Diff-in-Diff Regression: 6 Var, Interaction Terms & Estimate

In summary: To find significance, you can use p-values or confidence intervals. There are various resources available online that explain how to write a mixed logistic regression equation with multiple covariates. Additionally, there are software packages that can help with this type of analysis.
  • #1
je9183
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I am doing a difference-in-difference analysis on a set of survey data for a health education program and I need to find statistical significance for the difference-in-difference estimate. I know that I find this using a regression. I need to use a regression in a mixed logistic model including pre/post intervention, treatment group (intervention/control), gender, age, school, and classes in each school as covariates (6 variables). The issue is that I am not sure how to write the regression equation for all these variables.

I have only seen examples where a difference-in-difference estimate is made with a regression of two variables and their interaction term, like the following equation:
Y=α+β1Treat+β2Post+β3(Treat ⋅ Post) +ϵ
β3 will be the difference-in-difference estimate for this regression and will give the p-values for the difference-in-difference estimate.

How do I write the regression equation with all six variables? Which constant will be the difference-in-difference estimate in that equation?
Resources or explanations on how to do this would be much appreciated. If you just know the answer to the first question I would appreciate that too.
 
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  • #2
The equation you need to use is:Y=α+β1Treat+β2Post+β3Gender+β4Age+β5School+β6Classes+β7(Treat*Post)+ϵIn this equation, β7 will be the difference-in-difference estimate and will give the p-values for the difference-in-difference estimate.
 

FAQ: Diff-in-Diff Regression: 6 Var, Interaction Terms & Estimate

What is a Diff-in-Diff Regression?

A Diff-in-Diff Regression is a statistical method used to estimate the causal effect of a treatment or intervention on an outcome variable. It compares the changes in the outcome variable over time between a treatment group and a control group, taking into account other factors that may influence the outcome.

What are the 6 variables used in a Diff-in-Diff Regression?

The 6 variables used in a Diff-in-Diff Regression are: treatment status (whether the group received the treatment or not), time (before and after the treatment), treatment group indicator, control group indicator, outcome variable, and covariates (other factors that may influence the outcome).

What are interaction terms in a Diff-in-Diff Regression?

Interaction terms are additional variables that are included in the regression model to capture the differences in the treatment effect across different groups. They allow for a more nuanced analysis by taking into account the varying effects of the treatment on different subgroups.

How are estimates calculated in a Diff-in-Diff Regression?

Estimates in a Diff-in-Diff Regression are calculated by taking the difference in the outcome variable between the treatment and control group before and after the treatment, and then taking the difference between these differences. This provides an estimate of the causal effect of the treatment on the outcome variable.

What are the assumptions of a Diff-in-Diff Regression?

The assumptions of a Diff-in-Diff Regression include: parallel trends (the outcome variable would have followed a similar trend for both groups in the absence of the treatment), no spillover effects (the treatment only affects the treatment group and not the control group), and no selection bias (the treatment and control group are similar in all other aspects except for the treatment).

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