Score-Based Tests of Measurement Invariance: Use in Practice
In this paper, we consider a family of recently-proposed measurement invariance tests that are based on the scores of a fitted model. This family can be used to test for measurement invariance w.r.t. a continuous auxiliary variable, without pre-specification of subgroups. Moreover, the family can be used when one wishes to test for measurement invariance w.r.t. an ordinal auxiliary variable, yielding test statistics that are sensitive to violations that are monotonically related to the ordinal variable (and less sensitive to non-monotonic violations). The paper is specifically aimed at potential users of the tests who may wish to know (i) how the tests can be employed for their data, and (ii) whether the tests can accurately identify specific models parameters that violate measurement invariance (possibly in the presence of model misspecification). After providing an overview of the tests, we illustrate their general use via the R packages lavaan and strucchange. We then describe two novel simulations that provide evidence of the tests' practical abilities. As a whole, the paper provides researchers with the tools and knowledge needed to apply these tests to general measurement invariance scenarios.
|Date of creation:||Oct 2013|
|Date of revision:|
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- Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2001.
"Strucchange: An R package for testing for structural change in linear regression models,"
2001,26, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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- Edgar Merkle & Jinyan Fan & Achim Zeileis, 2014.
"Testing for Measurement Invariance with Respect to an Ordinal Variable,"
Springer, vol. 79(4), pages 569-584, October.
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Boston College Working Papers in Economics
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- Albert Satorra, 1989. "Alternative test criteria in covariance structure analysis: A unified approach," Psychometrika, Springer, vol. 54(1), pages 131-151, March.
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