The research questions (RQs) drive data collection and must be derived directly from the purpose of the study. This means they will also have to align with the research method. Although it is recommended that a single, central research question be developed to guide dissertation or applied doctoral project studies, this isn’t always possible. However, you should limit the number of research questions to avoid scope creep and taking on too much with your research. It is possible to develop sub-questions, but a single or a few questions should be the foundation that can guide your data collection and analysis, and help you reach conclusions.
Some students confuse research questions with interview or survey questions. They are not the same! Interview and survey questions are the detailed questions you will ask participants in your study; whereas the RQs reflect the high-level purpose of the study. The research question can cover several concepts and aspects of the purpose that can then be hashed out in an interview, focus group, or survey.
Qualitative and quantitative research questions differ considerably. The research method identified in your purpose statement should guide how the RQs are stated. Quantitative RQs must reflect testable relationships between variables, whereas qualitative RQs should indicate the exploratory and open-ended nature of the inquiry. Quantitative RQs are followed by hypotheses that reflect the researcher’s predictions about the nature of the relationships under study. These predictions should be firmly rooted in the researcher’s understanding of the literature and theory used to frame the study. Because qualitative research studies do not involve any testing of relationships between variables, qualitative RQs are not written by hypotheses.
RQ1: “What are the lived experiences of followers of mid-level managers in the financial services sector, regarding their well-being on the job?”
If the researcher wants to focus on a particular theory or aspects of the central RQ, the following sub-questions could be formulated:
RQ1a. “How do followers perceive the quality and adequacy of the leader-follower exchanges between themselves and their novice leaders?”
RQ1b. “Under what conditions does leader-member exchanges affect a follower’s own level of well-being?”
The researcher can now develop an interview protocol with specific questions that he or she plans to ask the participants of the study, structured by the two sub-RQs above.
RQ1. “What is the relationship between leadership competencies as measured by the Leader-Member Exchange (LMX) assessment framework and employee well-being?”
Associated hypotheses with this RQ include a null hypothesis (that proposes that no statistical significance exists among relationships between variables in the data); and an alternative hypothesis (that states there is a statistically significant relationship between two variables, based on the researcher’s prediction).
H10. None of the five leadership competencies from the Leader-Member Exchange (LMX) assessment framework of (a) motivation, (b) professionalism, (c) business acumen, (d) communication, and (e) relationship management are statistically significant predictors of follower well-being
H1a. At least one of the five leadership competencies from the Leader-Member Exchange (LMX) assessment framework of (a) motivation, (b) professionalism, (c) business acumen, (d) communication, and (e) relationship management is a statistically significant predictor of follower well-being
In the above example, you and your committee may decide that in order to adequately test a theory or a set of relationships between variables, you have to develop sub-questions and specific hypotheses related to each of the leadership variables (which are combined here into one RQ). The key is that for every research question, there is a set of null and alternative hypotheses statements. It is not generally permissible to have quantitative research questions for which there are no hypotheses, nor is it okay to have hypotheses that don’t connect to a research question.
Research questions should not be answerable with a “yes” or “no” because a “yes” or “no” does not tell you much about the subject being investigated. Knowing that performance-based pay systems for school teachers either do or do not improve student performance is just the first step. It is important to learn how or why such systems do or do not improve student performance. These questions are more informative in that they lead to a deeper understanding of the subject and can lead to action or more research down the line.
It is easy to confuse research questions with survey/interview questions. The questions above are interview questions—they are the questions that you will ask subjects in your interview or survey. They are not the underlying questions that you are trying to answer with those survey/interview questions. Your goal is to answer the underlying research questions, and survey/interview questions are the tool used to answer research questions; they are not the research questions themselves.
It is important to limit the number of variables being measured in a study because too many variables make it hard to connect one variable to another. There are too many things being measured by this question. To answer it, you must first catalogue the many attitudes of millennials, then catalog their many perceptions of virtual meetings, and then determine how the various attitudes are connected to the various perceptions. This makes for many correlations to track. It is better to pick a single attitude and a single perception to measure.
It is important not to appear biased in your study, such that you are attempting to establish a conclusion using a controversial premise. You might establish a controversial conclusion, but you must do it on solid footing.
The questions above are far too broad to be answered in a dissertation or applied doctoral project. There are a variety of factors related to each question and an enormous literature base to review before getting started. Trying to answer these broad questions is sometimes termed “Boiling the ocean.” Instead, a dissertation or applied doctoral project needs to be narrowly focused so that there are a limited number of variables to examine, and a literature that is sufficient to get going, but not so big that it is impossible to review.
1. What do NFL owners think are the major challenges facing the league in the future?
2. How do FBI agents view the organizational structure of the agency?
Any study gathering data from a survey needs to target an audience that can be identified, reached, and is willing to respond to questions in sufficient numbers to reach statistically significant results. While NFL owners are easy to identify, few, if any, will spend time answering a survey from someone they do not know. It also seems unlikely that it will be easy to find a list of FBI agents, and even more unlikely that they will be willing to discuss the organizational structure of their agency.
1. How have reports of player injuries influenced the training practices of high school football coaches? 2. What are the primary motivators of employment persistence among Federal employees?
Hypotheses are single, tentative guesses, good hunches –assumed for use in devising theory or planning experiments intended to be given a direct experimental test when possible (Eric Rogers, 1966).
A hypothesis is a conjectural statement of the relation between two or more variables (Kerlinger, 1956).
A hypothesis is a formal statement that presents the expected relationship between an independent and dependent variable (Creswell, 1994).
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Research Question |
Hypotheses |
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RQ1: To what extent does red light have an effect on reading speed? |
H0: Red light does not have a statistically significant effect on reading speed. |
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Ha: Red light has a statistically significant effect on reading speed. |
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RQ2: To what extent does red light have an effect on mood? |
H0: Red light does not have a statistically significant effect on mood. |
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Ha: Red light has a statistically significant effect on mood. |
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RQ3: To what extent does blue light have an effect on reading speed? |
H0: Blue light does not have a statistically significant effect on reading speed. |
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Ha: Blue light has a statistically significant effect on reading speed. |
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RQ4: To what extent does blue light have an effect on mood? |
H0: Blue light does not have a statistically significant effect on mood. |
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Ha: Blue light has a statistically significant effect on mood. |
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