Generating Research Questions

What makes a good research question? How can you generate good research questions, and how do you evaluate them?

Even after identifying an interesting topic you would like to study, good research must be directed by well-defined research questions. On this page, we provide some guidelines on how to design good research questions, some exercises to help you refine this skill, and some external resources for further study.

Overview

Developing a good research question is one of the first critical steps in the research process. A good research question will guide the project and the construction in a logical, thorough argument. These questions should be clear and focused questions that summarize the issue that you want to investigate.

  1. The first step is to identify a topic or a broader subject of interest that you would like to investigate. This can be any of a wide range of topics, and can likely lead to multiple research questions down the road.
  2. The next step is to perform preliminary research on this general topic area to figure out what research has already been done and what literature already exists on the subject. How much research has been done on this topic? What types of studies have been performed? Is there a unique area that has yet to be investigated, or clear holes in the literature? Is there a particular study that is worth replicating or confirming?
  3. With the broader scope of the literature in mind, you should then focus on narrowing down your topic. You should focus on "how" and "why" questions that will interest you but also provide sufficient opportunity for exploration.
  4. Finally, now that you have some candidate questions, you should figure out whether these research questions are good research questions. Is this research question of interest to you —- and your potential audience? Is it actually researchable, given your time frame and available resources? Is it measurable, and will this study produce data and results that can be replicated or contradicted in further work? Is the research question not too broad, but not too narrow?

Some examples of research questions and how they can be improved:

Too Narrow: What is the childhood obesity rate in Phoenix, AZ?
This question is too narrow because it can be answered with a simple statistic. Questions that can be answered with a simple "yes", "no", or a number should be avoided.
Improvement: How does the education level of parents impact the childhood obesity rates in Phoenix, AZ?
The results from this study would provide the opportunity for a constructive argument to be formed.
Too Broad: What are the effects of childhood obesity in the United States?
This question is too broad to be discussed in a typical research paper since it contains many implied sub-questions. The research methodology to address this question would be very difficult.
Improvement: How does childhood obesity correlate with academic performance in elementary school children?
This question has a much clearer focus for which data can be adequately collected, analyzed, and discussed.
Too Objective: How much time do children spend doing physical activity per day?
This question does allow the researcher to collect data that can provide an answer, but no scientific argument can be made since this answer would amount to simply factual information.
Improvement: What is the relationship between physical activity levels and childhood obesity?
This question is a bit more open-ended and can lead to the construction of an argument based on the results and analysis of the data collected.
Too Simple: How are school systems addressing childhood obesity?
This question can be answered with information that does not need to collect unique data. The information required to answer this question is publicly available and does not provide adequate opportunity for in-depth analysis.
Improvement: What are the effects of intervention programs in elementary schools on the rate of childhood obesity?
This question is more complex and requires an in-depth investigation and evaluation which will lead to a more structured argument.

Exercises

Color-by-Outline

Take the abstract of a paper you find interesting. Your task is to color the sentences in the abstract according to the part of the research question it answers; note that some abstracts may miss some questions.

  • What is the problem, and why is it important?
  • Why is this a hard problem, and why are current approaches insufficient?
  • What is the novelty of your work?
  • What did you do, and/or why should we believe your argument?
  • What are the future implications of this work?

Example:

Augur: Mining Human Behaviors from Fiction to Power Interactive Systems
From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors. Today our systems fall short of these visions, however, because this range of behaviors is too large for designers or programmers to capture manually. In this paper, we instead demonstrate it is possible to mine a broad knowledge base of human behavior by analyzing more than one billion words of modern fiction. Our resulting knowledge base, Augur, trains vector models that can predict many thousands of user activities from surrounding objects in modern contexts: for example, whether a user may be eating food, meeting with a friend, or taking a selfie. Augur uses these predictions to identify actions that people commonly take on objects in the world and estimate a user's future activities given their current situation. We demonstrate Augur-powered, activity-based systems such as a phone that silences itself when the odds of you answering it are low, and a dynamic music player that adjusts to your present activity. A field deployment of an Augur-powered wearable camera resulted in 96% recall and 71% precision on its unsupervised predictions of common daily activities. A second evaluation where human judges rated the system's predictions over a broad set of input images found that 94% were rated sensible. [Missing future implications]

20 Questions in 20 Minutes

Set a timer for 20 minutes. Your task is now to generate as many research questions as you can in this time limit. A useful formula for rapidly developing these questions is the (And)-But-Therefore structure.

Examples:

  • First-world countries often draw information from a variety of sources, but many third-world countries do not have as available information. Therefore, we present an argument for open-access research to all researchers regardless of institutional affiliation or paid access.
  • Surveys and literature reviews are critical to understanding the current state of the field, but it is often difficult to ensure that no “holes” have been left in the review. Therefore, we developed a social annotation platform for senior researchers to comment and review in-progress written surveys to enhance the final work.
  • When researching at the cutting edge of a field, you often need to be aware of all other concurrent research being conducted, but ongoing work is often unpublished and has little presence in the community. Therefore, we have developed a community and platform to share relevant but unpublished results.

External Resources

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