TRANSFORMATIONS FOR WITHIN-SUBJECT DESIGNS - A MONTE-CARLO INVESTIGATION
TRANSFORMATIONS FOR WITHIN-SUBJECT DESIGNS - A MONTE-CARLO INVESTIGATION
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DOI：
10.1037/0033-2909.113.3.566
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发表时间：
1993-05-01
影响因子：
22.4
通讯作者：
WOLFORD, G
中科院分区：
文献类型：
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作者：
BUSH, LK;HESS, U;WOLFORD, G
我们探索了使用变换来提高受试者内设计的功效，其中在每种条件下为每个 S 收集多个观察结果，例如反应时间和心理生理实验。通常，对处理中的多个测量值进行简单平均以产生单个数字，但也提出了其他转换。蒙特卡洛模拟用于研究这些变换对 I 类和 II 类错误概率的影响。对于正态分布的数据，Z 和范围校正变换导致功效比简单平均值大幅增加。对于高度倾斜的分布，最佳变换取决于多个变量，但 Z 和范围校正在各种条件下都表现良好。纠正异常值对于提高功效很有用，并且修剪比消除超出标准的所有点更有效。
We explored the use of transformations to improve power in within-subject designs in which multiple observations are collected for each S in each condition, such as reaction time and psycho-physiological experiments. Often, the multiple measures within a treatment are simply averaged to yield a single number, but other transformations have been proposed. Monte Carlo simulations were used to investigate the influence of those transformations on the probabilities of Type I and Type II errors. With normally distributed data, Z and range correction transformations led to substantial increases in power over simple averages. With highly skewed distributions, the optimal transformation depended on several variables, but Z and range correction performed well across conditions. Correction for outliers was useful in increasing power, and trimming was more effective than eliminating all points beyond a criterion.