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On the Use of Mixed Markov Models for Intensive Longitudinal Data

Datum:
28 september 2017
Auteur:
Auteur: Haan-Rietdijk, S. de, Kuppens, P., Bergeman, C. S., Sheeber, L. B., Allen, N. B., & Hamaker, E. L.
Opdrachtgever:
Cito

Markov modeling presents an attractive analytical framework for researchers who are interested in state-switching processes occurring within a person, dyad, family, group, or other system over time. This article focusses on the application of mixed Markov models to intensive longitudinal data sets in psychology. The article makes clear how specifications of a Markov model change when continuous random effect distributions are included, and how mixed Markov models can be used in the intensive longitudinal research context. Advantages of Bayesian estimation are discussed and the approach is illustrated by two empirical applications.

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