Applications and Extensions of Metric Stability Analysis

Abstract

Item response theory models and applications are affected by many sources of variability, including errors associated with item parameter estimation. Metric stability analysis (MSA) is one method to evaluate the effects of item parameter standard errors that quantifies how well a model determines the latent trait metric. This paper describes how to evaluate MSA in dichotomous and polytomous data and describes a Bayesian implementation of MSA that does not require a positive definite variance–covariance matrix among item parameters. MSA analyses are illustrated in the context of an oral-health-related quality of life measure administered before and after prosthodontic treatment. The R code to implement the methods described in this paper is provided.