השוואת שיטות

	אלפא של קרונבך (ניתוח מהימנות)	ניתוח רכיבים עיקריים
תחום	סטטיסטיקה	למידת מכונה
משפחה	Latent structure	Machine learning
שנת המקור	1951	2002
הוגה השיטה	Lee J. Cronbach	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
סוג	Reliability / internal consistency coefficient	Unsupervised dimensionality reduction
מקור מכונן	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.
כינויים	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
קשורות	4	3
תקציר	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.