Social Sciences: Setting up equation for difference in differences with multiple treatment groups The dataset I'm looking at is household energy consumption in a particular area. I'm looking to analyze the effect of interventions which were aimed to reduce energy consumption on particular times of day so that is the effect I'm trying to measure i.e how effective the intervention was.
I have data with 1 control group and 4 treatment groups - each of which differ from the other only marginally. For clarity, in this case the participants who are being studied are intimated of the event through different means - email, SMS etc. 
I set up dummy variables for each of my treatment groups as group2, group3, group4 and group5 along with dummies for treatment and post which are named as such.
I just want to make sure that I'm setting up my regression equation correctly.
The equation I have is:
Dependant var. = Treatment + Post + (Group1 + Group2 + Group3 + Group4)*Post

where each of the interaction variables signify the treatments for each of my groups.
Also I'm considering two days of pre-treatment data for my analysis. 
My treatment period only lasts for about 3 hours on a particular day so I have -
3 hours the day of and 3 hours for the two days before the event I'm trying to look at.
Any help would be appreciated! 
 A: If you are setting up the model in R, you'll notice that the formula you've specified will add the lower level effects for the Post * Group variable and additionally control for indicators of Group1, Group2, Group3, and Group4. This is the right way to go about modeling.
Testing for differences-in-differences is the usual way of analyzing prepost data. Setting up a model with controls for a pre/post indicator, indicators of treatment assignment, and the interaction between pre/post and treatment assignment will give you a T-test of pre/post differences for each group in the estimated effects for the interaction parameter.
Similarly, if you do a nested test for the difference between a reduced model with only treatment assignment and pre/post versus a full model with those effects and the interactions, you will obtain an equivalent to the ANOVA for any treatment having had a difference on testing which is different from control. This can have more power than each individual T-test, as I described above, and solve multiple testing issues.
So the model is almost correct, just be sure to add the lower level effects for group assignment. The reason why they need to be there is to account for spurious baseline differences in individuals who may have been assigned to each group. You make broad assumptions by having the same fitted values for all participants in the baseline measurement.
