Title: A Monte Carlo Investigation of the Fisher Z Transformation for Normal and Nonnormal Distributions
Abstract:The Fisher transformation of the sample correlation coefficient r (1915, 1921) and two related techniques by Gayen (1951) and Jeyaratnam (1992) are examined for robustness to nonnormality. Monte Carlo...The Fisher transformation of the sample correlation coefficient r (1915, 1921) and two related techniques by Gayen (1951) and Jeyaratnam (1992) are examined for robustness to nonnormality. Monte Carlo analyses compare combinations of sample sizes and population parameters for seven bivariate distributions. The Fisher, Gayen, and Jeyaratnam approaches are shown to provide useful results for a bivariate normal distribution with any population correlation coefficient rho and for nonnormal bivariate distributions when rho = 0. In contrast, the techniques are virtually useless for nonnormal bivariate distributions when rho not equal to 0.0. Surprisingly, small samples are found to provide better estimates than large samples for skewed and symmetric heavy-tailed bivariate distributions.Read More
Publication Year: 2000
Publication Date: 2000-12-01
Language: en
Type: article
Indexed In: ['crossref', 'pubmed']
Access and Citation
Cited By Count: 69
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot