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A Unifying Framework for Markov Modeling in Discrete Space and Discrete Time

Author

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  • ROLF LANGEHEINE

    (University of Kiel)

  • FRANK VAN DE POL

    (Netherlands Central Bureau of Statistics)

Abstract

The focus of this article is on Markov models for the analysis of panel data and, more specifically, on data obtained from repeated measurements of one categorical variable at several consecutive points in time. We first review developments in the field that attack the two main problems of the simple Markov model. The Mixed Markov model extends the simple model by allowing for population heterogeneity; the Latent Markov model incorporates measurement error and latent change into the simple model. Second, we present the more general Latent Mixed Markov model and show how both the Mixed Markov model and the Latent Markov model, as well as several more specific models, relate to this more general model. Finally, we reanalyze the Los Angeles panel data on depression with a focus on stability and change.

Suggested Citation

  • Rolf Langeheine & Frank Van De Pol, 1990. "A Unifying Framework for Markov Modeling in Discrete Space and Discrete Time," Sociological Methods & Research, , vol. 18(4), pages 416-441, May.
    • Handle: RePEc:sae:somere:v:18:y:1990:i:4:p:416-441
    DOI: 10.1177/0049124190018004002
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    References listed on IDEAS

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    1. Richard B. Davies & Robert Crouchley, 1986. "The Mover-Stayer Model," Sociological Methods & Research, , vol. 14(4), pages 356-380, May.
    2. Clifford Clogg & Leo Goodman, 1986. "On scaling models applied to data from several groups," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 123-135, March.
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    Cited by:

    1. Dimitris Pavlopoulos & Ruud Muffels & Jeroen K. Vermunt, 2009. "Training and Low‐pay Mobility: The Case of the UK and the Netherlands," LABOUR, CEIS, vol. 23(s1), pages 37-59, March.
    2. McDonough, Peggy & Worts, Diana & Sacker, Amanda, 2010. "Socioeconomic inequalities in health dynamics: A comparison of Britain and the United States," Social Science & Medicine, Elsevier, vol. 70(2), pages 251-260, January.
    3. Guglielmo D’Amico & Philippe Regnault, 2018. "Dynamic Measurement of Poverty: Modeling and Estimation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 305-340, November.
    4. Lee, Jae Hyun & Davis, Adam W. & Goulias, Konstadinos G., 2017. "Triggers of behavioral change: Longitudinal analysis of travel behavior, household composition and spatial characteristics of the residence," Journal of choice modelling, Elsevier, vol. 24(C), pages 4-21.
    5. We[beta]els, Holger & Von Eye, Alexander, 1996. "Using latent class analysis to detect behavioral patterns in systems of observational variables," Evaluation and Program Planning, Elsevier, vol. 19(4), pages 321-331, November.
    6. Montfort, Kees van & Bijleveld, Catrien, 1997. "Dynamic analysis of multivariate panel data with nonlinear transformations," Serie Research Memoranda 0054, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.

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