Matching Items (4)
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- All Subjects: Analytics
- All Subjects: Gender Discrimination
- Creators: Goegan, Brian
- Member of: Theses and Dissertations
- Member of: ASU Electronic 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
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
This paper attempts to introduce analytics and regression techniques into the National Hockey League. Hockey as a sport has been a slow adapter of analytics, and this can be attributed to poor data collection methods. Using data collected for hockeyreference.com, and R statistical software, the number of wins a team experiences will be predicted using Goals For and Goals Against statistics from 2005-2017. The model showed statistical significance and strong normality throughout the data. The number of wins each team was expected to experience in 2016-2017 was predicted using the model and then compared to the actual number of games each team won. To further analyze the validity of the model, the expected playoff outcome for 2016-2017 was compared to the observed playoff outcome. The discussion focused on team's that did not fit the model or traditional analytics and expected forecasts. The possible discrepancies were analyzed using the Las Vegas Golden Knights as a case study. Possible next steps for data analysis are presented and the role of future technology and innovation in hockey analytics is discussed and predicted.
ContributorsVermeer, Brandon Elliot (Author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Our research encompassed the prospect draft in baseball and looked at what type of player teams drafted to maximize value. We wanted to know which position returned the best value to the team that drafted them, and which level is safer to draft players from, college or high school. We decided to look at draft data from 2006-2010 for the first ten rounds of players selected. Because there is only a monetary cap on players drafted in the first ten rounds we restricted our data to these players. Once we set up the parameters we compiled a spreadsheet of these players with both their signing bonuses and their wins above replacement (WAR). This allowed us to see how much a team was spending per win at the major league level. After the data was compiled we made pivot tables and graphs to visually represent our data and better understand the numbers. We found that the worst position that MLB teams could draft would be high school second baseman. They returned the lowest WAR of any player that we looked at. In general though high school players were more costly to sign and had lower WARs than their college counterparts making them, on average, a worse pick value wise. The best position you could pick was college shortstops. They had the trifecta of the best signability of all players, along with one of the highest WARs and lowest signing bonuses. These were three of the main factors that you want with your draft pick and they ranked near the top in all three categories. This research can help give guidelines to Major League teams as they go to select players in the draft. While there are always going to be exceptions to trends, by following the enclosed research teams can minimize risk in the draft.
ContributorsValentine, Robert (Co-author) / Johnson, Ben (Co-author) / Eaton, John (Thesis director) / Goegan, Brian (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05