Markov Chain estimation for test theory without an answer key
Markov Chain estimation for test theory without an answer key
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DOI：
10.1007/bf02294733
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发表时间：
2003-09-01
期刊：
影响因子：
3
通讯作者：
Batchelder, WH
中科院分区：
文献类型：
--
作者：
Karabatsos, G;Batchelder, WH
本研究开发了马尔可夫链蒙特卡罗（MCMC）估计理论的一般孔多塞模型（GCM），一个项目反应模型的二分响应数据，不假设分析师知道正确的答案，测试先验（答案键）。除了答案的关键，回答能力，猜测偏差，和难度参数估计。关于数据拟合，研究比较可能的GCM配方，使用MCMC为基础的方法进行模型评估和模型选择。真实的数据应用和仿真研究表明，GCM可以准确地从少量的受访者重建答案的关键。
This study develops Markov Chain Monte Carlo (MCMC) estimation theory for the General Condorcet Model (GCM), an item response model for dichotomous response data which does not presume the analyst knows the correct answers to the test a priori (answer key). In addition to the answer key, respondent ability, guessing bias, and difficulty parameters are estimated. With respect to data-fit, the study compares between the possible GCM formulations, using MCMC-based methods for model assessment and model selection. Real data applications and a simulation study show that the GCM can accurately reconstruct the answer key from a small number of respondents.