I want to estimate a logit model with a binary dependent variable and two categorical independent variables, Concern and Motivation, each with five levels.
In my specification, I interact these variables as i.Concern##i.Motivation. When I examine the output of the model, most of the interaction term coefficients are statistically insignificant.
However, I understand that in nonlinear models like logit, the coefficients on interaction terms are not directly interpretable in the way they are in linear models. So instead, I used Stata’s margins command:
margins Motivation, dydx(Concern),
to calculate the marginal effects of each level of Concern at each level of Intrinsic Motivation.
This gives me a set of marginal effects that are all statistically significant, in contrast to the insignificant interaction coefficients from the logit model itself. I wanted to confirm whether this is the correct approach for interpreting the interaction — are these marginal effects the appropriate quantities to focus on in this case?
In my specification, I interact these variables as i.Concern##i.Motivation. When I examine the output of the model, most of the interaction term coefficients are statistically insignificant.
However, I understand that in nonlinear models like logit, the coefficients on interaction terms are not directly interpretable in the way they are in linear models. So instead, I used Stata’s margins command:
margins Motivation, dydx(Concern),
to calculate the marginal effects of each level of Concern at each level of Intrinsic Motivation.
This gives me a set of marginal effects that are all statistically significant, in contrast to the insignificant interaction coefficients from the logit model itself. I wanted to confirm whether this is the correct approach for interpreting the interaction — are these marginal effects the appropriate quantities to focus on in this case?
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