How to perform three-step latent class analysis in the presence of measurement non-invariance or differential item functioning

Jeroen K. Vermunt*, Jay Magidson

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

The practice of latent class (LC) modeling using a bias-adjusted three-step approach has become widely popular. However, the current three-step approach has one important drawback–its key assumption of conditional independence between external variables and latent class indicators is often violated in practice, such as when a (nominal) covariate represents subgroups showing measurement non-invariance (MNI) or differential item functioning (DIF). In this article, we demonstrate how the current three-step approach should be modified to account for MNI; that is, covariates causing DIF should be included in the step-one model and the step-three classification error adjustment should differ across the values of the DIF covariates. We also propose a model-building strategy that makes the new methodology practically applicable also when it is unknown which of the external variables cause DIF. The new approach, implemented in the program Latent GOLD, is illustrated using a synthetic and a real data example.

Original languageEnglish
Number of pages9
JournalStructural Equation Modeling
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • AUXILIARY VARIABLES
  • MODELS
  • auxiliary variables
  • item bias
  • mixture modeling
  • three-step modeling

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