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Title: Measurement invariance and latent Markov models: A model selection problem Authors:  Francesco Dotto - University of Roma Tre (Italy) [presenting]
Roberto Di Mari - Universita' di Catania, Dipartimento di Economia e Impresa (Italy)
Alessio Farcomeni - University of Rome Tor Vergata (Italy)
Antonio Punzo - University of Catania (Italy)
Abstract: A general approach is proposed to detect measurement non-invariance in latent Markov models for longitudinal data. We define different notions of differential item functioning in the context of panel data. We then present a model selection approach based on the Bayesian information criterion (BIC) to choose both the number of latent states and the measurement structure. We show the practical relevance by means of an extensive simulation study, and illustrate its use on two real-data examples from the social sciences. Our results indicate that BIC is able to select the correct measurement equivalence structure more than 95\% of the time.