Rasmussen, J. and Dunlap, W. (1991) Dealing with Non-Normal Data: Parametric Analysis of Transformed Data vs. Nonparametric Analysis. Educational and Psychological Measurement, 51, 809-820.
http://dx.doi.org/10.1177/001316449105100402

has been cited by the following article:

• TITLE: A Simulation Based Comparison of Correlation Coefficients with Regard to Type I Error Rate and Power

  AUTHORS: Elif Tuğran, Mehmet Kocak, Hamit Mirtagioğlu, Soner Yiğit, Mehmet Mendes

  KEYWORDS: Correlation Coefficient, Robustness, Type I Error Rate, Test Power, Non-Normality, Simulation

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.3 No.3, August 27, 2015

  ABSTRACT: In this simulation study, five correlation coefficients, namely, Pearson, Spearman, Kendal Tau, Permutation-based, and Winsorized were compared in terms of Type I error rate and power under different scenarios where the underlying distributions of the variables of interest, sample sizes and correlation patterns were varied. Simulation results showed that the Type I error rate and power of Pearson correlation coefficient were negatively affected by the distribution shapes especially for small sample sizes, which was much more pronounced for Spearman Rank and Kendal Tau correlation coefficients especially when sample sizes were small. In general, Permutation-based and Winsorized correlation coefficients are more robust to distribution shapes and correlation patterns, regardless of sample size. In conclusion, when assumptions of Pearson correlation coefficient are not satisfied, Permutation-based and Winsorized correlation coefficients seem to be better alternatives.