In item response theory (IRT), item response probabilities are a function of item characteristics and latent trait scores. Within an IRT framework, trait score misestimation results from (a) random error, (b) the trait score estimation method, (c) errors in item parameter estimation, and (d) model misspecification. This study investigated the relative effects of these error sources on the bias and confidence interval coverage rates for trait scores. Our results showed that overall, bias values were close to 0, and coverage rates were fairly accurate for central trait scores and trait estimation methods that did not use a strong Bayesian prior. However, certain types of model misspecifications were found to produce severely biased trait estimates with poor coverage rates, especially at extremes of the latent trait continuum. It is demonstrated that biased trait estimates result from estimated item response functions (IRFs) that exhibit systematic conditional bias, and that these conditionally biased IRFs may not be detected by model or item fit indices. One consequence of these results is that certain types of model misspecifications can lead to estimated trait scores that are nonlinearly related to the data-generating latent trait. Implications for item and trait score estimation and interpretation are discussed.
Keywords: estimation; item response theory; score interpretation.