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Multilevel analysis of matching behavior: A comparison of maximum likelihood and Bayesian estimation
Michael John Ilagan1, Pier-Olivier Caron2, Milica Miočević1
1McGill University, Montréal, QC, Canada.
Journal of the Experimental Analysis of Behavior
|June 16, 2023
Summary
This study compared maximum likelihood (ML) and Bayesian estimation (BE) for multilevel models in behavior analysis. ML estimation demonstrated superior performance in parameter recovery and hypothesis testing for matching behavior studies.
Area of Science:
- Behavioral science
- Psychometrics
- Statistical modeling
Background:
- Accounting for within- and between-subjects variance is crucial for behavioral laws.
- Multilevel modeling is increasingly advocated for analyzing matching behavior.
- Challenges exist in applying multilevel modeling within behavior analysis, particularly regarding sample size requirements.
Purpose of the Study:
- To compare parameter recovery and hypothesis rejection rates of maximum likelihood (ML) and Bayesian estimation (BE).
- To evaluate these estimation methods for multilevel models in matching behavior studies.
- To investigate the influence of sample size, measurements per subject, sensitivity, and random effect variance.
Main Methods:
- Simulation study comparing ML and BE for multilevel models.
- Investigated four factors: number of subjects, measurements per subject, slope (sensitivity), and random effect variance.
- Assessed parameter recovery and hypothesis rejection rates.
Main Results:
- Both ML and BE with flat priors showed acceptable statistical properties for fixed effects (intercept and slope).
- ML estimation generally exhibited less bias, lower Root Mean Square Error (RMSE), higher statistical power, and more accurate false-positive rates.
- BE with uninformative priors requires more informative priors for effective use in this context.
Conclusions:
- ML estimation is recommended over BE with uninformative priors for multilevel modeling in matching behavior research.
- Further research is needed to explore the use of informative priors in Bayesian multilevel modeling for behavior analysis.
Keywords:
Bayesian estimationmatching behaviormatching lawmaximum likelihoodmultilevel modelpooled datastatistical analysisMore Related Videos
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