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    On the Use of Mixed Markov Models for Intensive Longitudinal Data.

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    Author
    de Haan-Rietdijk, S; Kuppens, P; Bergeman, CS; Sheeber, LB; Allen, NB; Hamaker, EL
    Date
    2017-11
    Source Title
    Multivariate Behavioral Research
    Publisher
    Informa UK Limited
    University of Melbourne Author/s
    Allen, Nicholas
    Affiliation
    Melbourne School of Psychological Sciences
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    de Haan-Rietdijk, S., Kuppens, P., Bergeman, C. S., Sheeber, L. B., Allen, N. B. & Hamaker, E. L. (2017). On the Use of Mixed Markov Models for Intensive Longitudinal Data.. Multivariate Behav Res, 52 (6), pp.747-767. https://doi.org/10.1080/00273171.2017.1370364.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/255731
    DOI
    10.1080/00273171.2017.1370364
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5698102
    Abstract
    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. Markov modeling is flexible and can be used with various types of data to study observed or latent state-switching processes, and can include subject-specific random effects to account for heterogeneity. We focus on the application of mixed Markov models to intensive longitudinal data sets in psychology, which are becoming ever more common and provide a rich description of each subject's process. We examine 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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