Decisions on how to calibrate an item bank might have major implications in the subsequent performance of the adaptive algorithms. One of these decisions is model selection, which can become problematic in the context of cognitive diagnosis computerized adaptive testing, given the wide range of models available. This article aims to determine whether model selection indices can be used to improve the performance of adaptive tests. Three factors were considered in a simulation study, that is, calibration sample size, Q-matrix complexity, and item bank length. Results based on the true item parameters, and general and single reduced model estimates were compared to those of the combination of appropriate models. The results indicate that fitting a single reduced model or a general model will not generally provide optimal results. Results based on the combination of models selected by the fit index were always closer to those obtained with the true item parameters. The implications for practical settings include an improvement in terms of classification accuracy and, consequently, testing time, and a more balanced use of the item bank. An R package was developed, named cdcatR, to facilitate adaptive applications in this context.

关于如何校准项目库的决策可能会对自适应算法的后续性能产生重大影响。这些决策之一是模型选择，鉴于可用模型的范围广泛，这在认知诊断计算机化自适应测试的背景下可能会成为问题。本文旨在确定模型选择指标是否可用于提高自适应测试的性能。模拟研究考虑了三个因素，即校准样本大小、Q 矩阵复杂度和项目库长度。将基于真实项目参数、一般和单一简化模型估计的结果与适当模型组合的结果进行比较。结果表明，拟合单个简化模型或通用模型通常不会提供最佳结果。基于拟合指数选择的模型组合的结果总是更接近使用真实项目参数获得的结果。对实际设置的影响包括分类准确性的提高，从而缩短测试时间，以及更平衡地使用项目库。我们开发了一个名为 cdcatR 的R包，以促进这种情况下的自适应应用程序。