Dear all,
I have a question which can be expanded to the geeneral context of regression 
modelling in general. If you feel that this question is beyond the scope of this list, please say so and I will apologize. However, this has to do with teaching. 


Question 1:  I am revieweing a paper and the author uses a sample size of around 50,000 cases to run a logistic regression. He is using 22 independent 
variables. Using too many independent variables may cause collinearity 
problems. Beyond this, however, I am not aware of any other problems caused by 
using too many variables in a model. However, this is also related to the problem of massively throwing tens of variables in amodel and then waiting for statistically significant results. Can anyone suggest relevant literature to give to my students to read?


Question 2: Some coefficients of a diffrent logistic model in the same paper are marginally significant e.g. b=-0.18 and se=0.08. The only reason this is signficant is because the researcher used in this model a large sample size (around two thousand cases N=2000). The lower bound of the confidence interval is almost zero. Can anyone suggest a good reference to say that in such a case we should also check the "practical significance" and since the lower bound is so close to zero, we should be careful on what we claim about the effect?

Thank you for your time
Jason  


 
Dr. Iasonas Lamprianou
Department of Social and Political Sciences
University of Cyprus




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