Σύγκριση μεθόδων

	Ανάλυση Παραγόντων	Πολυεπίπεδη Μοντελοποίηση
Πεδίο	Ερευνητική Στατιστική	Ερευνητική Στατιστική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1931	1992
Δημιουργός	Louis Leon Thurstone	Anthony Bryk and Stephen Raudenbush
Τύπος	Method	Method
Θεμελιώδης πηγή	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Εναλλακτικές ονομασίες	EFA, CFA, latent variable modeling	HLM, mixed-effects models, random effects models, MLM
Συναφείς	3	3
Σύνοψη	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.