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	Analisi Fattoriale Esplorativa (AFE)	Modellazione Lineare Gerarchica (HLM / Modellazione Multilivello)	Analisi delle Componenti Principali
Campo	Statistica	Statistica	Apprendimento automatico
Famiglia	Latent structure	Hypothesis test	Machine learning
Anno di origine	—	1986	2002
Ideatore	—	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipo	Latent variable / dimension reduction	Parametric nested-data regression	Unsupervised dimensionality reduction
Fonte seminale	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	common factor analysis, açımlayıcı faktör analizi, factor analysis	HLM, MLM, multilevel modeling, multilevel analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Correlati	4	4	3
Sintesi	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.