Σύγκριση μεθόδων

	Διερευνητική Ανάλυση Παραγόντων (EFA)	Ανάλυση Διαμεσολάβησης	Πολυεπίπεδη Μοντελοποίηση
Πεδίο	Στατιστική	Στατιστική	Ερευνητική Στατιστική
Οικογένεια	Latent structure	Hypothesis test	Process / pipeline
Έτος προέλευσης	—	1986	1992
Δημιουργός	—	Baron & Kenny	Anthony Bryk and Stephen Raudenbush
Τύπος	Latent variable / dimension reduction	Indirect effects / path test	Method
Θεμελιώδης πηγή	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.	Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Εναλλακτικές ονομασίες	common factor analysis, açımlayıcı faktör analizi, factor analysis	indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS)	HLM, mixed-effects models, random effects models, MLM
Συναφείς	4	5	3
Σύνοψη	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.	Mediation analysis is a statistical procedure that tests whether the effect of an independent variable X on an outcome Y operates wholly or partly through a third variable M, called the mediator. Formalised by Baron and Kenny in 1986, it decomposes the total effect of X on Y into a direct path (c′) and an indirect path (a × b), quantifying how much of the relationship is carried by the mediating mechanism.	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.