Matching Items (4)
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- All Subjects: Regression Analysis
- All Subjects: Third Parties
- Creators: Goegan, Brian
- Creators: Beardsley, James
Description
The goal of our study is to identify socio-economic risk factors for depressive disorder and poor mental health by statistically analyzing survey data from the CDC. The identification of risk groups in a particular demographic could aid in the development of targeted interventions to improve overall quality of mental health in the United States. In our analysis, we studied the influences and correlations of socioeconomic factors that regulate the risk of developing Depressive Disorders and overall poor mental health. Using the statistical software STATA, we ran a regression model of selected independent socio-economic variables with the dependent mental health variables. The independent variables of the statistical model include Income, Race, State, Age, Marital Status, Sex, Education, BMI, Smoker Status, and Alcohol Consumption. Once the regression coefficients were found, we illustrated the data in graphs and heat maps to qualitatively provide visuals of the prevalence of depression in the U.S. demography. Our study indicates that the low-income and under-educated populations who are everyday smokers, obese, and/or are in divorced or separated relationships should be of main concern. A suggestion for mental health organizations would be to support counseling and therapeutic efforts as secondary care for those in smoking cessation programs, weight management programs, marriage counseling, or divorce assistance group. General improvement in alleviating poverty and increasing education could additionally show progress in counter-acting the prevalence of depressive disorder and also improve overall mental health. The identification of these target groups and socio-economic risk factors are critical in developing future preventative measures.
ContributorsGrassel, Samuel (Co-author) / Choueiri, Alexi (Co-author) / Choueiri, Robert (Co-author) / Goegan, Brian (Thesis director) / Holter, Michael (Committee member) / Sandra Day O'Connor College of Law (Contributor) / School of Molecular Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In this work we analyze just what makes the topic of third party voting so intriguing to voters and why it is different than voting for one of the major parties in American politics. First, we will discuss briefly the history of politics in America and what makes it exciting. Next, we will outline some of the works by other political and economic professionals such as Hotelling, Lichtman and Rietz. Finally, using the framework described beforehand this paper will analyze the different stances that voters, candidates, and others involved in the political process of voting have regarding the topic of third party voting.
ContributorsMcElroy, Elizabeth (Co-author) / Beardsley, James (Co-author) / Foster, William (Thesis director) / Goegan, Brian (Committee member) / Department of Economics (Contributor) / School of International Letters and Cultures (Contributor) / Economics Program in CLAS (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Today, statistical analysis can be used for a variety of different reasons. In sports, more particularly baseball, there is an increasing necessity to have better up to date analysis of players and their performance as they attempt to make it to the Major League. Athletes are constantly moving around within one or more organizations. Since they are moving around so often, clubs spend an ample amount of time determining whether or not it is for their benefit and betterment of the organization as a whole. The objective of this thesis is to utilize previous baseball statistics in StataSE to determine performance levels of players who played at the major league level. From these, regression-based performance models will be used to predict whether or not Major League Baseball organizations effectively and efficiently move players around from their farm systems to the big leagues. From this, teams will be able to see whether or not they in fact make the right decisions during the season. Several tasks were accomplished to achieve this outcome: 1. First, data was obtained from the Baseball-Reference statistics database and sorted in google sheets in order for me to perform analysis anywhere. 2. Next, all 1,354 players that entered the major leagues in the year 2016, were assessed as to whether or not they started in a given league and stayed, got promoted from the minor leagues to the majors, or demoted from the majors to the minor leagues. 3. Based off of prior baseball knowledge and offensive performance quantifications only, players' abilities were evaluated and only those who were called up or sent down were included in the overall analysis. 4. The statistical analysis software application, StataSE, was used to create a further analyze if any of the four major regression assumptions were violated. It was determined that logistic regression models would produce better results than that of a standard, linear OLS model. After testing multiple models, and slightly refining my hypothesis, the adjustments made developed a more accurate analysis of whether organizations were making an efficient move sending a player down to promote another player up. After producing the model, I decided to investigate at what level a player was deemed to be no longer able to perform at a Major League Baseball level.
ContributorsHayes, Andrew Joseph (Author) / Goegan, Brian (Thesis director) / Marburger, Daniel (Committee member) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Cannabis use has been purported to cause an amotivation-like syndrome among users. The purpose of this study was to investigate whether third party observers noticed amotivation among cannabis users. Participants in this study were 72 undergraduate university students, with a mean age of M=19.20 years old (SD=2.00). Participants nominated Informants who knew them well and these informants completed a version of the 18-item Apathy Evaluation Scale. Results indicated that more frequent cannabis use was associated with higher informant-reported levels of amotivation, even when controlling for age, sex, psychotic-like experiences, SES, alcohol use, tobacco use, other drug use, and depression symptoms (β=0.34, 95% CI: 0.04, 0.64, p=.027). A lack of motivation severe enough to be visible by a third party has the potential to have negative social impacts on individuals who use cannabis regularly.
ContributorsWhite, Makita Marie (Author) / Meier, Madeline (Thesis director) / Glenberg, Arthur (Committee member) / Pardini, Dustin (Committee member) / School of Art (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12