방법 비교

	탐색적 요인 분석(EFA)을 이용한 척도 개발	확인적 요인 분석 (CFA)	크론바흐 알파 (신뢰도 분석)	탐색적 요인 분석 (EFA)	주성분 분석
분야	심리측정학	심리측정학	통계학	통계학	머신러닝
계열	Latent structure	Latent structure	Latent structure	Latent structure	Machine learning
기원 연도	1904 (foundational); contemporary scale-development practice from 1990s onward	1969	1951	—	2002
창시자	Primarily Spearman (1904); psychometric scale application formalised by Thurstone (1930s)	Karl Gustav Jöreskog	Lee J. Cronbach	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
유형	Latent variable / dimension reduction	Hypothesis-testing latent variable model	Reliability / internal consistency coefficient	Latent variable / dimension reduction	Unsupervised dimensionality reduction
원전	Costello, A. B. & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
별칭	Açımlayıcı Faktör Analizi — Ölçek Geliştirme (EFA), psychometric EFA, scale construction factor analysis	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	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
관련	5	4	4	4	3
요약	Exploratory Factor Analysis for Scale Development is the psychometric application of EFA in which an item pool is administered and the resulting response data are analysed to discover the latent factor structure underlying the items. Originating with Spearman's (1904) factor theory and formalised for applied scale construction by Costello and Osborne (2005) and Fabrigar and colleagues (1999), this variant imposes a stricter sample requirement (n ≥ 100, subject-to-item ratio ≥ 5) and a higher loading threshold (≥ 0.40) than general EFA, and it treats the recovered factor structure as a draft to be subsequently validated by confirmatory analysis.	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.	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.