knowledge by encompassing an additional cognitive achievement, often referred to as a form of “grasping” (e.g., de Regt, 2009 ; Strevens, 2017 ; Grimm, 2014 ; Elgin, 2017 ; for a critique, see Khalifa, 2013 , ch. 3). The objects of grasping are “explanatory and other coherence-making relationships” (Kvanvig, 2003 , p. 192). There is no clear agreement on the precise meaning of “grasping” (Hannon, 2019 ), but for our purposes we might conceptualize it as a form of cognitive control that agents develop through the active engagement of their epistemic agency in delineating conceptual and explanatory links. Importantly, while grasping enables agents to mentally map a relational assembly (Grimm, 2014 ), it is not reducible to the experience of understanding (e.g., an “aha” moment): good explanations do not necessarily trigger a sense of understanding, while inadequate explanations sometimes do (Trout, 2002 ). While philosophers commonly concur that what is being grasped are explanations, aligning with the notion that the primary purpose of scientific explanation is to foster understanding (Lipton, 2001 ), opinions differ on what kind of explanations lead to understanding, such as deductive-nomological explanations (Hempel & Oppenheim, 1948 ), or mechanistic explanations, which explain phenomena by specifying the mechanisms that produce them (Salmon, 1984 ; Machamer et al., 2000 ). Footnote 6

Importantly, what counts as understanding, is – at least in a limited sense – context-sensitive . This can be interpreted in several ways. First, some argue that understanding is context-sensitive in the sense that the criteria for understanding can evolve even within a single scientific discipline (for historical examples, see De Regt, 2017 ; De Regt et al., 2009 ). This is in part because the capacity of an explanation to lead to understanding is partially contingent upon the disciplinary background and knowledge of individuals seeking to understand.

Second, and more importantly for our aims here, some hold that context-sensitivity is linked to the nature and aim of the particular scientific inquiry. For example, Craver ( 2013 ; Kendler et al., 2011 ) contends that mechanistic explanations are inherently contextual and “perspectival”, as they are framed within a specific explanatory framework that is chosen based on explanatory interests. While this point may be limited to mechanistic explanations, there are indications that objectual understanding displays some context-sensitivity across scientific fields. To illustrate this with a medical example, consider the study of cholesterol metabolism in medical science and chemistry. In medical science, a significant level of objectual understanding of cholesterol metabolism arguably encompasses an understanding of how cholesterol levels are regulated (e.g., by diet, genetics) and how they can be modified through interventions or lifestyle changes to reduce the risk of disease. From the perspective of chemistry, objectual understanding of cholesterol metabolism does not necessarily relate to cardiovascular health but instead focuses on explaining the biochemical pathways of cholesterol breakdown and synthesis, elucidating the precise molecular interactions involved. Thus, what constitutes some sufficient level of objectual understanding in medicine might differ from that in chemistry, primarily because the explanatory goals and interests in medicine are intrinsically tied to practical applications and clinical medicine. There is no inherent tension between context-sensitivity and objectual understanding: even if the threshold for sufficient objectual understanding can be consistent across disciplines, the kinds of explanations needed to reach this understanding vary according to the specific context and the explanatory, practical and other goals of each field.

3 Biomedical understanding

While the presented account of understanding does not purport to capture the intricacies of philosophical debates on the topic, it serves as a basis for exploring what it means to possess objectual understanding of a disease within the medical field. This will be referred to as biomedical understanding (see Varga, 2023 , 2024 ). To grasp what biomedical understanding entails, let us revisit the history of TB research.

Before the 19th century, tuberculosis (TB) was thought to result from heredity or environmental causes like bad air. Marten’s initial hypothesis that “minute living creatures” could spread TB was later validated by Villemin, who in 1865 provided experimental evidence of TB’s transmissibility. He linked its higher incidence to crowded, inadequately ventilated environments and noted a decrease in TB cases among soldiers when they were not confined to cramped barracks (Daniel et al., 1994 ; Bynum, 2012 ). Moreover, by removing liquid from tuberculous cavities of individuals who had died of TB and injecting it into healthy animals, Villemin successfully transmitted the disease from humans to rabbits, from cows to rabbits, and from rabbits to rabbits. Throughout his studies, he used the same amount of liquid and animals of similar origin, age, and habitat conditions, such that “everything indeed other than inoculation, were identical” (Villemin 1868/2015 , 257). While not all animals developed symptoms, autopsies three months later revealed that the vast majority developed extensive TB with massive dissemination of tubercles to the organs (Villemin 1868/2015 ; Barnes, 2000 ).

Clearly, Villemin’s findings helped distinguish between variables that had a direct effect on the development of TB and those that were correlated with it (e.g., certain professions, poverty, poor living conditions). However, while Villemin attained an important piece of explanatory understanding, it would be unwarranted to say that he obtained objectual understanding of TB in any noteworthy sense. Given that the explanatory goals and interests in medicine are closely tied to practical applications, such a claim might seem excessive because the explanatory understanding Villemin obtained did not form a coherent network that would have allowed him to consider how possible medical interventions could limit control the progression and spread of TB. After all, Villemin did not understand under what conditions TB developed, how it transmitted, and what the agent of the disease was, except that the tubercle (nodular lesion) contained it.

Let us now look closer at some shortcomings that could have prevented him from attaining objectual understanding of TB in any substantial sense. The first shortcoming stems from an incomplete understanding of the causal agent. Villemin lacked comprehension with respect to two critical aspects of the causal connection: stability and specificity (see Woodward, 2010 ). A causal link between the injected substance and TB is considered stable if the counterfactual dependence remains consistent across various background situations. Villemin’s studies did not provide much evidence with respect to stability, because they did not involve testing under different background circumstances. In addition, specificity refers to the grain level of counterfactual dependencies between the inoculated substance and TB. Because Villemin inoculated the same amount of substance in each case, his studies offered no knowledge about the extent to which the intensity of tuberculization depends on the amount of substance inoculated. Villemin had no way of determining whether the counterfactual dependencies between the inoculated substance and TB are fine-grained, in which case intervention on the inoculated substance would enable more precise control over how TB develops.

Moreover, Villemin’s incomplete understanding of the causal agent prevented him from ruling out the possibility that experimentally induced tuberculosis might follow a different pathway from ordinary TB or could even be a distinct disease altogether. When injecting liquids from organisms that succumbed from TB, one could argue that the effects obtained were not due to TB, but to the injection containing some “cadaveric material.” Although Villemin could show that the number and extent of lesions on the lungs are not correlated with the number and extent of lesions developed at the injection site, he himself noted a crucial limitation: “should we consider the entire chain of phenomena observed in experimental tuberculosis as the result of a traumatism due to inoculation? This is an enigma that we cannot resolve” (Villemin 1868/2015 , 259).

The second shortcoming concerns a lack of knowledge about the relevant mechanism. The causal knowledge Villemin attained did not permit “tracing” the causal process (Steel, 2008 ), which would have assisted grasping coherence-making relationships and comprehending how the elements of TB are configured. This seems to necessitate some degree of explanatory understanding and discerning the mechanism that is responsible for linking cause and effect. A mechanism for phenomenon P consists of parts and processes that are structured in a way such that they are responsible for P (Glennan et al., 2021 ). Explanations in the biomedical sciences are most frequently mechanistic, explaining a disease by identifying the spatiotemporal structure of a mechanism that is responsible for that disease and its symptoms (Thagard, 2005 ; Darrason, 2018 ; Williamson, 2019 ). Villemin’s study establishes a coarse-grained difference-making relationship, but it does not amount to biomedical understanding because it fails to discern the correct mechanism.

We could say that the lack of such a mechanism has crucially impacted Villemin’s ability to gather sufficient evidence for explanatory understanding. There are two possibilities here, depending on which thesis one subscribes to regarding the role of mechanisms in establishing causal claims (for discussions, see Russo & Williamson, 2007 ; Illari, 2011 ; Williamson, 2019 ). According to a strong thesis, establishing a causal relationship requires not only difference-making evidence but also evidence of a mechanism composed by entities (such as proteins) and processes (such as protein expression) that together link cause and effect. If one accepts the strong thesis, then Villemin has not met the criteria for establishing a causal relationship because he had no knowledge of the mechanism. According to a weaker thesis, difference-making can serve as evidence for a causal relationship. However, evidence of a mechanism, combined with difference-making evidence, significantly increases certainty that the observed correlation is not merely spurious and that the effect can be attributed to the experimental intervention rather than to confounding variables.

Having examined these two shortcomings, it appears likely that each has contributed to the failure to attain objectual understanding. However, it is unclear whether any of these factors are essential for achieving objectual understanding. In the sections that follow, we will explore the historical development of tuberculosis research to further investigate this issue.

3.1 Koch and beyond

A significant breakthrough with respect to the first two shortcomings came with Robert Koch’s 1882 discovery of the bacterium Mycobacterium tuberculosis (MTB) as the causative agent of TB (Keshavjee & Farmer, 2012 ). Footnote 7 Koch formalized a set of “postulates” for establishing causation, which required (a) coincidence of bacteria and disease, (b) isolation of bacteria in a pure culture, and (c) induction of disease by inoculation with bacteria from pure culture. As to (a), Koch was able to show that the MTB were always present in TB (but not in normal states), that they preceded tubercle formation, and that their number covaried with TB being progressive or quiescent. As to (b), Koch managed to isolate individual colonies of MTB in pure culture that allowed studying their growth characteristics. As to (c), he inoculated animals with MTB obtained from various origins (induced disease, spontaneous disease, and artificial culture). Koch found that injections led to the formation of tubercles with similar characteristics, and the number of tubercles corresponded to the amount of the inoculum used (Blevins & Bronze, 2010 ).

While Koch’s postulates can be interpreted in various ways (e.g., Broadbent, 2009 ), some have argued that Koch’s experimental distinction of causal from correlational relationships are best captured by the interventionist account of causation (Ross & Woodward, 2016 ). Interventionism posits that causal relationships are those that can be potentially harnessed for manipulation and control: very roughly, if intervening on C reliably leads to changes in E, then C is the cause of E. Woodward ( 2003 ) outlines the necessary and sufficient criteria for establishing causation as follows: C causes E if and only if (i) there is some possible intervention on C such that (ii) were this intervention to occur, there would be an association or correlation between C and E. The account highlights idealized experimental intervention as appropriate for the purposes of determining whether C causes E, as it eliminates possibility of confounding. As the induced change is not correlated with potential confounders, the presence of a correlation between C and E upon intervention on C means that C has a causal influence on E.

Interventionism fits Koch’s postulates, particularly his emphasis on (c), i.e., the induction of disease into a healthy animal by inoculation with bacteria from pure culture. In fact, Koch clearly maintains that determining causality between MTB and TB “can only be decided by inoculating pure bacilli,” thus step (c) (quoted in Ross & Woodward, 2016 , p. 44). Footnote 8 Of course, (b) can be seen as a procedure to ensure that (c) obtains the characteristics of a proper intervention: it excludes the possibility that confounding factors are contained in the inoculated material. Causal claims can only be established if the intervention is associated with a change in the incidence of TB (e.g., its presence, absence, rate of occurrence). In accordance with (M), if the inoculation of substances had not led to the occurrence of disease, Koch would not have identified them as the cause of the disease.

Although the discovery of the causal agent addressed the first shortcoming in Villemin’s research, it alone was insufficient to resolve the second shortcoming concerning the mechanism. However, this is clearly a significant issue, in part because it connects with important questions from a clinical perspective. Without an understanding of the mechanism, questions about what holds together the symptoms of TB, whether certain characteristics (e.g., diarrhea) are parts of TB or caused by TB, how MTB is disseminated to other organs, why most individuals with latent infection do not develop the disease, cannot be answered.

3.2 Twentieth-century discoveries

In the twentieth century, a notable breakthrough came with the identification of the mechanism through which MTB interacts intricately with the host’s immune system, leading to TB. Roughly, when MTB reaches the lungs, it is taken up by macrophages, which are immune cells that engulf and destroy foreign particles. However, MTB is able to survive and replicate within the macrophages, which leads to the formation of granulomas that surround the infected macrophages to contain the infection. MTB is sometimes able to resist destruction and containment, eventually causing the macrophages to burst and release more bacteria into the surrounding tissue. The infected tissue becomes inflamed, leading to the formation of the characteristic lesions, or granulomas, in the lungs and other organs. The granulomas can restrict the infection, leading to a latent TB infection, or they can break down, releasing MTB into the lungs, where it can be coughed up and spread to others (for reviews, see Delogu et al., 2013 ; Yan et al., 2022 ).

The mechanism was elucidated over several decades through the significant contributions of numerous researchers. Therefore, it is challenging to pinpoint exactly when and by which researchers a threshold was crossed, marking a stage at which we may speak of researchers having attained objectual understanding of TB. However, once a mechanistic explanation became available that referenced the configuration and activities of component entities, and identified both the normal functioning of macrophages and how MTB disrupts this process, it seems quite intuitive to say that researchers had achieved a significant level of objectual, biomedical understanding of TB. Researchers have progressed beyond merely explaining various aspects of TB; they have crossed a threshold into systematically, objectually understanding TB .

Of course, while this assertion may seem intuitively appealing, it alone raises a crucial question: what is it about mechanistic explanations that renders them necessary for achieving a significant level of objectual understanding? In what follows, the aim will be to show that mechanistic explanations have enabled achieving a level of coherence and integration, offering clear potential to refine theoretical frameworks and clinical practices, and to facilitate the development of more comprehensive taxonomies and classifications. But before doing so, it is worth emphasizing that a sufficient level of objectual biomedical understanding of TB has been achieved, not merely by grasping the relevant mechanistic explanations, but also by integrating this with other pieces of knowledge and understanding already obtained.

For this, we may start by noting how a mechanistic explanation not only overcomes the second shortcoming observed in the research of Villemin and Koch but also enables new insights that carry profound implications for diagnosis, treatment, and prevention strategies, directly affecting patient care and public health initiatives. This underscores an earlier argument that what constitutes a sufficient level of objectual understanding in medicine is context-sensitive and closely linked to a practical orientation. Let us now review a couple of important implications for research and clinical settings.

First, grasping the relevant mechanistic explanation, researchers were able to chart a much more fine-grained intricate web of counterfactual dependencies, which paves the road towards enhanced intervention possibilities concerning TB. Researchers can formulate new hypotheses around potential interventions, such as enhancing the macrophages’ capability to eradicate MTB or inhibiting MTB’s ability to prevent acidification within macrophages (for a review of current research, see e.g., Bo et al., 2023 ).

Second, comprehending the mechanism significantly enhances the ability to interpret and address a range of clinically relevant issues. It provides a unified view of TB, clarifying how its various elements are interrelated, and explaining how seemingly disparate symptoms are interconnected through a common cause. This comprehensive insight into the relationships between TB symptoms and the disease process improves diagnostic accuracy and aids in refining diagnostic criteria. It enables healthcare providers to more effectively differentiate TB from other conditions with similar symptoms, thereby reducing the risk of misdiagnosis. Moreover, this understanding is crucial in explaining why some individuals with latent TB infections do not progress to active disease, a key factor in managing public health risks.

Overall, comprehending the mechanism of TB has facilitated a significant milestone, crossing a threshold into what we may describe as an objectual, biomedical understanding of TB. This had key implications for identifying new treatment targets, enhancing diagnostic methods, and deepening our knowledge of disease transmission and resistance mechanisms—all of which are vital for improving clinical interventions and formulating effective public health strategies. Crossing this threshold is an important milestone, but it is entirely consistent with recognizing that further exploration and deeper understanding may still be necessary. It does not in any way imply that researchers have reached a final stage in their inquiry that would conclude investigation into TB. Indeed, as researcher recognize, many questions remain (for a recent review, see e.g., Bloom, 2023 ; WHO, 2023 ), driving increasingly detailed and nuanced insights to continuously refine existing approaches to treatment and prevention.

4 Concluding remarks

In light of the recent calls to reexamine the foundational aims of medicine, both in research and clinical practice, this paper emphasizes the importance of understanding as a unifying aim in these domains. As underscored by recent editorials cited in the introduction, there is an imperative to revisit not only the practical aims that medicine seeks, but also its epistemic aims. This is particularly salient in a time when the very essence of what constitutes medical science and clinical medicine is under scrutiny. Accordingly, this paper concentrated on the relevant epistemic aims. By exploring different forms of understanding, the paper uses TB as a focal point to argue that a grasp of mechanistic explanations is crucial for reaching a threshold of understanding at which we may speak of an objectual understanding of TB.

An important limitation of this paper is its focus on a single case: TB. Consequently, there are notable constraints on the breadth of conclusions that can be drawn. However, there are at least some reasons to believe that the findings may have broader applicability. One such reason is that an earlier study on noncommunicable diseases (Varga, 2023 ) have reached similar conclusion. That study revealed that in the case of scurvy, a mechanistic explanation of the condition is necessary for biomedical understanding, but this is not sufficient for understanding in a clinical setting. This earlier study, which examined an emblematic noncommunicable disease, reached a similar conclusion to the current study that focuses on a representative communicable disease. This suggests a potential pattern across various contexts of biomedical research. That said, additional research is required to reinforce this point by investigating whether these conclusions are applicable across a wide spectrum of diseases, including those that are rarer and less prominent. Additionally, it is worth noting that this might differ significantly for conditions where mechanistic explanations have proven challenging to establish. Mental disorders could serve as critical test cases to explore the applicability of our findings in contexts where the underlying mechanisms are less understood.

Interestingly, in an editorial published by the British Medical Journal (Marshall et al., 2018 ) the editors prompt a similar reflection on the purpose of clinical medicine. They challenge the prevailing emphasis on disease-centric care and encourage contemplation of whether a holistic therapeutic relationship with patients might better align with the true aim of medical practice. Though published separately, these editorials collectively highlight a growing movement towards a critical reevaluation of the aims and priorities of both medical science and clinical medicine. The question has sparked considerable interest, with various competing accounts proposing that there is a single, overarching aim (e.g., Broadbent, 2019 ) whereas others suggesting that medicine has multiple aims (e.g., Boorse, 2016 ; Brody & Miller, 1998 ; Schramme, 2017 ).

Munthe ( 2008 ) advocates for an integrated, multidimensional model, highlighting that recent decades have seen the introduction of new objectives focusing on autonomy and equality.

See Bird ( 2019 ) for a helpful discussion of an example from physics.

Other accounts maintain that progress in science occurs when theories come nearer to the truth or when it accumulates solutions to scientific puzzles that are neutral about questions of truth. For a critical review, see Bird ( 2007 ).

Practical understanding (“understanding-how”) typically involves skillful behaviors, relies often on non-propositional knowledge, and is neither explanatory nor susceptible to Gettier-style objections (Bengson, 2017 ). For example, a person may lack the resources to explain the workings of a device but may understand how the device works by way of her skill to adeptly use it.

A mechanism is typically defined as “a structure performing a function in virtue of its component parts, component operations, and their organization” (Bechtel & Abrahamsen, 2005 , p. 423).

For his research, Koch earned the Nobel Prize in 1905.

It makes sense to think that had Koch adhered to a view of causation as merely regularities involving necessary and sufficient conditions that could be discerned through observation, he would not have emphasized (c).

Ban, T. A. (2006). The role of serendipity in drug discovery. Dialogues in Clinical Neuroscience , 8:3 , 335–344.

Article   Google Scholar  

Barnes, D. S. (2000). Historical perspectives on the etiology of tuberculosis. Microbes and Infection , 2 (4), 431–440.

Baumberger, C., Beisbart, C., & Brun, G. (2017). What is understanding? An overview of recent debates in epistemology and philosophy of science. In Grimm, S., Bamberger, C, and Ammon, S. (Ed.). (2017). Explaining understanding: New perspectives from epistemology and philosophy of science London: Routledge. 1–34.

Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences , 36 (2), 4214–4241.

Bengson, J. (2017). The unity of understanding. In S. Grimm (Ed.), Making sense of the world: New essays on the philosophy of understanding (pp. 14–53). Oxford University Press.

Bird, A. (2007). What Is Scientific Progress? Nous , 41(1), 64–89.

Bird, A. (2019). The aim of belief and the aim of science. Theoria: An International Journal for Theory History and Foundations of Science , 34 (2), 1711–1793.

Google Scholar  

Blevins, S. M., & Bronze, M. S. (2010). Robert Koch and the ‘golden age’ of bacteriology. International Journal of Infectious Diseases , 14 (9), e744–e751.

Bloom, B. R. (2023). A half-century of research on tuberculosis: Successes and challenges. Journal of Experimental Medicine , 220 (9), e20230859.

Bo, H., Moure, U. A. E., Yang, Y., Pan, J., Li, L., Wang, M., & Cui, H. (2023). Mycobacterium tuberculosis-macrophage interaction: Molecular updates. Frontiers in Cellular and Infection Microbiology , 13 , 1062963.

Boorse, C. (2016). Goals of Medicine. In É. Giroux (Ed.), Naturalism in the Philosophy of Health (pp. 145–177). Springer International Publishing Carter and Gordon 2014).

Broadbent, A. (2009). Causation and models of disease in epidemiology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences , 40 (4), 302–311.

Broadbent, A. (2019). Philosophy of medicine . Oxford University Press.

Brody, H., & Miller, F. G. (1998). The internal morality of medicine: Explication and application to managed care. The Journal of Medicine and Philosophy , 23 (4), 384–410.

Bynum, H. (2012). Spitting blood: The history of tuberculosis . OUP Oxford.

Cambau, E., & Drancourt, M. (2014). Steps towards the discovery of Mycobacterium tuberculosis by Robert Koch, 1882. Clinical Microbiology and Infection , 20 (3), 196–201.

Craver, C. F. (2013). Functions and mechanisms: A perspectivalist view. Functions: Selection and mechanisms (pp. 133–158). Springer Netherlands.

Daniel, T. M. (2006). The history of tuberculosis. Respiratory Medicine , 100 (11), 1862–1870.

Daniel, T. M., Bates, J. H., & Downes, K. A. (1994). History of tuberculosis. In T. M. Daniel (Ed.), Tuberculosis: Pathogenesis, Protection, and control (pp. 13–24). American Society for Microbiology.

Darrason, M. (2018). Mechanistic and topological explanations in medicine: The case of medical genetics and network medicine. Synthese , 195 (1), 147–173.

De Regt, H. W. (2009). The epistemic value of understanding. Philosophy of Science , 76 (5), 585–597.

De Regt, H. W. (2017). Understanding scientific understanding . Oxford University Press.

De Regt, H. W., & Dieks, D. (2005). A contextual approach to scientific understanding. Synthese , 144 (1), 137–170.

De Regt, H. W., Leonelli, S., & Eigner, K. (2009). Focusing on scientific understanding. In, De Regt, H. W., Leonelli, S., and Eigner, K. (2009) Scientific understanding: Philosophical perspectives , Pittsburgh: University of Pittsburgh Press. 1–17.

Dellsén, F. (2016). Scientific progress: Knowledge versus understanding. Studies in History and Philosophy of Science Part A , 56 , 72–83.

Delogu, G., Sali, M., & Fadda, G. (2013). The biology of mycobacterium tuberculosis infection. Mediterranean Journal of Hematology and Infectious Diseases , 5 (1).

Doetsch, R. N. (1978). Benjamin Marten and his new theory of consumptions. Microbiological Reviews , 42 (3), 521–528.

Dupré, J. (2016). Towarda political philosophy of science. In M. Couch (Ed.), The philosophy of Philip Kitcher . Oxford University Press.

Elgin, C. (2007). Understanding and the facts. Philosophical Studies , 132 (1), 334–332.

Elgin, C. (2009a). Is understanding factive? In A. Haddock, A. Millar, & D. Pritchard (Eds.), Epistemic value (pp. 3223–3230). Oxford University Press.

Elgin, C. (2009b). Exemplification, idealization, and understanding. In M. Suárez (Ed.), Fictions in Science: Essays on idealization and modelling (pp. 779–770). Routledge.

Elgin, C. Z. (2017). True enough . MIT Press.

Ereshefsky, M. (2009). Defining ‘health’ and ‘disease’. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences , 40 (3), 221–227.

Glennan, S., Illari, P., & Weber, E. (2021). Six theses on mechanisms and mechanistic science. Journal for General Philosophy of Science , 1–19.

Grimm, S. (2011). Understanding In S. Bernecker and D. Pritchard (Eds.), The Routledge Companion to Epistemology. New York: Routledge.

Grimm, S. (2014). Understanding as knowledge of causes. In A. Fairweather (Ed.), Virtue Epistemology Naturalized: Bridges between Virtue Epistemology and Philosophy of Science . Springer.

Grimm, S. (2021). Understanding, The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N. Zalta (Ed.), https://plato.stanford.edu/archives/sum2021/entries/understanding/ .

Hannon, M. (2019). What’s the point of knowledge? A function-first Epistemology . Oxford University Press.

Hannon, M. (2021). Recent work in the epistemology of understanding. American Philosophical Quarterly , 58 (3), 269–290.

Hempel, C. G., & Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science , 15 (2), 1351–1375.

Illari, P., & McKay (2011). Disambiguating the russo–Williamson Thesis. International Studies in the Philosophy of Science , 25 , 139–157.

Kapur, V., Whittam, T. S., & Musser, J. M. (1994). Is Mycobacterium tuberculosis 15000 years old? Journal of Infectious Diseases , 170 , 1348–1349.

Kelp, C. (2021). Inquiry, knowledge and understanding. Synthese , 198 (7), 1583–1593.

Kendler, K. S., Zachar, P., & Craver, C. (2011). What kinds of things are Psychiatric disorders? Psychological Medicine , 41 (6), 1143–1115.

Keshavjee, S., & Farmer, P. E. (2012). Tuberculosis, drug resistance, and the history of modern medicine. New England Journal of Medicine , 367 (10), 931–936.

Khalifa, K. (2013). The role of explanation in understanding. The British Journal for the Philosophy of Science , 64 (1), 161–187.

Kitcher, P. (2001). Science, truth, and democracy . Oxford University Press.

Kvanvig, J. L. (2003). The value of knowledge and the pursuit of understanding . Cambridge University Press.

Kvanvig, J. (2009). The value of understanding. In A. Haddock, A. Millar, & D. Pritchard (Eds.), Epistemic value (pp. 95–111). Oxford University Press.

Kvanvig, J. (2013). Curiosity and the response-dependent special value of understanding. Knowledge, virtue and action: Putting epistemic virtues to work, 151–174.

Lipton, P. (2001). What good is an explanation? In G. Hon, & S. S. Rakover (Eds.), Explanation: Theoretical approaches and applications (pp. 53–59). Springer Science and Business Media.

Lipton, P. (2004). Inference to the best explanation . Routledge.

Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science , 67 (1), 12–15.

Marshall, M., Cornwell, J., & Collins, A. (2018). Rethinking medicine. bmj , 363 .

Munthe, C. (2008). The goals of publichealth: An integrated, multidimensional model. Public Health Ethics , 1 (1), 39–52.

Pennock, R. T. (2019). An instinct for truth: Curiosity and the moral character of science . MIT Press.

Potochnik, A. (2015). The diverse aims of science. Studies in History and Philosophy of Science Part A , 53 , 71–80.

Potochnik, A. (2017). Idealization and the aims of Science . University of Chicago Press.

Pritchard, D. (2010a). Knowledge and understanding., in Pritchard, D., Millar, A., and Haddock, A. (2010), The Nature and Value of. Knowledge: Three Investigations. Oxford: Oxford University Press

Ross, L. N., & Woodward, J. F. (2016). Koch’s postulates: An interventionist perspective. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences , 59 , 35–46.

Russo, F., & Williamson, J. (2007). Interpreting causality in the Health sciences. International Studies in the Philosophy of Science , 21 , 157–170.

Salmon, W. C. (1984). Scientific explanation and the causal structure of the world . Princeton University Press.

Schramme, T. (2017). Goals of Medicine. In T. Schramme, & S. Edwards (Eds.), Handbook of the philosophy of Medicine (pp. 121–128). Springer Netherlands.

Steel, D. (2008). Across the boundaries, Extrapolation in Biology and Social Science . Oxford University Press.

Strevens, M. (2010). Varieties of Understanding. In: Pacific Division meeting of the American Philosophical Association , San Francisco, CA, March (Vol. 31).

Strevens, M. (2017). How idealizations provide understanding. In S. R. Grimm (Ed.), Explaining understanding: New perspectives from epistemology and philosophy of science . Routledge, Taylor and Francis Group.

Thagard, P. (2005). What is a medical theory? Studies in Multidisciplinarity , 3 , 476–472.

The Lancet. (2013). What is the purpose of medical research? The Lancet , 381 (9864), 347.

Thornton, H. (2013). We need to askwhat is the purpose of research? BMJ , 347 .

Trout, J. D. (2002). Scientific explanation and the sense of understanding. Philosophy of Science , 69 (2), 2122–2133.

Varga, S. (2023). Understanding in Medicine. Erkenntnis . https://doi.org/10.1007/s10670-023-00665-8 .

Varga, S. (2024). Science, Medicine, and the aims of Inquiry: A philosophical analysis . Cambridge University Press.

Villemin, D. J. (1868/2015). On the virulence and specificity of tuberculosis. The International Journal of Tuberculosis and Lung Disease , 19 (3), 256–266.

White, F., Stallones, L., & Last, J. (2013). History, aims, and Methods of Public Health. In F. White, L. Stallones, & J. M. Last (Eds.), Global public health: Ecological foundations . Oxford University Press.

WHO. (2023). Global tuberculosis report 2023 . World Health Organization.

Williamson, J. (2019). Establishing Causal claims in Medicine. International Studies in the Philosophy of Science , 32 (1), 33–61.

Woodward, J. (2003). Making things happen: A theory of causal explanation . Oxford University Press.

Woodward, J. (2010). Causation in Biology: Stability, specificity, and the choice of levels of explanation. Biology and Philosophy , 25 (3), 287–318.

Yan, W., Zheng, Y., Dou, C., Zhang, G., Arnaout, T., & Cheng, W. (2022). The pathogenic mechanism of Mycobacterium tuberculosis: Implication for new drug development. Molecular Biomedicine , 3 (1), 48.

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Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

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July 10, 2024

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Researchers discover a new form of scientific fraud: Uncovering 'sneaked references'

by Lonni Besançon and Guillaume Cabanac, The Conversation

A researcher working alone—apart from the world and the rest of the wider scientific community—is a classic yet misguided image. Research is, in reality, built on continuous exchange within the scientific community: First you understand the work of others, and then you share your findings.

Reading and writing articles published in academic journals and presented at conferences is a central part of being a researcher. When researchers write a scholarly article, they must cite the work of peers to provide context, detail sources of inspiration and explain differences in approaches and results. A positive citation by other researchers is a key measure of visibility for a researcher's own work.

But what happens when this citation system is manipulated? A recent Journal of the Association for Information Science and Technology article by our team of academic sleuths—which includes information scientists, a computer scientist and a mathematician—has revealed an insidious method to artificially inflate citation counts through metadata manipulations: sneaked references.

Hidden manipulation

People are becoming more aware of scientific publications and how they work, including their potential flaws. Just last year more than 10,000 scientific articles were retracted . The issues around citation gaming and the harm it causes the scientific community, including damaging its credibility, are well documented.

Citations of scientific work abide by a standardized referencing system: Each reference explicitly mentions at least the title, authors' names, publication year, journal or conference name, and page numbers of the cited publication. These details are stored as metadata, not visible in the article's text directly, but assigned to a digital object identifier, or DOI—a unique identifier for each scientific publication.

References in a scientific publication allow authors to justify methodological choices or present the results of past studies, highlighting the iterative and collaborative nature of science.

However, we found through a chance encounter that some unscrupulous actors have added extra references, invisible in the text but present in the articles' metadata, when they submitted the articles to scientific databases. The result? Citation counts for certain researchers or journals have skyrocketed, even though these references were not cited by the authors in their articles.

Chance discovery

The investigation began when Guillaume Cabanac, a professor at the University of Toulouse, wrote a post on PubPeer , a website dedicated to post-publication peer review, in which scientists discuss and analyze publications. In the post, he detailed how he had noticed an inconsistency: a Hindawi journal article that he suspected was fraudulent because it contained awkward phrases had far more citations than downloads, which is very unusual.

The post caught the attention of several sleuths who are now the authors of the JASIST article . We used a scientific search engine to look for articles citing the initial article. Google Scholar found none, but Crossref and Dimensions did find references. The difference? Google Scholar is likely to mostly rely on the article's main text to extract the references appearing in the bibliography section, whereas Crossref and Dimensions use metadata provided by publishers.

A new type of fraud

To understand the extent of the manipulation, we examined three scientific journals that were published by the Technoscience Academy, the publisher responsible for the articles that contained questionable citations.

Our investigation consisted of three steps:

• We listed the references explicitly present in the HTML or PDF versions of an article.
• We compared these lists with the metadata recorded by Crossref, discovering extra references added in the metadata but not appearing in the articles.
• We checked Dimensions, a bibliometric platform that uses Crossref as a metadata source, finding further inconsistencies.

In the journals published by Technoscience Academy, at least 9% of recorded references were "sneaked references." These additional references were only in the metadata, distorting citation counts and giving certain authors an unfair advantage. Some legitimate references were also lost, meaning they were not present in the metadata.

In addition, when analyzing the sneaked references, we found that they highly benefited some researchers. For example, a single researcher who was associated with Technoscience Academy benefited from more than 3,000 additional illegitimate citations. Some journals from the same publisher benefited from a couple hundred additional sneaked citations.

We wanted our results to be externally validated, so we posted our study as a preprint , informed both Crossref and Dimensions of our findings and gave them a link to the preprinted investigation. Dimensions acknowledged the illegitimate citations and confirmed that their database reflects Crossref's data. Crossref also confirmed the extra references in Retraction Watch and highlighted that this was the first time that it had been notified of such a problem in its database. The publisher, based on Crossref's investigation, has taken action to fix the problem.

Implications and potential solutions

Why is this discovery important? Citation counts heavily influence research funding, academic promotions and institutional rankings. Manipulating citations can lead to unjust decisions based on false data. More worryingly, this discovery raises questions about the integrity of scientific impact measurement systems, a concern that has been highlighted by researchers for years. These systems can be manipulated to foster unhealthy competition among researchers, tempting them to take shortcuts to publish faster or achieve more citations.

To combat this practice we suggest several measures:

• Rigorous verification of metadata by publishers and agencies like Crossref.
• Independent audits to ensure data reliability.
• Increased transparency in managing references and citations.

This study is the first, to our knowledge, to report a manipulation of metadata. It also discusses the impact this may have on the evaluation of researchers. The study highlights, yet again, that the overreliance on metrics to evaluate researchers, their work and their impact may be inherently flawed and wrong.

Such overreliance is likely to promote questionable research practices, including hypothesizing after the results are known, or HARKing ; splitting a single set of data into several papers, known as salami slicing; data manipulation; and plagiarism. It also hinders the transparency that is key to more robust and efficient research. Although the problematic citation metadata and sneaked references have now been apparently fixed, the corrections may have, as is often the case with scientific corrections , happened too late.

Provided by The Conversation

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