השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מיפוי רב-ממדי (MDS)× | ניתוח גורמים גישוש (EFA)× | |
|---|---|---|
| תחום | סטטיסטיקה | סטטיסטיקה |
| משפחה | Latent structure | Latent structure |
| שנת המקור≠ | 1952–1964 | — |
| הוגה השיטה≠ | Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964) | — |
| סוג≠ | Dimensionality reduction / visualization | Latent variable / dimension reduction |
| מקור מכונן≠ | Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27. 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≠ | MDS, metric MDS, non-metric MDS, proximity scaling | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| קשורות≠ | 5 | 4 |
| תקציר≠ | Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data. | 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. |
| ScholarGateמערך נתונים ↗ |
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