পদ্ধতির তুলনা করুন

	অর্ডিনাল লজিস্টিক রিগ্রেশন (প্রপোর্শনাল অডস মডেল)	সুপ্ত শ্রেণী বিশ্লেষণ (Latent Class Analysis - LCA)
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Latent structure
উদ্ভবের বছর	2010	1950s–1968
প্রবর্তক	Agresti (textbook treatment); proportional odds model	Paul F. Lazarsfeld
ধরন	Ordinal logistic regression	Latent variable / person-centered classification
মৌলিক উৎস	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.
অপর নাম	proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds)	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
সম্পর্কিত	5	6
সারসংক্ষেপ	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.