Any coeducational postsecondary institution in the United States that participates in the federal student financial assistance programs and has an intercollegiate athletic program is required by the Higher Education Act of 1965, as amended, 20 USC 1092(g) (also known as the Equity in Athletics Disclosure Act [EADA]), to participate in the annual EADA data collection. Through this survey, data are collected by the US Department of Education Office of Postsecondary Education on athletic participation, staffing, and revenues and expenses, by men's, women's, and coed varsity teams. The Department of Education uses this information in preparing its required report to the Congress on gender equity in intercollegiate athletics. The dataset presented here includes data on all reporting institutions by school year for a consecutive twelve-month period of time designated by the institution for the purposes of the EADA Report. (Note that the year indicated refers to the beginning of the school year: eg, 2011 represents the period 2011-2012.)
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In Data Planet Statistical Datasets, the indicators included in the College Athletics dataset can be viewed individually to examine the many data points that comprise the time series. Statistics can be examined by state, institutions, and sport, and by gender of team. The chart below ranks numbers of players on the men's vs women's basketball teams at four institutions in Kentucky.
Compare statistics for states, institutions, sports, and/or by team gender. To select multiple indicators, hold down the control (Ctrl) key when clicking on the second (or third) item in the criteria panel. The trend display below provides an infographic of trends in revenues earned by men's and women's basketball teams at five Florida schools. Comparing these trends to team win-loss records might prove interesting!
You can also compare across indicators in other datasets in the repository, such as Graduation Rates, Academic Library Statistics, etc. Keep in mind that the graphs you create do not necessarily imply causality: the results may suggest a potential relationship between the variables you select, which may be an interesting line of inquiry for your own research.