Experimental personality designs: analyzing categorical by continuous variable interactions.
Theories hypothesizing interactions between a categorical and one or more continuous variables are common in personality research. Traditionally, such hypotheses have been tested using nonoptimal adaptations of analysis of variance (ANOVA). This article describes an alternative multiple regression-based approach that has greater power and protects against spurious conclusions concerning the impact of individual predictors on the outcome in the presence of interactions. We discuss the structuring of the regression equation, the selection of a coding system for the categorical variable, and the importance of centering the continuous variable. We present in detail the interpretation of the effects of both individual predictors and their interactions as a function of the coding system selected for the categorical variable. We illustrate 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 illustrated through the analysis of two data sets. We show how multiple regression can produce all of the information provided by traditional but less optimal ANOVA procedures.
West
S G
SG
Department of Psychology, Arizona State University, Tempe 85287-1104, USA.
Aiken
L S
LS
Krull
J L
JL
D000223:Adaptation, Psychological; D000704:Analysis of Variance; D005246:Feedback; D006801:Humans; D010553:Personality Development; D010555:Personality Inventory / Q000706:statistics & numerical data; D010556:Personality Tests / Q000706:statistics & numerical data*; D011594:Psychometrics; D012044:Regression Analysis; D015203:Reproducibility of Results; D012649:Self Concept; D012928:Social Desirability