I am building a binary logistic regression model. I am not sure if using the variables as interactions is a better choice than building separate models for level of a categorical variable. Is there a way to determine this? For ex, there are 2 categorical variable with 2 levels each - 1.Regions - East and West 2.AGI - High and Low The response variable behaves differently for each level i.e. East has higher number of positives response and so does high AGI. here is my question - Is it good to build separate models for each level i.e. have 4 models East and high, East and Low, etc.? Or use the 2 as categorical variables in one model? How do you determine when to split the data into separate models? I have built separate models when i knew the behaviour of the data (independent variables) are different in each level of a categorical variable.