强迫选择问卷的等级-2PL 模型的信息函数

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Jianbin Fu, Xuan Tan, Patrick C. Kyllonen
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引用次数: 0

摘要

本文介绍了强迫选择问卷中包含两个(成对)和三个(三连音)陈述的项目的 Rank 双参数逻辑模型(Rank-2PLM)的项目信息函数和测试信息函数。针对成对陈述的 Rank-2PLM 模型为 MUPP-2PLM(多维成对偏好),针对三重陈述的 Rank-2PLM 模型为 Triplet-2PLM。描述了费雪信息和方向信息,并区分了最大似然(ML)、最大 A 后验(MAP)和期望 A 后验(EAP)性状分数估计的测试信息。提出并绘制了不同水平的预期项目/测验信息指数,以提供项目和测验的诊断信息。由于典型测验的项目反应模式数量庞大,EAP 分数的预期测验信息指数可能难以计算。本文讨论了项目/测验信息与语句辨别参数、标准误差和特质分值估计的可靠性估计之间的关系,并使用真实数据进行了演示。此外,还提供了检查各种预期项目/测验信息指数和绘图的实用建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information Functions of Rank-2PL Models for Forced-Choice Questionnaires

This paper presents the item and test information functions of the Rank two-parameter logistic models (Rank-2PLM) for items with two (pair) and three (triplet) statements in forced-choice questionnaires. The Rank-2PLM model for pairs is the MUPP-2PLM (Multi-Unidimensional Pairwise Preference) and, for triplets, is the Triplet-2PLM. Fisher's information and directional information are described, and the test information for Maximum Likelihood (ML), Maximum A Posterior (MAP), and Expected A Posterior (EAP) trait score estimates is distinguished. Expected item/test information indexes at various levels are proposed and plotted to provide diagnostic information on items and tests. The expected test information indexes for EAP scores may be difficult to compute due to a typical test's vast number of item response patterns. The relationships of item/test information with discrimination parameters of statements, standard error, and reliability estimates of trait score estimates are discussed and demonstrated using real data. Practical suggestions for checking the various expected item/test information indexes and plots are provided.

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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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