ASSESSING SAMPLING VARIATION RELATIVE TO NUMBER-OF-FACTORS CRITERIA
ASSESSING SAMPLING VARIATION RELATIVE TO NUMBER-OF-FACTORS CRITERIA
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DOI：
10.1177/0013164490501004
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发表时间：
1990-03-01
影响因子：
2.7
通讯作者：
DURAND, RM
中科院分区：
文献类型：
--
作者：
LAMBERT, ZV;WILDT, AR;DURAND, RM
利用自举法来近似特征值的抽样变化是明确的，它的有用性是通过结合两个常用的因子数标准的说明被放大：特征值大于1和屏幕测试。对于对角线上具有1和平方多个相关系数的样本相关矩阵，特征值的置信区间是近似的。结果证明了自举方法在提供特征值的抽样可变性信息方面的有用性，这些信息为采用共同标准时关于因素数量的更明智的决策提供了基础。此外，这些信息可以很容易地获得，并且该方法避免了对称置信区间的脆弱假设。
Employment of the bootstrap method to approximate the sampling variation of eigenvalues is explicated, and its usefulness is amplified by an illustration in conjunction with two commonly used number-of-factors criteria: eigenvalues larger than one and the scree test. Confidence intervals for eigenvalues are approximated for sample correlation matrices that have ones and squared multiple correlation coefficients on the diagonals. The results demonstrate the usefulness of the bootstrap method in providing information about the sampling variability of eigenvalues-knowledge that affords a basis for more informed decisions regarding the number of factors when employing common criteria. Further, this information can be obtained with little difficulty, and the approach avoids tenuous assumptions of symmetric confidence intervals.