পদ্ধতির তুলনা করুন

	কাঠামোগত সমীকরণ মডেলিংয়ের জন্য পাওয়ার বিশ্লেষণ	সিমুলেশন-ভিত্তিক পাওয়ার অ্যানালাইসিস (মন্টে কার্লো পাওয়ার)
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Hypothesis test	Hypothesis test
উদ্ভবের বছর	1996	2011
প্রবর্তক	MacCallum, Browne & Sugawara	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension
ধরন	Sample size planning (multivariate / SEM)	Simulation-based (Monte Carlo)
মৌলিক উৎস	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.
অপর নাম	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)
সম্পর্কিত	6	6
সারসংক্ষেপ	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.