方法对比
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| 结构方程模型（SEM）的功效分析× | ANOVA 的功效分析× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Hypothesis test | Hypothesis test |
| 起源年份≠ | 1996 | 1988 |
| 提出者≠ | MacCallum, Browne & Sugawara | Jacob Cohen |
| 类型≠ | Sample size planning (multivariate / SEM) | Sample size determination |
| 开创性文献≠ | 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. DOI ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| 别名 | SEM sample size planning, covariance structure power analysis, MANOVA power analysis, SEM / Çok Değişkenli Güç Analizi | ANOVA power analysis, F-test power analysis, sample size for ANOVA, Güç Analizi — ANOVA |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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 ANOVA is a prospective statistical technique that determines the minimum sample size needed to detect a specified group mean difference with a chosen probability. Formalized by Jacob Cohen in his 1988 monograph, it translates a researcher's effect size expectation — expressed as Cohen's f — along with the desired Type I error rate (alpha) and statistical power (1 − beta) into a concrete per-group sample size recommendation for one-way or factorial ANOVA designs. |
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