Dear Statalist:
I have some queations

METHOD ONE: I created the following interaction variables using *. Where c1=no disease, c2=cancer c3=mental disease c4=other disease. Giving1= not caregiving, giving2=caregiving

NO CAREGIVING AND DISEASES
gen nocaregiving_nodisease= giving1*c1
gen nocaregiving_cancer= giving1*c2
gen nocaregiving_mental= giving1*c3
gen nocaregiving_other= giving1*c4

CAREGIVING AND DISEASES
gen caregiving_nodisease= giving2*c1
gen caregiving_cancer= giving2*c2
gen caregiving_mental= giving2*c3
gen caregiving_other= giving2*c4

The dependant variable es k6cat which is binary

Then proceed with the following command for logistic regression

logit k6cat nocaregiving_cancer nocaregiving_mental nocaregiving_other caregiving_nodisease caregiving_cancer caregiving_mental caregiving_other caregiverdisease caregiving sex agecat2 agecat3 agecat4 e2 e3 job setaiexpbyninsu_cat, or nolog

note: caregiverdisease omitted because of collinearity
note: caregiving omitted because of collinearity

Logistic regression Number of obs = 879,674
LR chi2(15) = 25077.54
Prob > chi2 = 0.0000
Log likelihood = -512669.93 Pseudo R2 = 0.0239

--------------------------------------------------------------------------------------
k6cat | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---------------------+----------------------------------------------------------------
nocaregiving_cancer | 2.166334 .0488408 34.29 0.000 2.072692 2.264206
nocaregiving_mental | 5.87672 .0993783 104.73 0.000 5.685135 6.074762
nocaregiving_other | 1.427238 .0075137 67.57 0.000 1.412587 1.442041
caregiving_nodisease | 1.415696 .0518512 9.49 0.000 1.317631 1.521059
caregiving_cancer | 3.454236 .6844809 6.26 0.000 2.342505 5.093585
caregiving_mental | 8.505128 1.54574 11.78 0.000 5.95637 12.14451
caregiving_other | 2.182677 .0617268 27.60 0.000 2.064987 2.307075
caregiverdisease | 1 (omitted)
caregiving | 1 (omitted)
sex | 1.220351 .0060654 40.07 0.000 1.208521 1.232297
agecat2 | .5647246 .003999 -80.69 0.000 .5569409 .5726171
agecat3 | .6406081 .005502 -51.85 0.000 .6299146 .651483
agecat4 | .8240018 .0098324 -16.22 0.000 .8049542 .8435001
e2 | .9156378 .0056034 -14.40 0.000 .904721 .9266864
e3 | .8764097 .0060818 -19.01 0.000 .8645704 .8884112
job | 1.194357 .0071747 29.57 0.000 1.180377 1.208502
setaiexpbyninsu_cat | .9502311 .0046335 -10.47 0.000 .9411928 .9593561
_cons | .2517346 .0032518 -106.78 0.000 .2454411 .2581894


METHOD 2: using ## with the "original variables". Where Caregiverdisease; 0= no disease, 1=cancer, 2=mental disease, 3=other disease. Caregiving; 0= not caregiving, 1=caregiving

logistic k6cat i.caregiverdisease##i.caregiving sex agecat2 agecat3 agecat4 e2 e3 job setaiexpbyninsu_cat, allbaselevels

Logistic regression Number of obs = 879,674
LR chi2(15) = 25077.54
Prob > chi2 = 0.0000
Log likelihood = -512669.93 Pseudo R2 = 0.0239

---------------------------------------------------------------------------------------------
k6cat | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
caregiverdisease |
0 | 1 (base)
1 | 2.166334 .0488408 34.29 0.000 2.072692 2.264206
2 | 5.87672 .0993783 104.73 0.000 5.685135 6.074762
3 | 1.427238 .0075137 67.57 0.000 1.412587 1.442041
|
caregiving |
0 | 1 (base)
1 | 1.415696 .0518512 9.49 0.000 1.317631 1.521059
|
caregiverdisease#caregiving |
0 0 | 1 (base)
0 1 | 1 (base)
1 0 | 1 (base)
1 1 | 1.126307 .2282772 0.59 0.557 .7570724 1.675623
2 0 | 1 (base)
2 1 | 1.022294 .1902165 0.12 0.906 .7098954 1.472169
3 0 | 1 (base)
3 1 | 1.080247 .0498851 1.67 0.095 .9867683 1.182582
|
sex | 1.220351 .0060654 40.07 0.000 1.208521 1.232297
agecat2 | .5647246 .003999 -80.69 0.000 .5569409 .5726171
agecat3 | .6406081 .005502 -51.85 0.000 .6299146 .651483
agecat4 | .8240018 .0098324 -16.22 0.000 .8049542 .8435001
e2 | .9156378 .0056034 -14.40 0.000 .904721 .9266864
e3 | .8764097 .0060818 -19.01 0.000 .8645704 .8884112
job | 1.194357 .0071747 29.57 0.000 1.180377 1.208502
setaiexpbyninsu_cat | .9502311 .0046335 -10.47 0.000 .9411928 .9593561
_cons | .2517346 .0032518 -106.78 0.000 .2454411 .2581894

***************************
As you can see the OR values in the first method nocaregiving_cancer, nocaregiving_mental, nocaregiving_other, caregiving_nodisease, are the same with 1,2, 3 from caregiverdisease and 1 in caregiving. The other OR values from the first method caregiving_cancer, caregiving_mental, caregiving_other; and second method: caregiverdisease#caregiving 11 2 1 31 are different.

Is there any explanation for these differences in OR? I think these should be equal.

Thank you for your attention,