Developing an interesting research question relies on understanding what is a research question, its attributes, and the approaches you can take to generate good ones. This requires a bit of philosophical work on the nature and intents of research programs. It is because research is too broad and varied an enterprise to pigeonhole into the lens of a single discipline or approach.
Research is conducted in many ways by various disciplines and schools of thought. Hence, research questions can be posed and answered in various ways. There are research questions that require answers through empirical observations (e.g., medical research like clinical testing). Others can only be answered by following abstract trains of thought (e.g., pure mathematics and logic). There are those that require a good deal of both (e.g., physics and archaeology). And, there are also types of research that only require introspection and first-hand experience (i.e., phenomenological research).
Thus, a substantial explanation as to what a research question is, cannot come in the form of a simple one-liner. So, this document will serve not only as a guide on how to find potential research questions for a journal, conference, or even a class paper. It will also offer a cursory discussion of the nature and intent of research questions. Moreover, some approaches you can take in finding knowledge gaps and research topics, applicable to different academic and industrial research fields, will be provided. And, to put more flesh into the abstract discussion, famous examples will be given.
Put simply, a research question is a question that a researcher wants to answer. Operationally, research questions are the main basis for how a research paper is made. As such, there are many types of research questions, depending on the field of study, accepted methodologies, and topics of interest you are dealing with. Research questions are also posed to fill in gaps in knowledge for a particular discipline or industry.
The knowledge gaps research questions seek to bridge generally fall into two main categories: (i) theoretical and (ii) empirical. This is because most disciplines, especially the sciences, employ both theories and empirical observations to build their body of knowledge. In addition, research questions have two main classes of desiderata or intent: (a) pure or basic research and (b) applied research. Understanding these concepts will help us grasp the nature and intent of research questions better.
Theoretical research is described as “a logical exploration of a system of beliefs and assumptions” about a system’s behavior and its implications (Edgar & Manz, 2017). It is mainly done in logical space or “the set of distinctions amongst ways for the world to be (Rayo, 2013)” or simply the space of possibilities. Theories are abstractly constructed to explain and predict phenomena or abstract research objects.
The term “theory” in science is used differently than in ordinary English. While in ordinary usage, “theory” is being used to mean rank speculation or a disputable hypothesis, among scientists, it is often used to describe an established subdiscipline with widely accepted laws, methods, foundations, and applications. It is a body of explanatory hypotheses with strong empirical support (Rosenberg, 2005).
So, crafting theoretical research questions is a delicate matter that often requires deep knowledge and understanding of the area of research.
On the other hand, empirical research is not solely conducted in logical space. These are done through experiments and observations often mediated by instruments. And, creating empirical research questions can be quite complex as experimentalists would need to pay much mind to methodologies, measures, and the calibration of their particular instruments.
In both basic and applied research, theoretical and empirical research questions play important roles in building knowledge or finding practical applications for research knowledge.
Scientific research in its purest form employs both theory and empirical observation to form conceptual architectures used by an entire discipline, or a research program within it, to understand their research objects. However, research is not done solely with pure understanding in mind. Research questions are also posed to how knowledge and technology can be applied. This brings us to two main kinds of research considering intentions: (a) basic research and (b) applied research.
The Organization for Economic Cooperative Development (OECD) defines basic research as an “experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view” (OECD, 2015). It is research for research’s sake.
Many seminal works in the sciences, especially mathematics, would fall into this classification. For instance, Andrew Wiles’ proof of Fermat’s Last theorem and the Banach-Tarski Paradox virtually does not have any practical use. However, many basic research works that initially did not have real-world applications turned out to have some. One example is group theory in mathematics that started as an abstract investigation. Later, researchers found applications in physics (Dresselhaus & Jorio, 2007) and music (Zhang, 2009).
Applied research, on the other hand, is defined as an investigation to acquire new knowledge “directed towards a specific practical aim or objective” (OECD, 2015). Basically, it is trying to find practical applications for research knowledge. Moreover, applied research is much related to what OECD calls “experimental development” in R&D. This class of research “draws from knowledge gained from research and practical experience and producing additional knowledge, which is directed at producing new products or processes or to improving existing products or processes (OECD, 2015).” And, products here mean actual goods and services.
The research works of engineers, marketers, medical researchers, and designers largely fall into these related classes. These types of research efforts produce knowledge and design processes or things that can make life better for many people.
Thus, from these main desired outcomes of research, we know what research is for. One can also infer how research questions are being valued. These can be summarized by the two following points: (a) the value of basic research is derived from how it helps research communities understand the world or research objects better, and (b) the value of applied research is derived from actual and perceived practical utility gained by its target beneficiaries.
As discussed in the previous section, good research bridges the gaps (a) in understanding and/or (b) practical efficiency. And, asking good research questions is central to bridging these knowledge gaps. In this section, we will expound on what makes research questions good. Below are the main attributes that make research questions useful. The rationale of why these are so will be discussed after.
And, particularly for applied research and experimental developments in industries:
Progress in research entails novelty. In basic research, it is thought that a research program is “theoretically progressive” when theoretical modifications generate novel predictions. It is considered to be “empirical progressive” if some novel predictions are corroborated by empirical observations. Failing to do so would make it degenerative (Lakatos, 1978).
Also, the novelty of research questions and answers is not valued for novelty’s sake. Novelty is valued because it drives research programs forward. It is the questions that researchers ask that become the fuel for more research. And, good research questions generate more research questions. They do not only help in building research knowledge, they also keep research programs alive.
Novelty, in these ways, is considered by many to be a standard factor in how relevant or “how good” a research question is.
This, though, begs the question of “to what or to whom should it be relevant to?”
The quick answer here, in everyday terms, is your target audience. And, when it comes to research, the usual audiences of research papers are those who are part of a research community, industry stakeholders, and policymakers. Thus, the relevancy of your research question depends much on the specific research interests of your target community. This is also why research questions need to be specific as well.
Good research questions are not only specific in terms of which issues they wish to shed light upon. These should also be specific in form or by sentence construction. And, this is helpful for at least three reasons.
Firstly, by structuring research questions to be specific, you can dissect and define concepts or physical research objects easier. Secondly, it helps you map out relationships like correlation, precedence, succession, and infer causation, among others. Thirdly, it makes it easier for you to differentiate your research from previous ones. Let us illustrate through an example.
Consider this simple example of a not-so-good research question: “Is anthropogenic climate change real?”
Why is it not so good? Right off the bat, this question can be answered by a yes or no question. It is too broad and not specific enough. Also, many researchers have already touched on the subject. Hence, it is also not a novel question.
One way to make it specific is by limiting the scope and making it measurable. An improved version could be: “Is there a correlation between the number of building constructions and the frequency of hurricanes forming in the US Atlantic Coast from 1990 to 2020?”
This version does not only puts limits on the geographical scope but also on the timeframe. It dissects which phenomena you wish to explore and gives you an idea of what kind of data you need. Moreover, it helps you map out the relationship between two sets of data. And, with this more specific version, it is easier to know whether there are identical research questions in research databases or Google Scholar.
Applied Research: More and Better Practical Applications
In applied research, however, the metrics for progress are much more lenient than in basic research in some sense. Practical utility and pragmatism hold significant sway. And, in many cases, the construction of models for truth and reality only act as handmaidens for target utility. Space flight is a good example.
Theoretically speaking, the classical view of force was already superseded by Einstein’s theory (see Stinner, 1994). But, this does not stop Newtonian mechanics from being useful. In fact, it is still useful enough to send rockets to space (NASA, 2017). This is even though it is a less accurate model of reality.
So, even though pure research in physics veered away from Newton’s program, classical mechanics is still very much alive in applied research. Hence, in applied research practice, the perceived and actual utility of research works may outweigh the quest for objective understanding. The practical utility of knowledge is thus a major measure for the relevance of applied research. And, therefore by extension, it is an attribute of a good applied research question.
Research does not exist in a vacuum. And, as great scientific minds like Thomas Kuhn, Karl Popper, and Imre Lakatos have noted in their careers, research is largely a communal activity with different traditions of going about it. So, for a researcher to come up with good research topics and questions, he or she must be attuned to the pulse of research communities and interested industries. Moreover, the researcher must be good at finding the research gaps. Below, a few tips and examples on how to achieve these are listed.
Tip # 1: Discuss with your supervisor, professors, and research colleagues for inspiring ideas to be turned into questions.
The first thing you should try is to tap into your own community. You can discuss topics that interest you with your supervisor or professors. This way, they may be able to share their knowledge, research papers, books, and even research contacts. They may even offer you topics and help you come up with research questions. Tap into their experience and stored knowledge.
Another thing you can do is to discuss research topics with your colleagues. They may have inspiring ideas or you can come up with great ideas together. In many research ventures, more heads can be better than one.
Discussions and debates are a good way to get an idea of what the zeitgeist is within your research community, industry, or discipline. So, it is also best to join conferences, attend talks, and be on social networks and connect to other people. This way, you will have a better idea of what your research community is interested in. So, start with your immediate community first.
Tip #2: Review state of the art literature
You can only find relevant research topics and questions when you are privy to what the current and relevant research topics and questions are in your field. And, the best way to look for them is through recent and landmark research literature.
To look for recent impactful literature, you can check out an author’s h-index on sites like Google Scholar and Scopus. The h-index measures both citation impact and the productivity of a researcher. The higher the number, the more likely it is to be impactful to other researchers in their fields. Moreover, you can also take a look at their publications and see which ones are being cited and talked about in the community.
And, thanks to research index sites, you can even see where these publications have been cited. In them, you can see issues, talking points, and directions for further research that can be a good topic to write about.
Tip #3: Read recent surveys/review papers
Furthermore, reading review papers, progress reports, responses, and rebuttals can give you a good idea of the context and the specifics of possible topics to write about and research questions to pose. In many fields in the sciences and the humanities, there are differing views when it comes to theory, interpretation of data, and the accuracy of methodologies. And, you can find these talking points and possible research topics in survey and review papers.
Survey and review papers are usually written by scholars to summarize the current view and understanding of a field with regards to a certain topic. It can be about the state of a theory, a methodology, a school of thought, a research program, or an entire disciplinary field. It is a great source of researches to follow and books or papers to read.
Moreover, these are excellent sources to find gaps in knowledge that can take many different forms. This will be the topic of our next subsection.
Now that you have an idea of how to tap into the current state of research, you need to develop the nose to find knowledge gaps. This is essential to finding interesting topics to formulate good research questions around. And, as mentioned, gaps in knowledge can come in different forms. A good way to identify them is to use the categories of research questions and the classes of research desiderata mentioned above: (1) theoretical and (2) empirical research questions; and (a) basic research and (b) applied research, respectively.
This kind of gap in knowledge can usually be found in theory-building work. However, it does not only apply to basic research but also to applied research. Basically, there is an explanatory gap in a theory when it fails to take into account or predict phenomena within its domain of interest. Here is an example.
In the 19th century, the then-current model of the solar system could not predict the orbit of Uranus accurately. The planet was falling behind its predicted position and George Airy, Britain’s Astronomer Royal, referred to the errors as “increasing with fearful rapidity” (Smart, 1946). This was not only the most puzzling problem in astronomy. It also put doubts on the popularly-favored Newtonian celestial mechanics, the prevailing theory at that time.
However, the pulse of the research community then was not to reject Newtonian celestial mechanics outright (Cleland & Brindel, 2013). So, they offered other hypotheses to be included in the theory. There were those that suggested that it had been struck by a comet before its discovery in 1781. Others hypothesized that there was an undiscovered planet affecting its orbit (Smart, 1946). These efforts then, including the celebrated mathematical or armchair prediction of Neptune by Urbain Le Verrier and John Couch Adams, tried to “rescue” or correct the prevailing theory then (they turned out to be right).
Put simply, if you work on explanatory gaps, you work on extending the explanatory power of a theory by modifying aspects, parameters, and even taking out or adding in new hypotheses (see, for instance, Westphal & Khanna, 2003). Often, you will see explicit gaps by reviewing the state of the art literature. Many times, other scholars will point them out, especially ones that publish critiques of a particular theory, hypothesis, or point of view.
Moreover, it is good to note that extending the explanatory power of a theory does not only mean accounting for anomalies. It could also mean seeing if a theory can also help explain phenomena that are usually situated outside their domains of interest. An interesting example of which is quantum biology where quantum mechanics and theoretical chemistry are applied to investigating biological processes like our sense of smell and avian navigation (McFadden & Al-Khalili, 2018).
Empirical gaps come in different forms as well. The main one is when there is no experiment yet to test a prediction or a hypothesis. Going back to our previous example, when Le Verrier and Couch brought their mathematical predictions of the location of Neptune forward, the research community could not accept their claims without empirical evidence. And, this empirical gap in their scientific claims, can only be filled by empirical observation.
In the case of Neptune, the first empirical observation coming after Le Verrier’s and Couch’s predictions came from the Berlin Observatory on September 24, 1946, by Johann Gottfried Galle. This story shows that experimentalists are needed by theoreticians to find empirical pieces of evidence for their predictions. This is because, in most contexts, experiments can be the arbiter of whether a scientific statement is true or not.
Other experimental gaps come in the form of faulty or less accurate experimental designs. As experiments are used to corroborate predictions and claims, it is crucial to get them as close as you can to approximate objective reality. Both experimental designs and instruments play a key role. And, by instruments, it is not only the physical tangible observation tools that you use but also the thinking tools, such as statistical frameworks and other metrics of measurement.
One way researchers look for gaps in empirical knowledge is to look into published experiment results and analyze methodologies and procedures. And, the acid test to know if experiments hold is to replicate them. One example of this approach was the huge replication study done in experimental psychology. The study found that there was a publication bias towards successful results as independent replication of 100 experiments, only 39 attempts were found successful (Baker, 2015). This is an ingenious way of contributing new knowledge to science and it showed problems plaguing the field of experimental psychology (Francis, 2012).
Critiques and analysis of experimental designs and mentions for the total lack of empirical observations can be found in state-of-the-art literature surveys and reviews. One must dive deep into these publications to look for plausibly accurate predictions that still lack empirical evidence and experiments that may have limited scope and less accurate methods.
Experimental Development Gaps
While experimental and explanatory gaps cuts across basic and applied research, gaps in experimental development are special to applied research and experimental development activities. Again, OECD categorizes experimental development as a separate research activity seeking not only to develop practical uses for scientific knowledge but also to generate and improve products and processes. Thus, this includes research activities from commercial purposes to social good efforts.
It covers the creation and development of products and processes from technological devices to social policies. And, this is what most research work in industries and the public sector revolve around. So, experimental design is the domain for those who have the aptitude for improving things or creating new things.
Prime historical examples of this would be the works in the tradition of Thomas Edison and Alexander Graham Bell, the works of inventors. The development of computers from Babbage and Turing to Jobs and Gates falls into this category. And, there still are many gaps. Inventing solutions may add even more.
Hot topics today revolve around social good issues, from climate change to deploying more ethical recommendation algorithms.
Gaps in experimental development can be found in industry reports, progress reports, and various writings outside academia. Sources can range from business journals to articles by journalists covering business, tech, and social policies.
Now that you have an idea of the kinds of knowledge gaps and where you can find them, it is time to discuss a few common approaches on how to deal with them. Keep in mind that any type of research requires the need to determine one of three problems: the outcomes could be enhanced, the current literature has conflicting results, or the evidence is insufficient (Fandino, 2019).
Of course, the suggested approaches listed will be very limited, considering the scope of general academic and industrial research. However, these will more or less hone in on some guiding principles that are ubiquitous in successful researches across various fields.
In the history of science and other academic disciplines, there are times when seemingly disparate concepts, theories, and methodologies have been found to work together well. Basically, research synthesis is the integration of existing research findings and knowledge about an issue. This aims to increase both the generality and applicability of existing findings to develop new knowledge (Wyborn et al., 2018).
Arguably, the greatest landmark achievement using this type of approach was that of Rene Descartes and Pierre de Fermat. Both independently invented analytic geometry, marrying the seemingly then-disparate areas of geometry and algebra to shed more light on the mathematical objects of interest in both areas (Stillwell, 2010).
Another prime example was the marrying of Charles Darwin’s natural selection with Gregor Mendel’s genetics into the Modern Synthesis in evolutionary biology. Two theories shed light on the same research interests while approaching it from two seemingly disparate levels. And, synthesizing theories amidst new experimental discoveries are still quite active in biology. Even to this day, many scholars are working towards a new theoretical synthesis in biology for richer explanatory power (see Pigliucci, 2007).
Of course, these examples are quite grand, and creating a revolutionary synthesis is a monumental task. However, this approach can be used for less conceptually complex, albeit not-so-simple concerns. One example of this approach in applied research would be to figure out the impacts of learning management systems (LMS) on education.
By using existing research findings and knowledge, one may very well create an integrated explanatory account for factors that affect knowledge acquisition via distance learning with the aid of an LMS. You can dip in different disciplines and integrate different points of view and theories. You may very well see that this research topic can be viewed from different lenses involving IT and analytics, psychology, education management, and many others. Thus, an integrative synthesis research approach can be used to form and answer particular questions.
Of course, drawing from different research findings and theories, you should fine-tune your research questions to investigate more specific problems. For instance, you can investigate the level of knowledge acquisition in mathematics and biology for elementary school or those in universities or institutions using LMS. This research topic has good potential for not just a research synthesis approach but also for an interdisciplinary approach.
The research synthesis or integrative approach is best suited to researchers that are good at recognizing similarities and abstracting generalities from various phenomena or abstract research objects.
In the sciences and the humanities, accounts of natural and social phenomena have undergone reconstruction over and over. Take history and archaeology as prime examples. In both disciplines, accounts of past events are often cast and re-casted in new lights, depending on new evidence acquired or novel postulates by scholars.
In fact, creating and reconstructing accounts of the past is central to various disciplines, such as archaeology, paleontology, and history. The first two, and to some extent the last one, even use empirical tools like dating techniques and forensics. Most historians, on the other hand, rely on public records, personal journals, and the works of past historians as well.
Account reconstructions primarily deal with answering specific “what likely could have happened” questions. However, there is another species of account reconstruction that can be an interesting subject as well as an approach to finding research questions. This mostly has to do with answering the questions of “what likely could have been thought” or “how was a concept understood. These questions can be directed at entire civilizations, several populations, a community, or a person.
Examples of this would be the reconstruction of a dead language (see Gamkrelidze & Ivanov, 2010), the reconstruction of the Babylonian sexagesimal number system (see Hodgkin, 2005), the reconstruction of Babbage’s analytical engine (see Markoff, 2011), and creating accounts of what Wittgenstein may have meant by his writings (see Mühlhölzer, 2016). Thus, reconstructing accounts also involve a good deal of interpretation of reinterpretation of the views that figures of academic interests held.
This is also not limited to scholars or phenomena that are the usual domains of far history. It could also pertain to interests in industries. For instance, economists conceptually reconstruct conditions for recessions and sociologists offer alternative accounts for the factors causing existing social problems. Also, this reconstruction of the past is being done by marketers and business analysts to gain insights for future business decisions.
Theory creation is one of, if not the most, valued activities in science. The accumulation of experimental facts and scientific observations are guided by or at least rely on scientific theories. For instance, public opinion surveys rely on the theories in probability and statistics and radio spectrometry used for cosmological observations relies on electromagnetic theory, among others.
Theory-making is ubiquitous in virtually all academic fields. Also, historically, theoretical work is the fecund birthplace of entire academic disciplines and even social movements. Work in theory creation and development does not stop. It may go on gradually or even in a quicker revolutionary way (Kuhn, 2012). Thus, it is also a fertile ground for many research questions.
One current example of theoretical work that has swept through different research disciplines spanning from cognitive science and philosophy of mind to mathematical psychology to neuroscience is Karl Friston’s free energy principle (FEP) or active inference. A synthesis of ideas and approaches that drew influence from physics to psychiatry, this theory, in simple terms, purports that living organisms keep themselves within survival range by changing internal parameters and actively sampling the environment by minimizing a quantity called free energy. Or, agents maximize their value by minimizing prediction error (Friston & Stephan, 2007). Its applications range from single neurons to an entire organism and even to a whole species. And, its formal models draw from the mathematics of machine learning, information theory, and game theory, among others. It has truly become an interdisciplinary interest with collaborators coming from different fields.
Other disciplines have very lively theory-making activities akin to the burgeoning FEP research program. And again, these kinds of lively theory-making activities can be identified by reviewing state-of-the-art literature, talking to colleagues, and literature survey papers. Also, as they are seen to be at the edge of their fields, community interests are at their peaks and fellow researchers are open to consultation and collaboration.
Work in theory development can come in different forms. Some researchers act as proponents of views. Others offer alternative views within the larger theory or research program. A researcher can also play the role of the devil’s advocate who tries to limit the scope of the theory’s explanatory domain. Others refute particular aspects of the theory, the theory as a whole, and even the research program as a whole. This can be done through theoretical arguments or by bringing to fore empirical observations of anomalies or phenomena that contradict a theory’s predictions.
These activities are pretty much the norm in theoretical work especially in a theory’s development stages within research programs. And, this, as mentioned, is always an ongoing enterprise within different disciplines and industries. Thus, theory development is a fertile place for developing research questions of your own.
Most examples mentioned here are landmark events in science. Not everyone can make revolutionary predictions, observations, or develop extraordinary products. However, everyone that made a discovery or has contributed useful research knowledge has started from somewhere. And, that somewhere is being connected to the loose network of individuals who think about and investigate similar things. They connect to the research or business community and build from shared cumulative knowledge.
Thus, it is best to follow in the footsteps of Isaac Newton and stand on the shoulders of giants so we could see a little further. Meaning, we should take the pulse of our target communities, understand how other researchers think of research objects in their domains of interests, and ask good research questions that can create useful knowledge further. This is through generating novel insights, theories, experiments, measurement methods, and even products and services. Remember, it is in the creation of useful novel knowledge and things that move the wheels of progress.
Also, as discussed in this article, novel things should not be necessarily totally about new things. They can be novel takes on research interests of the past. Just make sure that your novel contribution is relevant to the concerns of your target community, be ït a research program, your company, a market, your city, or your nation.
Hopefully, you have a better idea of (1) the nature of research questions and its intents, (2) the attributes of a good research question, (3) how to take the pulse of research or industry communities, and (4) some approaches to find good research questions.