পদ্ধতির তুলনা করুন

	ক্রোনব্যাকের আলফা (নির্ভরযোগ্যতা বিশ্লেষণ)	অনুসন্ধানী উপাদান বিশ্লেষণ (EFA)	প্রধান উপাদান বিশ্লেষণ	কাঠামোগত সমীকরণ মডেলিং (SEM)
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান	যন্ত্র শিখন	পরিসংখ্যান
পরিবার	Latent structure	Latent structure	Machine learning	Latent structure
উদ্ভবের বছর	1951	—	2002	1970
প্রবর্তক	Lee J. Cronbach	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Karl Jöreskog (LISREL framework, 1970s)
ধরন	Reliability / internal consistency coefficient	Latent variable / dimension reduction	Unsupervised dimensionality reduction	Latent variable / causal modeling
মৌলিক উৎস	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.	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
অপর নাম	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	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
সম্পর্কিত	4	4	3	5
সারসংক্ষেপ	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.	Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.