方法对比

	SIBTEST	认知诊断计算机化自适应测试	DINA模型	DINO模型
领域	心理测量学	心理测量学	心理测量学	心理测量学
方法族	Latent structure	Latent structure	Latent structure	Latent structure
起源年份	1993	2007	2001	2006
提出者	Richard Shealy, William F. Stout	Xueli Xu, Jean-Paul Fox	Brian Junker, Klaas Sijtsma	James Templin, Russell Henson
类型	Differential item functioning (DIF) assessment	Skill-adaptive testing with psychometric diagnostic classification	Discrete latent class model	Disjunctive latent class model
开创性文献	Shealy, R., & Stout, W. F. (1993). A model-based standardization approach that separates true bias/DIF from group differences and detects test bias/DTF. Psychometrika, 58(2), 159-194.	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.	Templin, J., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11(3), 287-305.
别名	—	CD-CAT	DINA	DINO
相关	5	5	4	4
摘要	SIBTEST (Simultaneous Item Bias Test) is a non-parametric method for detecting differential item functioning (DIF) and differential test functioning (DTF) developed by Shealy and Stout (1993). Unlike parametric approaches, SIBTEST does not assume a particular item response model and directly tests whether groups differ in their probability of correct responses at equal levels of overall ability.	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.	The DINO Model (Deterministic Inputs, Noisy Outputs—Disjunctive) is a cognitive diagnostic model that relaxes DINA's conjunctive (AND) skill requirement logic. DINO assumes an examinee only needs to master one of multiple possible skill pathways to answer an item correctly, making it suitable for scenarios where skills are substitutable or alternative routes to success exist.