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Related Concept Videos

How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data.

Sebastian Mildiner Moraga1, Emmeke Aarts1

  • 1Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University.

Multivariate Behavioral Research
|May 17, 2023
PubMed
Summary

The multilevel hidden Markov model (MHMM) effectively analyzes complex behavioral data. Using more variables and individuals improves model stability and accuracy for longitudinal studies.

Keywords:
Bayesian statisticsHidden Markov modelsMonte Carlo studiescategorical dataindividual random effectsintensive longitudinal datamultilevel modeling

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Area of Science:

  • Social and behavioral sciences
  • Quantitative psychology
  • Longitudinal data analysis

Background:

  • The multilevel hidden Markov model (MHMM) is valuable for analyzing intense longitudinal data in social and behavioral sciences.
  • MHMMs capture latent behavioral dynamics and individual differences but require performance evaluation.

Purpose of the Study:

  • To extensively simulate and assess the estimation performance of a Bayesian MHMM with categorical data.
  • To determine the impact of varying numbers of dependent variables, individuals, and observations on MHMM accuracy.

Main Methods:

  • Extensive simulation study of a Bayesian multilevel hidden Markov model.
  • Manipulation of key parameters: number of dependent variables (1-8), individuals (5-90), and observations per individual (100-1600).
  • Assessment of model performance across different levels of state distinctiveness and separation.

Main Results:

  • Multivariate data generally reduces required sample size and enhances result stability.
  • Inclusion of noisy variables did not significantly harm model performance.
  • Group-level parameter estimation is influenced by both individual and observation counts, while between-individual variability estimation depends primarily on the number of individuals.

Conclusions:

  • Guidelines for necessary sample sizes are provided based on state distinctiveness, separation, and research objectives.
  • Multivariate data and sufficient individual counts are crucial for robust MHMM analysis.
  • The study clarifies performance characteristics of MHMMs for complex longitudinal data.