Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
| From | Maarten Buis <maartenlbuis@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: dropping vars from analysis under conditions |
| Date | Tue, 17 Apr 2012 12:16:39 +0200 |
--- On Tue, Apr 17, 2012 at 11:55 AM, K.O. Ivanova wrote: > I am trying to run an identical model for several different countries The thing is that for some countries, the child residence dummies (ch_res_dum2, etc.) have too few cases for one (or both) of the genders (for example, in one country, there are only 15 women who say that they have shared custody of their kid). Basically, I want to run my syntax but then add a statement which tells Stata to drop one (or more than one) of these residence dummies from that list of predictors if the number of cases for that dummy is smaller than... It is tricky enough to compare groups (in your case countries) with non-linear models. See, for example Williams (2009). My stance on this issue is that you are fine as long as you interpret your results as descriptive and not causal, but many people (_not_ including me) are uncomfortable with "just" descriptive results. However, this problem becomes a lot worse when you use different sets of covariates. Unlike in linear models, in non-linear models this changes the meaning of the dependent variable. Think of it this way: Your dependent variable is a probability, this is a measure of central tendency but also a measure of uncertainty. That uncertainty comes from all (observed and unobserved) variables you did not include in your model. In essence you classified all those variables as "luck". So the choice of which variables you include in your model (and thus which variables you choose to leave out) determines which variables you treat as systematic and which are treated as just "luck". So when you compare countries and these countries differ also with respect to the covariates than the trick to interpret the differences as descriptive also starts to unravel. Hope this is not too depressing, Maarten Williams, R. 2009. Using heterogenous choice models to compare logit and probit coefficients across groups. Sociological Methods & Research 37: 531–559. -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/