Salīdzināt metodes

	Kognitīvās diagnostikas modeļi (DINA / G-DINA)	Latent Class Analysis (LCA)	Raša modelis
Nozare	Psihometrija	Statistika	Psihometrija
Saime	Latent structure	Latent structure	Latent structure
Izcelsmes gads	2011	1950s–1968	1960
Autors	Jimmy de la Torre	Paul F. Lazarsfeld	Georg Rasch
Tips	Latent variable diagnostic classification model	Latent variable / person-centered classification	Item Response Theory / Latent trait model
Pirmavots	de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.	Rasch, G. (1960). Probabilistic Models for Some Intelligence and Attainment Tests. Danish Institute for Educational Research, Copenhagen.
Citi nosaukumi	Diagnostic Classification Model, Skills Assessment Model, Attribute Mastery Model, Bilişsel Tanı Modeli	LCA, latent class model, latent categorical analysis, finite mixture of multinomials	1PL IRT, one-parameter logistic model, Rasch Modeli — 1PL IRT, 1PL model
Saistītās	2	6	6
Kopsavilkums	Cognitive Diagnosis Models (CDMs) are a family of latent variable models designed to classify examinees according to their mastery of a set of discrete cognitive attributes or skills. The Generalized DINA (G-DINA) framework, introduced by Jimmy de la Torre in 2011, provides a unifying structure that encompasses many specific CDMs — including the DINA, DINO, ACDM, and LLM models — as special cases, enabling fine-grained diagnostic feedback beyond a single total score.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.	The Rasch model, introduced by Georg Rasch in 1960, is the simplest member of the Item Response Theory (IRT) family. It assigns a single difficulty parameter to each test item and places both item difficulties and person abilities on the same logit scale, enabling direct, sample-independent comparison of items and persons.