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बहुभिन्नरूपी मॉडल परीक्षण अनुसंधान×पुष्टिकारीय कारक विश्लेषण (CFA)×
क्षेत्रअनुसंधान अभिकल्पमनोमिति
परिवारProcess / pipelineLatent structure
उद्भव वर्ष1970s–1980s (multivariate model testing as a distinct approach)1969
प्रवर्तकKarl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis)Karl Gustav Jöreskog
प्रकारQuantitative confirmatory research designHypothesis-testing latent variable model
मौलिक स्रोतTabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗
उपनामmultivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT researchCFA, confirmatory FA, measurement model, restricted factor analysis
संबंधित54
सारांशMultivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path 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.
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ScholarGateविधियों की तुलना करें: Multivariate Model Testing Research · Confirmatory factor analysis. 2026-06-17 को यहाँ से प्राप्त https://scholargate.app/hi/compare