Bootstrapping to test for nonzero population correlation coefficients using univariate sampling

被引:26
作者
Beasley, William Howard [1 ]
DeShea, Lise [2 ]
Toothaker, Larry E. [1 ]
Mendoza, Jorge L. [1 ]
Bard, David E. [3 ]
Rodgers, Joseph Lee [1 ]
机构
[1] Univ Oklahoma, Dept Psychol, Norman, OK 73019 USA
[2] Oklahoma Hlth Care Author, Oklahoma City, OK USA
[3] Univ Oklahoma, Hlth Sci Ctr, Norman, OK 73019 USA
关键词
correlation; Monte Carlo simulation; univariate sampling; bootstrap; nonzero and nil hypotheses;
D O I
10.1037/1082-989X.12.4.414
中图分类号
B84 [心理学];
学科分类号
04 [教育学]; 0402 [心理学];
摘要
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 >= 10, and rho <= 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 rho s of moderate size.
引用
收藏
页码:414 / 433
页数:20
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