방법 비교

	잠재 계층 분석(Latent Class Analysis, LCA)	잠재 프로파일 분석 (Latent Profile Analysis, LPA)
분야	통계학	심리측정학
계열	Latent structure	Latent structure
기원 연도	1950s–1968	2010
창시자	Paul F. Lazarsfeld	Lazarsfeld & Henry; Collins & Lanza
유형	Latent variable / person-centered classification	Person-centered finite mixture model
원전	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.	Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley. ISBN: 978-0-470-22839-7
별칭	LCA, latent class model, latent categorical analysis, finite mixture of multinomials	Continuous Latent Class Analysis, Gaussian Profile Mixture Model, Person-Centered Cluster Analysis, Gizil Profil Analizi
관련	6	2
요약	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.	Latent Profile Analysis (LPA) is a person-centered finite mixture modeling technique that identifies unobserved subgroups — called profiles — within a population based on patterns of scores across multiple continuous indicators. Rooted in Lazarsfeld and Henry's latent structure tradition and formally synthesized for applied behavioral research by Collins and Lanza (2010), LPA assumes that observed heterogeneity in continuous data arises from a discrete number of latent classes, each characterized by a unique multivariate mean profile.