Statistical analysis is the language of research, but it can be confusing when you’re first getting started, especially when you don’t know what the terms actually mean. One of the most commonly confused sets of terms in statistics are moderation and mediation, which are two different types of relationships between variables in a research study. Let’s explore each one more closely and see how they work together!
What is mediation?
In statistics, mediation is a type of interaction effect in which a dependent variable is affected by an independent variable through an intervening (mediating) variable. In other words, the mediating variable explains or clarifies the relationship between the independent and dependent variables. A mediator may be another variable that influences both the independent and dependent variables. The term mediation may also refer to a statistic that measures this influence, such as the indirect effect, which can be calculated as the difference between total effects on Y when X=0 versus when X=1. There are different types of mediation analyses: 1) Asymetric 2) Bidirectional 3) Multiple-step 4) Parallel process 5) Path-analytic 6) Cross-sectional 7) Longitudinal 8 ) Psychometric 9 ) Qualitative 10 ) Structural Equation Modeling 11). Moderation is a concept in research used to measure how two variables interact with each other. It usually consists of two parts: First, one variable will have a significant effect on the outcome (e.g., gender affects salary); Second, there will be some level of moderating effect where at certain levels one variable interacts with the other to produce either stronger or weaker results than we would expect if they were unrelated (for example, performance improves at higher ability levels).
With moderation, the first part is still important but it is considered together with how much of an effect the second variable has on its own. If, for instance, men make more money than women do while high school graduates make more money than those without degrees, then the moderator could be what degrees people have attained. If having a graduate degree lessens the gender gap while high school graduation doesn’t affect salary then it would be safe to say that graduate degrees are moderating the gap.
How to know if there is a moderating effect?
When you see a moderating effect, it means that the relationship between your independent and dependent variables changes depending on the level of another variable. In other words, the direction and/or strength of the relationship varies. For example, let’s say you’re looking at the relationship between hours of studying and test scores. You might find that when students study more than 10 hours, there is a positive relationship between hours of studying and test scores. But when they study less than 10 hours, there is no relationship between hours of studying and test scores. There is an interaction effect because the relationship changes as levels of one variable change. A mediation effect occurs when an independent variable (IV) is related to two different dependent variables (DV). One DV can be mediated by the IV to explain some or all of its relationship with the other DV. Mediation analyses are used to investigate whether changes in one set of relationships are attributable to a mediator (another causal factor) or whether both sets of relationships need to be taken into account. The mediator is also known as the intervening variable. To do this, we examine whether changes in one set of relationships are attributable to a mediator (intervening variable) or if both sets of relationships need to be taken into account. The mediatee is also known as the intervening variable.
How to know if there is a mediating effect?
You can test for a mediating effect using regression analysis. In regression, you include the predictor variable, the outcome variable, and the mediator variable. You then test to see if the predictor variable is still associated with the outcome variable when you control for the mediator variable. If it is not, then you have found evidence for mediation. To test for moderating effects, you use an interaction term. For example, let’s say that we are interested in whether gender moderates the relationship between life satisfaction and job satisfaction. We would include an interaction term of gender by job satisfaction as well as life satisfaction by job satisfaction. If there is no significant interaction (i.e., the coefficient on the interaction term is zero), then we find no evidence for moderation. If there is a significant interactive term, then it tells us that something about either job satisfaction or life satisfaction has a different impact on men than women. If the value of this interaction term is positive, then job satisfaction matters more for men. Alternatively, if the value of this interaction term is negative, then life satisfaction matters more for women.
Other examples of Moderating & Mediating effects
In social science, a moderator is a third variable that affects the relationship between an independent variable and a dependent variable.
A mediator is a third variable that explains the relationship between an independent variable and a dependent variable. Moderators can be categorical (e.g., gender) or continuous (e.g., age).
Mediators can also be categorical or continuous. A mediating variable is considered a causal mechanism through which the effect of an independent variable on a dependent variable operates. For example, if one wanted to study how anger affected performance on a math test, then anger would be the independent variable, and performance on the math test would be the dependent variable. If one were to find that high levels of anger led to lower performance on the math test, but only for men, then there would be a moderating effect because men are experiencing something different than women. If there was no moderating effect when looking at anger and performance for both genders, it could mean either: 1) Anger does not affect performance on a math test for both genders 2) Anger has different effects depending on gender ; for instance, high levels of anger may lead to better performance on a math test for women. When looking at mediation, one would need to look at the effect of anger on female performance as well as the effect of female performance on male performance. If they found that high levels of anger led to higher performance on the math test for females but did not have any effect on males, then this would show a mediating variable. On the other hand, if high levels of anger had a negative impact on both males and females without any variation in level or degree, this would show a moderative effect. These two terms differ slightly because moderators measure how the strength of one effect changes based on another variable while mediators measure whether another variable impacts the strength of an original effect. One needs to understand which term will provide a more accurate answer in order to get closer to understanding what type of causality might exist among variables.
