So sánh phương pháp

	Phân tích sức mạnh dựa trên mô phỏng (Sức mạnh Monte Carlo)	Phân tích lực (Power Analysis) cho Mô hình Đa cấp và Hỗn hợp
Lĩnh vực	Thống kê	Thống kê
Họ	Hypothesis test	Hypothesis test
Năm ra đời	2011	1993
Người khởi xướng	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension	Snijders & Bosker; Hox, Moerbeek & van de Schoot
Loại	Simulation-based (Monte Carlo)	Sample-size planning for hierarchical designs
Công trình gốc	Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.	Snijders, T.A.B. & Bosker, R.J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). SAGE. ISBN: 978-1849202015
Tên gọi khác	Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)	HLM power analysis, mixed-effects power analysis, clustered design power analysis, Çok Düzeyli / Karma Model Güç Analizi
Liên quan	6	4
Tóm tắt	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.	Multilevel power analysis is a sample-size planning procedure designed for hierarchical, clustered, or longitudinal study designs in which observations are nested within higher-level units such as students within schools or patients within clinics. Formalized in the multilevel modeling literature by Snijders and Bosker (1993, expanded 2012) and Hox, Moerbeek, and van de Schoot (2017), it accounts for the intraclass correlation (ICC) and the design effect that arises when data are clustered, ensuring that both the number of clusters and the cluster size are adequate to detect a target effect.