Zheng Chanjin, Meng Xiangbin, Guo Shaoyang, Liu Zhengguang
School of Psychology, Jiangxi Normal University, Nanchang, China.
Faculty of Education, Northeast Normal University, Changchun, China.
Front Psychol. 2018 Jan 5;8:2302. doi: 10.3389/fpsyg.2017.02302. eCollection 2017.
Stable maximum likelihood estimation (MLE) of item parameters in 3PLM with a modest sample size remains a challenge. The current study presents a mixture-modeling approach to 3PLM based on which a feasible Expectation-Maximization-Maximization (EMM) MLE algorithm is proposed. The simulation study indicates that EMM is comparable to the Bayesian EM in terms of bias and RMSE. EMM also produces smaller standard errors (SEs) than MMLE/EM. In order to further demonstrate the feasibility, the method has also been applied to two real-world data sets. The point estimates in EMM are close to those from the commercial programs, BILOG-MG and flexMIRT, but the SEs are smaller.
在样本量适中的情况下，对三参数逻辑斯蒂克模型（3PLM）中的项目参数进行稳定的最大似然估计（MLE）仍然是一个挑战。当前研究提出了一种基于混合建模的3PLM方法，并在此基础上提出了一种可行的期望最大化最大化（EMM）MLE算法。模拟研究表明，在偏差和均方根误差方面，EMM与贝叶斯期望最大化（EM）相当。与最大边际似然估计/期望最大化（MMLE/EM）相比，EMM产生的标准误差（SEs）也更小。为了进一步证明其可行性，该方法还应用于两个实际数据集。EMM中的点估计与商业程序BILOG-MG和flexMIRT的点估计接近，但SEs更小。
