Salīdzināt metodes

	Ordinālā loģistikās regresijas modelis (proporcionālo izredžu modelis)	Latent Class Analysis (LCA)
Nozare	Statistika	Statistika
Saime	Regression model	Latent structure
Izcelsmes gads	2010	1950s–1968
Autors	Agresti (textbook treatment); proportional odds model	Paul F. Lazarsfeld
Tips	Ordinal logistic regression	Latent variable / person-centered classification
Pirmavots	Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Citi nosaukumi	proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds)	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Saistītās	5	6
Kopsavilkums	Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model.	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.