পদ্ধতির তুলনা করুন

	Group-Based Trajectory Model	সুপ্ত শ্রেণী বিশ্লেষণ (Latent Class Analysis - LCA)
ক্ষেত্র	Criminology	পরিসংখ্যান
পরিবার	Regression model	Latent structure
উদ্ভবের বছর	1993	1950s–1968
প্রবর্তক	Daniel S. Nagin & Kenneth C. Land	Paul F. Lazarsfeld
ধরন	Finite-mixture model of longitudinal developmental trajectories	Latent variable / person-centered classification
মৌলিক উৎস	Nagin, D. S., & Land, K. C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed Poisson model. Criminology, 31(3), 327–362.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
অপর নাম	GBTM, Group-Based Modeling of Development, Nagin Trajectory Model, Semiparametric Group-Based Modeling	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
সম্পর্কিত	4	6
সারসংক্ষেপ	Group-based trajectory modeling (GBTM) is a finite-mixture method that identifies clusters of individuals who follow similar developmental paths of a behavior — most famously offending — over age or time. Introduced to criminology by Daniel Nagin and Kenneth Land in 1993, it replaces the assumption of a single average trajectory with a small number of distinct latent groups, each described by its own polynomial curve and its share of the population.	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.