Guadagnoli E, Velicer W
Multivariate Behav Res. 1991 Apr 1;26(2):323-43. doi: 10.1207/s15327906mbr2602_7.
A common problem in multivariate applications is the comparison of pattern matrices obtained from two independent studies. We compared the performance of four pattern matching indices (the coefficient of congruence [c], the s-statistic [s], Pearson's r [r], and kappa [k]) under a variety of experimental conditions. We constructed population pattern matrices by varying (a) saturation or the size of the loadings, (b) sample size, (c) the number of observed variables, and (d) the number of derived variables. Sample patterns were computer generated and matched, employing each index, to their population pattern. With the exception of r, little difference in matching performance between indices was observed. In general, an increase in either saturation or sample size resulted in more accurate index values.
多变量应用中的一个常见问题是比较来自两项独立研究的模式矩阵。我们在各种实验条件下比较了四种模式匹配指数(一致性系数[c]、s统计量[s]、皮尔逊相关系数[r]和卡帕[k])的性能。我们通过改变以下因素构建总体模式矩阵:(a)饱和度或载荷大小、(b)样本量、(c)观测变量的数量和(d)派生变量的数量。样本模式由计算机生成,并使用每个指数将其与总体模式进行匹配。除了r之外,各指数之间在匹配性能上几乎没有差异。一般来说,饱和度或样本量的增加会导致指数值更准确。