方法对比

	潜剖面分析 (Latent Profile Analysis, LPA)	潜在类别分析 (Latent Class Analysis, LCA)
领域	心理测量学	统计学
方法族	Latent structure	Latent structure
起源年份	2010	1950s–1968
提出者	Lazarsfeld & Henry; Collins & Lanza	Paul F. Lazarsfeld
类型	Person-centered finite mixture model	Latent variable / person-centered classification
开创性文献	Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley. ISBN: 978-0-470-22839-7	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
别名	Continuous Latent Class Analysis, Gaussian Profile Mixture Model, Person-Centered Cluster Analysis, Gizil Profil Analizi	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
相关	2	6
摘要	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.	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.