Expectation-Maximization-Maximization: A Feasible MLE Algorithm for the Three-Parameter Logistic Model Based on a Mixture Modeling Reformulation

Frontiers in Psychology

Xiangbin Meng²  Zhengguang Liu³  Chanjin Zheng⁴  Shaoyang Guo⁴ 

[1] Faculty of Education, Northeast Normal University, Changchun, China;Key Laboratory of Applied Statistics of Moe, Northeast Normal University, Changchun, China;National Cooperative Innovation Center for Assessment and Improvement of Basic Education Quality, Beijing Normal University, Beijing, China;School of Psychology, Jiangxi Normal University, Nanchang, China;

关键词: EMM;    Bayesian EM;    MLE;    mixture modeling;    3PL;   

DOI  :  10.3389/fpsyg.2017.02302

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown