As behavioral science research advances, third variables have increasingly been considered in the relationship between independent and dependent variables in many research. Research has commonly examined the role of mediating and/or moderating variables. A moderating variable indicates a variable that has an interaction effect on a dependent variable, producing a joint effect with an independent variable. Given that mediation (or indirect) effects can be easily tested in structural equation models that are very widely used, mediation effects are more commonly tested than interaction effects. One of the main possible reasons interaction models are underutilized is that testing interaction effects can be complicated and thus many researchers often experience difficulties. In view of this, analysis methods for testing the interaction effects are explained and discussed in detail using regression analysis and structural equation models in the present study. First, mean centering, correction of standardized interaction coefficient, and reliability issues of interaction variables are explained in regression analysis. Next, constrains of parameters and unconstrained methods as well as the abovementioned three issues are emphasized in structural equation models. In addition, regression analysis and structural equation models are applied to a real data set to explain the procedures of the analysis. Finally, several issues that are commonly misunderstood by many researchers are presented and clarified.