Implementing a Standardized Effect Size in the POLYSIBTEST Procedure.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
ACS Applied Bio Materials Pub Date : 2023-04-01 Epub Date: 2022-02-28 DOI:10.1177/00131644221081011
James D Weese, Ronna C Turner, Xinya Liang, Allison Ames, Brandon Crawford
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引用次数: 0

Abstract

A study was conducted to implement the use of a standardized effect size and corresponding classification guidelines for polytomous data with the POLYSIBTEST procedure and compare those guidelines with prior recommendations. Two simulation studies were included. The first identifies new unstandardized test heuristics for classifying moderate and large differential item functioning (DIF) for polytomous response data with three to seven response options. These are provided for researchers studying polytomous data using POLYSIBTEST software that has been published previously. The second simulation study provides one pair of standardized effect size heuristics that can be employed with items having any number of response options and compares true-positive and false-positive rates for the standardized effect size proposed by Weese with one proposed by Zwick et al. and two unstandardized classification procedures (Gierl; Golia). All four procedures retained false-positive rates generally below the level of significance at both moderate and large DIF levels. However, Weese's standardized effect size was not affected by sample size and provided slightly higher true-positive rates than the Zwick et al. and Golia's recommendations, while flagging substantially fewer items that might be characterized as having negligible DIF when compared with Gierl's suggested criterion. The proposed effect size allows for easier use and interpretation by practitioners as it can be applied to items with any number of response options and is interpreted as a difference in standard deviation units.

在 POLYSIBTEST 程序中实施标准化效应大小。
我们开展了一项研究,利用 POLYSIBTEST 程序对多态数据使用标准化效应大小和相应的分类指南,并将这些指南与之前的建议进行比较。其中包括两项模拟研究。第一项研究确定了新的非标准化测试启发式方法,用于对具有三到七个响应选项的多项式响应数据的中度和高度差异项目功能(DIF)进行分类。这些启发式是为使用 POLYSIBTEST 软件研究多项式数据的研究人员提供的,该软件已于之前发布。第二项模拟研究提供了一对标准化效应大小启发式方法,可用于具有任意数量回答选项的项目,并比较了 Weese 提出的标准化效应大小的真阳性率和假阳性率,以及 Zwick 等人提出的标准化效应大小和两种非标准化分类程序(Gierl;Golia)的真阳性率和假阳性率。在中等和较大的 DIF 水平下,所有四种程序的假阳性率一般都低于显著性水平。不过,Weese 的标准化效应大小不受样本量的影响,其真阳性率略高于 Zwick 等人和 Golia 的建议,同时与 Gierl 建议的标准相比,标记出的可忽略 DIF 的项目要少得多。建议的效应大小更便于从业人员使用和解释,因为它可以应用于具有任意数量回答选项的项目,并解释为标准差单位的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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