Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ мощности для структурного моделирования уравнений× | Анализ статистической мощности для множественной регрессии× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Hypothesis test | Hypothesis test |
| Год появления≠ | 1996 | 1988 |
| Автор метода≠ | MacCallum, Browne & Sugawara | Jacob Cohen |
| Тип≠ | Sample size planning (multivariate / SEM) | A priori sample size determination |
| Основополагающий источник≠ | MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. DOI ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| Другие названия | SEM sample size planning, covariance structure power analysis, MANOVA power analysis, SEM / Çok Değişkenli Güç Analizi | regression power analysis, sample size estimation regression, f² power analysis, Güç Analizi — Regresyon |
| Связанные≠ | 6 | 4 |
| Сводка≠ | Power analysis for SEM and other multivariate procedures determines the minimum sample size required to detect a model misfit of a specified magnitude with adequate probability. The dominant approach, introduced by MacCallum, Browne, and Sugawara in 1996, expresses effect size as the Root Mean Square Error of Approximation (RMSEA) and derives power from the noncentral chi-square distribution. | Power analysis for multiple regression is a pre-study procedure, formalised by Jacob Cohen (1988), that calculates the minimum sample size needed to detect a regression effect of a given size with adequate statistical power. It uses the anticipated R² (or the equivalent Cohen's f² effect size) and the number of predictors to determine how many observations must be collected before data collection begins. |
| ScholarGateНабор данных ↗ |
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