Linganisha mbinu

	Cognitive Diagnostic Computerized Adaptive Testing	Mfumo wa DINA	Uchambuzi wa Hali Muhimu
Nyanja	Saikometriki	Saikometriki	Saikometriki
Familia	Latent structure	Latent structure	Latent structure
Mwaka wa asili	2007	2001	2016
Mwanzilishi	Xueli Xu, Jean-Paul Fox	Brian Junker, Klaas Sijtsma	Jan Dul
Aina	Skill-adaptive testing with psychometric diagnostic classification	Discrete latent class model	Set-theoretic configurational analysis
Chanzo asilia	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.
Majina mbadala	CD-CAT	DINA	NCA
Zinazohusiana	5	4	5
Muhtasari	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.