Creating an interaction term with 2 continuous variables: What to do? I want to create an interaction term in SPSS on two continuous variables (ticket price and household income) in order to use this interaction term in a multiple regression model and test whether my main effects or interaction effects model is better.
Do I:


*

*Compute a new variable for each IV (the score minus the mean) and then an interaction term between these new variables?

*Or compute an interaction term between the original IVs? (I have 105 observations)

*Or do I re-code each of the IVs into categories e.g. low, med, high income/ticket price and then compute an interaction term that multiplied the two new categorical variables?
I had just created a new categorical variable for income (low,mid, high) and left the ticket price as is, but now I'm doing option 1 as I read this online. 
 A: First see When conducting multiple regression, when should you center your predictor variables & when should you standardize them?—there's no substantive† difference between models 1 & 2, both containing two linear terms plus an interaction term.
Second see What is the benefit of breaking up a continuous predictor variable?—discretizing continuous variables as in model 3 isn't generally a good idea. If your concern is to allow for curvilinear relationships you might consider a full second-order model for the expected response,
$$\operatorname{E}Y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_{11} x_1^2 + \beta_{22} x_2^2 + \beta_{12} x_1 x_2$$
where $x_1$ & $x_2$ are your predictors, & the $\beta$s the coefficients to be estimated; this has five regression degreees of freedom. For only one more degree of freedom than the eight you would spend on modelling the interaction between two three-level categorical variables you could even try a full third-order model, but at increased risk of over-fitting.
† By that I mean one model is just a reëxpression of the other—the predictors are redefined through an affine transformation & the interpretation of the coefficents changes accordingly, but the predictions & inferences remain the same.
