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| Моделиране на структурни уравнения (МСУ)× | Конфирматорният факторен анализ (CFA)× | Експлораторният факторен анализ (EFA)× | |
|---|---|---|---|
| Област≠ | Статистика | Психометрия | Статистика |
| Семейство | Latent structure | Latent structure | Latent structure |
| Година на възникване≠ | 1970 | 1969 | — |
| Създател≠ | Karl Jöreskog (LISREL framework, 1970s) | Karl Gustav Jöreskog | — |
| Тип≠ | Latent variable / causal modeling | Hypothesis-testing latent variable model | Latent variable / dimension reduction |
| Основополагащ източник≠ | Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ | 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. DOI ↗ |
| Други названия≠ | Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling | CFA, confirmatory FA, measurement model, restricted factor analysis | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Свързани≠ | 5 | 4 | 4 |
| Резюме≠ | 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. | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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