用于多分类变量焦点组的非参数综合组 DIF 指数

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Corinne Huggins-Manley, Anthony W. Raborn, Peggy K. Jones, Ted Myers
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引用次数: 0

摘要

本研究的目的是开发一种非参数 DIF 方法,该方法(a)可将焦点组直接与将用于开发报告测试得分量表的综合组进行比较,(b)允许从业人员探索与源自多类别变量的焦点组相关的 DIF,这些焦点组在整个测试人群中只占很小的比例。我们提出了非参数根期望比例平方差(REPSD)指数,该指数可评估源自多类别焦点变量的相对较小焦点组的复合组 DIF 的统计显著性,统计显著性的判定依据的是在零分布下对 DIF 统计量进行蒙特卡罗排列所获得的准精确 p 值。我们进行了一次模拟,以评估该指数在哪些条件下可产生可接受的 I 类错误和幂率,并将其应用于学区评估。实践者可以通过我们在 R 环境中创建的免费软件包计算 REPSD 指数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Nonparametric Composite Group DIF Index for Focal Groups Stemming from Multicategorical Variables

The purpose of this study is to develop a nonparametric DIF method that (a) compares focal groups directly to the composite group that will be used to develop the reported test score scale, and (b) allows practitioners to explore for DIF related to focal groups stemming from multicategorical variables that constitute a small proportion of the overall testing population. We propose the nonparametric root expected proportion squared difference (REPSD) index that evaluates the statistical significance of composite group DIF for relatively small focal groups stemming from multicategorical focal variables, with decisions of statistical significance based on quasi-exact p values obtained from Monte Carlo permutations of the DIF statistic under the null distribution. We conduct a simulation to evaluate conditions under which the index produces acceptable Type I error and power rates, as well as an application to a school district assessment. Practitioners can calculate the REPSD index in a freely available package we created in the R environment.

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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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