Σύγκριση μεθόδων

	Ανάλυση Ισχύος για Μοντελοποίηση Δομικών Εξισώσεων	Ανάλυση Ισχύος για Πολλαπλή Παλινδρόμηση
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Hypothesis test	Hypothesis test
Έτος προέλευσης	1996	1988
Δημιουργός	MacCallum, Browne & Sugawara	Jacob Cohen
Τύπος	Sample size planning (multivariate / SEM)	A priori sample size determination
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	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
Συναφείς	6	4
Σύνοψη	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.