EXPERIMENTAL PERSONALITY DESIGNS - ANALYZING CATEGORICAL BY CONTINUOUS VARIABLE INTERACTIONS

Theories hypothesizing interactions between a categorical and one or m ore continuous variables are common in personality research. Tradition ally, such hypotheses have been tested using nonoptimal adaptations of analysis of variance (ANOVA). This article describes an alternative m ultiple regression-based approach that has greater power and protects against spurious conclusions concerning the impact of individual predi ctors on the outcome in the presence of interactions. We discuss the s tructuring of the regression equation, the selection of a coding syste m for the categorical variable, and the importance of centering the co ntinuous variable. We present in detail the interpretation of the effe cts of both individual predictors and their interactions as a function of the coding system selected for the categorical variable. We illust rate two- and three-dimensional graphical displays of the results and present methods for conducting post hoc tests following a significant interaction. The application of multiple regression techniques is illu strated through the analysis of two data sets. We show how multiple re gression can produce all of the information provided by traditional bu t less optimal ANOVA procedures.