เปรียบเทียบวิธี

	การวิเคราะห์ปัจจัยยืนยัน (Confirmatory Factor Analysis: CFA)	การวิเคราะห์ปัจจัยเชิงสำรวจ (Exploratory Factor Analysis, EFA)	การวิเคราะห์องค์ประกอบหลัก
สาขาวิชา	การวัดทางจิตวิทยา	สถิติศาสตร์	การเรียนรู้ของเครื่อง
ตระกูล	Latent structure	Latent structure	Machine learning
ปีกำเนิด	1969	—	2002
ผู้ริเริ่ม	Karl Gustav Jöreskog	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
ประเภท	Hypothesis-testing latent variable model	Latent variable / dimension reduction	Unsupervised dimensionality reduction
แหล่งต้นตำรับ	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
ชื่อเรียกอื่น	CFA, confirmatory FA, measurement model, restricted factor analysis	common factor analysis, açımlayıcı faktör analizi, factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
ที่เกี่ยวข้อง	4	4	3
สรุป	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.	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.