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	Analisi di potenza per modelli di equazioni strutturali	Analisi di potenza per la regressione multipla
Campo	Statistica	Statistica
Famiglia	Hypothesis test	Hypothesis test
Anno di origine	1996	1988
Ideatore	MacCallum, Browne & Sugawara	Jacob Cohen
Tipo	Sample size planning (multivariate / SEM)	A priori sample size determination
Fonte seminale	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
Alias	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
Correlati	6	4
Sintesi	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.