General factor mean difference estimation in bifactor models with ordinal data

Research output: Contribution to journalArticlepeer-review

Abstract

A simulation study was conducted to explore robustness of general factor mean difference estimation in bifactor measurement models with ordered-categorical measures. Common analysis misspecifications in which generated bifactor data were fitted using a unidimensional model and/or ordered-categorical data were treated as continuous were compared under generation conditions varying by sample size, number of response categories, effect size of the general factor mean difference, and loadings on the specific factors. Fitting bifactor data using unidimensional models resulted in estimation bias in the general factor mean difference, with magnitude largely determined by degree of unidimensionality and size of the general factor mean difference. Although bifactor models produced less estimation bias, estimates were less precise. Modeling the data as categorical and employing the WLSMV estimator provided somewhat more power, whereas modeling the data as continuous and applying MLR produced relatively less estimation bias when unidimensional models were specified for strongly bifactor data.

Original languageEnglish (US)
JournalStructural Equation Modeling
DOIs
StateAccepted/In press - 2020

Keywords

  • Bifactor models
  • general factor mean difference
  • multiple-group categorical CFA
  • ordinal data

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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