Dear John, thanks for Your quick reply.
John Fox wrote: Dear Kimmo, MCA is a rather old name (introduced, I think, in the 1960s by Songuist and Morgan in the OSIRIS package) for a linear model consisting entirely of factors and with only additive effects -- i.e., an ANOVA model will no interactions.
It is true, that MCA is an old name, but the technique itself is still robust, I think. The problem I am facing is that I have a research project where I try to find out which factors affect measured knowledge of a specific issue. As predictors I have formal education, interest, gender and consumption of different medias (TV, newspapers etc.). Now, these are correlated predictors and running e.g. a simple anova (anova(lm(...)) as You suggested) won't - if I have understood correctly - consider the problem of correlated predictors. MCA would do this.
A colleague of mine has run anova and MCA in SPSS and the results differ significantly. Because I am more familiar with R, I just hoped that this marvelous statistical package could handle MCA, too :)
Typically, the results of an MCA are reported using "adjusted means." You could compute these manually, or via the effects package.
Well, I am interested in the eta and beta values, too. I have tried to use the effects package but my attempts with all.effects resulted in errors. I have to figure out what's going wrong here :)
Kind regards, Kimmo Elo -- University of Turku, Finland Dep. of political science ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
