Multi-Group Confirmatory Factor Analysis for Testing Measurement Invariance in Mixed Item Format Data

By Kim Koh, Bruno D. Zumbo. 2008

80 citations recorded by the source corpus.

This simulation study investigated the empirical Type I error rates of using the maximum likelihood estimation method and Pearson covariance matrix for multi-group confirmatory factor analysis (MGCFA) of full and strong measurement invariance hypotheses with mixed item format data that are ordinal in nature. The results indicate that mixed item formats and sample size combinations do not result in inflated empirical Type I error rates for rejecting the true measurement invariance hypotheses. Therefore, although the common methods are in a sense sub-optimal, they don’t lead to researchers claiming that measures are functioning differently across groups – i.e., a lack of measurement invariance.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/40e902b57906dc2520e601452660bc9ee4d41510

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In Computer Networks and Communications

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Assessing statistical aspects of test fairness with structural equation modelling (2013, methodology): “These results suggest that the use of sub-optimal methods within the scope of the variables tested by Koh and Zumbo (2008) may not systematically lead researchers to reject true measurement invariance hypotheses.”
Multiple-Group Confirmatory Factor Analysis in R--A Tutorial in Measurement Invariance with Continuous and Ordinal Indicators. (2014, methodology): “…using categorical data and WLSMV estimation would be especially useful to close the gap between single-group CFA where these estimation methods are widely used and multiple-group CFA for which most researchers still use ML estimation irrespective of the nature of the data (Koh & Zumbo, 2008).”