Beasley William Howard, DeShea Lise, Toothaker Larry E, Mendoza Jorge L, Bard David E, Rodgers Joseph Lee
Department of Psychology, University of Oklahoma.
Oklahoma Health Care Authority.
Psychol Methods. 2007 Dec;12(4):414-433. doi: 10.1037/1082-989X.12.4.414.
This article proposes 2 new approaches to test a nonzero population correlation (rho): the hypothesis-imposed univariate sampling bootstrap (HI) and the observed-imposed univariate sampling bootstrap (OI). The authors simulated correlated populations with various combinations of normal and skewed variates. With alpha set=.05, N> or =10, and rho< or =0.4, empirical Type I error rates of the parametric r and the conventional bivariate sampling bootstrap reached .168 and .081, respectively, whereas the largest error rates of the HI and the OI were .079 and .062. On the basis of these results, the authors suggest that the OI is preferable in alpha control to parametric approaches if the researcher believes the population is nonnormal and wishes to test for nonzero rhos of moderate size.
本文提出了两种用于检验非零总体相关性（rho）的新方法：假设强加单变量抽样自举法（HI）和观测强加单变量抽样自举法（OI）。作者用正态和偏态变量的各种组合模拟了相关总体。当α设定为0.05、N≥10且rho≤0.4时，参数r和传统双变量抽样自举法的经验性I型错误率分别达到0.168和0.081，而HI和OI的最大错误率分别为0.079和0.062。基于这些结果，作者建议，如果研究者认为总体是非正态的且希望检验中等大小的非零rho，那么在控制α方面，OI比参数方法更可取。
