Robust maximum marginal likelihood (RMML) estimation for item response theory models

被引:24
作者
Hong, Maxwell R. [1 ]
Cheng, Ying [1 ]
机构
[1] Univ Notre Dame, Dept Psychol, Notre Dame, IN 46556 USA
关键词
Item response theory; Careless responses; Person fit; Robust estimation; Robust maximum marginal likelihood; PERSON FIT STATISTICS; LEVERAGE OBSERVATIONS; PARAMETER-ESTIMATION; LATENT ABILITY; ERROR; RELIABILITY; OUTLIERS; CHOICE; PSYCHOLOGY; VALIDITY;
D O I
10.3758/s13428-018-1150-4
中图分类号
B841 [心理学研究方法];
学科分类号
040201 ;
摘要
Self-report data are common in psychological and survey research. Unfortunately, many of these samples are plagued with careless responses, due to unmotivated participants. The purpose of this study was to propose and evaluate a robust estimation method to detect careless or unmotivated responders, while leveraging item response theory (IRT) person-fit statistics. First, we outlined a general framework for robust estimation specific for IRT models. Subsequently, we conducted a simulation study covering multiple conditions in order to evaluate the performance of the proposed method. Ultimately, we showed that robust maximum marginal likelihood (RMML) estimation significantly improves detection rates for careless responders and reduces bias in item parameters across conditions. Furthermore, we applied our method to a real data set, to illustrate the utility of the proposed method. Our findings suggest that robust estimation coupled with person-fit statistics offers a powerful procedure to identify careless respondents for further review and to provide more accurate item parameter estimates in the presence of careless responses.
引用
收藏
页码:573 / 588
页数:16
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