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Evaluating Measurement Invariance in Categorical Data Latent Variable Models with the EPC-Interest

Published online by Cambridge University Press:  04 January 2017

Daniel L. Oberski ,
Jeroen K. Vermunt  and
Guy B. D. Moors
Daniel L. Oberski*
Affiliation:
Department of Methodology and Statistics, Tilburg University, Room P 1107, PO Box 90153, 5000 LE Tilburg, The Netherlands
Jeroen K. Vermunt
Affiliation:
Department of Methodology and Statistics, Tilburg University, Room P 1114, PO Box 90153, 5000 LE Tilburg, The Netherlands, e-mail: [email protected]
Guy B. D. Moors
Affiliation:
Department of Methodology and Statistics, Tilburg University, Room P 1110, PO Box 90153, 5000 LE Tilburg, The Netherlands, e-mail: [email protected]
*
e-mail: [email protected] (corresponding author)
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Abstract

Many variables crucial to the social sciences are not directly observed but instead are latent and measured indirectly. When an external variable of interest affects this measurement, estimates of its relationship with the latent variable will then be biased. Such violations of “measurement invariance” may, for example, confound true differences across countries in postmaterialism with measurement differences. To deal with this problem, researchers commonly aim at “partial measurement invariance” that is, to account for those differences that may be present and important. To evaluate this importance directly through sensitivity analysis, the “EPC-interest” was recently introduced for continuous data. However, latent variable models in the social sciences often use categorical data. The current paper therefore extends the EPC-interest to latent variable models for categorical data and demonstrates its use in example analyses of U.S. Senate votes as well as respondent rankings of postmaterialism values in the World Values Study.

Type
Articles
Information
Political Analysis , Volume 23 , Issue 4 , Autumn 2015 , pp. 550 - 563
Copyright
Copyright © The Author 2015. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: The authors are grateful to two anonymous reviewers, the editor, Katrijn van Deun, Lianne Ippel, Eldad Davidov, Zsuzsa Bakk, Verena Schmittmann, and participants of EAM2014 for their comments. Replication materials for this article can be obtained from http://dx.doi.org/10.7910/DVN/I7Y3G2, Harvard Dataverse, V1. Supplementary materials for this article are available on the Political Analysis Web site.

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