Two IRT Characteristic Curve Linking Methods Weighted by Information

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED Journal of Educational Measurement Pub Date : 2022-04-17 DOI:10.1111/jedm.12315
Shaojie Wang, Minqiang Zhang, Won-Chan Lee, Feifei Huang, Zonglong Li, Yixing Li, Sufang Yu
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Abstract

Traditional IRT characteristic curve linking methods ignore parameter estimation errors, which may undermine the accuracy of estimated linking constants. Two new linking methods are proposed that take into account parameter estimation errors. The item- (IWCC) and test-information-weighted characteristic curve (TWCC) methods employ weighting components in the loss function from traditional methods by their corresponding item and test information, respectively. Monte Carlo simulation was conducted to evaluate the performances of the new linking methods and compare them with traditional ones. Ability difference between linking groups, sample size, and test length were manipulated under the common-item nonequivalent groups design. Results showed that the two information-weighted characteristic curve methods outperformed traditional methods, in general. TWCC was found to be more accurate and stable than IWCC. A pseudo-form pseudo-group analysis was also performed, and similar results were observed. Finally, guidelines for practice and future directions are discussed.

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两种信息加权的IRT特征曲线连接方法
传统的IRT特征曲线连接方法忽略了参数估计误差,这可能会影响连接常数估计的准确性。提出了两种考虑参数估计误差的连接方法。项目加权特征曲线法(IWCC)和测试信息加权特征曲线法(TWCC)分别利用传统方法中对应的项目和测试信息对损失函数进行加权。通过蒙特卡罗仿真对新连接方法的性能进行了评价,并与传统连接方法进行了比较。在共同项目非等效组设计下,对连接组之间的能力差异、样本量和测试长度进行处理。结果表明,两种加权特征曲线方法总体上优于传统方法。TWCC比IWCC更准确、更稳定。伪形式伪组分析也进行,并观察到类似的结果。最后,对实践指导和未来发展方向进行了讨论。
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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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