השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| ניתוח גורמים מאשר (Confirmatory Factor Analysis - CFA)× | אלפא של קרונבך (ניתוח מהימנות)× | ניתוח גורמים גישוש (EFA)× | |
|---|---|---|---|
| תחום | סטטיסטיקה | סטטיסטיקה | סטטיסטיקה |
| משפחה | Latent structure | Latent structure | Latent structure |
| שנת המקור≠ | 1969 | 1951 | — |
| הוגה השיטה≠ | Karl Jöreskog | Lee J. Cronbach | — |
| סוג≠ | Confirmatory latent variable model | Reliability / internal consistency coefficient | Latent variable / dimension reduction |
| מקור מכונן≠ | Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363 | Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. 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 ↗ |
| כינויים≠ | Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model | coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha) | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| קשורות | 4 | 4 | 4 |
| תקציר≠ | 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. | 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. |
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