เปรียบเทียบวิธี

	Factor Analysis	การวินิจฉัยอิทธิพล (ระยะห่างของคุ้ก, DFFITS, Leverage)	การวิเคราะห์องค์ประกอบหลัก	การประมาณค่าความแปรปรวนร่วมที่ทนทาน (MCD)
สาขาวิชา	สถิติการวิจัย	สถิติศาสตร์	การเรียนรู้ของเครื่อง	สถิติศาสตร์
ตระกูล	Process / pipeline	Regression model	Machine learning	Regression model
ปีกำเนิด	1931	1977	2002	1999
ผู้ริเริ่ม	Louis Leon Thurstone	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
ประเภท	Method	Regression diagnostic	Unsupervised dimensionality reduction	Robust multivariate location-scatter estimator
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.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
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	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
ที่เกี่ยวข้อง	3	5	3	4
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.	Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation.