Bias-Adjusted Three-Step Latent Markov Modeling With Covariates. Issue 5 (2nd September 2016)

• Record Type: Journal Article
• Authors: Di Mari, Roberto; Oberski, Daniel L.; Vermunt, Jeroen K.
• Is Part Of: Structural equation modeling. Volume 23:Issue 5(2016)
• Page Start: 649
• Page End: 660
• Publication Date: 2016-09-02
• Subjects: bias-correction -- classify-analyze -- hidden Markov model -- latent class analysis -- latent Markov model -- latent transition analysis -- three-step approach; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2016.1191015
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Latent Markov models with covariates can be estimated via 1-step maximum likelihood. However, this 1-step approach has various disadvantages, such as that the inclusion of covariates in the model might alter the formation of the latent states and that parameter estimation could become infeasible with large numbers of time points, responses, and covariates. This is why researchers typically prefer performing the analysis in a stepwise manner; that is, they first construct the measurement model, then obtain the latent state classifications, and subsequently study the relationship between covariates and latent state memberships. However, such a stepwise approach yields downward-biased estimates of the covariate effects on initial state and transition probabilities. This article, shows how to overcome this problem using a generalization of the bias-corrected 3-step estimation method proposed for latent class analysis (Asparouhov & Muthén, 2014; Bolck, Croon, & Hagenaars, 2004; Vermunt, 2010). We give a formal derivation of the generalization to latent Markov models and discuss how it can be used with many time points by incorporating it into a Baum–Welch type of expectation-maximization algorithm. We evaluate the method through a simulation study and illustrate it using an application on household financial portfolio change. Our study shows that the proposed correction method yields unbiased parameter estimates and accurate standard errors, except for situations with very poorly separated classes and a small sample.