方法对比

	认知诊断计算机化自适应测试	DINA模型	必要条件分析
领域	心理测量学	心理测量学	心理测量学
方法族	Latent structure	Latent structure	Latent structure
起源年份	2007	2001	2016
提出者	Xueli Xu, Jean-Paul Fox	Brian Junker, Klaas Sijtsma	Jan Dul
类型	Skill-adaptive testing with psychometric diagnostic classification	Discrete latent class model	Set-theoretic configurational analysis
开创性文献	Choi, K. M., Lee, Y. S., & Park, Y. S. (2015). What CDM can tell about examinees' strengths and weaknesses: Cognitive diagnostic information in TIMSS. Journal of Educational Evaluation for Policy Analysis, 24(1), 79-100.	Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25(3), 258-272.	Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of "necessary but not sufficient" causality. Organizational Research Methods, 19(1), 10-52.
别名	CD-CAT	DINA	NCA
相关	5	4	5
摘要	Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) combines computerized adaptive testing (CAT) with cognitive diagnostic models (CDMs) to efficiently assess students' specific skill profiles. Rather than producing a single overall ability score, CD-CAT adaptively selects items to quickly identify which skills a student has mastered and which need development.	The DINA Model (Deterministic Inputs, Noisy Outputs) is a cognitive diagnostic model developed by Junker and Sijtsma (2001) that classifies examinees into latent skill classes based on their item response patterns. DINA assumes a deterministic relationship between skill mastery and correct responses, with probabilistic error accounting for guessing and slips.	Necessary Condition Analysis (NCA) is a set-theoretic method developed by Dul (2016) that identifies conditions necessary (but not necessarily sufficient) for an outcome to occur. Unlike regression, which estimates average effects, NCA identifies absolute thresholds: conditions that must be present at a certain level for the outcome to be possible, regardless of other factors.