Tests of Measurement Invariance Without Subgroups: A Generalization of Classical Methods
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The issue of measurement invariance commonly arises in factor-analytic contexts, with methods for assessment including likelihood ratio tests, Lagrange multiplier tests, and Wald tests. These tests all require advance definition of the number of groups, group membership, and offending model parameters. In this paper, we study tests of measurement invariance based on stochastic processes of casewise derivatives of the likelihood function. These tests can be viewed as generalizations of the Lagrange multiplier test, and they are especially useful for: (i) identifying subgroups of individuals that violate measurement invariance along a continuous auxiliary variable without prespecified thresholds, and (ii) identifying specific parameters impacted by measurement invariance violations. The tests are presented and illustrated in detail, including an application to a study of stereotype threat and simulations examining the tests’ abilities in controlled conditions.
Key wordsmeasurement invariance parameter stability factor analysis structural equation models
This work was supported by National Science Foundation grant SES-1061334. The authors thank Jelte Wicherts, who generously shared data for the stereotype threat application, Yves Rosseel, who provided feedback and code for performing the tests with the lavaan package, Kris Preacher, who provided helpful comments on the manuscript, and the participants of the Psychoco 2012 workshop on psychometric computing for helpful discussion.
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