เปรียบเทียบวิธี

	การวิเคราะห์โปรไฟล์แฝง (Latent Profile Analysis - LPA)	[NEEDS TRANSLATION]
สาขาวิชา	การวัดทางจิตวิทยา	สถิติศาสตร์
ตระกูล	Latent structure	Latent structure
ปีกำเนิด	2010	1950s–1968
ผู้ริเริ่ม	Lazarsfeld & Henry; Collins & Lanza	Paul F. Lazarsfeld
ประเภท	Person-centered finite mixture model	Latent variable / person-centered classification
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	2	6
สรุป	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.