caa a22 4500 606242252 CHVBK 20210128101317.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11336-013-9399-0 doi (NATIONALLICENCE)springer-10.1007/s11336-013-9399-0 Wang Chun University of Minnesota, 75 East River Road, Elliott Hall, N658, 55455, Minneapolis, MN, USA aut On Latent Trait Estimation in Multidimensional Compensatory Item Response Models [Elektronische Daten] [Chun Wang] Making inferences from IRT-based test scores requires accurate and reliable methods of person parameter estimation. Given an already calibrated set of item parameters, the latent trait could be estimated either via maximum likelihood estimation (MLE) or using Bayesian methods such as maximum a posteriori (MAP) estimation or expected a posteriori (EAP) estimation. In addition, Warm's (Psychometrika 54:427-450, 1989) weighted likelihood estimation method was proposed to reduce the bias of the latent trait estimate in unidimensional models. In this paper, we extend the weighted MLE method to multidimensional models. This new method, denoted as multivariate weighted MLE (MWLE), is proposed to reduce the bias of the MLE even for short tests. MWLE is compared to alternative estimators (i.e., MLE, MAP and EAP) and shown, both analytically and through simulations studies, to be more accurate in terms of bias than MLE while maintaining a similar variance. In contrast, Bayesian estimators (i.e., MAP and EAP) result in biased estimates with smaller variability. The Psychometric Society, 2014 maximum likelihood estimation (MLE) nationallicence weighted maximum likelihood estimation (WLE) nationallicence multivariate weighted maximum likelihood estimation (MWLE) nationallicence Bayesian estimation nationallicence Psychometrika Springer US; http://www.springer-ny.com 80/2(2015-06-01), 428-449 0033-3123 80:2<428 2015 80 11336 https://doi.org/10.1007/s11336-013-9399-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11336-013-9399-0 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Wang Chun University of Minnesota, 75 East River Road, Elliott Hall, N658, 55455, Minneapolis, MN, USA aut NATIONALLICENCE 773 0- Psychometrika Springer US; http://www.springer-ny.com 80/2(2015-06-01), 428-449 0033-3123 80:2<428 2015 80 11336