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Internship Abstract
Title: 2016 Presidential Lens
Name: Victor Arroyo
Preceptors: Professor Debra Borie-Holtz, Assistant Teaching Professor
Agency: Edward J. Bloustein School of Planning and Public Policy
Purpose: To identify whether a swing state's composition of race, gender, political culture and workforce
participation, controlled by election type, primary versus caucus, had an effect on the 2016 presidential
primary outcomes.
Significance: Swing states are important battleground states for US presidential primary candidates since
the voter preferences do not always predict general election outcomes. Swing states and early voting
states receive more attention from US presidential primary candidates than states where polling for a
specific presidential candidate or party is skewed towards a favorite. Understanding what factors
influence voters in these states can inform policy and political scholars and experts making projections
about electoral decision-making.
Method/Approach: This project employs a quantitative strategy to measure if there is a statistically
significant relationship between the socioeconomic factors (IV) and political outcomes (DV). To conduct
this analysis, bivariate statistics and correlations were measured to assess the degree the IVs may have
had on contest outcomes. A regression analysis will be modeled to estimate whether a state’s composition
of race,gender, political culture and workforce can explain contest outcomes or predict the remaining
contests.
Outcomes: Descriptive statistics indicate the higher the percentage of non-minorities per state, the more
likely leading party candidates were to be selected. There was also an inverse relationship observed
between the Democrat and Republican candidates. Workforce factors appear to be correlated to the
percentage of victory by “outsider” candidates. At the time of publication, the regression analysis has not
been modeled.
Evaluation: Both descriptive and inferential statistics (bivariate, correlations and Chi squared statistics)
will be triangulated to evaluate the hypothesis that socioeconomic factors are predictive of primary
contest outcomes.

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ArroyoVictor rev 4.21.16

  • 1. Internship Abstract Title: 2016 Presidential Lens Name: Victor Arroyo Preceptors: Professor Debra Borie-Holtz, Assistant Teaching Professor Agency: Edward J. Bloustein School of Planning and Public Policy Purpose: To identify whether a swing state's composition of race, gender, political culture and workforce participation, controlled by election type, primary versus caucus, had an effect on the 2016 presidential primary outcomes. Significance: Swing states are important battleground states for US presidential primary candidates since the voter preferences do not always predict general election outcomes. Swing states and early voting states receive more attention from US presidential primary candidates than states where polling for a specific presidential candidate or party is skewed towards a favorite. Understanding what factors influence voters in these states can inform policy and political scholars and experts making projections about electoral decision-making. Method/Approach: This project employs a quantitative strategy to measure if there is a statistically significant relationship between the socioeconomic factors (IV) and political outcomes (DV). To conduct this analysis, bivariate statistics and correlations were measured to assess the degree the IVs may have had on contest outcomes. A regression analysis will be modeled to estimate whether a state’s composition of race,gender, political culture and workforce can explain contest outcomes or predict the remaining contests. Outcomes: Descriptive statistics indicate the higher the percentage of non-minorities per state, the more likely leading party candidates were to be selected. There was also an inverse relationship observed between the Democrat and Republican candidates. Workforce factors appear to be correlated to the percentage of victory by “outsider” candidates. At the time of publication, the regression analysis has not been modeled. Evaluation: Both descriptive and inferential statistics (bivariate, correlations and Chi squared statistics) will be triangulated to evaluate the hypothesis that socioeconomic factors are predictive of primary contest outcomes.