How to write good research questions: What even is a research question?

J Kasmire

Dr J. Kasmire, who works at the UK Data Service and the Cathie Marsh Institute at the University of Manchester, discusses what research questions are, and what makes a good one. 

Naturally, the first thing to say is that a research question is the question that a piece of research has answered or is expected to answer. It may seem obvious, but many researchers are confronted by a need to be sure that their partially complete research matches up with a specific question. I know this because every time I run a workshop on how to write good research questions audience members lament that they wish they had known all of this before they were asked to write their PhD research questions.

The confusion they, and undoubtably many others, experience may derive from the way published work tends to focus on the methods, results and conclusions more than motivating questions. Even when the motivation is included, there is not always a question, leaving readers to intuit the question from what was done. Readers thus find themselves in the position of the hyper-intelligent pan-dimensional beings that appear as mice in the Hitchhiker’s Guide to the Galaxy; they have the answer to life, the universe and everything (the answer is 42) but they don’t know the question so are left to conclude that “How many roads must a man walk down?” is probably good enough.

In fact, not having a good research question in advance of doing the research probably happens fairly often. In addition to the frustrated attendees at my workshops, history suggests that many important discoveries resulted from non-questions such as “Huh. That’s funny.” or vague questions like “What happens if I do this?”. Successful as such answer-lead research may be, I still recommend spending some time thinking about the research question.

I also encourage you to think about good research questions that are clear, focussed, concise and, if possible, also novel, arguable, objective and appropriate

  • Clear means specifying all the necessary details and context for the target audience to understand. Clear questions bring the audience along and inspires responses like “why did you choose that?” or “what do you expect?” rather than alienating them and prompting responses such as “what does that word mean?” or “how could you possibly answer that?”.
  • Focus is about asking the right size question for the time, resources and data available. Researchers may aspire to big questions with big implications in the course of their career, but the specific piece of research (e.g. their PhD thesis, a conference presentation, a chapter in a collected work, etc.) should ask focussed questions that match the scope at hand.
  • Concise means using the fewest words needed in context. Concise is rarely the fewest words possible as straightforward grammar and sensible language may be longer than impenetrable, jargony text. Being understood is more impressive than being confusing, so avoid using “big words” just to sound important or clever.
  • Novel requires questions that are not already sufficiently answered. This works well with being clear because specifying details and context are likely to show how your research differs from existing research.
  • Arguable is what distinguishes real research from “pseudo-science”, “straw man arguments” and other kinds of “I’m-just-asking” questions. Arguable questions cannot be answered with simple repetition of facts or with single word answers.
  • Objective is about avoiding questions that rely on judgments like “good”, “bad” or “worst”. Very few things are universally considered to be good or bad, so researchers should work to be more objective (and also clearer) by asking “what benefits does it have?” rather than “is it good?”.
  • Appropriate questions are those can be understood to match well to data and methods. For example, questions that ask about populations should probably include details about quantitative data and methods while questions about individuals are likely to benefit from specifying qualitative or mixed methods.

Let’s look at some examples!

‘How should social networking sites address the harm they cause?’ is not as clear as ‘What action should social networking sites like Twitter and Facebook take to protect users’ personal information and privacy?’ because it does not specify which social media sites or what kind of harm is considered (which undermines the idea that such harm exists).

‘What effect have anti-climate change innovations had?’ is WAY TOO BIG for anyone to research, but ‘What effect have UK government green grants had on the number of heat pump installations and heat pump installer certifications between 2008 and 2022?’ is a reasonable question for a multi-year project.

‘Are photoshopped images in advertising bad for the mental health of young people?’ is very subjective and not even very novel while ‘What measurable effects do photoshopped images in online advertising have on self-reported depression scores of young people with at least 4 hours or more of daily screen time?’ is more clear, focussed, objective and arguable (even if not astonishingly novel).

‘What changes can we see in urban residents over the past decade?’ is bafflingly vague and does not even tell you whether you are talking about individual residents or populations of residents while ‘How have UK cities changed between the 2011 and 2021 UK census in terms of the age, language, health, work, income, and family status of urban residents?’ provides genuinely useful details that give the research an understandable structure.

‘Do people claim to have a trans or non-binary gender identity?’ sounds very click-baity and does not really add anything to discourse but ‘What are the most popular social media platforms for users to broadcast their own trans or non-binary gender identities?’ does actually provide new and useful information.

At this point, it may help to know that no one writes a good research question in one go. Instead, it may take months or even years to write a good research question at the beginning of a big project. Writing a good sub-research question for a part of an ongoing project can take weeks. This is because writing good research projects is an iterative process that begins by:

  1.  choosing something interesting like a pattern, a problem or just a topic,
  2.  doing some preliminary research to see what has been done or not done on that topic,
  3.  drafting some basic and almost certainly not-good questions about that topic that are not yet answered and then
  4.  refining those questions until they have the features listed above.

At any stage, researchers may need to go back and re-do a previous stage if they find, for example, that the interesting thing has already been extensively researched or that the gaps in existing research are too big or too small for the scope of the project at hand.

In conclusion, researchers are encouraged to think about their research questions carefully and to take time to make them better. At the same time, researchers should feel encouraged by the fact that good research questions are not easy to write and that no one is expected to write them well right away.

To find out more on topics like this, and what being a computational social scientist is all about, register for our upcoming training session, “How to become a computational social scientist”, which will take place online on November 21st. 

About the author

J. Kasmire researches and teaches on how to use new forms of data for social scientists with the UK Data Service and the Cathie Marsh Institute at the University of Manchester.

They approach this task as an interesting combination of thinking like a computer (essential for data sciences) and thinking like a human (essential for social sciences) in the context of complex adaptive systems. They are deeply committed to equality, diversity and inclusivity and are currently dabbling with stand-up comedy as a form of science communication.

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