A conceptual framework is an illustration of what to expect from research. It helps to define all the relevant variables of the research and helps to map out how each of the variables relates to one another.

Before collecting data, it is essential to construct a conceptual framework that can be easily depicted through a visual format. This article will describe the construction of a proper conceptual framework for an anticipated cause and effect relationship by combining all the variables and how their interaction might influence their relationships.

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What is a conceptual framework?
Generally developed from the literature review of existing theories, a conceptual framework in simple terms is the expected relationship between variables represented in a visual or written representation. Thus, the variables are simply the properties and the parameters that are to be studied.

Research example: If you are looking to determine if longer study hours equate to better marks, you will need to conduct surveys and use the data to determine the relationship between the variables.

Before you begin to collect the data, you will first need to create a conceptual framework to determine the variables of your survey and how they will relate to each other.

There are several ways to design a conceptual framework, and it depends on the kind of research or study that is being conducted.

What are Independent and dependent variables?
If you are looking to test a cause-effect relationship, you will first need to identify the dependent and the independent variable. The study hours are the independent variable in the example cited above, while the marks or the grades are the dependent variables. In simpler terms, the marks of the exam depend on the hours of study. We can assume our hypothesis that more study hours equate to better marks. Such causal relationships often will involve an independent variable that affects the dependent variable. To keep the complexity to a minimum, let us consider only one independent variable of study hours.

To better visualise the effect cause relation of the variables, it is better to use basic design elements like arrows and boxes. The boxes indicate the variables and then use the arrows to indicate which variable is dependent on the other.

Now try to identify any other variable that might be influencing the relationship between the dependent and the independent variables. Moderators, mediators and other control variables are common variables influencing the relationship.

Moderating Variables:
The conceptual framework should now be expanded by including a moderating variable. These moderators alter the effect of the independent variable on the dependent one. Thus the moderator works to change the impact of cause-effect relationship, which is also known as the interaction effect.

In our above example of the relation between the hours of study and the marks, let us include a moderating variable, “Student IQ”. Higher the IQ, the lesser the hours the student needs to study to get higher marks. In simpler words, the variable, Student IQ, is moderating the effect of the hours of studies on the student’s marks. Therefore, a moderating variable can cause the dependency on the independent variable to be altered.

Mediating variables:
The conceptual framework can also be increased by including a mediating variable. A mediating variable helps to explain better the cause-effect relationship of the dependent and the independent variable.

To better understand, let us include a mediating variable of “number of practice questions solved” in our above example. This is a mediating variable as the greater number of practice questions solved translates to greater study hours on which the marks of the students depend. Here the mediator works to explain why studying longer hours can give better results. This is because the more hours a student invest in studying, the more practice questions they can solve. Therefore, by adding the mediating variable, we help to explain the relationship between the dependent and the independent variables.

Moderator vs Mediator:
It is very common for many to get confused between mediators and moderators. To better understand the ideas, try to relate their relation with the independent variable. In the case of a mediating variable, it is affected by the independent variable while having an effect on the dependent variable. On the other hand, a moderating variable remains unaffected by the independent variable while affecting the dependent variable.

Control Variables:
To understand the cause-effect relationship, you will also need to think of other variables which are not particularly important to measure but have a potential impact on the relationship. These variables are known as control variables. Control variables are set as constant, so they don’t have any influence on the result. For example, a student’s health plays a vital role in their performance, and therefore, our study will assume all the students in the study group are healthy on the day of taking their test.

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