title: Improving measurement-invariance assessments: correcting entrenched testing deficiencies.
abstract: Factor analysis historically focused on measurement while path analysis employed observed variables as though they were error-free. When factor- and path-analysis merged as structural equation modeling, factor analytic notions dominated measurement discussions - including assessments of measurement invariance across groups. The factor analytic tradition fostered disregard of model testing and consequently entrenched this deficiency in measurement invariance assessments.
  
  
  
  Applying contemporary model testing requirements to the so-called configural model initiating invariance assessments will improve future assessments but a substantial backlog of deficient assessments remain to be overcome. This article summarizes the issues, demonstrates the problem using a recent example, illustrates a superior model assessment strategy, and documents disciplinary entrenchment of inadequate testing as exemplified by the journal Organizational Research Methods. Employing the few methodologically and theoretically best, rather than precariously-multiple, indicators of latent variables increases the likelihood of achieving properly causally specified structural equation models capable of displaying measurement invariance. Just as evidence of invalidity trumps reliability, evidence of configural model misspecification trumps invariant estimates of misspecified coefficients.
authors.affiliation: Department of Sociology, University of Alberta, Edmonton, T6G 2H4, Canada. LHayduk@ualberta.ca.
authors.forename: Leslie A
authors.initials: LA
authors.lastname: Hayduk
mesh_terms: D000704:Analysis of Variance; D005163:Factor Analysis, Statistical; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D012107:Research Design
keywords: Close fit; Factor analysis; Invariance; SEM; Structural equation model; Testing