השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| רגרסיה בייסיאנית× | ניתוח גורמים מאשר (Confirmatory Factor Analysis - CFA)× | ניתוח גורמים גישוש (EFA)× | |
|---|---|---|---|
| תחום≠ | בייסיאני | סטטיסטיקה | סטטיסטיקה |
| משפחה≠ | Bayesian methods | Latent structure | Latent structure |
| שנת המקור≠ | — | 1969 | — |
| הוגה השיטה≠ | — | Karl Jöreskog | — |
| סוג≠ | Bayesian linear model | Confirmatory latent variable model | Latent variable / dimension reduction |
| מקור מכונן≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363 | 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 ↗ |
| כינויים | bayesian linear regression, probabilistic regression, bayesian regresyon | Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| קשורות≠ | 2 | 4 | 4 |
| תקציר≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships. | 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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