เปรียบเทียบวิธี

	การวิเคราะห์อำนาจการทดสอบสำหรับแบบจำลองสมการโครงสร้าง	การวิเคราะห์อำนาจการทดสอบโดยการจำลอง (Monte Carlo Power)
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์
ตระกูล	Hypothesis test	Hypothesis test
ปีกำเนิด	1996	2011
ผู้ริเริ่ม	MacCallum, Browne & Sugawara	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension
ประเภท	Sample size planning (multivariate / SEM)	Simulation-based (Monte Carlo)
แหล่งต้นตำรับ	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.	Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.
ชื่อเรียกอื่น	SEM sample size planning, covariance structure power analysis, MANOVA power analysis, SEM / Çok Değişkenli Güç Analizi	Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)
ที่เกี่ยวข้อง	6	6
สรุป	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.	Simulation-based power analysis estimates the statistical power and required sample size of a study by repeating a full analysis pipeline thousands of times on artificially generated data. Because it relies on Monte Carlo simulation rather than closed-form equations, it is applicable to designs — mixed models, complex measurement structures, non-standard outcomes — where analytical power formulas do not exist. The approach was systematically described for applied research by Arnold et al. in 2011, and the mixed-model implementation via the SIMR package was formalised by Green and MacLeod in 2016.