Ulrich R, Miller J
University of Konstanz, Germany.
J Exp Psychol Gen. 1994 Mar;123(1):34-80. doi: 10.1037//0096-3445.123.1.34.
Many reaction time (RT) researchers truncate their data sets, excluding as spurious all RTs falling outside a prespecified range. Such truncation can introduce bias because extreme but valid RTs may be excluded. This article examines biasing effects of truncation under various assumptions about the underlying distributions of valid and spurious RTs. For the mean, median, standard deviation, and skewness of RT, truncation bias is larger than some often-studied experimental effects. Truncation can also seriously distort linear relations between RT and an independent variable, additive RT patterns in factorial designs, and hazard functions, but it has little effect on statistical power. The authors report a promising maximum likelihood procedure for estimating properties of an untruncated distribution from a truncated sample and present in an appendix a set of procedures to control for truncation biases when testing hypotheses.
许多反应时（RT）研究人员会截断他们的数据集，将超出预先设定范围的所有反应时都作为虚假数据排除。这种截断可能会引入偏差，因为极端但有效的反应时可能会被排除。本文研究了在关于有效和虚假反应时的潜在分布的各种假设下，截断的偏差效应。对于反应时的均值、中位数、标准差和偏度，截断偏差比一些经常研究的实验效应更大。截断还会严重扭曲反应时与自变量之间的线性关系、析因设计中的相加反应时模式以及风险函数，但对统计功效影响很小。作者报告了一种很有前景的最大似然程序，用于从截断样本估计未截断分布的属性，并在附录中给出了一组在检验假设时控制截断偏差的程序。
