เปรียบเทียบวิธี

	Factor Analysis	การวินิจฉัยอิทธิพล (ระยะห่างของคุ้ก, DFFITS, Leverage)	การวิเคราะห์องค์ประกอบหลัก
สาขาวิชา	สถิติการวิจัย	สถิติศาสตร์	การเรียนรู้ของเครื่อง
ตระกูล	Process / pipeline	Regression model	Machine learning
ปีกำเนิด	1931	1977	2002
ผู้ริเริ่ม	Louis Leon Thurstone	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
ประเภท	Method	Regression diagnostic	Unsupervised dimensionality reduction
แหล่งต้นตำรับ	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
ชื่อเรียกอื่น	EFA, CFA, latent variable modeling	Cook's distance, DFFITS, leverage, influential observation detection	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
ที่เกี่ยวข้อง	3	5	3
สรุป	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.	Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients.	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.