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From | Maarten Buis <maartenlbuis@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: RE: RE: RE: Reference group for categorical interactions |
Date | Thu, 26 Sep 2013 09:36:32 +0200 |
On Thu, Sep 26, 2013 at 1:53 AM, Hussein, Mustafa wrote: > Though widely used, ORs mask the heterogeneity in the marginal effects across subjects, and their interpretation in the presence of interaction terms is not straightforward. I would suggest sticking to the marginal effects at the means, if that's meaningful, or estimate them at some relevant representative values for other covariates. A different take on this issue is that a marginal effect is a linear model estimated on the results of a non-linear (logit) model. If you need a second model to interpret the results of your original model, then there is something wrong with your original model. The purpose of a model is to simplify what you have seen (your data) such that it is interpretable, and if you think you need to estimate a second model to interpret the results of your first (logit) model, then your first model is not doing what it is supposed to be doing. I would recommend to stick to the interpretation of the model in terms of its natural parameters in their natural form as the main form of interpretation, marginal effects can play a useful role as a secondary interpretation. So you would need to choose your model such that its natural parameters correspond with what you and your audience are comfortable with: If you want risk differences you would estimate a linear probability model, if you want risk ratios you estimate a model with a log link (e.g. -poisson-), if you want odds ratios you estimate a logit. It may be that you will find that a linear probability model or a Poisson model does not fit the data well, and you will need to move on to a logit model. That is a good thing: by estimating these models directly you can easily detect whether your model makes sense. If instead you had estimated it indirectly by first estimating a logit model and then estimating marginal effects, you probably would not have seen that the final model (the marginal effects _not_ the logit) does not fit the data. When it comes to the interpretation of interaction terms in a logit model, see: M.L. Buis (2010) "Stata tip 87: Interpretation of interactions in non-linear models", The Stata Journal, 10(2), pp. 305-308. <http://www.maartenbuis.nl/publications/interactions.html> Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/