Statistical power for the two-factor repeated measures ANOVA
Statistical power for the two-factor repeated measures ANOVA
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DOI：
10.3758/bf03207805
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发表时间：
2000-05-01
期刊：
影响因子：
--
通讯作者：
Schutz, RW
中科院分区：
文献类型：
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作者：
Potvin, PJ;Schutz, RW
确定一个先验的权力，单变量重复测量（RM）方差分析设计与两个或两个以上的主题内的因素，具有不同的相关模式之间的因素是目前困难的，由于准确的方法来估计使用的功率计算误差方差。本研究的主要目的是确定一个RM因子中的水平之间的相关性对另一个RM因子的功效的影响。在不同的效应量实验条件下，使用Monte Carlo模拟程序估计2 x 3、2 x 6、2 x 9、3 x 3、3 x 6和3 x 9设计的A、B和AB检验的功效（小、中、大）、平均相关性（.4和.8）、α（.01和.05）和样本量（n = 5、10、15、20、25和30）。结果表明，因子A水平之间的平均相关性与AB矩阵中的平均相关性之间的差异幅度越大，因子B的功效越低（反之亦然）。通过检查不同相关矩阵的把握度和均方误差趋势，构建了用于估计双向模型各检验的误差方差的方程。支持这些公式的准确性，从而允许在未来的研究中直接分析功率计算。
Determining a priori power for univariate repeated measures (RM) ANOVA designs with two or more within-subjects factors that have different correlational patterns between the factors is currently difficult due to the unavailability of accurate methods to estimate the error variances used in power calculations. The main objective of this study was to determine the effect of the correlation between the levels in one RM factor on the power of the other RM factor. Monte Carlo simulation procedures were used to estimate power for the A, B, and AB tests of a 2 x 3, a 2 x 6, a 2 x 9, a 3 x 3, a 3 x 6, and a 3 x 9 design under varying experimental conditions of effect size (small, medium, and large), average correlation (.4 and .8), alpha (.01 and .05), and sample size (n = 5, 10,15, 20, 25, and 30). Results indicated that the greater the magnitude of the differences between the average correlation among the levels of Factor A and the average correlation in the AB matrix, the lower the power for Factor B (and vice versa). Equations for estimating the error variance of each test of the two-way model were constructed by examining power and mean square error trends across different correlation matrices. Support for the accuracy of these formulae is given, thus allowing for direct analytic power calculations in future studies.