Wang Chun
University of Minnesota, 75 East River Road, Elliott Hall, N658, Minneapolis, MN, 55455, USA,
Psychometrika. 2015 Jun;80(2):428-49. doi: 10.1007/s11336-013-9399-0. Epub 2014 Mar 7.
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.
从基于项目反应理论（IRT）的测试分数中进行推断需要准确可靠的人员参数估计方法。给定一组已经校准的项目参数，可以通过最大似然估计（MLE）或使用贝叶斯方法（如最大后验估计（MAP）或期望后验估计（EAP））来估计潜在特质。此外，Warm（《心理测量学》54:427 - 450，1989）提出了加权似然估计方法，以减少单维模型中潜在特质估计的偏差。在本文中，我们将加权MLE方法扩展到多维模型。这种新方法称为多元加权MLE（MWLE），旨在即使对于短测试也能减少MLE的偏差。将MWLE与其他估计方法（即MLE、MAP和EAP）进行比较，通过分析和模拟研究表明，在偏差方面，MWLE比MLE更准确，同时保持相似的方差。相比之下，贝叶斯估计方法（即MAP和EAP）会导致偏差估计且变异性较小。
