A trifactor model for integrating ratings across multiple informants.
Robert A. Zucker, Patrick J. Curran, Ruth E. Baldasaro, Daniel J. Bauer, Howard, Andrea L., Andrea M. Hussong, Laurie Chassin · Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2020
Psychologists often obtain ratings for target individuals from multiple informants such as parents or peers. In this paper we propose a tri-factor model for multiple informant data that separates target-level variability from informant-level variability and item-level variability. By leveraging item-level data, the tri-factor model allows for examination of a single trait rated on a single target. In contrast to many psychometric models developed for multitrait-multimethod data, the tri-factor model is predominantly a measurement model. It is used to evaluate item quality in scale development, test hypotheses about sources of target variability (e.g., sources of trait differences) versus informant variability (e.g., sources of rater bias), and generate integrative scores that are purged of the subjective biases of single informants.