基于测试集的测试中单模型与多维模型的比较

IF 2 3区 心理学 Q2 PSYCHOLOGY, MATHEMATICAL
Alejandro Hernández‐Camacho, J. Olea, F. J. Abad
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引用次数: 4

摘要

双因子模型(BM)和小测试响应模型(TRM)是应用于基于小测试的最常见的多维模型。常见的程序是使用不同的估计方法来估计这些模型(例如,见DeMars,2006)。这样做的一个可能后果是,以前关于将错误模型拟合到数据中的影响的发现可能与他们使用的估计程序相混淆。考虑到这一点,本研究使用相同的方法(使用降维的最大边际似然[MML])将单一和多维策略与基于测试集的测试进行比较,并评估各种相对拟合指数的性能。数据在三个不同的模型下进行了模拟,即BM、TRM和一维模型。记录了项目参数的恢复、可靠性估计和相对拟合指数的选择率。结果与通过不同方法获得的结果基本一致(DeMars,2006),表明估计方法的影响可以忽略不计。关于拟合指数,Akaike信息准则(AIC)显示出最佳的选择率,而Bayes信息准则(BIC)倾向于选择比真实模型更简单的模型。这项工作最后提出了对从业者的建议和对未来研究的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Uni- and Multidimensional Models Applied in Testlet-Based Tests
The bifactor model (BM) and the testlet response model (TRM) are the most common multidimensional models applied to testlet-based tests. The common procedure is to estimate these models using different estimation methods (see, e.g., DeMars, 2006). A possible consequence of this is that previous findings about the implications of fitting a wrong model to the data may be confounded with the estimation procedures they employed. With this in mind, the present study uses the same method (maximum marginal likelihood [MML] using dimensional reduction) to compare uni- and multidimensional strategies to testlet-based tests, and assess the performance of various relative fit indices. Data were simulated under three different models, namely BM, TRM, and the unidimensional model. Recovery of item parameters, reliability estimates, and selection rates of the relative fit indices were documented. The results were essentially consistent with those obtained through different methods (DeMars, 2006), indicating that the effect of the estimation method is negligible. Regarding the fit indices, Akaike Information Criterion (AIC) showed the best selection rates, whereas Bayes Information Criterion (BIC) tended to select a model which is simpler than the true one. The work concludes with recommendations for practitioners and proposals for future research.
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来源期刊
CiteScore
2.70
自引率
6.50%
发文量
16
审稿时长
36 weeks
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