Generating non-normal data for simulation of structural equation models using Mattson's method

    Authors

    W. J. Reinartz; R. Echambadi;W. W. Chin

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Multivariate Behav. Res.

    Keywords

    RANDOM-NUMBER GENERATOR; NONNORMAL DATA; COVARIANCE; Mathematics, Interdisciplinary Applications; Social Sciences, ; Mathematical Methods; Psychology, Experimental; Statistics & Probability

    Abstract

    SEM researchers use Monte-Carlo simulations to ascertain the robustness of statistical estimators and the performance of various fit indices under varying conditions of nonnormality. The efficacy of these Monte-Carlo simulations is closely related to the generation of non-normal data. Traditionally, SEM researchers have used approaches proposed by Fleishman (1978) and Vale and Maurelli (1983) to generate multivariate nonnormal random numbers. However, both approaches do not provide a method to determine univariate skewness and kurtosis of the observed variables when a non-normal distribution is specified for some or all of the latent variables. Mattson (1997) proposed a method for generating data on the latent variables with controlled skewness and kurtosis of the observed variables. We empirically test the applicability of Mattson's theoretical method in a Monte-Carlo simulation. Specifically, we assess the impact of data generation, selection of transformation method, sample size and degree of skewness/kurtosis on the performance of the method. Our results suggest that Mattson's method seems to be a good approach to generate data with defined levels of skewness and kurtosis. In addition, based on the results of our analysis, we provide practicing researchers recommendations regarding their empirical implementation schemes.

    Journal Title

    Multivariate Behavioral Research

    Volume

    37

    Issue/Number

    2

    Publication Date

    1-1-2002

    Document Type

    Article

    Language

    English

    First Page

    227

    Last Page

    244

    WOS Identifier

    WOS:000177414900003

    ISSN

    0027-3171

    Share

    COinS