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Date:         Wed, 28 Sep 2005 12:20:11 -0300
Reply-To:     Hector Maletta <hmaletta@fibertel.com.ar>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Hector Maletta <hmaletta@fibertel.com.ar>
Subject:      Re: Interpretation of logistic regression ' results
Comments: To: "S. E. Hayman-Abello" <hayman2@uwindsor.ca>
In-Reply-To:  <BF60222F.19E2D%hayman2@uwindsor.ca>
Content-Type: text/plain; charset="US-ASCII"

Changing from an extreme value to another (worst to best or the reverse) should not change the results by much. It is moving from one intermediate to one extreme category what counts, in my previous message sense. However, your case is related to NOMREG, which is a different procedure. My advice in the previous message regarded categorical independent variables, not the categories of a dependent polithomous outcome.

Hector

> -----Original Message----- > From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] > On Behalf Of S. E. Hayman-Abello > Sent: Wednesday, September 28, 2005 11:34 AM > To: SPSSX-L@LISTSERV.UGA.EDU > Subject: Re: Interpretation of logistic regression ' results > > Hector and all, > > Continuing with logistic regression. How do you interpret the > same finding as Jan reports (i.e., significant predictor at > model level but parameter estimate not significant) with > continuous variables? > > I have 3 outcome (DV) categories (Worse/No Change/Better) and > two of my continuous variables show significance at the model > level but not within either the 'Worse' or the 'No Change' > parameter estimates. My reference category is 'Better'. I > wonder now, based on Hector's advice to Jan, what would > happen if I changed the reference category to 'Worse'. Would > I do that in SPSS NOMREG by assigning the Worse category the > highest rather than the lowest value? > > There are the current findings: > > Model > ANARTIQ (chi) 9.826 2 .007 > > > (Par. Est.) B S.E. Wald df Sig > Exp(B) > > 'Worse' > ANARTIQ -.089 .049 3.310 1 .069 .915 > > 'No Change' > ANARTIQ .041 .054 .578 1 .447 1.042 > > > > Thank you for any help, > Sue > > PS I also have a significant dichotomous categorical > predictor (MEDPROB: > yes/no) that has a negative parameter estimate (B = -1.730) > and a very low odds ratio (Exp(B) = .177). I'm having a brain > cramp about interpreting this finding. My take is that the > odds of a subject being in reference (Better) group (compared > to the No Change group) are lower (bc. of the negative B?) > for subjects with Yes on MEDPROB. If that's right, what do I > say more specifically about the .177 odds ratio? I'm used to > thinking in terms of odds ratios >1. > > > > Hector Maletta said ... > > > The coefficients of dummies measure the significance of the > difference > > between one category and the reference category. In your case, the > > reference category seems to be "in the middle", since two > of the other > > categories have a negative coefficient and one category > (represented > > by the DIAG(3) dummy) has a positive coefficient. I suspect that if > > you use the category represented by DIAG(3) as your reference > > category, the other two shown in the table would show a more > > significant (i.e. lower) probability, because they have a larger > > difference with that category than they have with your > current reference category. > > However, I think what is more important is the coefficients of the > > categories, and not so much the overall effect of the categorical > > variable as such. If DIAG alludes to diagnostic, I suppose > it is more > > important what the diagnostic is (i.e. which category), > than the fact > > that some diagnostic existed. > > > > Hector > > __________ Informacisn de NOD32 1.1235 (20050928) __________ > > Este mensaje ha sido analizado con NOD32 Antivirus System > http://www.nod32.com > >


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