Estimating Latent Traits from Expert Surveys: An Analysis of Sensitivity to Data Generating Process

Abstract

Models for converting expert-coded data to point estimates of latent concepts assume different data-generating processes. In this paper, we simulate ecologically-valid data according to different assumptions, and examine the degree to which common methods for aggregating expert-coded data can recover true values and construct appropriate coverage intervals from these data. We find that hierarchical latent variable models and the bootstrapped mean perform similarly when variation in reliability and scale perception is low; latent variable techniques outperform the mean when variation is high. Hierarchical A-M and IRT models generally perform similarly, though IRT models are often more likely to include true values within their coverage intervals. The median and non-hierarchical latent variable modeling techniques perform poorly under most assumed data generating processes.