Evaluating model fit with ordered categorical data within a measurement invariance framework : A comparison of estimators
Sass, Daniel Schmitt, Thomas Marsh, Herbert W.
Sass, Daniel
Schmitt, Thomas
Marsh, Herbert W.
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.
Keywords
Date
2014
Type
Journal article
Journal
Structural Equation Modeling: A Multidisciplinary Journal
Book
Volume
21
Issue
2
Page Range
167-180
Article Number
ACU Department
Institute for Positive Psychology and Education
Faculty of Education and Arts
Faculty of Education and Arts