Re: [R] MCA in R

From: K. Elo <maillists_at_nic.fi>
Date: Fri, 13 Jun 2008 08:42:53 +0300

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

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Received on Fri 13 Jun 2008 - 07:09:36 GMT

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