เปรียบเทียบวิธี

	การวิเคราะห์ปัจจัยยืนยัน (Confirmatory Factor Analysis: CFA)	สัมประสิทธิ์อัลฟาของครอนบาค (การวิเคราะห์ความเชื่อมั่น)	การวิเคราะห์ปัจจัยเชิงสำรวจ (Exploratory Factor Analysis, EFA)
สาขาวิชา	การวัดทางจิตวิทยา	สถิติศาสตร์	สถิติศาสตร์
ตระกูล	Latent structure	Latent structure	Latent structure
ปีกำเนิด	1969	1951	—
ผู้ริเริ่ม	Karl Gustav Jöreskog	Lee J. Cronbach	—
ประเภท	Hypothesis-testing latent variable model	Reliability / internal consistency coefficient	Latent variable / dimension reduction
แหล่งต้นตำรับ	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.	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.
ชื่อเรียกอื่น	CFA, confirmatory FA, measurement model, restricted factor analysis	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	common factor analysis, açımlayıcı faktör analizi, factor analysis
ที่เกี่ยวข้อง	4	4	4
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.	Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research.	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.