A comparison of alternative models for testlets
A comparison of alternative models for testlets
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DOI：
10.1177/0146621605275414
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发表时间：
2006-01-01
影响因子：
1.2
通讯作者：
Fu, JB
中科院分区：
文献类型：
--
作者：
Li, YM;Bolt, DM;Fu, JB
当测试由Testlet组成时，标准的项目反应理论(IRT)模型通常不适合，因为公共Testlet中的项目之间存在局部依赖。最近发展了一个基于测试题的IRT模型来模拟考生在这种条件下的反应(Bradlow，Wainer，&Wang，1999)。Bradlow、Wainer和Wang模型引入了单独的被试因素来解释这种依赖，并对一般能力和被试因素应用了一个共同的项目区分参数。这项研究调查了几种解释局部依赖的替代方法，这些方法对受试者因素对项目绩效的影响做出了不同的假设。作者实现了几个贝叶斯模型选择标准，以使用具有Testlet结构的几个真实测试数据集来比较模型。结果表明，将区分参数分别应用于一般能力和受试者因素的另一种模型提供了更好的拟合这些数据，尽管其更复杂。
When tests are made up of testlets, standard item response theory (IRT) models are often not appropriate due to the local dependence present among items within a common testlet. A testlet-based IRT model has recently been developed to model examinees' responses under such conditions (Bradlow, Wainer, & Wang, 1999). The Bradlow, Wainer, and Wang model introduces separate testlet factors to account for this dependence and applies a common item discrimination parameter to both the general ability and testlet factor. This study investigates several alternative ways of accounting for local dependence that make different assumptions regarding the influence of testlet factors on item performance. The authors implement several Bayesian model selection criteria to compare models using several real test data sets that have a testlet structure. Results suggest that an alternative model in which separate discrimination parameters are applied to the general ability and testlet factors provides a better fit to these data despite its greater complexity.