Rietveld Toni, van Hout Roeland
Centre for Language Studies, Radboud University, Erasmusplein 1, 6525 HT Nijmegen, The Netherlands.
Centre for Language Studies, Radboud University, Erasmusplein 1, 6525 HT Nijmegen, The Netherlands.
J Commun Disord. 2017 Sep;69:44-57. doi: 10.1016/j.jcomdis.2017.07.002. Epub 2017 Jul 25.
In this tutorial we review current practice in the analysis of data obtained in designs involving two dependent samples and evaluate two conventional statistics: the t test for paired samples and its non-parametric alternative, the Wilcoxon Signed Ranks test (WSR). It is a sequel to our tutorial on the analysis of designs with two independent samples on the basis of non-count data (Rietveld & van Hout, 2015). The frequency with which these statistics are used is assessed on the basis of publications on disordered communication in Clinical Linguistics & Phonetics, Journal of Communication Disorders and Journal of Speech, Language and Hearing Research for the time interval 2006-2015. We conclude with a number of recommendations for the analysis and presentation of data.
Researchers should more consistently present the relevant characteristics of their data (means, medians, SD, skewness, tailedness, outliers etc.) and explicitly consider the assumptions that apply to their statistical methods, such as correlations between data obtained on two occasions, interactions between participants and treatment, and the symmetry of difference scores, many of which are hardly ever reported or even tested. Two recommendations are particularly relevant. First, the WSR is not a proper test for central tendencies as a replacement of the conventional t test for paired samples whenever assumptions about the dependent variable are in doubt. Second, researchers should choose statistical procedures on the basis of the null hypothesis (H0) to be tested and not primarily on the basis of the type of data (ordinal or interval). Two relevant H0's in the field of speech-language pathology are: (1) μ=μ (the mean obtained in condition 1 is equal to the mean in condition 2) and (2) p=0.5, which says: the probability to obtain (for instance) higher scores in condition 2 than in condition 1 is 0.5. We recommend the permuted t test for paired samples to test the first H0 and the permuted Brunner-Munzel rank test to test the second.
在本教程中，我们回顾了在涉及两个相关样本的设计中对所得数据进行分析的当前实践，并评估了两种传统统计方法：配对样本t检验及其非参数替代方法——威尔科克森符号秩检验（WSR）。它是我们之前关于基于非计数数据的两个独立样本设计分析教程的续篇（里特维尔德和范霍特，2015年）。根据2006 - 2015年期间发表在《临床语言学与语音学》《沟通障碍杂志》以及《言语、语言和听力研究杂志》上有关言语紊乱的出版物，评估了这些统计方法的使用频率。我们最后给出了一些关于数据分析和呈现的建议。
研究人员应更一致地呈现其数据的相关特征（均值、中位数、标准差、偏度、尾性、异常值等），并明确考虑适用于其统计方法的假设，例如两次获取的数据之间的相关性、参与者与治疗之间的相互作用以及差异分数的对称性，但其中许多假设几乎从未被报告甚至检验过。有两条建议尤为重要。第一，每当对因变量的假设存疑时，WSR并非用于检验中心趋势的恰当方法，不能替代传统的配对样本t检验。第二，研究人员应根据要检验的零假设（H0）来选择统计程序，而不是主要基于数据类型（有序或区间）。言语语言病理学领域的两个相关H0是：（1）μ = μ（条件1中获得的均值等于条件2中的均值）和（2）p = 0.5，即：例如，在条件2中获得比条件1更高分数的概率为0.5。我们建议使用配对样本置换t检验来检验第一个H0，使用置换布鲁纳 - 蒙泽尔秩检验来检验第二个。
