Vertaile menetelmiä

	Kognitiiviset diagnoosimallit (DINA / G-DINA)	Latent Class Analysis (LCA)
Tieteenala	Psykometriikka	Tilastotiede
Menetelmäperhe	Latent structure	Latent structure
Syntyvuosi	2011	1950s–1968
Kehittäjä	Jimmy de la Torre	Paul F. Lazarsfeld
Tyyppi	Latent variable diagnostic classification model	Latent variable / person-centered classification
Alkuperäislähde	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.
Rinnakkaisnimet	Diagnostic Classification Model, Skills Assessment Model, Attribute Mastery Model, Bilişsel Tanı Modeli	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Liittyvät	2	6
Tiivistelmä	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.