What is a good way to judge which groups to exclude from logistic regression analysis? I've built a logistic regression model to find predictors of having a performance issue in the workplace. Staff are either 1 - they have a performance issue, or 0 - they do not.
Approx 50 out of 3000 staff have a performance issue. I've excluded some groups from the dataset before i run the analysis as they have zero individuals in the performance issue group. The standard errors for their coefficients had come up as very large and seemed to indicate an issue. These groups were particular staff grade levels and divisions.
Should i also exclude groups which have only 1, 2 or 3 etc occurrences also? I wonder if they lead to less accurate estimates of the other coefficients. If i do then it affects what characteristics come up as statistically significant. E.g. in one model age is significant, in another disability, in another work location. The p-values move from being just above 0.05 to just below.
Is there a relatively easy way to justify which groups to include/exclude?
Thanks
Rob

Small group sizes combined with a small number of cases is a recipe for problems.
There isn't much you can do about the small proportion other than collect more data. As for the group sizes, yes it would be a good idea to combine them into another group, (or with another group if that makes sense). P-values will, of course, change when you change the data, but try not to be too concerned with p-values - they are little more than a function of sample size.
