Wagstaff David A, Elek Elvira, Kulis Stephen, Marsiglia Flavio
College of Health and Human Development, The Pennsylvania State University, 153 Henderson Building, University Park, PA 16802, USA.
J Prim Prev. 2009 Sep;30(5):497-512. doi: 10.1007/s10935-009-0191-y. Epub 2009 Aug 15.
A nonparametric bootstrap was used to obtain an interval estimate of Pearson's r, and test the null hypothesis that there was no association between 5th grade students' positive substance use expectancies and their intentions to not use substances. The students were participating in a substance use prevention program in which the unit of randomization was a public middle school. The bootstrap estimate indicated that expectancies explained 21% of the variability in students' intentions (r = 0.46, 95% CI = [0.40, 0.50]). This case study illustrates the use of a nonparametric bootstrap with cluster randomized data and the danger posed if outliers are not identified and addressed. EDITORS' STRATEGIC IMPLICATIONS: Prevention researchers will benefit from the authors' detailed description of this nonparametric bootstrap approach for cluster randomized data and their thoughtful discussion of the potential impact of cluster sizes and outliers.
采用非参数自助法来获得皮尔逊相关系数r的区间估计，并检验五年级学生积极的物质使用预期与他们不使用物质的意图之间不存在关联这一零假设。这些学生参与了一项物质使用预防项目，其中随机化单位是一所公立中学。自助法估计表明，预期解释了学生意图中21%的变异性（r = 0.46，95%置信区间 = [0.40, 0.50]）。本案例研究说明了在聚类随机数据中使用非参数自助法以及如果未识别和处理异常值所带来的危险。编辑的战略启示：预防研究人员将受益于作者对这种用于聚类随机数据的非参数自助法的详细描述，以及他们对聚类大小和异常值潜在影响的深入讨论。
