مقایسهٔ روش‌ها

	تحلیل رخ‌نمای پنهان (LPA)	تحلیل عاملی تأییدی (CFA)	تحلیل طبقه نهفته (LCA)
حوزه	روان‌سنجی	آمار	آمار
خانواده	Latent structure	Latent structure	Latent structure
سال پیدایش	2010	1969	1950s–1968
پدیدآور	Lazarsfeld & Henry; Collins & Lanza	Karl Jöreskog	Paul F. Lazarsfeld
نوع	Person-centered finite mixture model	Confirmatory latent variable model	Latent variable / person-centered classification
منبع بنیادین	Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley. ISBN: 978-0-470-22839-7	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
نام‌های دیگر	Continuous Latent Class Analysis, Gaussian Profile Mixture Model, Person-Centered Cluster Analysis, Gizil Profil Analizi	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
مرتبط	2	4	6
خلاصه	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.	Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.	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.