CatPCA and CatReg in SPSS might fit your situation.

Are your variables strictly nominal? or dichotomous, or merely ordered, or equal-appearing? Are there subsets of the independent variables designed to measure pretty much the same thing? E.g., are they attitude questions?

If you describe your data in more detail list members would be better able to give their reactions.

Art Kendall Social Research Consultants

On 2/2/2011 5:02 AM, butasbutauskas wrote: > Hi, > > I know that if you want to reduce variable size you have to use for instance > principal component analysis. In addition to this, this method could > eliminate multicollinearity problem in regression analysis. > > > In my research I have a lot of variables (about 30) of categorical data, > which I want to reduce, and then use these results to predict categorical > outcome (i.e. through logistic regression). > > The questions would be: > 1. Is it possible to get scores from correspondence analysis (similar as in > Principal Component analysis) and to use it in logistic regression? > 2. Is Correspondence analysis could eliminate multicollinearity problem? > > Thanks in advance:) > > Regard > -- > View this message in context: http://spssx-discussion.1045642.n5.nabble.com/correspondence-analysis-and-logistic-regression-tp3367586p3367586.html > Sent from the SPSSX Discussion mailing list archive at Nabble.com. > > ===================== > To manage your subscription to SPSSX-L, send a message to > LISTSERV@LISTSERV.UGA.EDU (not to SPSSX-L), with no body text except the > command. To leave the list, send the command > SIGNOFF SPSSX-L > For a list of commands to manage subscriptions, send the command > INFO REFCARD >

===================== To manage your subscription to SPSSX-L, send a message to LISTSERV@LISTSERV.UGA.EDU (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD