The Power to Detect and Predict Individual Differences in Intra-Individual Variability Using the Mixed-Effects...

Area of Science:

• Psychometrics
• Quantitative Psychology
• Behavioral Statistics

Background:

• Intra-individual variability is crucial for understanding psychological phenomena.
• Existing statistical models may not adequately capture individual differences in this variability.
• The mixed-effects location-scale model offers a promising framework for such investigations.

Purpose of the Study:

• To equip empirical scientists with practical tools for analyzing individual differences in intra-individual variability.
• To evaluate the performance of the mixed-effects location-scale model regarding Type I error rates and statistical power.
• To offer empirically-based guidelines for constructing scale models within this framework.

Main Methods:

• Evaluation of Type I error rates and power for detecting individual differences in intra-individual variability.
• Simulation studies to assess model performance under various design conditions.
• Development of practical guidelines for model building, including random and fixed effects.
• Provision of power simulation programs for a priori power analyses.

Main Results:

• Statistical power increased with more individuals, more repeated occasions, larger proportions of explainable variance, and larger effect sizes.
• Type I error rates were acceptable across tested scenarios, including those with and without initially detectable individual differences.
• The presence of individual-level predictors in the scale model influenced the detection of variability.

Conclusions:

• The mixed-effects location-scale model is a viable tool for studying individual differences in intra-individual variability.
• Researchers can enhance statistical power by optimizing study design parameters.
• Practical advice is provided for study design and model construction to facilitate the application of this model.