The Detection and Interpretation of Interaction Effects Between Continuous Variables in Multiple Regression

James Jaccard, Choi K. Wan, Robert Turrisi

Research output: Contribution to journalArticle

Abstract

Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent articles by Cronbach (1987) and Dunlap and Kemery (1987) suggested the use of two transformations to reduce “problems” of multicollinearity. These transformations are discussed in the context of the conditional nature of multiple regression with product terms. It is argued that although additive transformations do not affect the overall test of statistical interaction, they do affect the interpretational value of regression coefficients. Factors other than multicollinearity that may account for failures to observe interaction effects are noted.

Original languageEnglish (US)
Pages (from-to)467-478
Number of pages12
JournalMultivariate Behavioral Research
Volume25
Issue number4
DOIs
StatePublished - Oct 1 1990

Fingerprint

Interaction Effects
Multiple Regression
Continuous Variables
Multicollinearity
Regression Analysis
Regression Coefficient
Term
Interaction
Interpretation

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Statistics and Probability
  • Experimental and Cognitive Psychology

Cite this

The Detection and Interpretation of Interaction Effects Between Continuous Variables in Multiple Regression. / Jaccard, James; Wan, Choi K.; Turrisi, Robert.

In: Multivariate Behavioral Research, Vol. 25, No. 4, 01.10.1990, p. 467-478.

Research output: Contribution to journalArticle

@article{0f22f6418f4449e4a849e187d8f37478,
title = "The Detection and Interpretation of Interaction Effects Between Continuous Variables in Multiple Regression",
abstract = "Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent articles by Cronbach (1987) and Dunlap and Kemery (1987) suggested the use of two transformations to reduce “problems” of multicollinearity. These transformations are discussed in the context of the conditional nature of multiple regression with product terms. It is argued that although additive transformations do not affect the overall test of statistical interaction, they do affect the interpretational value of regression coefficients. Factors other than multicollinearity that may account for failures to observe interaction effects are noted.",
author = "James Jaccard and Wan, {Choi K.} and Robert Turrisi",
year = "1990",
month = "10",
day = "1",
doi = "10.1207/s15327906mbr2504_4",
language = "English (US)",
volume = "25",
pages = "467--478",
journal = "Multivariate Behavioral Research",
issn = "0027-3171",
publisher = "Psychology Press Ltd",
number = "4",

}

TY - JOUR

T1 - The Detection and Interpretation of Interaction Effects Between Continuous Variables in Multiple Regression

AU - Jaccard, James

AU - Wan, Choi K.

AU - Turrisi, Robert

PY - 1990/10/1

Y1 - 1990/10/1

N2 - Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent articles by Cronbach (1987) and Dunlap and Kemery (1987) suggested the use of two transformations to reduce “problems” of multicollinearity. These transformations are discussed in the context of the conditional nature of multiple regression with product terms. It is argued that although additive transformations do not affect the overall test of statistical interaction, they do affect the interpretational value of regression coefficients. Factors other than multicollinearity that may account for failures to observe interaction effects are noted.

AB - Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent articles by Cronbach (1987) and Dunlap and Kemery (1987) suggested the use of two transformations to reduce “problems” of multicollinearity. These transformations are discussed in the context of the conditional nature of multiple regression with product terms. It is argued that although additive transformations do not affect the overall test of statistical interaction, they do affect the interpretational value of regression coefficients. Factors other than multicollinearity that may account for failures to observe interaction effects are noted.

UR - http://www.scopus.com/inward/record.url?scp=84948868626&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84948868626&partnerID=8YFLogxK

U2 - 10.1207/s15327906mbr2504_4

DO - 10.1207/s15327906mbr2504_4

M3 - Article

AN - SCOPUS:84948868626

VL - 25

SP - 467

EP - 478

JO - Multivariate Behavioral Research

JF - Multivariate Behavioral Research

SN - 0027-3171

IS - 4

ER -