เปรียบเทียบวิธี

	Factor Analysis	การถดถอยแบบทนทาน
สาขาวิชา	สถิติการวิจัย	สถิติศาสตร์
ตระกูล	Process / pipeline	Regression model
ปีกำเนิด	1931	1964
ผู้ริเริ่ม	Louis Leon Thurstone	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
ประเภท	Method	Regression with outlier resistance
แหล่งต้นตำรับ	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
ชื่อเรียกอื่น	EFA, CFA, latent variable modeling	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
ที่เกี่ยวข้อง	3	6
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.	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.