Detecting Latent Interaction Effects in Behavioral Data

Harsh discipline is a well-replicated risk factor for aggressive, antisocial and delinquent behavior. In this paper we investigate the moderator hypothesis that parental perceptions of early child manageability problems, moderate parental discipline responses to the child’s disruptive behavior. We describe the application of an interaction model to the analysis of a behavioral data set, show how the data are initially screened for an interaction effect, and implement the model by use of the newly developed Quasi-ML method for analysis of latent interaction effects. The new methodology detects the hypothesized interaction effect by use of a likelihood ratio test. A quantitative interpretation of the estimated model parameters is provided, and conclusions for the analysis of synergistic effects in behavioral data by implementing appropriate interaction hypotheses are drawn.