Abstract: Analysis of interaction, the phenomenon that the effect (including the size and direction) of a certain independent variable (or predictor) depends on the state (size, value) of another independent variable, has always been important in psychological or social research. Methods for the analysis of interaction effects between observed variables were briefly reviewed. The main concern of the article was the detailed comparison and discussion on the analysis of latent variable interactions. Four basic approaches, including regression on factor scores, multiple-group structural equation modeling, structural equation modeling with product terms, and two-stage least square regression, were illustrated and contrasted. Advances in these and other analytical methods, including recently developed latent moderated structural equations (LMS) approach and generalized appended product indicator (GAPI) procedure, were also described and evaluated

Key words: latent variable, interaction effect, regression, structural equation modeling (SEM)