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Research Questions, Variables, and Statistics

Working with students over the years has led me to the conclusion that often students do not understand the connection between variables, quantitative research questions and the statistical tools

used to answer these questions. In other words, students will take statistics and pass the class. Then they will take research methods, collect data, and have no idea how to analyze the data even though they have the necessary skills in statistics to succeed.

This means that the students have a theoretical understanding of statistics but struggle in the application of it. In this post, we will look at some of the connections between research questions and statistics.


Variables are important because how they are measured affects the type of question you can ask and get answers to. Students often have no clue how they will measure a variable and therefore have no idea how they will answer any research questions they may have.

Another aspect that can make this confusing is that many variables can be measured more than one way. Sometimes the variable “salary” can be measured in a continuous manner or in a categorical manner. The superiority of one or the other depends on the goals of the research.

It is critical to support students to have a thorough understanding of variables in order to support their research.

Types of Research Questions

In general, there are two types of research questions. These two types are descriptive and relational questions. Descriptive questions involve the use of descriptive statistic such as the mean, median, mode, skew, kurtosis, etc. The purpose is to describe the sample quantitatively with numbers (ie the average height is 172cm) rather than relying on qualitative descriptions of it (ie the people are tall).

Below are several example research questions that are descriptive in nature.

  1. What is the average height of the participants in the study?
  2. What proportion of the sample is passed the exam?
  3. What are the respondents perceptions towards the cafeteria?

These questions are not intellectually sophisticated but they are all answerable with descriptive statistical tools. Question 1 can be answered by calculating the mean. Question 2 can be answered by determining how many passed the exam and dividing by the total sample size. Question 3 can be answered by calculating the mean of all the survey items that are used to measure respondents perception of the cafeteria.

Understanding the link between research question and statistical tool is critical. However, many people seem to miss the connection between the type of question and the tools to use.

Relational questions look for the connection or link between variables. Within this type there are two sub-types. Comparison question involve comparing groups. The other sub-type is called relational or an association question.

Comparison questions involve comparing groups on a continuous variable. For example, comparing men and women by height. What you want to know is whether there is a difference in the height of men and women. The comparison here is trying to determine if gender is related to height. Therefore, it is looking for a relationship just not in the way that many student understand. Common comparison questions include the following.male

  1. Is there a difference in height by gender among the participants?
  2. Is there a difference in reading scores by grade level?
  3. Is there a difference in job satisfaction in based on major?

Each of these questions can be answered using ANOVA or if we want to get technical and there are only two groups (ie gender) we can use t-test. This is a broad overview and does not include the complexities of one-sample test and or paired t-test.

Relational or association question involve continuous variables primarily. The goal is to see how variables move together. For example, you may look for the relationship between height and weight of students. Common questions include the following.

  1.  Is there a relationship between height and weight?
  2. Does height and show size explain weight?

Questions 1 can be answered by calculating the correlation. Question 2 requires the use of linear regression in order to answer the question.


The challenging as a teacher is showing the students the connection between statistics and research questions from the real world. It takes time for students to see how the question inspire the type of statistical tool to use. Understanding this is critical because it helps to frame the possibilities of what to do in research based on the statistical knowledge one has.

Identifying a Research Problem

The research problem is the issue or concern in a particular setting that motivates and guides the need for conducting a study. Identifying a research problem is important because it lays the foundation for an entire project. If the foundation is shaky the entire project is doomed to failure. This is why absolute caution is necessary for the initial stages of a research project.

In addition to a research problem, there are several terms closely related to it. These terms are

  • research topic
  • research problem (already discussed)
  • purpose
  • research questions

We will now look at each of these with an appropriate example.

Research Topic

A research topic is the subject matter of a study. This is the broadest aspect of research and many people begin here. An example would be student satisfaction. What we are going to do and how is not explained yet. This is only pointing us in a certain direction without knowing what our destination is.

Research Problem

As mentioned previously, the research problem is the issue being addressed. The problem helps in narrowing the topic down to something that is reasonable for conducting a study. Continuing with our topic of student satisfaction, we may look at student satisfaction of teaching methods at the university level. The problem is that student satisfaction is often low at many universities and we want to do a study to explain some reasons for this.


The purpose is the objective of the study. For our student satisfaction example, the purpose could be To identify how various teaching methods influence student satisfaction at a university. At this point, we are explaining what we are going to do.

It is common for people to confuse the research problem and the purpose. The problem simply identifies a problem. The purpose explains how you will study the problem or what you are going to do. The purpose looks at the problem and states what you are going to look at in order to generate data about the problem that could be used one day to solve the problem.

This distinction is important because small missteps in the beginning will lead to major issues in completing a study in the future.  Think that problems are passive in terms of what you will do while purpose is active in terms of what you will do.

Research Questions

Research questions narrow the purpose down into questions that provide evidence for addressing the research problem. For example, in our student satisfaction example, below are two questions that could be used for the study

  1. How does a lecture method approach to teaching affect student satisfaction?
  2. How does a discussion method approach to teaching affect student satisfaction?

From here the study can move forward.


The initial step of research involves several sub-steps which are the research topic, problem, purpose, and questions. The topic is the broadest aspect of research and the research question(s) is the most focus aspects of the initial aspects of a study. The purpose of this process is narrow and shape a study in order to make it feasible and relevant.