Voting with their feet: Migration, partisanship, and party-safe elections in Florida

Political scientists have long noted that Congressional elections are often uncompetitive, often extremely so. Many scholars argue that the cause lies in the partisan redistricting of Congressional districts, or “gerrymandering”. Other scholars emphasize polarization created by a fragmented news media, or the candidate choices made by a more ideological primary electorate. All these explanations identify the cause of party-safe elections in institutions of various kinds. This dissertation, by contrast, presents a structural explanation of uncompetitive elections. My theory is that population composition and patterns of migration are significant causes and predictors of election results in Florida. I test this theory empirically by comparing the predictions from four hypotheses against aggregate data, using the county as the unit of analysis. The first hypothesis is that Florida can be divided into clearly distinguishable, persistent partisan sections. This hypothesis is confirmed. The second hypothesis is that Florida voters have become increasingly partisan over time. This hypothesis is confirmed. The third hypothesis is that the degree of migration into a county predicts how that county will vote. This hypothesis is partially confirmed, for the migration effect appears to have waned over time. The last hypothesis is that the degree of religiosity of a county population is a predictor of how that county will vote. This hypothesis is also supported by the results of statistical analysis. By identifying the structural causes of party-safe elections, this dissertation not only broadens our understanding of elections in Florida, but also sheds light on the current polarization in American politics.

Organizational Behavior: Forgotten Variable in Safe Food

The war on foodborne illness in hotels and restaurants is based on microbiology and critical control points. The author argues that cooks, managers, instructors, researchers, and regulators need to start looking beyond this narrow base to include more organizational behavior processes in their arsenal.

Personnel selection, safety performance, and job performance: Are safe workers better workers?

The present dissertation consists of two studies that combine personnel selection, safety performance, and job performance literatures to answer an important question: are safe workers better workers? Study 1 tested a predictive model of safety performance to examine personality characteristics (conscientiousness and agreeableness), and two novel behavioral constructs (safety orientation and safety judgment) as predictors of safety performance in a sample of forklift loaders/operators (N = 307). Analyses centered on investigating safety orientation as a proximal predictor and determinant of safety performance. Study 2 replicated Study 1 and explored the relationship between safety performance and job performance by testing an integrative model in a sample of machine operators and construction crewmembers (N = 323). Both Study 1 and Study 2 found conscientiousness, agreeableness, and safety orientation to be good predictors of safety performance. While both personality and safety orientation were positively related to safety performance, safety orientation proved to be a more proximal determinant of safety performance. Across studies, results surrounding safety judgment as a predictor of safety performance were inconclusive, suggesting possible issues with measurement of the construct. Study 2 found a strong relationship between safety performance and job performance. In addition, safety performance served as a mediator between predictors (conscientiousness, agreeableness and safety orientation) and job performance. Together these findings suggest that safe workers are indeed better workers, challenging previous viewpoints to the contrary. Further, results implicate the viability of personnel selection as means of promoting safety in organizations.^

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.