On the solution multiplicity of the Fleishman method and its impact in simulation studies. (11th January 2018)

• Record Type: Journal Article
• Authors: Olvera Astivia, Oscar L.; Zumbo, Bruno D.
• Is Part Of: British journal of mathematical & statistical psychology. Volume 71:Part 3(2018)
• Page Start: 437
• Page End: 458
• Publication Date: 2018-01-11
• Subjects: Fleishman method -- kurtosis -- Monte Carlo -- multivariate nonnormal -- simulation -- skewness -- structural equation modeling -- Vale Maurelli method; Psychometrics -- Periodicals; Psychology -- Mathematical models -- Periodicals; Psychology -- Statistical methods -- Periodicals; 150.727
• DOI: 10.1111/bmsp.12126
• Languages: English
• ISSNs: 0007-1102

Abstract

Abstract : The Fleishman third‐order polynomial algorithm is one of the most‐often used non‐normal data‐generating methods in Monte Carlo simulations. At the crux of the Fleishman method is the solution of a non‐linear system of equations needed to obtain the constants to transform data from normality to non‐normality. A rarely acknowledged fact in the literature is that the solution to this system is not unique, and it is currently unknown what influence the different types of solutions have on the computer‐generated data. To address this issue, analytical and empirical investigations were conducted, aimed at documenting the impact that each solution type has on the design of computer simulations. In the first study, it was found that certain types of solutions generate data with different multivariate properties and wider coverage of the theoretical range spanned by population correlations. In the second study, it was found that previously published recommendations from Monte Carlo simulations could change if different types of solutions were used to generate the data. A mathematical description of the multiple solutions to the Fleishman polynomials is provided, as well as recommendations for users of this method.