Variables: Mediator and Moderator

There are two types of variables that play an important role in the format of academic research.

A mediator or a mediating variable refers to the process of research in which two variables are related.

A moderator or a moderating variable demonstrates the direction and close connection between the two variables.

When you include a mediator and moderator in the research, you will receive a more complete picture of relationships between the two variables. They are especially important when you study the complex cause-effect and correlational relationships.

However, you need to understand the difference between them to use these variables in your research efficiently. The problem is that they are often confused by researchers because these terms are so similar. Even though these two variables are correlational by nature, there are big differences in their use when you investigate the relationships between independent and dependent variables.

So, let’s consider these differences by answering the questions about what a mediator and moderator variables are and how to spot them in the study.

What Are the Main Differences Between Mediators and Moderators?

Let’s determine the most apparent differences between these two kinds of variables.
✔️ A mediator means a ‘middleman’, or something that stands between the two variables and determines their relationships. It is the reason for the effect that the independent variable has on the dependent variable. It means that without the mediator you will not receive the causal link between these two variables or it will not be understandable.
✔️ A mediator contextualizes the effect made on the dependent variable by the independent variable. Therefore, it is a causal result that the dependent variable experiences from the independent variable.
✔️ A moderator is a variable that can change the causal effect, its strength or direction.
✔️ A moderator never provides the context of the causal effect of the independent variable on the dependent variable.

You may still have trouble while you try to get a more detailed understanding of these differences. So, try to realize the purpose of each variable first by considering these explanations and examples.

Start with understanding a mediator as something that goes between the two variables. For example, the level of a student's income (an independent variable) can influence the student’s academic progress (a dependent variable) via the mediator of time for self-education. When you have the relationships of mediation, you can draw an imaginary arrow from an independent variable to this mediator and from the latter to the dependent variable.

On the other hand, a moderator does not show this cause-effect relationship but influences the relationships between the independent and dependent variables by changing the direction or strength of the overall influence.

Thus, if you include mediators and moderators in your research process, you make the whole picture more complex and understanding of the causal relationships between independent and dependent variables more detailed and complete.

Role of Mediators in Research

Let’s define the role of mediating variables in research. Mediators explain ‘why’ and ‘how’ relationships between the variables, so they contextualize or highlight the cause that leads to a specific effect.

When you use the mediation analysis, you cannot expect that the independent variable has an immediate influence on the dependent variable. This effect is seen only via a mediator between these two. Therefore, if we attempt to remove the mediator, this cause-effect relationship will disappear.

If some variables can be considered mediators, they originate from an independent variable, influence a dependent variable, and increase the statistical correlation between the independent and dependent variables. Mediation is usually used in statistical analysis with methods such as linear regression analysis and ANOVA.

Mediation can be full and partial.

In full mediation, the relationship between the independent and dependent variable exists only with the mediation. Without it, you cannot follow the cause-effect correlation.

Partial mediation means that there is a relationship between the variables even without a mediator because it explains this relationship only from one aspect.

Let’s come back to our example. In the study of relationships between the levels of students’ incomes and their academic progress, you make a hypothesis that more time for self-education is crucial for the progress, so it is a mediator. It means that if students need to work part-time, they cannot make academic progress because of the lack of time for studies.

Here, you can use a descriptive research design. When you have collected all the data on the main variables, make a statistical analysis to see whether:

1. Higher incomes predetermine more time for studies.
2. Having more time for studies increases the students’ chances to make progress.
3. Higher incomes correlate with making more academic progress via having more time for self-education.

Now, let’s have a look at what moderation means in statistical analysis and how it helps the research process.

Role of Moderators in Research

Moderators change the relationships between independent and dependent variables. So, moderation can contextualize the causal changes in relationships between the variables when they interact with a moderator. The latter can affect the level and direction of these relationships. It reveals when, under what circumstances, or for whom the relationships are important. So, it helps make conclusions about the external validity of the research because it can define the limitations of it. For example, the student’s part-time job reduces the time for self-education in low-income students. The presence of free time for more studies is a moderator here.

There can be other moderators related to:

• categories: religion, health condition, ethnicity, favorite foods, etc.;
• quantities: height, income, weight, age, etc.

This implies that the relationships between the students’ income and their academic progress is moderated by their part-time job or the lack of necessity for it.

When you use a moderator as a variable, you can make a mistake because it is always difficult to measure the moderation effect. The helpful way of avoiding it is to use purposeful sampling when you include the extreme cases for both levels, so you need to survey those students who have pretty high and pretty low incomes alongside the medium-income ones. The size of your samples also matters. You need to make your samples larger for using moderation to test the effect of the moderator on the final results of the research.

There is one more tip here to make the effect of moderation more apparent. Choose the samples for your purpose, taking into account the criteria that are especially interesting for you, such as the size of the income, students’ interests, ways of spending free time, and their gender. You may check your hypothesis and be sure that no other variables, apart from the moderating ones, influence the final conclusions.

A moderating analysis is a distinct a straightforward process that consists of the following:

• developing standardized values for the independent variable and a moderator;
• calculation exact values for the moderating variable;
• analyzing the interaction effect with the help of the multiple linear regression.

Final Thoughts

Understanding the differences between mediator and moderator variables is essential for the accuracy of the received data and the final results of the research work. Remember that a mediator is a result caused by an independent variable, and it should come before the effect demonstrated by a dependent variable. On the other hand, a moderator does not have any causal reference to an independent variable. It influences the dependent variable via its capacity to change the size, strength, or direction of both variables.

When you start your research, you always predict how much the variables are dependent on each other or in what way an independent variable can influence the dependent one. In fact, you build up a theory that needs certain support and proof.

Very often, this theory does not receive this support or, if you insist on your hypothesis, your research can mislead you in a wrong direction. Using mediator and moderator variables in your research can help you avoid the mistakes and formulate the correct approaches, theories, and hypotheses with all their limitations, topics for debate, and issues for further research.