Learn How to differentiate mediator and moderator variables
The first thing you need to know is that both variables convey different things; when differentiating mediator vs. moderator variables, the easiest way is to think of the mediating variable helps us understand how two variables are related. In contrast, a moderating variable can cause an alteration in the strength and direction of a relationship between two variables.
If you want to go beyond just a simple study and get a more vivid picture of the world, you must incorporate both variables in your research. Both variables are equally important when considering causal and correlational relationships between two variables.
Mediator vs. moderator variables – The difference
As explained earlier, a mediator has an intermediary position between two variables. For example, we know that a sleep-deprived mind would not be alert, which can affect your academic achievements. In this scenario, sleep quality is an independent variable, academic achievement is a dependent variable, and alertness is the mediator variable that connects both.
On the other hand, a moderator directly affects the relationship between two variables and can be a factor that changes the direction in which they are going or how strongly they are bound to each other. In the above example, the moderator can be your mental health, which affects your academic achievements and sleep quality. As a result, the bond between the two may be stronger in a case where there are no problems as opposed to a case where problems might arise.
Mediator vs. moderator variables – The definition
Mediating variables are always independent, and they impact a dependent variable. However, it is part of the causes as to how and why an effect is happening.
You can identify a mediator if it has the following characteristics:
- It is the effect that is caused by an independent variable
- The effect is on the dependent variable
- When a mediator is considered in a statistical correlation (between the two variables, independent and dependent), the value is higher than when the statistical correlation is not considered.
Mediation analysis is the process of identifying whether a variable is also a mediator or not. The methods used are linear regression analyses or ANOVAs.
It is called full mediation if the relationship between an independent and dependent variable exists just because the mediator or the mediator fully explains their bond.
When a mediator only partially explains the relationship between two variables, and even without it, the two share a statistical relationship, it is known as partial mediation.
When we see mediator vs. moderator variables, a moderator directly affects the relationship and its factors. It can impact the direction, the level, and even the existence of a relationship among variables.
Moderators are usually used to identify the limitations of a relationship between variables. In other words, it helps you judge the study’s external validity. The greatest example can be how social media can be used to detect levels of loneliness among users. Amongst all kinds of users, the relationship may be tougher in the case of teenagers than for adults. It shows that in the study, the moderator is age.
There are numerous examples of moderators, like race, health status, gender, sexuality, etc. These are all categorical variables.
Quantitative variables can be age, income, weight, and many more
FAQs of moderator vs. mediator variables
What is a mediating variable?
Answer: A mediating variable explains to us the process of how two variables are related to each other. In other words, it acts as a link between an independent and a dependent variable.
What is a moderating variable?
Answer: A moderating variable is a factor that directly affects a relationship. It is a part of the cause through which an effect takes place.
How to differentiate between mediator vs. moderator variables?
Answer: A moderator variable can show you the circumstances under which a relationship might succeed and its limitations. In contrast, a mediator variable explains the relationship between two variables and how an independent variable affects a dependent one.
