Estimating Multiple Types of Error Concurrently Using the Multitrait-Multierror (MTME) Approach
Response errors of different types, including acquiescence, social desirability, and random error, are well-known to be present in surveys simultaneously and to bias substantive results. Nevertheless, most methods developed to estimate and correct for such errors concentrate on a single error type at a time. Consequently, estimation of response errors is inefficient and their relative importance unknown. In this talk we propose a method to estimate and control for multiple types of measurement errors concurrently, which we call the “multitrait-multierror” (MTME) approach. MTME combines the theory of experimental design with latent variable modeling to efficiently estimate response errors of different types simultaneously and evaluate which are most impactful on a given question. We demonstrate the usefulness of our method using both cross-sectional and longitudinal data focusing on attitudinal and political values scales in two countries.
