Likert scales, levels of measurement and the "laws" of statistics
Likert scales, levels of measurement and the "laws" of statistics
复制标题
DOI：
10.1007/s10459-010-9222-y
复制
发表时间：
2010-12-01
影响因子：
4
通讯作者：
Norman, Geoff
中科院分区：
文献类型：
--
作者：
Norman, Geoff
研究报告的审稿人经常批评统计方法的选择。虽然其中一些批评是有根据的，但经常使用各种参数方法，如方差分析，回归，相关性是错误的，因为：（a）样本量太小，（B）数据可能不是正态分布的，或（c）数据来自Likert量表，这是有序的，所以不能使用参数统计。在本文中，我剖析了这些论点，并表明，许多研究，可以追溯到20世纪30年代一致表明，参数统计是强大的违反这些假设。因此，上述挑战是没有根据的，可以使用参数方法，而不必担心“得到错误的答案”。
Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for "getting the wrong answer".