For a research I am conducting I am looking at the effect of two categorical variables (dummy with values 0 and 1 for each group) on the speed and quantity with which customers make future purchases. The variables are simply made as follows:
- speed is measured as the days until the next purchase
- Quantity is measured as a count variable counting the number of products purchased at the next purchase My goal is to understand how the two categorical variables affect the speed and quantity. For Speed I used a hazard Cox regression to analyze the time until the event of the next purchase. For quantity I used a negative binomial model (to accoutn for overdispersion) to analyse the quantity of products. In both analyses I add first one variable, and then both and then their interaction in a hierarchical fashion.
However the signs of the coefficient of one categorical variable change everytime I add the other one. I checked multicollinearity because the correlation between the variables is .130 and the VIF scores are around 1.
Here is an example of the negative binomial count model results (*significant below .001) :
|Models||Model 1||Model 2||Model 3|
|Variables||B coefficient||B coefficient||B coefficient|
Could anyone help me explain why this is happening or suggest how to deal with this issue? I do not know how to explain this and it happens both with the cox regression as with the negative binomial model..
Thank you already!