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Selecting the best unbalanced repeated measures model.

Guillermo Vallejo1, M Paula Fernández, Pablo E Livacic-Rojas

  • 1Department of Psychology, University of Oviedo, Plaza de Benito Feijóo, s/n 33003, Oviedo, Spain. gvallejo@uniovi.es

Behavior Research Methods
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Summary

This study evaluated statistical model selection criteria performance in repeated measures analysis. Consistent criteria excelled with simple covariance patterns, while efficient criteria performed better with complex patterns, especially under non-normal data.

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Area of Science:

  • Statistics
  • Biostatistics
  • Quantitative Psychology

Background:

  • Model selection is crucial in statistical analysis, particularly for longitudinal data with missing values.
  • Existing criteria in statistical packages vary in performance depending on data structure and estimation methods.
  • Understanding these variations is key for accurate data interpretation in repeated measures designs.

Purpose of the Study:

  • To assess the performance of various model selection criteria for mean and covariance structures in repeated measures.
  • To investigate the impact of unbalanced designs, missing data, and different estimation strategies on criterion effectiveness.
  • To compare consistent versus efficient criteria under diverse data conditions, including varying distribution shapes.

Main Methods:

  • Conducted simulation studies using unbalanced designs with moderate to large numbers of repeated measurements.
  • Varied total sample sizes, estimation strategies, and adjustments for calculating information criteria.
  • Examined the influence of different distribution shapes on criterion performance.

Main Results:

  • Consistent criteria outperformed efficient criteria under simple covariance patterns, whereas efficient criteria were superior for complex patterns.
  • Consistent criteria based on the number of subjects were more effective than those based on total observations, and vice versa for efficient criteria.
  • Efficient criteria were more sensitive to the lack of normality in datasets with missing values compared to consistent criteria.

Conclusions:

  • The choice between consistent and efficient criteria depends on the underlying covariance structure of the data.
  • Subject-based consistent criteria and observation-based efficient criteria show differential effectiveness.
  • Normality is a significant factor affecting the performance of efficient criteria in the presence of missing data.