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Simulating Correlated Multivariate Nonnormal Distributions: Extending the Fleishman Power Method

Published online by Cambridge University Press:  01 January 2025

Todd C. Headrick  and
Shlomo S. Sawilowsky
Todd C. Headrick
Affiliation:
Evaluation and Research, College of Education, Wayne State University
Shlomo S. Sawilowsky*
Affiliation:
Evaluation and Research, College of Education, Wayne State University
*
Requests for reprints should be sent to Shlomo S. Sawilowsky, #351 EDUC, College of Educaton, Wayne State University, Detroit, MI, 48202. E-mail: shlomo@edstat.coe.wayne.edu
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Abstract

A procedure for generating multivariate nonnormal distributions is proposed. Our procedure generates average values of intercorrelations much closer to population parameters than competing procedures for skewed and/or heavy tailed distributions and for small sample sizes. Also, it eliminates the necessity of conducting a factorization procedure on the population correlation matrix that underlies the random deviates, and it is simpler to code in a programming language (e.g., FORTRAN). Numerical examples demonstrating the procedures are given. Monte Carlo results indicate our procedure yields excellent agreement between population parameters and average values of intercorrelation, skew, and kurtosis.

Keywords

simulationspseudo-random numberscorrelated datanonnormality

Information

Type
Original Paper
Information
Psychometrika , Volume 64 , Issue 1 , March 1999 , pp. 25 - 35
Copyright
Copyright © 1999 The Psychometric Society

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