mca or various ca (multivariate analysis)

Question

I will make a analysis about some information of my company.

I thought making a ca to representate the association between two variables. I have 3 variables: Category, Tag, Valoration. My idea is to make 2 analysis, one to view the association between Category - Valorarion and a second analysis between Tag - Valoration.

But I think that this representation is possible with a mca.

What do you recomment me?

Thank You

Answer 1

Assuming that all variables are categorical, you can use multiple classification analysis to gain an understanding of the associations between the variables. There was a good article on the topic from the European Consortium for Politics back in 2k7 but I can't find it on my drive, I'm sure google will have it somewhere. I can't "see" your data so I can't say with any certainty that MCA will be better than regression or GLM but the article I'm referring to has a discussion on this topic specifically to do with MCA vs. GLM vs. Regression.

Alternatively, you could use pearson product-moment correlations to identify the coefficients. Close to 1 = positive linear relationship, close to -1 = negative linear relationship, close to 0 = no linear relationship.

Answer 2

Various classification or association rule mining algorithms could be of much help too. You could check the Weka toolbench for machine learning and data mining.

Answer 3

I came across VGAM package for categorical data analysis. You could check this too