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Analisis Kelas Tersembunyi (LCA)×Analisis Profil Tersembunyi (LPA)×
BidangStatistikPsikometrik
KeluargaLatent structureLatent structure
Tahun asal1950s–19682010
PengasasPaul F. LazarsfeldLazarsfeld & Henry; Collins & Lanza
JenisLatent variable / person-centered classificationPerson-centered finite mixture model
Sumber perintisGoodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley. ISBN: 978-0-470-22839-7
AliasLCA, latent class model, latent categorical analysis, finite mixture of multinomialsContinuous Latent Class Analysis, Gaussian Profile Mixture Model, Person-Centered Cluster Analysis, Gizil Profil Analizi
Berkaitan62
RingkasanLatent 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.
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ScholarGateBandingkan kaedah: Latent Class Analysis · Latent Profile Analysis. Dicapai 2026-06-18 daripada https://scholargate.app/ms/compare