Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Надежный анализ элементов× | Эксплораторный факторный анализ (ЭФА)× | |
|---|---|---|
| Область≠ | Психометрия | Статистика |
| Семейство | Latent structure | Latent structure |
| Год появления≠ | 1980s–2000s | — |
| Автор метода≠ | Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues | — |
| Тип≠ | Diagnostic / item-level evaluation | Latent variable / dimension reduction |
| Основополагающий источник≠ | Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838 | 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 ↗ |
| Другие названия | robust item statistics, outlier-resistant item analysis, robust classical item analysis | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Связанные≠ | 5 | 4 |
| Сводка≠ | Robust item analysis applies outlier-resistant statistical methods to the evaluation of individual test or scale items. Instead of classical means and Pearson correlations — both sensitive to extreme scores — it uses trimmed means, Winsorized correlations, or M-estimators to obtain item difficulty and item-total discrimination indices that remain stable when respondent distributions are skewed or contaminated by outliers. | 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. |
| ScholarGateНабор данных ↗ |
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