Salīdzināt metodes

	Daudzlīmeņu apstiprinošā faktoru analīze (MCFA)	Latent Profile Analysis (LPA)
Nozare	Psihometrija	Psihometrija
Saime	Latent structure	Latent structure
Izcelsmes gads	1994	2010
Autors	Bengt O. Muthen	Lazarsfeld & Henry; Collins & Lanza
Tips	Latent variable model / measurement model	Person-centered finite mixture model
Pirmavots	Muthen, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398.	Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley. ISBN: 978-0-470-22839-7
Citi nosaukumi	MCFA, multilevel measurement model, two-level CFA, hierarchical CFA	Continuous Latent Class Analysis, Gaussian Profile Mixture Model, Person-Centered Cluster Analysis, Gizil Profil Analizi
Saistītās	6	2
Kopsavilkums	Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the standard tool for validating psychometric scales administered within natural groups such as classrooms, clinics, or organisations.	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.