Generalized linear models (covariates and splitting files) I am using generalized linear models with one response variable and 6 predictors (1 covariate and 5 factors). I want to assess the effect of smoking on my response variable. When I split my participants in sex groups and then in three smoking groups (smokers, ex-smokers, non-smokers) I find weaker associations than when I add smoking in the model (as a factor since it is a categorical variable) and split the participants in sex groups.
Could anyone please explain why is there this difference and what would be the best practice in this model? 
Thank you!
ps: when I split in three smoking groups, the associations are similar per smoking group, they are just weaker.
 A: After your comments I think the sample size is not a big issue.  But like Peter I would be concerned about the missing data and understanding why so many samples have missing information.  If your software won't let you fit some data because if missing covariates see which covariates are missing.  
If it is just a couple that cause you to lose so much data drop them and then fit the model.  Now many observations will enter the model because they will not have missing covariates. Maybe based on what you observed smoking doesn't really have much effect and any relationship you might see with a model that relate smoking to BMI where smoking is the only covariate may be because it is the smoker that drink that tend to be obese.  I have seen many a smoker that is pretty thin. 
Compare a model that includes smoking, alcohol and an interaction term between the two and also look at a model with alcohol alone.  If the more detailed model is not doing much better fit to the same data then maybe it is okay to drop the smoking groups.
But before taking any of these recommendation get to understand better why covariates are missing in so many cases.  Maybe you can looked at the dropped cases and compare the demographics (gender, age etc) for the fitted sample with what it is for the missing data.  There could be bias due to covariate imbalance.  Vance Berger's book which I have previously cited here on CV could help you with that.
A: If I am following you, you are doing one of two things:
1) Controlling for what you are trying to analyze. In the non-smoking group, there can be no association between smoking and BMI (or anything else). 
2) Comparing the three groups (non-smokers, ex-smokers, current smokers) somehow, after separating them. I am not sure how you would then find associations: At most you could find different levels of BMI.
