Vertaile menetelmiä

	Vahvistava faktorianalyysi	Hierarkkinen lineaarinen mallinnus (HLM / monitasomallinnus)
Tieteenala	Psykometriikka	Tilastotiede
Menetelmäperhe	Latent structure	Hypothesis test
Syntyvuosi	1969	1986
Kehittäjä	Karl Jöreskog	Raudenbush & Bryk (popularized); Goldstein (parallel development)
Tyyppi	Measurement model / latent variable analysis	Parametric nested-data regression
Alkuperäislähde	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). Guilford Press. ISBN: 978-1462515363	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
Rinnakkaisnimet	Doğrulayıcı Faktör Analizi — Ölçek Doğrulama (CFA), confirmatory factor analysis, measurement model testing	HLM, MLM, multilevel modeling, multilevel analysis
Liittyvät	6	4
Tiivistelmä	Confirmatory factor analysis is a measurement modelling technique that tests whether a hypothesised factor structure — typically derived from theory or an earlier exploratory analysis — fits observed data from a new sample. Developed by Karl Jöreskog in 1969, it became the dominant tool for validating psychological scales because it requires the researcher to specify in advance which items belong to which latent factor and then assesses the adequacy of that specification against explicit statistical fit criteria.	Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.