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Related Experiment Videos

Measuring change: mixed Markov models for ordinal panel data.

U Böckenholt1

  • 1Department of Psychology, University of Illinois, Champaign 61820, USA.

The British Journal of Mathematical and Statistical Psychology
|June 25, 1999
PubMed
Summary
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This study introduces mixed Markov models for ordinal panel data, effectively separating within- and between-subject variability. The models incorporate covariates to analyze emotions and personality factors over time.

Area of Science:

  • Statistics
  • Psychology
  • Longitudinal Data Analysis

Background:

  • Analyzing longitudinal data requires distinguishing between within- and between-subject variability.
  • Existing models may not adequately capture both sources of variation in ordinal panel data.
  • Understanding temporal relationships between psychological constructs is crucial.

Purpose of the Study:

  • To present mixed Markov models for ordinal data that account for both within- and between-subject variability.
  • To incorporate covariates for individual differences and time-specific changes.
  • To test hypotheses about the dynamics of emotions and personality factors over time.

Main Methods:

  • Development of mixed Markov models for ordinal longitudinal data.
  • Specification of an observation-driven process at the individual level.

Related Experiment Videos

  • Inclusion of parametric or semi-parametric random parameter variation.
  • Application to a three-week diary study analyzing emotions and personality.

    • Main Results:

    • The proposed models successfully distinguish between within- and between-subject variability in ordinal panel data.
    • Covariates effectively captured inter-individual differences and temporal changes.
    • The analysis provided insights into the dynamic relationships between emotions and personality factors.

    Conclusions:

    • Mixed Markov models offer a robust framework for analyzing ordinal panel data with multiple sources of variation.
    • The approach is suitable for datasets with numerous time points.
    • The study demonstrates the utility of these models in psychological research for understanding temporal dynamics.