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A different approach to multiple correspondence analysis (MCA) than that of specific MCA

机译:与特定MCA不同的多重对应分析(MCA)方法

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In multiple correspondence analysis, each nominal variable affects the analysis with a different amount of inertia, depending on the number of its modalities or categories. Usually in variables with many modalities – categories created infrequent (weak classes) modalities which contribute disproportionally to the inertia of the corresponding variable. Often these modalities contribute heavily to the determination of the first factorial axes and as a result this cannot clearly represent the investigated problem. Specific multiple correspondence analysis deals with the problem of infrequent (weak) modalities by removing them. That is, it simply ignores them in the calculation of distances between individuals [Le Roux B., 1999; Le Roux B., Rouanet H., 2004].In this paper we deal with this problem in a different manner. We keep the weak modalities in the analysis. Replacing the khi2 metric by a new metric which also takes into account the number of modalities of each variable, a reasonable effect of the weak modalities and a balancing of all the nominal variables is achieved in the analysis.We also encounter uniformly the weak modalities, whether they derive from many or few variables, even though the most “dangerous” case is the one variables where have many modalities. Only variables of two modalities are not affected.
机译:在多重对应分析中,每个标称变量以不同的惯性量影响分析,具体取决于其模态或类别的数量。通常在具有许多模态的变量中–很少创建的类别(弱类)模态会不成比例地贡献相应变量的惯性。通常,这些模态对第一阶乘轴的确定起很大作用,因此这不能清楚地代表所研究的问题。特定的多重对应分析通过删除它们来解决不频繁(弱)模态的问题。也就是说,在计算个人之间的距离时,它只是忽略了它们[Le Roux B.,1999; Le Roux B.,Rouanet H.,2004]。在本文中,我们以不同的方式处理此问题。我们在分析中保留了弱模式。用新的度量取代khi2度量,该度量还考虑了每个变量的模态数量,在分析中实现了弱模态的合理效果以及所有名义变量的平衡。我们还统一遇到了弱模态,即使它们是从多个变量还是从几个变量衍生而来的,即使最“危险”的情况是具有多种模式的一个变量。仅两种方式的变量不受影响。

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