Re: standardized odds ratio interpretation

Posted by Rich Ulrich on Jun 24, 2016; 6:26am
URL: http://spssx-discussion.165.s1.nabble.com/standardized-odds-ratio-interpretation-tp5732518p5732525.html

I think you show that whatever two codes there are for the outcome
in a logistic regression, SPSS will accept them and treat them the same.
The only surprise here might be there is no restriction on the codes, not
to (0,1)  or even to something integer. 

Similarly, it seems that it will accept any two (or more?) codes when you specify
    /contrast =
and treat them as the same dummy contrasts every time.

If you want to get something that might be called a standardized OR,
you could "standardize" your dichotomies for dummy variables and
enter them as continuous covariates.  - Maybe I have been missing some
literature, so take this with a grain of salt:  That does not sound like a sensible
thing to do, and if I were a reviewer I would probably demand a strong
justification before accepting it.

--
Rich Ulrich

> Date: Thu, 23 Jun 2016 13:47:10 -0700

> From: [hidden email]
> Subject: Re: standardized odds ratio interpretation
> To: [hidden email]
>
> I don't understand the results you posted, in that case. How did you
> standardize the variables? It would help of you posted your syntax for
> that, as well as syntax to estimate to estimate the two models.
>
> Meanwhile, try the following demonstration.
>
> * Change path below to where you store the sample datasets.
> GET FILE='C:\SPSSdata\survey_sample.sav'.
> COMPUTE Male = sex EQ 1.
> FORMATS Male(F1).
> CROSSTABS sex by Male.
>
> * Get Z-score versions of Male and marital status.
> DESCRIPTIVES Male marital /SAVE.
>
> * Logistic regression with Y = Male, X = marital (as factor variable).
> LOGISTIC REGRESSION VARIABLES Male
> /METHOD=ENTER marital
> /CONTRAST (marital)=Indicator
> /PRINT=CI(95)
> /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).
>
> * Logistic regression with Y = Male, X = Zmarital (as factor variable).
> LOGISTIC REGRESSION VARIABLES Male
> /METHOD=ENTER Zmarital
> /CONTRAST (Zmarital)=Indicator
> /PRINT=CI(95)
> /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).
>
> * Logistic regression with Y = Zmale, X = Zmarital (as factor variable).
> LOGISTIC REGRESSION VARIABLES Zmale
> /METHOD=ENTER Zmarital
> /CONTRAST (Zmarital)=Indicator
> /PRINT=CI(95)
> /CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).
>
> I get exactly the same table of coefficients for all 3 of these models
> (apart from the value labels for the the maritial variable). Here are my
> coefficients:
>
> Marital Status B1 B2 B3
> Marital status(1) -0.176 -0.176 -0.176
> Marital status(2) -1.530 -1.530 -1.530
> Marital status(3) -0.332 -0.332 -0.332
> Marital status(4) -0.296 -0.296 -0.296
>
> B1 = coefficient from first model
> B2 = coefficient from second model
> B3 = coefficient from third model
>
> HTH.
>
>
> Greg wrote
> > Correct and thank you for the quick reply. I converted my variables to
> > z-scores and then ran the log regression. (All the predictors are
> > categorical.)
>
>
>
>
>
> -----
> --
> Bruce Weaver
> [hidden email]
> http://sites.google.com/a/lakeheadu.ca/bweaver/
>
> "When all else fails, RTFM."
>
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