Hi all,
I have a three-part question from a logistic regression. In the case of this study, I have three categorical predictors: gender (M/F), age group (2, 3, 4), and country of origin (1,2,3,4,5). Both the logit and odds ratio outputs are below.
Here are my questions:
1.) Which confidence intervals do I report? The ones associated with the coefficient or with the odds ratio?
2.) What happens when CI and p-values don't agree? For example, in the first output, ageGroup 3 has an OR = 0.207-1.2, which crosses the "1" threshold, but has a p=0.005. Crossing the 1 means insignificant, correct? But p<0.05 is significant. Does one trump the other? Or does this mean something is wrong with my data?
3.) This might be answered by #2, but how do I interpret an OR greater than 2? I know and OR of 0.76 can be interpreted at "24% less likely" (correct?), but what about and OR of 2.02?

Thanks

Code:
logit stage4 i.ageGroup i.country i.sex

Iteration 0:   log likelihood = -612.81892  
Iteration 1:   log likelihood = -580.34846  
Iteration 2:   log likelihood = -579.85354  
Iteration 3:   log likelihood = -579.85215  
Iteration 4:   log likelihood = -579.85215  

Logistic regression                             Number of obs     =        940
                                                LR chi2(7)        =      65.93
                                                Prob > chi2       =     0.0000
Log likelihood = -579.85215                     Pseudo R2         =     0.0538

------------------------------------------------------------------------------
      stage4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ageGroup |
          3  |   .7038665   .2534184     2.78   0.005     .2071756    1.200557
          4  |    1.47736    .253085     5.84   0.000      .981322    1.973397
             |
     country |
          2  |  -.2736965    .208729    -1.31   0.190    -.6827979     .135405
          3  |  -.2919848   .2617669    -1.12   0.265    -.8050386    .2210689
          4  |  -.1398201   .2613874    -0.53   0.593    -.6521299    .3724897
          5  |  -.7572049    .354703    -2.13   0.033     -1.45241   -.0619997
             |
       1.sex |  -.3730072   .1425399    -2.62   0.009    -.6523802   -.0936342
       _cons |  -1.228277     .24262    -5.06   0.000    -1.703803   -.7527503
------------------------------------------------------------------------------
Code:
logit stage4 i.ageGroup i.country i.sex, or

Iteration 0:   log likelihood = -612.81892  
Iteration 1:   log likelihood = -580.34846  
Iteration 2:   log likelihood = -579.85354  
Iteration 3:   log likelihood = -579.85215  
Iteration 4:   log likelihood = -579.85215  

Logistic regression                             Number of obs     =        940
                                                LR chi2(7)        =      65.93
                                                Prob > chi2       =     0.0000
Log likelihood = -579.85215                     Pseudo R2         =     0.0538

------------------------------------------------------------------------------
      stage4 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    ageGroup |
          3  |   2.021554    .512299     2.78   0.005     1.230199    3.321968
          4  |   4.381361   1.108857     5.84   0.000     2.667981    7.195077
             |
     country |
          2  |   .7605629   .1587516    -1.31   0.190     .5052015       1.145
          3  |   .7467799   .1954823    -1.12   0.265     .4470707    1.247409
          4  |   .8695146   .2272801    -0.53   0.593      .520935    1.451344
          5  |   .4689754    .166347    -2.13   0.033     .2340056    .9398832
             |
       1.sex |   .6886603   .0981616    -2.62   0.009     .5208047    .9106158
       _cons |   .2927967   .0710383    -5.06   0.000     .1819901    .4710692
------------------------------------------------------------------------------
Note: _cons estimates baseline odds.