Generating univariate and multivariate nonnormal data
Sunbok Lee
Center for Family Research
University of Georgia
Athens, GA
[email protected]
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Abstract. Because the assumption of normality is common in statistics, the robustness of
statistical procedures to the violation of the normality assumption is often of
interest. When one examines the impact of the violation of the normality
assumption, it is important to simulate data from a nonnormal distribution with
varying degrees of skewness and kurtosis. Fleishman (1978, Psychometrika
43: 521–532) developed a method to simulate data from a univariate
distribution with specific values for the skewness and kurtosis. Vale and
Maurelli (1983, Psychometrika 48: 465–471) extended Fleishman’s
method to simulate data from a multivariate nonnormal distribution. In this
article, I briefly introduce these two methods and present two new commands,
rnonnormal and rmvnonnormal, for simulating data from the
univariate and multivariate nonnormal distributions.
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Sunbok Lee
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rnonnormal, rmvnonnormal, nonnormal data, skewness, kurtosis
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