Hello Statalist,
I am running an mlogit in which the dependent variable is a measure of inter-generational social mobility (1=upward, 2=none, 3=downward) and my main independent variables of interest are sibling exposure (which is continuous and measured as shared person-years) and birth cohort (which is a factor variable with three categories). The model includes an interaction term of these two variables and their base effects, plus other controls and using robust standard errors. The model looks like this:
mlogit mobility c.sibling_exposure i.cohort c.sibling_exposure#i.cohort x3 x4 x5, vce(robust) baseoutcome(2)
When I run the model, the interaction of the two variables produces no significant result, but the base effects are significant, suggesting that when exposure=0 there are distinct differences between cohorts, and when the cohort=1, the association of exposure and mobility is significantly different from zero. When I calculate the Average Adjusted Predictions at specific values of exposure as:
margins cohort, at(sibling_exposure=(0(1)30)) predict(outcome(1))
the confidence intervals of the predictions for the three cohorts do not overlap at higher levels of exposure (above sibling_exposure=15 roughly), suggesting to me that they are statistically distinct from one another. But I am struggling how to reconcile the difference between confidence intervals of the adjusted predictions and the significance tests of the interaction terms in the mlogit output, and which one is more reasonable to use to interpret the results. Does anyone have any advice? Is my thinking here completely misguided?
Thanks.
Joe
I am running an mlogit in which the dependent variable is a measure of inter-generational social mobility (1=upward, 2=none, 3=downward) and my main independent variables of interest are sibling exposure (which is continuous and measured as shared person-years) and birth cohort (which is a factor variable with three categories). The model includes an interaction term of these two variables and their base effects, plus other controls and using robust standard errors. The model looks like this:
mlogit mobility c.sibling_exposure i.cohort c.sibling_exposure#i.cohort x3 x4 x5, vce(robust) baseoutcome(2)
When I run the model, the interaction of the two variables produces no significant result, but the base effects are significant, suggesting that when exposure=0 there are distinct differences between cohorts, and when the cohort=1, the association of exposure and mobility is significantly different from zero. When I calculate the Average Adjusted Predictions at specific values of exposure as:
margins cohort, at(sibling_exposure=(0(1)30)) predict(outcome(1))
the confidence intervals of the predictions for the three cohorts do not overlap at higher levels of exposure (above sibling_exposure=15 roughly), suggesting to me that they are statistically distinct from one another. But I am struggling how to reconcile the difference between confidence intervals of the adjusted predictions and the significance tests of the interaction terms in the mlogit output, and which one is more reasonable to use to interpret the results. Does anyone have any advice? Is my thinking here completely misguided?
Thanks.
Joe
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