Detecting Differential Item Functioning Using Posterior Predictive Model Checking: A Comparison of Discrepancy Statistics

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Seang-Hwane Joo, Philseok Lee
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引用次数: 1

Abstract

This study proposes a new Bayesian differential item functioning (DIF) detection method using posterior predictive model checking (PPMC). Item fit measures including infit, outfit, observed score distribution (OSD), and Q1 were considered as discrepancy statistics for the PPMC DIF methods. The performance of the PPMC DIF method was evaluated via a Monte Carlo simulation manipulating sample size, DIF size, DIF type, DIF percentage, and subpopulation trait distribution. Parametric DIF methods, such as Lord's chi-square and Raju's area approaches, were also included in the simulation design in order to compare the performance of the proposed PPMC DIF methods to those previously existing. Based on Type I error and power analysis, we found that PPMC DIF methods showed better-controlled Type I error rates than the existing methods and comparable power to detect uniform DIF. The implications and recommendations for applied researchers are discussed.

用后验预测模型检验检测差异项目功能:差异统计的比较
本文提出了一种新的基于后验预测模型检验的贝叶斯差分项目功能(DIF)检测方法。项目拟合措施包括infit、outfit、观察得分分布(OSD)和Q1被认为是PPMC DIF方法的差异统计。通过蒙特卡罗模拟对样本大小、DIF大小、DIF类型、DIF百分比和亚种群性状分布进行了评价。参数DIF方法,如Lord卡方法和Raju面积法,也包括在仿真设计中,以比较所提出的PPMC DIF方法与先前存在的DIF方法的性能。基于I型误差和功率分析,我们发现PPMC DIF方法比现有方法具有更好的I型错误率控制,并且具有相当的检测均匀DIF的能力。讨论了对应用研究人员的启示和建议。
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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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