Testing Measurement Invariance in Longitudinal Data with Ordered-Categorical Measures

By Yu Liu, R. Millsap, S. West, J. Tein, R. Tanaka, Kevin J. Grimm. Psychological methods, 2016

295 citations recorded by the source corpus.

A goal of developmental research is to examine individual changes in constructs over time. The accuracy of the models answering such research questions hinges on the assumption of longitudinal measurement invariance: The repeatedly measured variables need to represent the same construct in the same metric over time. Measurement invariance can be studied through factor models examining the relations between the observed indicators and the latent constructs. In longitudinal research, ordered-categorical indicators such as self- or observer-report Likert scales are commonly used, and these measures often do not approximate continuous normal distributions. The present didactic article extends previous work on measurement invariance to the longitudinal case for ordered-categorical indicators. We address a number of problems that commonly arise in testing measurement invariance with longitudinal data, including model identification and interpretation, sparse data, missing data, and estimation issues. We also develop a procedure and associated R program for gauging the practical significance of the violations of invariance. We illustrate these issues with an empirical example using a subscale from the Mexican American Cultural Values scale. Finally, we provide comparisons of the current capabilities of 3 major latent variable programs (lavaan, Mplus, OpenMx) and computer scripts for addressing longitudinal measurement invariance.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/614a5de73218c117ea07c91365a4978aa36d88c1

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Citations in the corpus

The development of emotional and behavioral self-regulation and their effects on academic achievement in childhood (2018, background): “Table 1 summarizes the necessary parameter restrictions in the longitudinal measurement invariance testing procedure (for another approach see Liu et al., 2016).”
Relations among math self-efficacy, interest, intentions, and achievement: a social cognitive perspective (2018, methodology): “These longitudinal tests were conducted in line with a novel taxonomy of longitudinal invariance tests with ordered categorical data (Liu et al., 2016; Perera, McIlveen, Burton, & Corser, 2015).”