Despite the increasing popularity, cognitive diagnosis models have been criticized for limited utility for small samples. In this study, the authors proposed to use Bayes modal (BM) estimation and monotonic constraints to stabilize item parameter estimation and facilitate person classification in small samples based on the generalized deterministic input noisy “and” gate (G-DINA) model. Both simulation study and real data analysis were used to assess the utility of the BM estimation and monotonic constraints. Results showed that in small samples, (a) the G-DINA model with BM estimation is more likely to converge successfully, (b) when prior distributions are specified reasonably, and monotonicity is not violated, the BM estimation with monotonicity tends to produce more stable item parameter estimates and more accurate person classification, and (c) the G-DINA model using the BM estimation with monotonicity is less likely to overfit the data and shows higher predictive power.

尽管认知诊断模型越来越受欢迎，但因其对小样本的实用性有限而受到批评。在本研究中，作者提出使用贝叶斯模态（BM）估计和单调约束来稳定项目参数估计，并基于广义确定性输入噪声“与”门（G-DINA）模型促进小样本中的人员分类。模拟研究和实际数据分析都用于评估 BM 估计和单调约束的实用性。结果表明，在小样本中，（a）采用 BM 估计的 G-DINA 模型更容易成功收敛，（b）在合理指定先验分布且不违反单调性的情况下，采用单调性的 BM 估计往往会产生更多的结果。稳定的项目参数估计和更准确的人员分类，以及（c）使用具有单调性的 BM 估计的 G-DINA 模型不太可能过度拟合数据并显示出更高的预测能力。