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Date:         Thu, 17 Nov 2005 15:53:43 -0500
Reply-To:     "Woodring, Jonathan" <JWoodring@PPV.org>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         "Woodring, Jonathan" <JWoodring@PPV.org>
Subject:      Re: Analysis of categorical data - is Chi-Square my only option?
Comments: To: "E. Griffin" <ergriffin@gmail.com>
Content-Type: text/plain; charset="us-ascii"

Logistic regression is the "kitchen sink" approach you seek, though you should certainly educate yourself on the assumptions of the model and appropriate diagnostics. If your "quit" variable is simply a binary variable without taking time into account (i.e. someone quit or not over a given period of time), then logistic regression will supply you with odds ratios for each of your predictors (you should also read up on how to interpret odds ratios, as they are often misinterpreted). But if you have data on WHEN someone quits, or can create such a variable given your data set, then survival analysis may be a possibility.

-----Original Message----- From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of E. Griffin Sent: Thursday, November 17, 2005 3:33 PM To: SPSSX-L@LISTSERV.UGA.EDU Subject: Re: Analysis of categorical data - is Chi-Square my only option?

You are absolutely correct - my vocabulary may be my undoing here. I guess the ultimately question is, when looking at my population, I want to see what variables have a significant relationship (or effect) with the "quit" variable. That is why at first I was using Chi-Squares, but since I am looking at 6 or so influencing the "quit" variable, it gets cumbersome.

I was wondering (hoping) if there was a "kitchen sink" approach that throws all of them in the pot and comes back with what matters and what does not.

Thanks for the help.

On 11/17/05, Aric Zion <Aric.Zion@asu.edu> wrote: > You said that your IV is the quit variable. Do you mean instead that your DV (i.e. the thing you want to predict) is the quit variable? > > You could also use logistic regression to look at your problem. That would also give you significance tests for each category of each of your IVs (i.e. predictors). Of course, if use logistic regression, you will have to decide which category is your reference category for each of your IVs. > > Aric > > -----Original Message----- > From: SPSSX(r) Discussion on behalf of E. Griffin > Sent: Thu 11/17/2005 12:18 PM > To: SPSSX-L@LISTSERV.UGA.EDU > Cc: > Subject: Analysis of categorical data - is Chi-Square my only option? > > > > I have tried to search for an answer, but need to appeal to all for > help. I have a 10,000 case data set consisting of demographic data in > categorical format. The data set is for a group of people, and the IV > I ultimately am looking at is whether they stay in the group or quit. > I have this coded as a variable - a '1' if they quit and a '0' if they > did not. > > I have run crosstabs to look at relationships between demographic data > (gender, ethnicity, etc.) and this "Quit" variable and produce a > Chi-Square. Is this the best way to look at this, or is there a > better way? > > Since I am looking at perhaps 5 or 6 possible categorical DV I feel > like maybe I am going about this the wrong way. Thanks for any help. > > > >


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