विधियों की तुलना करें

	क्रोनबैक का अल्फा (विश्वसनीयता विश्लेषण)	अन्वेषणात्मक कारक विश्लेषण (EFA)	पदानुक्रमिक रैखिक मॉडलिंग (एचएलएम / बहुस्तरीय मॉडलिंग)	प्रिंसिपल कंपोनेंट एनालिसिस
क्षेत्र	सांख्यिकी	सांख्यिकी	सांख्यिकी	मशीन अधिगम
परिवार	Latent structure	Latent structure	Hypothesis test	Machine learning
उद्भव वर्ष	1951	—	1986	2002
प्रवर्तक	Lee J. Cronbach	—	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
प्रकार	Reliability / internal consistency coefficient	Latent variable / dimension reduction	Parametric nested-data regression	Unsupervised dimensionality reduction
मौलिक स्रोत	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.	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
उपनाम	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	common factor analysis, açımlayıcı faktör analizi, factor analysis	HLM, MLM, multilevel modeling, multilevel analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
संबंधित	4	4	4	3
सारांश	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.	Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.	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.