Сравнение методов

	Анализ мощности для структурного моделирования уравнений	Анализ мощности для дисперсионного анализа (ANOVA)
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)	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.	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	ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA
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 ANOVA is a prospective statistical technique that determines the minimum sample size needed to detect a specified group mean difference with a chosen probability. Formalized by Jacob Cohen in his 1988 monograph, it translates a researcher's effect size expectation — expressed as Cohen's f — along with the desired Type I error rate (alpha) and statistical power (1 − beta) into a concrete per-group sample size recommendation for one-way or factorial ANOVA designs.