Mediator and moderator variables are often used incorrectly or are not given the importance they require in research designs. They serve different functions and are used to explore the causal relationship between independent and dependent variables. This blog deals with their differences and uses in research.
What is a mediator and moderator variable?
A mediator variable is a variable that plays a role in either the cause or the effect of a studied variable. It is not the sole cause or the main cause, but a variable that plays an important role in the determination of the result. A moderator variable is a variable that either maintains or reduces the influence of the main variable. It can either increase or decrease the variation in the dependent variable. These variables are used to explore the causal relationship between independent and dependent variables.
Differences in mediator and moderator variables
One of the most common ways to use mediator variables is in bidirectional moderation. In this design, researchers compare the effect of two variables on one another. A good example of this is the study found by Lekach and Banaji on “aging in American culture”. In this study, researchers compare being old to being smart and find that the former has a greater effect on the latter. In this case, the mediator variable is age and the moderator variable is intelligence. By measuring these two variables and seeing which one has the greater effect, researchers can determine why the former has such a larger impact on the latter. Another common approach is to compare two variables in isolation. If researchers want to test the effect of having a child on parents’ satisfaction, they can look at the Big Five Factor of personality and see if having a child has an effect on parents’ satisfaction.
Implementation of mediator and moderator variables
There are two basic ways to use mediator and moderator variables in your research.
The first is to use them as independent variables. For example, you could run a study on how having a job affects the stress level of teenagers. You would randomly assign some teens to work and some not to work. Then, you could compare how the teens who were assigned to work compared with the ones who weren’t assigned to work in terms of their stress level.
In this example, your independent variable would be whether or not a teen worked. The dependent variable would be how stressed out that teen was.
The second way is to use them as dependent variables. This means that you use one of these variables in order to predict another variable. For example, let’s say you want to study how having a job affects students’ stress levels. You could randomly assign some students to work and some not to work and then compare their stress levels before and after they got jobs so that you can see if working has any effect on stress levels
Summing up
There are many ways to use mediation in research and all have their place, but the majority of the time, researchers use them in an integrative fashion. In an integrative design, researchers combine the effects of both variables to determine the overall effect of the study. The most common way to do this is with the mediation model. The mediation model is a statistical tool that can be used to determine if two variables are linked. If they are, researchers can use the variables to make causal claims. However, the model assumes that the dependent variable is influenced by both factors and that these factors are inversely related. If one factor is much higher or lower than the other, researchers will not be able to make causal claims about the relationship between that factor and the dependent variable.