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多维等级反应模型统计量的性能

Performance of the Statistic for the Multidimensional Graded Response Model.

作者信息

Su Shiyang, Wang Chun, Weiss David J

机构信息

University of Central Florida, Orlando, FL, USA.

University of Washington, Seattle, WA, USA.

出版信息

Educ Psychol Meas. 2021 Jun;81(3):491-522. doi: 10.1177/0013164420958060. Epub 2020 Sep 23.

DOI:10.1177/0013164420958060
PMID:33994561
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8072952/
Abstract

is a popular item fit index that is available in commercial software packages such as MIRT. However, no research has systematically examined the performance of for detecting item misfit within the context of the multidimensional graded response model (MGRM). The primary goal of this study was to evaluate the performance of under two practical misfit scenarios: first, all items are misfitting due to model misspecification, and second, a small subset of items violate the underlying assumptions of the MGRM. Simulation studies showed that caution should be exercised when reporting item fit results of polytomous items using within the context of the MGRM, because of its inflated false positive rates (FPRs), especially with a small sample size and a long test. performed well when detecting overall model misfit as well as item misfit for a small subset of items when the ordinality assumption was violated. However, under a number of conditions of model misspecification or items violating the homogeneous discrimination assumption, even though true positive rates (TPRs) of were high when a small sample size was coupled with a long test, the inflated FPRs were generally directly related to increasing TPRs. There was also a suggestion that performance of was affected by the magnitude of misfit within an item. There was no evidence that FPRs for fitting items were exacerbated by the presence of a small percentage of misfitting items among them.

摘要

是一种在诸如MIRT等商业软件包中可用的流行项目拟合指数。然而,尚无研究在多维等级反应模型(MGRM)的背景下系统地检验其检测项目不拟合的性能。本研究的主要目标是在两种实际不拟合情况下评估其性能:第一,由于模型设定错误,所有项目均不拟合;第二,一小部分项目违反了MGRM的基本假设。模拟研究表明,在MGRM背景下使用时报告多分类项目的项目拟合结果时应谨慎,因为其假阳性率(FPR)过高,尤其是在样本量小且测试时间长的情况下。在检测总体模型不拟合以及违反顺序假设时一小部分项目的项目不拟合方面表现良好。然而,在一些模型设定错误或项目违反同质区分假设的条件下,即使在小样本量与长测试相结合时其真阳性率(TPR)很高,但过高的FPR通常与TPR的增加直接相关。还有迹象表明,的性能受项目内不拟合程度的影响。没有证据表明其中存在一小部分不拟合项目会加剧拟合项目的FPR。

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本文引用的文献

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Assessing Item-Level Fit for Higher Order Item Response Theory Models.评估高阶项目反应理论模型的项目水平拟合度。
Appl Psychol Meas. 2018 Nov;42(8):644-659. doi: 10.1177/0146621618762740. Epub 2018 Mar 21.
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Assessing Item-Level Fit for the DINA Model.评估DINA模型的项目水平拟合度。
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