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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

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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.