Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Експлораторний факторний аналіз для розробки шкал (EFA)× | Метод головних компонент× | |
|---|---|---|
| Галузь≠ | Психометрія | Машинне навчання |
| Родина≠ | Latent structure | Machine learning |
| Рік появи≠ | 1904 (foundational); contemporary scale-development practice from 1990s onward | 2002 |
| Автор методу≠ | Primarily Spearman (1904); psychometric scale application formalised by Thurstone (1930s) | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Тип≠ | Latent variable / dimension reduction | Unsupervised dimensionality reduction |
| Основоположне джерело≠ | 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 ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Інші назви≠ | Açımlayıcı Faktör Analizi — Ölçek Geliştirme (EFA), psychometric EFA, scale construction factor analysis | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Пов'язані≠ | 5 | 3 |
| Підсумок≠ | 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. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
| ScholarGateНабір даних ↗ |
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