Zumbo B D
Psychology Program, University of Northern British Columbia, Prince George, Canada.
Percept Psychophys. 1996 Apr;58(3):471-8. doi: 10.3758/bf03206822.
Coupled data arise in perceptual research when subjects are contributing two scores to the data pool. These two scores, it can be reasonably argued, cannot be assumed to be independent of one another; therefore, special treatment is needed when performing statistical inference. This paper shows how the Type I error rate of randomization-based inference is affected by coupled data. It is demonstrated through Monte Carlo simulation that a randomization test behaves much like its parametric counterpart except that, for the randomization test, a negative correlation results in an inflation in the Type I error rate. A new randomization test, the couplet-referenced randomization test, is developed and shown to work for sample sizes of 8 or more observations. An example is presented to demonstrate the computation and interpretation of the new randomization test.
当受试者为数据集贡献两个分数时，耦合数据便出现在知觉研究中。可以合理地认为，这两个分数并非相互独立；因此，在进行统计推断时需要特殊处理。本文展示了基于随机化的推断的I型错误率是如何受到耦合数据影响的。通过蒙特卡洛模拟表明，随机化检验的表现与其参数对应检验非常相似，只是对于随机化检验而言，负相关会导致I型错误率膨胀。开发了一种新的随机化检验——对联参考随机化检验，并证明其适用于8个或更多观测值的样本量。给出了一个例子来说明新随机化检验的计算和解释。
