Definition:

An interaction effect refers to the combined impact of two or more independent variables on a dependent variable that is different from the individual effects of each independent variable alone.

Explanation:

When analyzing the effects of multiple independent variables on a dependent variable, an interaction effect occurs when the relationship between these variables is not additive. In other words, the effect of one variable is modified by the presence of another variable, leading to a non-linear or multiplicative impact on the dependent variable.

Example:

Let’s consider a study that examines the effect of both age and gender on income. While both age and gender individually have an influence on income, an interaction effect may exist. For instance, the impact of age on income may differ between males and females. In this case, the interaction effect occurs when the influence of age on income varies based on whether the individual is male or female.

As a result, the interaction effect implies that the combined effect of age and gender on income cannot be predicted by simply summing up the individual effects of these variables. Instead, it requires evaluating the joint influence of both variables to understand their combined impact on the dependent variable.