Matching Items (4)
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- All Subjects: Gender Discrimination
- All Subjects: Regression Analysis
- Creators: Goegan, Brian
- Member of: Theses and Dissertations
Description
Overt forms of sexism have become less frequent (Swim Hyers, Cohen & Ferguson, 2001; Sue & Capodilupo, 2008). Nonetheless, scholars contend that sexism is still pervasive but often manifests as female microaggressions, which have been defined as often subtle, covert forms of gender discrimination (Capodilupo et al., 2010). Extant sexism scales fail to capture female microaggresions, limiting understanding of the correlates and consequences of women’s experiences of gender discrimination. Thus, the purpose of the current study was to develop the Female Microaggressions Scale (FeMS) based on an existing theoretical taxonomy and content analysis of social media data, which identifies diverse forms of sexism. Two separate studies were conducted for exploratory factor analysis (N = 582) and confirmatory factor analysis (N = 325). Exploratory factor analyses supported an eight-factor, correlated structure and confirmatory factor analyses supported a bifactor model, with eight specific factors and one general FeMS factor. Overall, reliability and validity of the FeMS (general FeMS and subscales) were mostly supported in the two present samples of diverse women. The FeMS’ subscales and body surveillance were significantly positively correlated. Results regarding correlations between the FeMS subscales and anxiety, depression, and life satisfaction were mixed. The FeMS (general FeMS) was significantly positively correlated with anxiety, body surveillance, and another measure of sexism but not depression or life satisfaction. Furthermore, the FeMS (general FeMS) explained variance in anxiety and body surveillance (but not depression, self-esteem, or life satisfaction) above and beyond that explained by an existing sexism measure and explained variance in anxiety and depression (but not self-esteem) above and beyond that explained by neuroticism. Implications for future research are discussed.
ContributorsMiyake, Elisa (Author) / Tran, Giac-Thao Thanh (Thesis advisor) / Bernsten, Bianca (Committee member) / Tracey, Terence (Committee member) / Arizona State University (Publisher)
Created2018
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
Early in the development of American's interest in athletics there has been a conditioning of the mind toward promoting and rewarding male athletes, while ignoring and undercutting female athletes. There is substantial evidence of the existence of monetary and promotional time given to male athletes and very little support given to their female counterparts. The gender pay gap in professional sports is a culmination of gender discrimination within the entire sports realm. It appears to start at the high school level, continue on into the collegiate sector, and is finally magnified in the professional arena. In high school, male sport's programs are given preference to game and practice times, locations, as well as promotions. In college, male athletic programs are advertised and highlighted as being the premier events to go to. This is also seen in college bookstores with the dominating male event merchandise for sale. In the professional arena, the astronomical value of male athletes' salaries, which go into the multi-millions, makes the gender pay gap glaring. These discrepancies between men and women at each level of sport are in part caused by the underlying informal systems or societal norms and values currently present and encouraged in American culture and communities. These informal systems are often countered by formal systems, such as Title IX. Change cannot truly take place until the two systems are aligned. Thankfully, society today seems to be headed in a more equitable direction; therefore, promoting hope and promise for a more equal future between male and female athletes and their programs.
ContributorsBaldwin, Macy Jeanette (Author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / School of Accountancy (Contributor) / WPC Graduate Programs (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