.png){kind=icon}
Return
Evaluating Robust Tests for Longitudinal Invariance Testing
.png){kind=icon}
OA
AI
DOI：10.1080/10705511.2026.2714956.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Testing parameter constraints in CFA under non-normality is routinely done with robust chi-square difference tests, yet the finite-sample behavior of the available procedures remains unsettled. Pavlov et al. (Citation2020) identified the Mplus mean- and variance-adjusted test DMLMV as the most accurate established procedure in a two-model longitudinal CFA design. Here, we extend their study in three ways: we add penalized eigenvalue (PE) methods; we vary three estimation choices that Pavlov et al. held fixed—Satorra’s (2000) direct estimator of
the unbiased estimator of
and the reweighted least-squares (RLS) base statistic; and we broaden the distributional conditions. While the performance of DMLMV was acceptable in the smaller model (
), it was too liberal in the larger model (
). Our results suggest that PE and the scaled-and-shifted (SS) test, combined with the 2000 method, deserve priority in longitudinal invariance testing. In our simulations, size-adjusted power was very similar across all procedures. In terms of Type I error control, we recommend RLS-based PE with the unbiased
estimator, and the RLS-based SS test, as the preferred procedures.
Keywords:
Chi-square difference test
confirmatory factor analysis
longitudinal invariance testing
nested model comparison
non-normality
penalized eigenvalues
Journal
S
IF:
3.2
Papers:
95
Citations:
2.1W
Organization
Cited Papers
No cited papers available