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.

中文翻译：

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基于Testlet的测试中一维和多维模型的比较

双因素模型（BM）和睾丸反应模型（TRM）是应用于基于睾丸的测试中最常见的多维模型。通用程序是使用不同的估计方法来估计这些模型（例如，参见DeMars，2006）。其可能的结果是，先前关于将错误模型拟合到数据的含义的发现可能与他们采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可能性[MML]）将一维和多维策略与基于睾丸的测试进行比较，并评估各种相对拟合指数的性能。在三种不同的模型（BM，TRM和一维模型）下模拟了数据。恢复项目参数，可靠性估算，并记录了相对适合指数的选择率。结果与通过不同方法获得的结果基本一致（DeMars，2006年），表明估计方法的影响可忽略不计。关于拟合指数，Akaike信息标准（AIC）表现出最好的选择率，而Bayes信息标准（BIC）倾向于选择比真实模型更简单的模型。这项工作以对从业者的建议和对未来研究的建议作为结尾。而贝叶斯信息准则（BIC）倾向于选择比真实模型更简单的模型。该工作以对从业者的建议和对未来研究的建议作为结尾。而贝叶斯信息准则（BIC）倾向于选择一种比真实模型更简单的模型。这项工作以对从业者的建议和对未来研究的建议作为结尾。