Measurement error in the analysis of interaction effects between continuous predictors using multiple regression: Multiple indicator and structural equation approaches

James Jaccard, Choi K. Wan

Research output: Contribution to journalArticle

Abstract

Unreliability of measures produces bias in regression coefficients. Such measurement error is particularly problematic with the use of product terms in multiple regression because the reliability of the product terms is generally quite low relative to its component parts. The use of confirmatory factor analysis as a means of dealing with the problem of unreliability was explored in a simulation study. The design compared traditional regression analysis (which ignores measurement error) with approaches based on latent variable structural equation models that used maximum-likelihood and weighted least squares estimation criteria. The results showed that the latent variable approach coupled with maximum-likelihood estimation methods did a satisfactory job of interaction analysis in the presence of measurement error in terms of Type I and Type II errors.

Original languageEnglish (US)
Pages (from-to)348-357
Number of pages10
JournalPsychological Bulletin
Volume117
Issue number2
StatePublished - 1995

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Structural Models
Least-Squares Analysis
Statistical Factor Analysis
Regression Analysis
Multiple Regression
Predictors
Interaction
Measurement Error
Structural Equations

ASJC Scopus subject areas

  • History and Philosophy of Science
  • Psychology(all)

Cite this

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abstract = "Unreliability of measures produces bias in regression coefficients. Such measurement error is particularly problematic with the use of product terms in multiple regression because the reliability of the product terms is generally quite low relative to its component parts. The use of confirmatory factor analysis as a means of dealing with the problem of unreliability was explored in a simulation study. The design compared traditional regression analysis (which ignores measurement error) with approaches based on latent variable structural equation models that used maximum-likelihood and weighted least squares estimation criteria. The results showed that the latent variable approach coupled with maximum-likelihood estimation methods did a satisfactory job of interaction analysis in the presence of measurement error in terms of Type I and Type II errors.",
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