Sinharay Sandip
CTB/McGraw-Hill, 20 Ryan Ranch Rd., Monterey, CA 93940, USA.
J Appl Meas. 2013;14(2):149-58.
The application of the existing test statistics to determine differential item functioning (DIF) requires large samples, but test administrators often face the challenge of detecting DIF with small samples. One advantage of a Bayesian approach over a frequentist approach is that the former can incorporate, in the form of a prior distribution, existing information on the inference problem at hand. Sinharay, Dorans, Grant, and Blew (2009) suggested the use of information from past data sets as a prior distribution in a Bayesian DIF analysis. This paper suggests an extension of the method of Sinharay et al. (2009). The suggested extension is compared to the existing DIF detection methods in a realistic simulation study.
应用现有的检验统计量来确定项目功能差异（DIF）需要大样本，但测试管理者常常面临着利用小样本检测DIF的挑战。贝叶斯方法相对于频率论方法的一个优势在于，前者能够以前验分布的形式纳入关于手头推理问题的现有信息。辛哈雷、多兰斯、格兰特和布卢（2009年）建议在贝叶斯DIF分析中，将来自过去数据集的信息用作先验分布。本文提出了对辛哈雷等人（2009年）方法的扩展。在一项实际模拟研究中，将所建议的扩展方法与现有的DIF检测方法进行了比较。
