Using randomization techniques to analyse behavioural data
Using randomization techniques to analyse behavioural data
复制标题
DOI：
10.1006/anbe.1996.0077
复制
发表时间：
1996-04-01
期刊：
影响因子：
2.5
通讯作者：
Anthony, CD
中科院分区：
文献类型：
--
作者：
Adams, DC;Anthony, CD
行为研究的数据通常是非正态分布的，不能用传统的参数统计进行分析。相反，行为主义者必须依赖等级转换测试，这会丢失数据中存在的潜在有价值的信息。然而，最近，其他学科的生物学家已经通过使用reservation方法解决了类似的统计困难。Kruskal-Wallis非参数方差分析和随机化检验的结果进行了比较两个行为数据集。结果发现，随机化检验比Kruskal-Wallis检验更有效，因此可以检测到数据中存在的较小效应量。此外，在500 - 10 000的8个重复水平下计算P值周围的方差，以确定随机化试验的最佳重复次数。随着重复次数的增加，P值周围的方差减小。P值稳定在5000次重复，因此建议至少使用5000次重复进行行为数据的随机化检验。(C)1996年动物行为研究协会
Data from behavioural studies are frequently non-normally distributed and cannot be analysed with traditional parametric statistics. Instead, behaviourists must rely on rank-transformation tests, which lose potentially valuable information present in the data. Recently, however, biologists in other disciplines have resolved similar statistical difficulties by using resampling methods. Results from Kruskal-Wallis non-parametric ANOVA and randomization tests were compared for two behavioural data sets. It was found that randomization tests were more powerful than Kruskal-Wallis, and could thus detect smaller effect sizes present in the data. In addition, the variance was calculated around the P-value at eight levels of replication ranging from 500 to 10 000, to determine the optimal number of replications for the randomization test. The variance around the P-value decreased as the number of replications increased. The P-value stabilized at 5000 replications, and thus it is recommended that at least 5000 replications be used for randomization tests on behavioural data. (C) 1996 The Association for the Study of Animal Behaviour