Sun Jianan, Chen Yunxiao, Liu Jingchen, Ying Zhiliang, Xin Tao
Beijing Forestry University, Beijing, China.
Emory University, Atlanta, USA.
Psychometrika. 2016 Dec;81(4):921-939. doi: 10.1007/s11336-016-9529-6. Epub 2016 Oct 3.
We develop a latent variable selection method for multidimensional item response theory models. The proposed method identifies latent traits probed by items of a multidimensional test. Its basic strategy is to impose an [Formula: see text] penalty term to the log-likelihood. The computation is carried out by the expectation-maximization algorithm combined with the coordinate descent algorithm. Simulation studies show that the resulting estimator provides an effective way in correctly identifying the latent structures. The method is applied to a real dataset involving the Eysenck Personality Questionnaire.
我们为多维项目反应理论模型开发了一种潜在变量选择方法。所提出的方法可识别多维测试项目所探测的潜在特质。其基本策略是对对数似然施加一个[公式：见正文]惩罚项。计算通过期望最大化算法与坐标下降算法相结合来进行。模拟研究表明，所得估计器为正确识别潜在结构提供了一种有效方法。该方法应用于一个涉及艾森克人格问卷的真实数据集。
