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