# Logistic regression: if only some classes of a categorical variable appear significant

I am performing (rare events) logistic regression analyses in R and want to test several categorical variables consisting of more than two classes. I understood that I can do this by using factor(). However, I am not sure what to do in case some, but not all categories are significant. Can I (1) just leave out the ones that are not significant, assuming their coefficients equal 0, or (2) do I have to include them all in the model, or (3) do I somehow have to recalibrate the logistic regression model with only the significant dummies?

• I have been searching for an answer similar maybe you guys can help me. I am also trying to interpret categorical variables with more than two classes. Some are significant whilst other classes are not. what can I infer from the insignificant ones? does this mean the insignificant ones and the reference category are equally influence the dependent? For example: ETHNICITY Reference- Indian Other Asian - Sig = .273 Exp (b) = 1.123 African - Sig = .000 Exp (b) = .148 Many Thanks!
– Kate
Apr 20, 2014 at 14:03
• Welcome to the site, @Kate. This isn't an answer to the OP's question. Please only use the "Your Answer" field to provide answers. If you have your own question, click the [ASK QUESTION] at the top & ask it there, then we can help you properly. Since you are new here, you may want to take our tour, which contains information for new users. Apr 20, 2014 at 14:49

• Everything said until the last sentence is correct and useful. The last remark is not correct, though, because it is based on an illusion: once you have included all the original levels in the model you have used all those degrees of freedom (DF) in the analysis and that needs to be respected in all subsequent analyses of the same data. Combining certain levels and then pretending this was originally intended creates an artificial apparent reduction in DF, which thereby can (in some cases) increase measures such as reduced $R^2$ and AIC. This, however, is just a way of deceiving yourself.