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So far, we have been preparing and analyzing data to understand voting behavior based on various factors such as civic engagement and treatment types. We have addressed potential issues with validity, stability, representativeness, and unconfoundedness in our data analysis approach. We also defined key components of justice in data science, such as accuracy and fairness, to ensure that our conclusions are robust and ethical.
We are employing a Bayesian regression model using brm() to estimate the effects of various predictors on voting behavior. The model includes a range of predictors and their interactions with the dependent variable, which is whether a person voted in the 2006 primary election. For instance, one of our predictors, civic_duty, is expected to have a positive relationship with the likelihood of voting, meaning that as civic duty increases, so does the probability of voting.