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Date:         Wed, 28 Sep 2005 10:34:24 -0400
Reply-To:     "S. E. Hayman-Abello" <>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         "S. E. Hayman-Abello" <>
Subject:      Re: Interpretation of logistic regression ' results
In-Reply-To:  <S32875AbVI1Ngd/>
Content-type: text/plain; charset="US-ASCII"

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

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