Premium
Experimental Personality Designs: Analyzing Categorical by Continuous Variable Interactions
Author(s) -
West Stephen G.,
Aiken Leona S.,
Krull Jennifer L.
Publication year - 1996
Publication title -
journal of personality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.082
H-Index - 144
eISSN - 1467-6494
pISSN - 0022-3506
DOI - 10.1111/j.1467-6494.1996.tb00813.x
Subject(s) - categorical variable , spurious relationship , regression analysis , psychology , personality , feature selection , coding (social sciences) , regression , statistics , variance (accounting) , continuous variable , variable (mathematics) , variables , analysis of variance , econometrics , computer science , artificial intelligence , mathematics , social psychology , mathematical analysis , accounting , business
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.