...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Educational and Psychological Measurement >Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality
【24h】

Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality

机译：减少非正态相关系数的偏差和误差

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared with its major alternatives, including the Spearman rank-order correlation, the bootstrap estimate, the Box-Cox transformation family, and a general normalizing transformation (i.e., rankit), as well as to various bias adjustments. Nonnormality caused the correlation coefficient to be inflated by up to +.14, particularly when the nonnormality involved heavy-tailed distributions. Traditional bias adjustments worsened this problem, further inflating the estimate. The Spearman and rankit correlations eliminated this inflation and provided conservative estimates. Rankit also minimized random error for most sample sizes, except for the smallest samples (n = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated.
机译：教育和心理数据不正常比近似正常更为常见。这种趋势可能导致皮尔逊相关系数的点估计中存在偏差和误差。在一系列的蒙特卡洛模拟中，在正常数据和非正常数据的条件下检查了皮尔森相关性，并将其与主要替代方法进行了比较，包括斯皮尔曼秩相关，自举估计，Box-Cox变换族和一般的归一化转换（即等级），以及各种偏差调整。非正态性导致相关系数膨胀高达+.14，特别是当非正态性涉及重尾分布时。传统的偏差调整使这个问题更加恶化，使估计值进一步膨胀。 Spearman和Rankit的相关性消除了这种通货膨胀，并提供了保守的估计。对于最小的样本（n = 10），Rankit还使大多数样本大小的随机误差最小化，因为自举更有效。总的来说，当违反正态性时，结果证明使用精心选择的替代Pearson相关性是合理的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号