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| 시뮬레이션 기반 검정력 분석 (몬테카를로 검정력)× | 다층 및 혼합 효과 모형을 위한 검정력 분석× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 2011 | 1993 |
| 창시자≠ | Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension | Snijders & Bosker; Hox, Moerbeek & van de Schoot |
| 유형≠ | Simulation-based (Monte Carlo) | Sample-size planning for hierarchical designs |
| 원전≠ | Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94. DOI ↗ | Snijders, T.A.B. & Bosker, R.J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). SAGE. ISBN: 978-1849202015 |
| 별칭 | 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 |
| 관련≠ | 6 | 4 |
| 요약≠ | 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. |
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