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.
Keywords
MulticollinearityRegression diagnosticRegression analysisRegressionEconometricsContext (archaeology)StatisticsInterpretation (philosophy)InteractionLinear regressionAffect (linguistics)PsychologyVariablesMathematicsComputer sciencePolynomial regression
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Publication Info
- Year
- 1990
- Type
- article
- Volume
- 25
- Issue
- 4
- Pages
- 467-478
- Citations
- 729
- Access
- Closed
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Cite This
James Jaccard,
Choi K. Wan,
Rob Turrisi
(1990).
The Detection and Interpretation of Interaction Effects Between Continuous Variables in Multiple Regression.
Multivariate Behavioral Research
, 25
(4)
, 467-478.
https://doi.org/10.1207/s15327906mbr2504_4
Identifiers
- DOI
- 10.1207/s15327906mbr2504_4