A Direct Comparison Approach for Testing Measurement Invariance

Gordon W. Cheung, The Chinese University of Hong Kong, and Rebecca S. Lau, The Open University of Hong Kong, published “A Direct Comparison Approach for Testing Measurement Invariance” on November 3rd, 2011 in Organizational Research Methods‘ OnlineFirst section. Other OnlineFirst articles can be found here.

The abstract:

Measurement equivalence/invariance (ME/I) is a condition that should be met before meaningful comparisons of survey results across groups can be made. As an alternative to the likelihood ratio test (LRT), the change in comparative fit index (DCFI) rules of thumb, and the modification index (MI), this teaching note demonstrates the procedures for establishing bias-corrected (BC) bootstrap confidence intervals for testing ME/I. Unlike the LRT and DCFI methods, which need a different model estimation per item, the BC bootstrap confidence intervals approach can examine itemlevel ME/I tests using a single model. This method greatly simplifies the search for an invariant item as the reference indicator in the factor-ratio test. Also demonstrated here is how the factor-ratio test and the list-and-delete method can be extended from the metric invariance test to the scalar invariance test. Finally, the BC bootstrap confidence interval procedures for comparing uniqueness variances, factor variances, factor covariances, and latent means across groups are shown.

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