Hong Hyokyoung, Wang Chun, Lim Youn Seon, Douglas Jeff
Michigan State University, East Lansing, USA.
University of Minnesota, Minneapolis, USA.
Appl Psychol Meas. 2015 Jan;39(1):31-43. doi: 10.1177/0146621614524981. Epub 2014 Apr 14.
The issue of latent trait granularity in diagnostic models is considered, comparing and contrasting latent trait and latent class models used for diagnosis. Relationships between conjunctive cognitive diagnosis models (CDMs) with binary attributes and noncompensatory multidimensional item response models are explored, leading to a continuous generalization of the Noisy Input, Deterministic "And" Gate (NIDA) model. A model that combines continuous and discrete latent variables is proposed that includes a noncompensatory item response theory (IRT) term and a term following the discrete attribute Deterministic Input, Noisy "And" Gate (DINA) model in cognitive diagnosis. The Tatsuoka fraction subtraction data are analyzed with the proposed models as well as with the DINA model, and classification results are compared. The applicability of the continuous latent trait model and the combined IRT and CDM is discussed, and arguments are given for development of simple models for complex cognitive structures.
本文考虑了诊断模型中潜在特质粒度的问题，比较并对比了用于诊断的潜在特质模型和潜在类别模型。探讨了具有二元属性的联合认知诊断模型（CDM）与非补偿性多维项目反应模型之间的关系，从而实现了噪声输入确定性“与”门（NIDA）模型的连续泛化。提出了一种结合连续和离散潜在变量的模型，该模型包括一个非补偿性项目反应理论（IRT）项和一个遵循认知诊断中离散属性确定性输入噪声“与”门（DINA）模型的项。使用所提出的模型以及DINA模型对Tatsuoka分数减法数据进行分析，并比较分类结果。讨论了连续潜在特质模型以及IRT与CDM相结合模型的适用性，并为开发针对复杂认知结构的简单模型提供了论据。
