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Votes in the Balance: A Statistical Analysis of Voting Behavior in the 2016 Presidential Election
Full text linkThe 2016 Presidential Election was unprecedented in its results. The predicted winner, Hillary Clinton, ended up winning the popular vote, but lost in the Electoral College count to Donald Trump. I examine voting for the winner, Donald Trump, and what factors caused people to vote for him or for another candidate. Although there are several factors that go into how people vote, I analyze demographic, geographic, and ideological predictors for why people voted the way they did. To do this, I will run a regression model on my dataset. Since my dependent variable is dichotomous, meaning there are only two options, I will use a logistic, or logit, regression model. My independent variables include following the media, favoring taxes on millionaires, social class, feelings on feminists, feelings on Christians, racial discrimination, and state where registered. Following the media, feelings on Christians, racial discrimination, and state where registered caused people to vote for the winner, Donald Trump, while favoring taxes on millionaires, social class, and feelings on feminists caused people to not vote for Trump. My analysis discovers that all variables, except social class, are statistically significant and therefore explain how people voted in the election. My research overall showed significance that these factors can predict why respondents voted for or against Donald Trump and what helped influence a respondent’s voting decision in the 2016 Presidential Election
Analeigh Thomas
No full texthttps://scholarworks.harding.edu/lectureship-presenters/1536/thumbnail.jp
Joseph Randolph
No full texthttps://scholarworks.harding.edu/lectureship-presenters/1522/thumbnail.jp
