Abstract

This study aims to examine the effects of the underlying population distribution (normal, non-normal) and OLs on the magnitude of Pearson, Spearman and Pearson Winzorized correlation coefficients through Monte Carlo simulation. The study is conducted using Monte Carlo simulation methodology, with sample sizes of 50, 100, 250, 250, 500 and 1000 observations. Each, underlying population correlations of 0.12, 0.20, 0.31 and 0.50 under conditions of bivariate Normality, bivariate Normality with Outliers (discordant, contaminants) and Non-normal with different values of skewness and kurtosis. The results show that outliers have a greater effect compared to the data distributions; specifically, a substantial effect occurs in Pearson and a smaller one in Spearman and Pearson Winzorized. Additionally, the outliers are shown to have an impact on the assessment of bivariate normality using Mardia’s test and problems with decisions based on skewness and kurtosis for univariate normality. Implications of the results obtained are discussed

摘要

本研究旨在通过蒙特卡罗模拟检验潜在总体分布（正态、非正态）和 OL 对 Pearson、Spearman 和 Pearson Winzorized 相关系数大小的影响。该研究采用蒙特卡罗模拟方法进行，样本量为 50、100、250、250、500 和 1000 个观测值。在双变量正态性、具有异常值（不一致、污染物）的双变量正态性和具有不同偏度和峰度值的非正态性条件下，每个基本总体相关性为 0.12、0.20、0.31 和 0.50。结果表明，与数据分布相比，异常值的影响更大；具体来说，Pearson 产生了显着的影响，而 Spearman 和 Pearson Winzorized 产生了较小的影响。此外，异常值对使用 Mardia 检验的双变量正态性评估以及基于单变量正态性的偏度和峰度决策的问题有影响。讨论了所获得结果的含义