مقایسهٔ روش‌ها

	تحلیل عاملی تأییدی (CFA)	آلفای کرونباخ (تحلیل پایایی)	تحلیل مؤلفه‌های اصلی
حوزه	آمار	آمار	یادگیری ماشین
خانواده	Latent structure	Latent structure	Machine learning
سال پیدایش	1969	1951	2002
پدیدآور	Karl Jöreskog	Lee J. Cronbach	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
نوع	Confirmatory latent variable model	Reliability / internal consistency coefficient	Unsupervised dimensionality reduction
منبع بنیادین	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
نام‌های دیگر	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
مرتبط	4	4	3
خلاصه	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.	Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research.	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.