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Cited by 421

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Evaluating Model Fit With Ordered Categorical Data Within a Measurement Invariance Framework: A Comparison of Estimators

Sass, Daniel A., Schmitt, Thomas A., Marsh, Herbert W.·2014·Structural Equation Modeling: A Multidisciplinary Journal
DOI 10.1080/10705511.2014.882658
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Abstract

A paucity of research has compared estimation methods within a measurement invariance (MI) framework and determined if research conclusions using normal-theory maximum likelihood (ML) generalizes to the robust ML (MLR) and weighted least squares means and variance adjusted (WLSMV) estimators. Using ordered categorical data, this simulation study aimed to address these queries by investigating 342 conditions. When testing for metric and scalar invariance, Δχ2 results revealed that Type I error rates varied across estimators (ML, MLR, and WLSMV) with symmetric and asymmetric data. The Δχ2 power varied substantially based on the estimator selected, type of noninvariant indicator, number of noninvariant indicators, and sample size. Although some the changes in approximate fit indexes (ΔAFI) are relatively sample size independent, researchers who use the ΔAFI with WLSMV should use caution, as these statistics do not perform well with misspecified models. As a supplemental analysis, our results evaluate and suggest cutoff values based on previous research.

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Metadata

Authors
Sass, D. A., Schmitt, T. A., & Marsh, H. W.
Venue
Structural Equation Modeling: A Multidisciplinary Journal
Publisher
Informa UK Limited
Published
2014-04-03
Volume
21
Issue
2
Pages
167-180
ISSN
  • 1070-5511 (ISSN-L)
  • 1532-8007
DOI
10.1080/10705511.2014.882658
OpenAlex
W2024071025
Cited by
421
References
10
FWCI
21.77
Type
journal article
Language
en
Open access
closed

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