Determining the significance of scale values from multidimensional scaling profile analysis using a resampling method
Determining the significance of scale values from multidimensional scaling profile analysis using a resampling method
复制标题
DOI：
10.3758/bf03206396
复制
发表时间：
2005-02-01
影响因子：
5.4
通讯作者：
Ding, CS
中科院分区：
文献类型：
--
作者：
Ding, CS
虽然多维标度（MDS）轮廓分析被广泛用于研究个体差异，但没有客观的方法来评估估计量表值的统计学意义。在本研究中，一个restaurant技术（bootstrapping）被用来构建从MDS配置文件分析估计的规模值的置信限。反过来，这些bootstrap置信限用于评价特征的标志物变量的显著性。模拟数据和真实的数据的分析结果表明，自助法可能是有效的，并可用于评估MDS档案的标志物变量的统计学意义的假设。
Although multidimensional scaling (MDS) profile analysis is widely used to study individual differences, there is no objective way to evaluate the statistical significance of the estimated scale values. In the present study, a resampling technique (bootstrapping) was used to construct confidence limits for scale values estimated from MDS profile analysis. These bootstrap confidence limits were used, in turn, to evaluate the significance of marker variables of the profiles. The results from analyses of both simulation data and real data suggest that the bootstrap method may be valid and may be used to evaluate hypotheses about the statistical significance of marker variables of MDS profiles.