Measurement Invariance in Latent Transition Models

• In Event: Issues of Measurement Invariance in Mixture Models for Educational Data

Abstract

Latent transition analysis (LTA) provides an opportunity for the study of development in discrete states over time, providing a flexible modeling framework for educational researchers to address pressing research questions. Most applications of the LTA model found in the literature make the assumption of measurement invariance-- that is, they assume that both the number and type of latent classes that emerge across measurement occasions are the same. While the assumption of measurement invariance (MI) certainly makes interpretation of transition probabilities more straightforward, it is not a necessary assumption for model identification or valid interpretation, and is often used in applications without an empirical or theoretical justification. Additionally, MI significantly reduces the number of measurement parameters estimated at each time point, significantly increasing model estimating time and complexity.

Authors

• Karen L. Nylund-Gibson, University of California - Santa Barbara

• Katherine E. Masyn, Georgia State University