well... you kinda do what lazar already told you to do.

since you only say "relationship betweens status and well-being" let's assume (wlog) that your predictor variable is status and your criterion variable is well being... so, steps:

1) create new variables centering sense of control and social status (i.e. you substract the mean of each from every single data point in your data-column)

2) create the interaction effect (from the centered data) just as lazar said, multiplying both your predictor (social status) and your moderator (control)

3) run an ols multiple regression with and without the interaction term. if the interaction term is statistically significnat, then you can conclude that control is moderating the relationship between status and well-being (and i know a few people like to check whether the increase in R^2 is also statistically significant or not, which would also bring more evidence in favour of the moderator effect).

4) if the interaction is not significant then you can interpret the main effects only. if it is significant then you should run a simple slopes analysis to understand the interaction...

this is an extremely brief summary of what is usually an, alebeit not complicated, kinda cumbersome process (at least to me. i dont really like moderated regression). i'd really suggest for you to take a look at the classic of classics Cohen, Cohen, West & Aiken's book on regression/correlation analysis.