השוואת שיטות

	ניתוח גורמים מאשר (Confirmatory Factor Analysis - CFA)	ניתוח גורמים מאשר (CFA)	ניתוח גורמים גישוש (EFA)
תחום	סטטיסטיקה	פסיכומטריה	סטטיסטיקה
משפחה	Latent structure	Latent structure	Latent structure
שנת המקור	1969	1969	—
הוגה השיטה	Karl Jöreskog	Karl Gustav Jöreskog	—
סוג	Confirmatory latent variable model	Hypothesis-testing latent variable model	Latent variable / dimension reduction
מקור מכונן	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	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.
כינויים	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	CFA, confirmatory FA, measurement model, restricted factor analysis	common factor analysis, açımlayıcı faktör analizi, factor analysis
קשורות	4	4	4
תקציר	Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.	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.