Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Modele de Diagnostic Cognitiv (DINA / G-DINA)× | Analiza claselor latente (LCA)× | |
|---|---|---|
| Domeniu≠ | Psihometrie | Statistică |
| Familie | Latent structure | Latent structure |
| Anul apariției≠ | 2011 | 1950s–1968 |
| Autorul original≠ | Jimmy de la Torre | Paul F. Lazarsfeld |
| Tip≠ | Latent variable diagnostic classification model | Latent variable / person-centered classification |
| Sursa seminală≠ | de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199. DOI ↗ | Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗ |
| Denumiri alternative | Diagnostic Classification Model, Skills Assessment Model, Attribute Mastery Model, Bilişsel Tanı Modeli | LCA, latent class model, latent categorical analysis, finite mixture of multinomials |
| Înrudite≠ | 2 | 6 |
| Rezumat≠ | 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. |
| ScholarGateSet de date ↗ |
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