Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Vipengele vya Uchunguzi kwa Ajili ya Uundaji wa Kipimo (EFA)× | Uchanganuzi wa Vipengele vya Uchunguzi (EFA)× | |
|---|---|---|
| Nyanja≠ | Saikometriki | Takwimu |
| Familia | Latent structure | Latent structure |
| Mwaka wa asili≠ | 1904 (foundational); contemporary scale-development practice from 1990s onward | — |
| Mwanzilishi≠ | Primarily Spearman (1904); psychometric scale application formalised by Thurstone (1930s) | — |
| Aina | Latent variable / dimension reduction | Latent variable / dimension reduction |
| Chanzo asilia≠ | Costello, A. B. & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9. link ↗ | 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 ↗ |
| Majina mbadala | Açımlayıcı Faktör Analizi — Ölçek Geliştirme (EFA), psychometric EFA, scale construction factor analysis | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | Exploratory Factor Analysis for Scale Development is the psychometric application of EFA in which an item pool is administered and the resulting response data are analysed to discover the latent factor structure underlying the items. Originating with Spearman's (1904) factor theory and formalised for applied scale construction by Costello and Osborne (2005) and Fabrigar and colleagues (1999), this variant imposes a stricter sample requirement (n ≥ 100, subject-to-item ratio ≥ 5) and a higher loading threshold (≥ 0.40) than general EFA, and it treats the recovered factor structure as a draft to be subsequently validated by confirmatory analysis. | 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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