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Group-Based Trajectory Model×Análise de Classes Latentes (LCA)×
ÁreaCriminologyEstatística
FamíliaRegression modelLatent structure
Ano de origem19931950s–1968
Autor originalDaniel S. Nagin & Kenneth C. LandPaul F. Lazarsfeld
TipoFinite-mixture model of longitudinal developmental trajectoriesLatent variable / person-centered classification
Fonte seminalNagin, 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. DOI ↗Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗
Outros nomesGBTM, Group-Based Modeling of Development, Nagin Trajectory Model, Semiparametric Group-Based ModelingLCA, latent class model, latent categorical analysis, finite mixture of multinomials
Relacionados46
ResumoGroup-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.
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ScholarGateComparar métodos: Group-Based Trajectory Model · Latent Class Analysis. Recuperado em 2026-06-24 de https://scholargate.app/pt/compare