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A Method to Explore the Best Mixed-Effects Model in a Data-Driven Manner with Multiprocessing: Applications in Public
1Educational Psychology Program, University of Alabama, Tuscaloosa, AL 35487, USA.
Summary
A new R module, explore.models, efficiently identifies optimal multilevel models for public health research using Akaike information criterion (AIC) and Bayesian information criterion (BIC). This validated tool aids in selecting superior models faster than traditional methods.
Area of Science:
- Public Health
- Health Psychology
- Educational Research
Background:
- Multilevel modeling is crucial for analyzing complex public health data with nested structures.
- Selecting the best candidate model from numerous possibilities is a significant challenge in multilevel analysis.
- Existing methods for model selection can be computationally intensive and time-consuming.
Purpose of the Study:
- To develop and validate an R module, explore.models, for efficient multilevel model selection.
- To compare the performance of explore.models against established model selection criteria.
- To assess the reliability and feasibility of the module for public health research.
Main Methods:
- Developed the R module 'explore.models' to compare candidate multilevel models using Akaike information criterion (AIC) and Bayesian information criterion (BIC) with multiprocessing.
- Tested the module on three public health datasets examining psychological well-being, preventive measure compliance, and vaccine intent.
- Cross-validated module results with model Bayes Factors to ensure accuracy.
Main Results:
- The explore.models module accurately nominated the best candidate models using AIC and BIC.
- Nomination outcomes were consistently supported by model Bayes Factors.
- Identified models were superior to full models based on model Bayes Factors.
- The module demonstrated significantly shorter processing times compared to model Bayes Factor calculations.
Conclusions:
- explore.models is a reliable, valid, and feasible R package for data-driven multilevel model exploration in public health, health psychology, and education.
- The module offers a computationally efficient alternative for selecting optimal models.
- Utilizing AIC and BIC with multiprocessing provides a robust approach to model selection in complex datasets.
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