手法を比較

	構造方程式モデリングにおける検出力分析	重回帰分析のための検出力分析
分野	統計学	統計学
系統	Hypothesis test	Hypothesis test
提唱年	1996	1988
提唱者	MacCallum, Browne & Sugawara	Jacob Cohen
種類	Sample size planning (multivariate / SEM)	A priori sample size determination
原典	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
別名	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
関連	6	4
概要	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.