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- All Subjects: Gender Discrimination
- All Subjects: NBA
- Creators: Goegan, Brian
- Status: Published
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 NBA Draft has become one of the most exciting and unique events in sports. Draft decisions are so monumental; so crucial to be right, so disastrous to be wrong. The purpose of this project is to build a model that would help teams to predict which types of players perform at a high level upon entering the league. By using regression analysis to predict the rookie year PER (performance efficiency rating) as a dependent variable, teams would have some idea of whether their rookies were underperforming, excelling, or performing at a level they could expect. The independent variables and their statistical significance could help answer a host of questions that front offices have about players: If a player came from a worse conference, can we expect them to have a harder time adjusting? Will their shorter wingspan have a negative effect on their play in the NBA? Do guards or forwards tend to have higher PERs upon entering the league? To answer these questions, I've gathered data on every first round NBA draft pick from 2001-2014 who played at least one season of Division 1 NCAA basketball. The data consist of anthropometric measurements (height, wingspan, standing reach, etc.), NBA draft combine results (agility drills, sprint times, etc.) and their college statistics per 40 minutes in their final season of college basketball (points, rebounds, assist-to-turnover ratio, etc.). I then separated the data into seven different sets: aggregate, backcourt, frontcourt, guard, wing, forward, and big. For each of these data sets, I built a predictive model for rookie PER. In doing so, I aimed to gain both a broad understanding of what factors lead to translation of college basketball play to professional play, and also a precise understanding of how those factors change for each distinct position.
ContributorsMurphy, Benjamin Joseph (Author) / Goegan, Brian (Thesis director) / Marburger, Daniel (Committee member) / Economics Program in CLAS (Contributor) / School of Historical, Philosophical and Religious Studies (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
The NBA operates under a unique system with both forms of the salary cap. The league has a team salary cap that sets a limit that teams can spend on their entire roster. The NBA has a soft cap and a luxury tax system, meaning if teams spend over a determined amount, they are taxed for the salaries in excess. The league also has a player salary cap. The 1999 NBA collective bargaining agreement first introduced the individual player salary cap in the league. This cap sets a limit on what the best players can earn, otherwise known as the maximum contract. In an economic system with a soft team cap, the introduction of the player salary cap has important implications. The stated outcome of such a salary cap is to improve competitive balance and better distribute star players throughout the league. This study evaluated the 1990-2015 regular seasons to measure the impact of the player salary cap on competitive balance, the distribution of team payrolls, and the dispersion of star players. In accordance with the Rottenberg's invariance hypothesis, the player salary cap has hurt the players and benefited the owners by redistributing income from one party to the other, without impacting the distribution of talent in the league. The rule change has not affected competitive balance, while team payrolls have converged and star players have become more dispersed throughout the league. These changes hurt the league overall, preventing the maximization of revenues. Despite this inefficiency, the chance of the league moving to eliminate the player salary cap is low.
ContributorsWelu, Brian Andrew (Author) / Marburger, Daniel (Thesis director) / Goegan, Brian (Committee member) / Sandra Day O'Connor College of Law (Contributor) / Department of Economics (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12