The effect of error correlation on interfactor correlation in psychometric measurement

Peter H. Westfall, Kevin S.S. Henning, Roy D. Howell

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This article shows how interfactor correlation is affected by error correlations. Theoretical and practical justifications for error correlations are given, and a new equivalence class of models is presented to explain the relationship between interfactor correlation and error correlations. The class allows simple, parsimonious modeling of error correlations via prespecifying reliabilities. Within the class, the correlation between latent factors can be as high as 1.0, and as low as the correlation between certain component scores. The models are indistinguishable in terms of parameter parsimony, identifiability, and fit statistics, implying that interfactor correlation is not identifiable within the class. The existence of the class is problematic for psychometric measurement, because estimates of interfactor correlation form the foundation of much of the literature.

Original languageEnglish
Pages (from-to)99-117
Number of pages19
JournalStructural Equation Modeling
Volume19
Issue number1
DOIs
StatePublished - 2012

Keywords

  • Confirmatory factor analysis
  • Fit statistics
  • Latent variable
  • Measurement error
  • Structural equation models

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