Department of Psychology, University of Notre Dame, Corbett Family Hall, Notre Dame, IN, 46556, USA.
Psychological Sciences, University of California, Merced, CA, USA.
Behav Res Methods. 2020 Jun;52(3):939-946. doi: 10.3758/s13428-019-01291-5.
In social and behavioral sciences, data are typically not normally distributed, which can invalidate hypothesis testing and lead to unreliable results when being analyzed by methods developed for normal data. The existing methods of generating multivariate non-normal data typically create data according to specific univariate marginal measures such as the univariate skewness and kurtosis, but not multivariate measures such as Mardia's skewness and kurtosis. In this study, we propose a new method of generating multivariate non-normal data with given multivariate skewness and kurtosis. Our approach allows researchers to better control their simulation designs in evaluating the influence of multivariate non-normality.
在社会和行为科学中，数据通常不是正态分布的，这可能会使基于正态数据开发的方法进行的假设检验无效，并导致结果不可靠。现有的生成多元非正态数据的方法通常根据特定的单变量边缘度量（如单变量偏度和峰度）而不是多元度量（如 Mardia 的偏度和峰度）来创建数据。在本研究中，我们提出了一种新的生成给定多元偏度和峰度的多元非正态数据的方法。我们的方法允许研究人员在评估多元非正态性的影响时更好地控制他们的模拟设计。
