How to identify items to be dropped out in MCA or in categorical PCA in order to increse eigenvalues?

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Diana Surová

Thanks Alessandro, I appreciate your answer.

I will definitely try it .

I also wonder if there are some specific rules for variable ommition. I found one example, where they excluded those having small MEAN discriminance value (DV) across first two dimensions. But these first two dimensions explained only around 30% of total variance.

And this is happening with my data as well. Some variables do not discriminate at first two but for example at 3. or at 5. dimension. Should I ommit them and only look at first two dimensions?

Till now I tried different options to eliminate certain variables with low MEAN or ABSOLUTE discriminance measures. The best solution (from my subjective point of view) appear when I eliminate variables having MEAN DV

Thanks for your help Alessandro.

The leave one out approach is very helpful for my data set.

After that I applied the VARIMAX rotation and I can see the main discriminants on particular dimensions, even if % of variance explained doesnt change.

In relation to bootstrap - I use SPSS and as I know it doesnt permit to apply it on MCA or PCA outputs...

Thanks Alessandro I appreciate your answers.

These rotations can be applied in PCA which is as I know not applicable for categorical variables . So instead of PCA I use Multiple correspondence analysis and till now leave one out approach was the most convenient for me.