So sánh phương pháp

	Phân tích nhân tố	Chẩn đoán ảnh hưởng (Khoảng cách Cook, DFFITS, Đòn bẩy)	Ước lượng hiệp phương sai mạnh mẽ (MCD)
Lĩnh vực	Thống kê nghiên cứu	Thống kê	Thống kê
Họ	Process / pipeline	Regression model	Regression model
Năm ra đời	1931	1977	1999
Người khởi xướng	Louis Leon Thurstone	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Loại	Method	Regression diagnostic	Robust multivariate location-scatter estimator
Công trình gốc	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.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Tên gọi khác	EFA, CFA, latent variable modeling	Cook's distance, DFFITS, leverage, influential observation detection	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Liên quan	3	5	4
Tóm tắt	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.	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.