Joo Seang-Hwane, Lee Philseok, Stark Stephen
University of Kansas, Lawrence, KS, USA.
George Mason University, Fairfax, VA, USA.
Appl Psychol Meas. 2022 Jan;46(1):3-18. doi: 10.1177/01466216211051717. Epub 2021 Oct 11.
Collateral information has been used to address subpopulation heterogeneity and increase estimation accuracy in some large-scale cognitive assessments. The methodology that takes collateral information into account has not been developed and explored in published research with models designed specifically for noncognitive measurement. Because the accurate noncognitive measurement is becoming increasingly important, we sought to examine the benefits of using collateral information in latent trait estimation with an item response theory model that has proven valuable for noncognitive testing, namely, the generalized graded unfolding model (GGUM). Our presentation introduces an extension of the GGUM that incorporates collateral information, henceforth called . We then present a simulation study that examined Explanatory GGUM latent trait estimation as a function of sample size, test length, number of background covariates, and correlation between the covariates and the latent trait. Results indicated the Explanatory GGUM approach provides scoring accuracy and precision superior to traditional expected a posteriori (EAP) and full Bayesian (FB) methods. Implications and recommendations are discussed.
在一些大规模认知评估中，辅助信息已被用于解决亚群体异质性问题并提高估计准确性。在已发表的研究中，尚未开发和探索将辅助信息纳入专门为非认知测量设计的模型中的方法。由于准确的非认知测量变得越来越重要，我们试图使用一种已被证明对非认知测试有价值的项目反应理论模型——广义分级展开模型（GGUM），来检验在潜在特质估计中使用辅助信息的益处。我们的展示介绍了GGUM的一种扩展，它纳入了辅助信息，此后称为解释性GGUM。然后，我们进行了一项模拟研究，考察了解释性GGUM潜在特质估计作为样本量、测试长度、背景协变量数量以及协变量与潜在特质之间相关性的函数。结果表明，解释性GGUM方法提供的评分准确性和精度优于传统的期望后验（EAP）和全贝叶斯（FB）方法。我们还讨论了其意义和建议。
