Kuo Tzu-Chun, Sheng Yanyan
Department of Counseling, Quantitative Methods, and Special Education, Southern Illinois University Carbondale Carbondale, IL, USA.
Front Psychol. 2016 Jun 10;7:880. doi: 10.3389/fpsyg.2016.00880. eCollection 2016.
This study compared several parameter estimation methods for multi-unidimensional graded response models using their corresponding statistical software programs and packages. Specifically, we compared two marginal maximum likelihood (MML) approaches (Bock-Aitkin expectation-maximum algorithm, adaptive quadrature approach), four fully Bayesian algorithms (Gibbs sampling, Metropolis-Hastings, Hastings-within-Gibbs, blocked Metropolis), and the Metropolis-Hastings Robbins-Monro (MHRM) algorithm via the use of IRTPRO, BMIRT, and MATLAB. Simulation results suggested that, when the intertrait correlation was low, these estimation methods provided similar results. However, if the dimensions were moderately or highly correlated, Hastings-within-Gibbs had an overall better parameter recovery of item discrimination and intertrait correlation parameters. The performances of these estimation methods with different sample sizes and test lengths are also discussed.
本研究使用相应的统计软件程序和软件包，比较了多单维等级反应模型的几种参数估计方法。具体而言，我们通过使用IRTPRO、BMIRT和MATLAB，比较了两种边际极大似然（MML）方法（博克 - 艾特金期望最大化算法、自适应求积法）、四种完全贝叶斯算法（吉布斯抽样、梅特罗波利斯 - 黑斯廷斯算法、吉布斯内部的黑斯廷斯算法、分块梅特罗波利斯算法）以及梅特罗波利斯 - 黑斯廷斯罗宾斯 - 门罗（MHRM）算法。模拟结果表明，当特质间相关性较低时，这些估计方法提供了相似的结果。然而，如果维度具有中度或高度相关性，吉布斯内部的黑斯廷斯算法在项目区分度和特质间相关性参数的总体参数恢复方面表现更好。还讨论了这些估计方法在不同样本量和测验长度下的表现。
