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| ألفا كرونباخ (تحليل الموثوقية)× | تحليل العوامل التأكيدي (CFA)× | التحليل العاملي الاستكشافي (EFA)× | |
|---|---|---|---|
| المجال≠ | الإحصاء | القياس النفسي | الإحصاء |
| العائلة | Latent structure | Latent structure | Latent structure |
| سنة النشأة≠ | 1951 | 1969 | — |
| صاحب الطريقة≠ | Lee J. Cronbach | Karl Gustav Jöreskog | — |
| النوع≠ | Reliability / internal consistency coefficient | Hypothesis-testing latent variable model | Latent variable / dimension reduction |
| المصدر التأسيسي≠ | Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. DOI ↗ | 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 ↗ |
| الأسماء البديلة≠ | coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha) | CFA, confirmatory FA, measurement model, restricted factor analysis | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| ذات صلة | 4 | 4 | 4 |
| الملخص≠ | 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. | 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. |
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