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Area of Science:

  • Psychological Sciences
  • Statistics
  • Data Analysis

Background:

  • Ecological Momentary Assessment (EMA) collects real-time data.
  • Traditional frequentist methods have limitations for complex EMA data.
  • Bayesian statistics offers a flexible alternative for analyzing psychological data.

Purpose of the Study:

  • Introduce Bayesian generalized linear mixed-effects models for EMA data analysis.
  • Highlight practical and conceptual advantages of the Bayesian approach.
  • Provide a reproducible workflow for EMA data analysis using Bayesian methods.

Main Methods:

  • Application of Bayesian generalized linear mixed-effects models.
  • Demonstration using EMA data to predict alcohol outcomes.
  • Comparison of Bayesian versus frequentist approaches.

Main Results:

  • Bayesian methods facilitate incorporation of prior knowledge.
  • Bayesian models accommodate diverse outcome distributions.
  • Quantification of effect-size uncertainty and evidence for hypotheses is enabled.

Conclusions:

  • Bayesian workflow enhances EMA data analysis in psychological sciences.
  • Researchers can adopt these methods for robust data interpretation.
  • Reproducible examples and code are provided for practical application.