We compare two alternative approaches of modeling longitudinal data in order to find patterns of change according to the available covariates, accounting for the unobserved heterogeneity in the population of interest. We refer to the Latent Markov (LM) model and to the Growth Mixture Model (GMM). We illustrate the main results by using real data on the self-perceived health status.
Pennoni, F., Romeo, I. (2015). Latent Markov and growth mixture models: a comparison. In Book of Abstract of the 10th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (SIS) (pp.181-184). Cagliari : CUEC.
Latent Markov and growth mixture models: a comparison
PENNONI, FULVIA;ROMEO, ISABELLA
2015
Abstract
We compare two alternative approaches of modeling longitudinal data in order to find patterns of change according to the available covariates, accounting for the unobserved heterogeneity in the population of interest. We refer to the Latent Markov (LM) model and to the Growth Mixture Model (GMM). We illustrate the main results by using real data on the self-perceived health status.
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