Body image disturbances: the effects of media on self-appraisal and ideal mate selection

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 22-24).Previous research indicates that while media images of women and men are becoming more thin and muscular (respectively), the size and weight of American people is increasing. Several researchers have found that media images of ideal body types are highly related to body dissatisfaction and negative self-evaluation. Although some researchers have found that interventions regarding media images (i.e. education about the unrealistic nature of these images) negate the effect of media images on body dissatisfaction, most researchers have found no effect. The purpose of this study is to determine the effects of printed media images and interventions on participants' perceived body image, ideal body image, ideal body shape for the opposite sex, and current mood state. Five hundred and twelve undergraduates (males n=249, females n=263) viewed a slideshow containing either ideal, average, or overweight male and female images. After the slideshow, half of the participants (n=258) received an intervention, which consisted of a handout explaining techniques commonly used by the media to enhance the appearance of models (e.g., airbrushing, image splicing, and resizing). Measures of body dissatisfaction, perceived body image, ideal body image, ideal body shape of the opposite sex, depression, anger, anxiety, and vigor will be compared from pre-test to post-test using Analysis of Variance, in order to determine the effects of the images and intervention on the dependent variables. Because of the lack of an effect of the manipulation, there was limited ability to test the effectiveness of the intervention. Future research similar to the current study should not present participants with both male and female images. To prevent possible confounds, participants should view either ideal, average, or overweight men or women

The Development of a Pollution Prevention Plan

This study focuses on the reasons for implementing a pollution prevention program, and the format that such an effort should take . The numerous laws and regulations created by government place a significant burden on the manufacture, handling, and use of hazardous substances, and significant resources are required of industry as a result. Preventing pollution makes economic sense by increasing operating efficiencies and reducing the costs associated with pollution . Congress and the EPA have made pollution prevention a current priority. The EPA is incorporating pollution prevention into all aspects of the Agency\u27s activities . With regard to industry, the EPA is relying on market incentives and cooperative efforts with industry to promote pollution prevention. Industry, utility companies, and Union Electric have already taken significant steps to prevent pollution . However, it makes sense for Union Electric to formalize activities into a program which will improve the company\u27 s approach in responding to regulatory and business incentives to pollution prevention. A formal program using an assessment process is the most effective way to identify opportunities and select successful alternatives to reduce pollution. A pollution prevention program was developed to present the means to achieve pollution prevention in a way which meets the needs of the company while also fulfilling applicable regulatory requirements . The subjects reviewed the program, and in general, determined that it would be effective in achieving pollution prevention. Many specific, constructive comments and suggestions were provided and incorporated into the program

Investigating the Possible Relationship Between Participation in High School Athletics and First-Generation College Student Persistence to College Graduation

The purpose of this mixed-methods study was to investigate ways in which participation in high school varsity athletics impacted academic success of first generation college students. Through an anonymous online survey, this study compared quantitative demographic data of first-generation college students who participated in high school varsity athletics to those who did not. In addition, the qualitative research in this study explored first-generation college student perceptions of why they have been successful during college. Athletic focus group participants were asked questions related to college transition, what they gained through athletics, and long-term academic benefits of their participation in high school athletics. Prior research correlated the relationship between participation in high school athletics and improved school attendance, grades, ACT scores, and graduation rates (Lumpkin & Favor, 2012) while the athletes were enrolled in high school. However, few studies have explored the long-term academic benefits in terms of college persistence and bachelor’s degree completion. With consideration of the academic benefits, this study pinpointed characteristics, academic behaviors, and life skills enhanced through participation in high school varsity athletics that contributed to positive college outcomes for these first-generation college students. Two first-generation cohorts were utilized in the study: (a) college students who graduated from high school in 2015 and returned for their second year of college at Suburban Private University during the fall of 2016 and (b) college seniors who graduated from high school since 2011 and applied for graduation during the 2016-2017 school year. The findings indicated that first-generation college students, who were high iii school varsity athletes have a statistically significant higher high school grade point averages and college grade point averages after two semesters, compared to college athletes and nonathletes. Also, former high-school-only athletes graduated from college in fewer semesters than either of the other two groups. Most notably, based on the sample utilized in this study, there was statistically significant evidence that there are more first generation college graduates that were former high school athletes than first-generation graduates who were not high school varsity athletes. The results of this mixed-methods study indicated a possible relationship between participation in varsity high school athletics and successful first-generation college transition to college and persistence to graduation. As the study participants expressed, their participation in varsity level athletics assisted them to be academically prepared for college when they first arrived and were self-confident that with hard work they would one-day become first-generation college graduates. This researcher believes more future first-generation college students should participate in school-sponsored athletics alongside their teammates for all four years of high school, not necessarily with the motivation of more playing time in high school or to secure an athletic college scholarship, but to enhance the personal characteristics, academic focus, and resiliency that could help them graduate from college

Cost Effective Analysis of Big Data

Executive Summary Big data is everywhere and businesses that can access and analyze it have a huge advantage over those who can’t. One option for leveraging big data to make more informed decisions is to hire a big data consulting company to take over the entire project. This method requires the least effort, but is also the least cost effective. The problem is that the know-how for starting a big data project is not commonly known and the consulting alternative is not very cost effective. This creates the need for a cost effective approach that businesses can use to start and manage big data projects. This report details the development of an advisory tool to cut down on consulting costs of big data projects by taking an active role in the project yourself. The tool is not a set of standard operating procedures, but simply a guide for someone to follow when embarking on a big data project. The advisory tools has three steps that consist of data wrangling, statistical analysis, and data engineering. Data wrangling is the process of cleaning and organizing data into a format that is ready for statistical analysis. The guide recommends using the open source software and programming language of R. The next step is the statistical analysis portion of the process which takes the form of exploratory data analysis and the use of existing models and algorithms. The use of existing methods should always be attempted to the highest performance before justifying the costs to pay for big data analytics and the development of new algorithms. Data engineering consists of creating and applying statistical algorithms, utilizing cloud infrastructure to distribute processing, and the development of a complete platform solution. The experimentation for the design of our advisory toolwas carried out through analysis of many large data sets. The data sets were analyzed to determine the best explanatory variables to predict a selected response. The iterative process of data wrangling, statistical analysis, and model building was carried out for all the data sets. The experience gained, through the iterations of data wrangling and exploratory analysis, was extremely valuable in evaluating the usefulness of the design. The statistical analysis improved every time the iterative loop of wrangling and analysis was navigated. In house data wrangling, before submission to a data scientist, is the primary cost justification of using the advisory tool. Data wrangling typically occupies 80% of data scientist’s time in big data projects. So, if data wrangling is self-performed before a data scientist receives the data, then less time will be spent wrangling by the data scientist. Since data scientists are paid very high hourly wages, extra time saved wrangling equates to direct cost savings. This is assuming that the data wrangling performed before a data scientist takes over is of adequate quality. The results of applying the advisory tool may vary from case to case, depending on the critical skills the user possesses and the development of such skills. The critical skills begin with coding in R and Python as well as knowledge in the statistical methods of choice. Basic knowledge of statistics, and any programming language is a must to begin utilizing this guide. Statistical proficiency is the limiting factor in the advisory tool. The best start for doing a big data project on one’s own is to first learn R and become familiar with the statistical libraries it contains. This allows data wrangling and exploratory analysis to be performed at a high level. This project pushed the boundaries of what can be done with big data using traditional computer framework without cloud usage. Storage and processing limits of traditional computers were tested and in some cases reached, which verified the eventual need to operate in the cloud environment

Here Comes the Future: Embedding Library Leaders of Tomorrow

Additional contributions to the administrative documents were made by Emma Marshall, Elizabeth Nicholson, Anthony Strand, Ashley Nelson, Jodi Millard, Shannon Western Mawhiney, Kristy Farrington, and John Henry Muhrer.Through an Institute of Museum and Library Services (IMLS) grant the University of Missouri has provided 20 masters students with the opportunity to be embedded within university libraries under the guidance of library leaders

Violets under the snow; a collection of short poems, by Louisa E. Litzsinger.

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