Research question format

Research problem carry inherent research questions if it is a good research problem. With the help of the specific research objectives and the research knowledge gaps dominating literature review, a researcher will easily frame questions which are researchable. With good research questions, it make it easy to tailor make the general statement to be addressed and provide a framework for the research work ahead.

The research questions may assume three formats as per research knowledge gaps guidelines,


-Quantifiable Questions to estimate

-Descriptive Questions to describe or narrate character

-Correlational Questions to link relationships

These THREE formats of research questions are further elaborated as follows with examples for your clear understandability. To achieve this purpose of driving the point to you, the explanation links each format with the corresponding mainstream research gaps which are supposed to be derived from chapter two of literature review.


1.1 Quantifiable Questions to Estimate (QQE)

The research questions should be able to guide the researcher to break down the statement of the problem into sub-statements which can further translate into variables that are quantifiable or measurable in terms of numbers or units. In other words, the researcher should be able to narrow down the research problem in to researchable terms affiliated to specific characteristic(s) that can be measured from the unit of observation (the respondent). So, if a research problem cannot lead to measurable study variables, then that is not a research problem.

To be more precise to you, I always tell my colleagues, postgraduate students, both for Masters and PhD levels and those I mentor in research that;

“If You Cannot Measure a Study Variable, then that Study Variable Does Not Exist”

The research problem should lead you to universally accepted measurement or gauge commonly referred to as Key Performance Indicators (KPIs). Even those organizations which want to succeed, will always identify their KPIs for they help them to measure the extent to which they have achieved their set goals and specific objectives of their firms within a specific period of time.

You see, I always relate setting of KPIs to fixing of goal posts for a football match to be sensible to both the players and other stakeholders not forgetting to mention even the fans. For every team player to participate, the motivation is that the goal post must remain in position. If the goal post are twisted or shifted, the game is over!


Now suppose the whistle to start round one is blown and after five minutes somebody pulls down the goal posts! Guest what will happen. Nobody will tell the players to down their tools. It will be automatic that they will stop playing. Why? The answer is obvious, no KPIs! How will they know when they score?

Goal posts and of course being in their rightly agreed position by all players acts as their KPIs for them to play objectively which again determine their rewarding. So if within five minutes the players will down their tools and the game is over, what about an organization without KPIs? What about a researcher or student like you moving on with a research problem that cannot translate to study variables which are measurable because you lack KPIs in your research?

Therefore, to ensure embarrassment don’t catch up with you, then you better identify a research problem that can result to measurable variables. This can be achieved by interrogating past studies to establish the authentic way used to gauge a particular study variable. As you build the methodological gap, which can take many forms, you may realize the way a variable has been measured is not appropriate in your study. Yes, it could have been applicable in the past studies but a different methodology of measuring the same study variable may be necessary in your current research. This approach of using past literature by carrying literature review mostly in chapter two before chapter one in the proposal document is paramount for it will make you get the right measurement for your study variable.

1.1.1 My short story when I was a small boy

I remember when I was a small boy, once in a while I would show signs of malaria for my mother would use the traditional methodology of assessing whether I was sick.



You can see here my mother is using the back of her hand to test what am suffering from. With this instrument/gauge, she has noted I have fever. Then she used to conclude that I had malaria.

I remember very well that the two alternative courses of action by my mother could adopt were;

One, buy me malaria tablets with prescriptions from the shopkeeper who is not an expert.

Two, Rush me to hospital where the doctor will use another measurement-the clinical thermometer and if the results portray that I have fever with body temperature is more than 360 c. Again, am given the tough malaria injection plus some tablets.

 But who said I had malaria precisely?

Who said with FEVER, one is sick of malaria?

For your information, there are many body disorderliness that cause fever. For example, stomach upsets, Typhoid, whooping cough, and Pneumonia has similar symptoms of fever.

The BIG question is are those sicknesses treated the same way? Of course no.

Of recent past, other methods of testing malaria symptoms such as use of microscope to scan blood specimen to confirm whether the fever is associated with plasmodium which causes malaria is in use. This is a more appropriate methodology for it precisely defines whether the fever is of malaria or otherwise. The goodness of such a gauge is that it helps the doctor to make the right prescription of the right medicine. This again avoids confusion of high order which could lead to death or more complications to the patient if the problem is not specifically addressed.


The message I have for you as a researcher, tutor or student is this;

“Identification of the correct or appropriate measurement of the study variable is key in realizing valid and reliable results for future use by many stakeholders.”


1.2 Descriptive Questions to Describe or Narrate Character (DQDC)

The research question should be able to address the qualitative character of the unit of observation and make it measurable. Like in the case of quantitative perspective, the research questions should be able to guide the researcher to break down the qualitative statement of the problem into qualitative sub-statements which can further translate in to variables that are measurable in qualitative terms. In other words, the researcher should be able to narrow down the research problem into researchable terms affiliated to specific characteristic(s) that can be measured from the unit of observation (the respondent).


Under Qualitative measurement methodology right measurement are such as interval scale using Likert scale tool which gives a good touch on how to measure a qualitative characteristic or study variable. For instance, a scale of between 1 to5 can be utilized where each number represent a certain level of opinion, such as;

1=Very Much Satisfied



4=Not Satisfied

5=Not Satisfied at All  


With this scale, we are able to infer characteristic/or behavior of a study variable without necessarily having quantitative figures in place. This is achievable when you consider both methodological and contextual gaps as it may be portrayed in chapter two of literature review. For with chapter two on literature review, it is possible to find out how the same study variable you are using in your research was measured or defined under a different contextual settings.


For example, consider Small and Medium Enterprises (SMEs), there is no one universally accepted way or approach of defining this sub-sector. Different regions define SMEs in dissimilar manner and in the same way, the SME affiliated variables also differ from one region to another. Let’s have a look at the following regions;


1.2.1 Regional Contextual Definition and Measurement


For Japan case, SMEs are defined as establishments employing 4 to 299 employees and Large Enterprises as those employing 300 or more. SMEs are defined as establishments’ capitalization at less than 100 million yen, and Large Enterprises as those capitalized at 100 million yen or over.



In USA, Small and Medium-sized Enterprises (SMEs) are non-subsidiary, independent firms which employ fewer than a given number of employees. United States considers SMEs to include firms with fewer than 500 employees.


On the other hand, UK government definition of SMEs encompasses micro (less than 10 employees and an annual turnover under €2 million), small (less than 50 employees and an annual turnover under €10 million) and medium-sized (less than 250 employees and an annual turnover under €50 million) businesses.


President of the European Commission. 'The category of Micro, Small and Medium-Sized Enterprises (SMEs) is made up of enterprises which employ fewer than 250 persons and which have an annual turnover not exceeding EUR 50 million, and/or an annual balance sheet total not exceeding EUR 43 million.

South Africa

A comprehensive definition of an SME in South Africa is, therefore, an enterprise with one or more of the following characteristics: Fewer than 200 employees, Annual turnover of less than R64 million, Capital assets of less than R10 million, Direct managerial involvement by owners.

South Korea

Korean SMEs were defined when the 'Small and Medium Enterprises Act' was enacted and promulgated in 1966. ... According to the SME Act, SMEs in the area of manufacturing are considered as companies which have less than 300 employees or its capital worth under KRW 8 billion

This are just but a few cases chosen to portray that contextual definition or measurement of the unit of analysis, i.e. the SMEs differ from one region to another and it will be erroneous to just use any definition in your research. This would result to misleading research findings.


1.2.3 Organizational Contextual Definition or Measurement

Similarly, the study variables used also vary contextually in terms of the nature of the organization is concerned. Depending on the firm being focused on, you need to consider their culture, industry or their common practice when it comes to measurement of the study variables. For instance, a variable such as firm performance is usually measured in different ways based on the set specific performance objectives. For each firm, either in a particular sector or industry, they may have dissimilar KPIs which make logic to them and not to others.


Look at this example;

Financial performance

For firms listed in the security exchange market, possibly they use Return on Equity (ROE), Return on Assets (ROA), Return on Investment (ROI), Return on Capital Employed (ROCE), Earning per Share (EPS) etc. First, this is made possible because the data is in the public domain and can be accessed by the researcher or any other interested party. Two, it is the common practice in the respective industries and hence it is universally accepted method of measuring financial performance which aid in assessing the overall performance of the population elements. This is what is logical to these firms and if the research question guides the researcher to use a different KPI or proxy for estimating financial performance, then the results may lack and not even may, they will definitely lack plausibility for the method used to gauge the financial performance variable is not logical to the users.


NB: Remember that the research questions should be logical.

In fact I have a personal experience of this nature. When I was writing my PhD thesis, my dependent variable was Dividend Policy (google my Journal Article-Financial Performance and Dividend Policy or use link-Doi: 10.19044/esj.2017.v13n28p138). In my endeavors to measure, this variable, I came up with several proxies that I felt they were ok. To my disbelief, the results were not statistically significant. The relationship between financial performance and dividend policy were insignificant. It is until I considered Dividend Pay Out (DPO) ratio that the regression results were statistically significant.

The reason for such statistically significant outcome is very simple, contextually, DPO ratio is logical to the listed firms for this is what is used by these firms practically in the Kenyan market.


Contrary, for firms which are small such as micro small and medium enterprises, contextually they do not rely on Dividend Payout Ratio to assess profit share or use Return on Equity (ROE), Return on Assets (ROA), Return on Investment (ROI), Return on Capital Employed (ROCE), Earning Per Share (EPS) to measure profitability level of the business. Why, because in the first place, one common characteristics these firms have is that they do not keep proper books of accounts and there is no statutory requirement for them to publish their accounts. So one challenge to the researcher, tutors or the postgraduate students doing their degree is that they will not access financial details for they are unavailable. Also it is not possible to get the data from the firms themselves due to secrecy protection practices they have. So you reach a deadlock.


Therefore, the researcher need to review literature in chapter two to identify the best and the most suitable study variable method to use. He or she will be well guided by considering the contextual way in which a similar variable has been utilized and measured.


1.3 Correlational Questions to Link Relationships (CQR)

The research question should portray an element of correlation of variables in the physical and natural phenomenon. This is a conceptual knowledge gap matter. From literature review, we should derive at new concepts from what other researchers have done. Conceptualization in research focuses on how the same study variables are classified by different authors in different studies. Some treat the variables as pure predictors and others treat the same variable as a dependent or criterion variable. Even other scholars consider the same variables as either extraneous, intervening or moderating variables. All these philosophical way of arguing their cases represent conceptual knowledge gaps. You as a researcher can pick it from there and introduce another perspective of how the same variables may relate with one another in your study.

For example, in the African culture, most of the times the man approach the woman for marriage. Man, referring to the male being is the influencer or predictor of the behavior or character change of the female being. But this is not always the case, for in some circumstances, in Africa we have witnessed the female personality approaching the male being for marriage. In this case the predictor variable is the behavior of the female persons while the dependent variable is the character or behavior of the male personality. This is termed as an abnormal circumstance. However in other countries, the normality is that man is approached by the lady for marriage. Contextually, this is also logical and the regression results will be logical. So as a researcher, contextual gap should guide you to bring logic to your study.


Remember as you consider the conceptual gap to fill in your study, the theoretical knowledge gap is also being incorporated in your study for in your new viewpoint of which variable predicts which other variable, this should be underpinned by a theory. A theory makes logical argument on your concept. Therefore, if the research question does not portray efforts of introducing a theory to your concept, then the research question used is not a good research question. In other words, the research question should relate the specifics of what is being examined to a more general background of theory which aids to interpret the results and connect it to the field.

About the Author - Dr Geoffrey Mbuva(PhD-Finance) is a lecturer of Finance and Accountancy at Kenyatta University, Kenya. He is an enthusiast of teaching and making accounting & research tutorials for his readers.