# 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.

• What is your sample size? It could be that that the sample size is not very large and now you have essentially created 6 groups. The effects may be real but different by gender. What are the 5 factors? Keep in mind that 3 genders with three smoking groups makes 3x2=6 groups. Commented Jul 23, 2012 at 10:40
• I have 32,484 (20,008 women and 14,619 men) people. But when I run the models, it says that only about 5,ooo are included (probably because of missing values in some covariates). My dependent variable is body mass index and the factors are alcohol drinking frequency, alcohol amount, economic status, index of deprivation, physical activity level. Commented Jul 23, 2012 at 10:45
• The fact that most of your observations have missing data is worrisome. Commented Jul 23, 2012 at 11:21
• Shouldn't 20,008 women + 14,619 men = 34,627 people, instead of 32,484? Commented Jul 23, 2012 at 13:49
• It sounds like you tried your model 2 ways, entering smoking first or entering sex first, and found 2 different patterns of results. Is this the basis for your question? Commented Jul 23, 2012 at 14:02