I have 1 dependent (continuous) variable, 3 explanatory (continuous) variables and a bunch of control variables. The explanatory variables are my interest variables.I want to do a categorical analysis.
So far, I've conducted a simple categorical analysis through multiple linear regression, where I include my 3 explanatory variables along with 3 additional interaction terms, i.e. I interact my category variable (0 for group 1, 1 for group 2) with my 3 explanatory interest variables to test for a statistically significant difference between the two groups along my 3 interest variables.
Now, however, I want to take into account control variables, i.e. my observed results could equally well be due to an omitted control variable. I could make a regression where I also include the control variables along with the interactions described above. But then, the two groups are only allowed to differ on the explanatory interest variables and none of the control variables, which is not a good solution, unless the two groups are not expected to differ on the control variables. Alternatively, I could include the control variables along with the interactions described above, plus interactions between the categorical variable and all the control variables. In this way, the two groups are allowed to differ on all the control variables, and I can "isolate" the effect of the explanator interest variable that is unique to each group. Yet there are also problems with this method, i.e. artificial multicollinearity. So both method (1) and method (2) could result in inappropriate results. As a third alternative, I've also considered ANCOVA. As far as I can tell from the Internet, however, this test is best suited for analysis of categorical variables alone, whereas I am interested in the interaction between the categorical variable and the explanatory interest variable. But could it work?
I hope anyone can help me resolve this issue! Thanks a lot guys