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Who is and is not "average"? Random effects selection with spike-and-slab priors.

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This study introduces a novel spike-and-slab prior for mixed-effects models, enhancing the analysis of individual differences in psychological science by identifying unique individual effects beyond population averages.

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Area of Science:

  • Psychological Science
  • Statistical Modeling

Background:

  • Mixed-effects models are standard for studying individual differences in psychology.
  • Current methods often focus on overall variability rather than individual specifics.

Purpose of the Study:

  • To propose and evaluate the spike-and-slab prior for random effect selection in mixed-effects models.
  • To enable a more nuanced understanding of individual differences by identifying specific individuals' deviations from the average.

Main Methods:

  • Introduced a spike-and-slab prior, a mixture of a point-mass at zero and a diffuse component for nonzero values.
  • Applied the method to select random intercepts in logistic regression and random slopes in cognitive tasks.
  • Conducted simulation studies to validate the method's performance in identifying individual differences.

Main Results:

  • Demonstrated the utility and flexibility of the spike-and-slab prior in identifying individual differences in random intercepts and slopes.
  • Showcased significant individual differences in cognitive task performance despite minimal slope variability.
  • Simulation studies confirmed accurate identification of non-average individuals without compromising estimates.

Conclusions:

  • The spike-and-slab prior offers a powerful tool for detailed individual differences research in psychological science.
  • This methodology facilitates answering specific questions about individual deviations from population-level effects.
  • Future research directions include extending the methodology to more complex models and applications.