The University of Auckland, New Zealand.
East Bay Specialist Centre, Whakatane, New Zealand.
J Exp Anal Behav. 2019 Mar;111(2):342-358. doi: 10.1002/jeab.502. Epub 2019 Feb 7.
We advocate for rank-permutation tests as the best choice for null-hypothesis significance testing of behavioral data, because these tests require neither distributional assumptions about the populations from which our data were drawn nor the measurement assumption that our data are measured on an interval scale. We provide an algorithm that enables exact-probability versions of such tests without recourse to either large-sample approximation or resampling approaches. We particularly consider a rank-permutation test for monotonic trend, and provide an extension of this test that allows unequal number of data points, or observations, for each subject. We provide an extended table of critical values of the test statistic for this test, and both a spreadsheet implementation and an Oracle® Java Web Start application to generate other critical values at https://sites.google.com/a/eastbayspecialists.co.nz/rank-permutation/.
我们提倡对行为数据进行零假设显著性检验时使用排序随机检验，因为这些检验既不需要关于数据来源总体的分布假设，也不需要关于我们的数据是在等距量表上测量的测量假设。我们提供了一种算法，可以实现这些检验的精确概率版本，而无需使用大样本逼近或重采样方法。我们特别考虑了一种用于单调趋势的排序随机检验，并提供了此检验的扩展，允许每个主体的数据点或观察值数量不相等。我们提供了此检验的检验统计量的扩展临界值表，以及一个电子表格实现和一个 Oracle® Java Web Start 应用程序，可在 https://sites.google.com/a/eastbayspecialists.co.nz/rank-permutation/ 生成其他临界值。
