期刊
BEHAVIOR RESEARCH METHODS
卷 52, 期 3, 页码 939-946出版社
SPRINGER
DOI: 10.3758/s13428-019-01291-5
关键词
Multivariate non-normal data; Multivariate skewness; Multivariate kurtosis; Random number generation
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.
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