# Interaction terms and effect sizes in multiple regression

I know there are similar questions on here, but I can't quite find an answer that covers all of what I need. I am running multiple regression in r with two predictor variables and sometimes an interaction term e.g.:

model1 = lm(Measure1 ~ Variable 1 + Variable 2)
model2 = lm(Measure1 ~ Variable 1 + Variable 2 + Variable2:Variable 3)


I am first wondering, what is the best way to calculate the effect size specifically of variable 2 in both instances. I know because the second formula includes an interaction I can't necessarily use the standard coefficient values, and I'd like to get the effect size in a consistent way between the two formulas. Also, if it's important, the DV is continuous, but the variables are dummy coded variables (e.g. on/off a drug and gender). Along these lines, is there a good way to determine when I should use an interaction in the equation when I have many dependent variables I want to look at? Creating a plot of each manually doesn't seem like the most efficient way to do so…