In psychological research, what is something that affects the relationship between two variables?

A mediating variable (or mediator) explains the process through which two variables are related, while a moderating variable (or moderator) affects the strength and direction of that relationship.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. These variables are important to consider when studying complex correlational or causal relationships between variables.

What’s the difference?

You can think of a mediator as a go-between for two variables. For example, sleep quality (an independent variable) can affect academic achievement (a dependent variable) through the mediator of alertness. In a mediation relationship, you can draw an arrow from an independent variable to a mediator and then from the mediator to the dependent variable.

In contrast, a moderator is something that acts upon the relationship between two variables and changes its direction or strength. For example, mental health status may moderate the relationship between sleep quality and academic achievement: the relationship might be stronger for people without diagnosed mental health conditions than for people with them.

In a moderation relationship, you can draw an arrow from the moderator to the relationship between an independent and dependent variable.

A mediator is a way in which an independent variable impacts a dependent variable. It’s part of the causal pathway of an effect, and it tells you how or why an effect takes place.

If something is a mediator:

1. It’s caused by the independent variable.
2. It influences the dependent variable
3. When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

Mediation analysis is a way of statistically testing whether a variable is a mediator using linear regression analyses or ANOVAs.

In full mediation, a mediator fully explains the relationship between the independent and dependent variable: without the mediator in the model, there is no relationship.

In partial mediation, there is still a statistical relationship between the independent and dependent variable even when the mediator is taken out of a model: the mediator only partially explains the relationship.

You use a descriptive research design for this study. After collecting data on each of these variables, you perform statistical analysis to check whether:

1. Socioeconomic status predicts parental education levels,
2. Parental education levels predicts child reading ability,
3. The correlation between socioeconomic status and child reading ability is greater when parental education levels are taken into account in your model.

Moderating variables

A moderator influences the level, direction, or presence of a relationship between variables. It shows you for whom, when, or under what circumstances a relationship will hold.

Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds. For example, while social media use can predict levels of loneliness, this relationship may be stronger for adolescents than for older adults. Age is a moderator here.

Moderators can be:

• Categorical variables such as ethnicity, race, religion, favorite colors, health status, or stimulus type,
• Quantitative variables such as age, weight, height, income, or visual stimulus size.

• years of work experience predicts salary, when controlling for relevant variables,
• gender identity moderates the relationship between work experience and salary.

This means that the relationship between years of experience and salary would differ between men, women, and those who do not identify as men or women.

To test this statistically, you perform a multiple regression analysis for the data on work experience and salary, with gender identity added in the model. You compare the statistical significance of the model with and without gender identity included to determine whether it moderates the relationship between work experience and salary.

Why should you include mediators and moderators in a study?

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

This Scribbr article

Bhandari, P. (July 21, 2022). Mediator vs. Moderator Variables | Differences & Examples. Scribbr. Retrieved October 10, 2022, from https://www.scribbr.com/methodology/mediator-vs-moderator/