An Empirical Bayes Approach to Subscore Augmentation: How Much Strength Can We Borrow?

This article examines a subscore augmentation procedure. The approach uses empirical Bayes adjustments and is intended to improve the overall accuracy of measurement when information is scant. Simulations examined the impact of the method on subscale scores in a variety of realistic conditions. The authors focused on two popular scoring methods: summed scores and item response theory scale scores for summed scores. Simulation conditions included number of subscales, length (hence, reliability) of subscales, and the underlying correlations between scales. To examine the relative performance of the augmented scales, the authors computed root mean square error, reliability, percentage correctly identified as falling within specific proficiency ranges, and the percentage of simulated individuals for whom the augmented score was closer to the true score than was the nonaugmented score. The general findings and limitations of the study are discussed and areas for future research are suggested.