For context, here is what I have now for my regressions:
Code:
glm muslim_support_index i.immigrant_status i.francophone_status i.region i.gender_status i.age_group i.education_status i.religious_status i.income_group i.urban_status i.party_id_status i.soc_net_vis_status c.ideology_index, family(binomial) vce(robust)
Code:
glm aboriginal_support_index i.immigrant_status i.francophone_status i.region i.gender_status i.age_group i.education_status i.religious_status i.income_group i.urban_status i.party_id_status i.soc_net_vis_status c.ideology_index, family(binomial) vce(robust)
Code:
margins, dydx(immigrant_status) post estimates store MargMusImm est restore Reg1 margins, dydx(francophone_status) post estimates store MargMusFranc est restore Reg1 margins, dydx(region) post estimates store MargMusReg est restore Reg1 margins, dydx(gender_status) post estimates store MargMusGender est restore Reg1 margins, dydx(age_group) post estimates store MargMusAge est restore Reg1 margins, dydx(education_status) post estimates store MargMusEd est restore Reg1 margins, dydx(religious_status) post estimates store MargMusRelgion est restore Reg1 margins, dydx(income_group) post estimates store MargMusInc est restore Reg1 margins, dydx(urban_status) post estimates store MargMusUrb est restore Reg1 margins, dydx(party_id_status) post estimates store MargMusPID est restore Reg1 margins, dydx(soc_net_vis_status) post estimates store MargMusSocNet est restore Reg1 margins, dydx(ideology_index) post estimates store MargMusIdeo est restore Reg1
sureg (muslim_support_index immigrant_status region francophone_status gender_status age_group education_status religious_status income_group urban_status party_id_status soc_net_vis_status ideology_index)(aboriginal_support_index immigrant_status region francophone_status gender_status age_group education_status religious_status income_group urban_status party_id_status soc_net_vis_status ideology_index)(racial_support_index immigrant_status region francophone_status gender_status age_group education_status religious_status income_group urban_status party_id_status soc_net_vis_status ideology_index)
The covariates are the same for each DV (of which there are three) - made a macro that included all controls/covariates, to make it less wordy, but that gave me an error for some reason when using it in the sureg command. Anyways, what does the SUR regression output actually say? How does it help me address the problems of:
1) If the two DVs (or three, if you want to also include the racial minority index) can indeed even be compared in terms of the direction and magnitude of their marginal effects, and if not, if a SUR regression can make them comparable
2) How to actually compare the marginal effects given the SUR regression
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