Experimental Personality Designs: Analyzing Categorical by Continuous Variable Interactions

Stephen West, Leona S. Aiken, Jennifer L. Krull

Research output: Contribution to journalArticle

434 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
JournalJournal of Personality
Volume64
Issue number1
DOIs
StatePublished - Jan 1 1996

Fingerprint

Personality
Analysis of Variance
Research Design
Research
Datasets
Power (Psychology)

ASJC Scopus subject areas

  • Social Psychology

Cite this

Experimental Personality Designs : Analyzing Categorical by Continuous Variable Interactions. / West, Stephen; Aiken, Leona S.; Krull, Jennifer L.

In: Journal of Personality, Vol. 64, No. 1, 01.01.1996.

Research output: Contribution to journalArticle

@article{ab794af92996420bb290e4c1c2661c81,
title = "Experimental Personality Designs: Analyzing Categorical by Continuous Variable Interactions",
abstract = "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.",
author = "Stephen West and Aiken, {Leona S.} and Krull, {Jennifer L.}",
year = "1996",
month = "1",
day = "1",
doi = "10.1111/j.1467-6494.1996.tb00813.x",
language = "English (US)",
volume = "64",
journal = "Journal of Personality",
issn = "0022-3506",
publisher = "Wiley-Blackwell",
number = "1",

}

TY - JOUR

T1 - Experimental Personality Designs

T2 - Analyzing Categorical by Continuous Variable Interactions

AU - West, Stephen

AU - Aiken, Leona S.

AU - Krull, Jennifer L.

PY - 1996/1/1

Y1 - 1996/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85045503098&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045503098&partnerID=8YFLogxK

U2 - 10.1111/j.1467-6494.1996.tb00813.x

DO - 10.1111/j.1467-6494.1996.tb00813.x

M3 - Article

C2 - 8656311

AN - SCOPUS:85045503098

VL - 64

JO - Journal of Personality

JF - Journal of Personality

SN - 0022-3506

IS - 1

ER -