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Identification of Confirmatory Factor Analysis Models of Different Levels of Invariance for Ordered Categorical Outcomes

Published online by Cambridge University Press:  01 January 2025

Hao Wu Open the ORCID record for Hao Wu [Opens in a new window]  and
Ryne Estabrook
Hao Wu*
Affiliation:
Boston College
Ryne Estabrook
Affiliation:
Northwestern University
*
Correspondence should be made to Hao Wu, Boston College, Chestnut Hill, USA. Email: [email protected]
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Abstract

This article considers the identification conditions of confirmatory factor analysis (CFA) models for ordered categorical outcomes with invariance of different types of parameters across groups. The current practice of invariance testing is to first identify a model with only configural invariance and then test the invariance of parameters based on this identified baseline model. This approach is not optimal because different identification conditions on this baseline model identify the scales of latent continuous responses in different ways. Once an invariance condition is imposed on a parameter, these identification conditions may become restrictions and define statistically non-equivalent models, leading to different conclusions. By analyzing the transformation that leaves the model-implied probabilities of response patterns unchanged, we give identification conditions for models with invariance of different types of parameters without referring to a specific parametrization of the baseline model. Tests based on this approach have the advantage that they do not depend on the specific identification condition chosen for the baseline model.

Keywords

ordered categorical datainvariance testingmodel identification
Type
Article
Information
Psychometrika , Volume 81 , Issue 4 , December 2016 , pp. 1014 - 1045
Copyright
Copyright © 2016 The Psychometric Society

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Footnotes

Roger Millsap, whose work inspired this paper, unexpectedly passed away when we were preparing this manuscript. We would like to honor him for his pioneering work in measurement invariance.

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