Title IX Compliance among NCAA schools

Title IX was brought into consideration in 1972 to eliminate gender based discrimination in education. Most notably, it has been used to reconfigure how we determine gender equality in sports. It is often in discussion for reasons of which teams will receive more funding, what sports are offered at schools, and if these institutions are properly meeting the standards set by the US government on handling Title IX expectations. This study will look into the data on all NCAA schools in the United States and compare the findings on the schools that are most in compliance with these standards and which areas are failing.

The first step was to run the data and filter all colleges in the data sheet.

library(readr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ dplyr   1.0.9
## ✔ tibble  3.1.8     ✔ stringr 1.4.1
## ✔ tidyr   1.2.1     ✔ forcats 0.5.2
## ✔ purrr   0.3.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(readr)
titlenine <- read_csv("~/Desktop/titlenine.csv")
## Rows: 2073 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (12): Institution, Overall, Grade - Participation, Scholarships, Scholar...
## dbl  (2): Participation gap, Total Enrollment
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
library(readr)
totalgap <- read_csv("~/Desktop/totalgap.csv")
## Rows: 54 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): State
## dbl (1): total
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
titlenine %>% 
 filter(grepl("NCAA", Classification, ignore.case = TRUE)) -> ncaa_schools



gsub('[^[:alnum:] ]', '', titlenine$`Scholarship Gap`) -> titlenine$`Scholarship Gap`

gsub('[^[:alnum:] ]', '', ncaa_schools$`Scholarship Gap`) -> ncaa_schools$`Scholarship Gap`
as.numeric(ncaa_schools$`Scholarship Gap`) -> ncaa_schools$`Scholarship Gap`
## Warning: NAs introduced by coercion
as.numeric(titlenine$`Scholarship Gap`) -> titlenine$`Scholarship Gap`
## Warning: NAs introduced by coercion
titlenine %>% 
  group_by(State) %>% 
  summarize(total = sum(as.numeric(`Scholarship Gap`), na.rm = TRUE)) %>% 
  arrange(desc(total)) -> total_gap

Passing and Failing Grades Awarded to Institutions

With a combination of looking at the student athlete percentage, the male and female student body ratio, along with the scholarship and recruiting numbers, schools were labeled as passing or failing in terms of meeting Title IX standards. These schools were then ranked by enrollment to show the largest institutions that are passing or failing.

ncaa_schools %>%
  filter(Overall == "Pass") %>%
  arrange(desc(`Total Enrollment`)) %>%
  count(Overall, sort = TRUE)
## # A tibble: 1 × 2
##   Overall     n
##   <chr>   <int>
## 1 Pass       39
ncaa_schools %>%
  filter(Overall == "Pass") %>%
  arrange(desc(`Total Enrollment`)) %>%
  select(Institution)
## # A tibble: 39 × 1
##    Institution                            
##    <chr>                                  
##  1 Utah Valley University                 
##  2 University of California-Santa Cruz    
##  3 Rowan University                       
##  4 University of North Florida            
##  5 University of Maryland-Baltimore County
##  6 Farmingdale State College              
##  7 Texas Woman's University               
##  8 New Jersey Institute of Technology     
##  9 Rensselaer Polytechnic Institute       
## 10 Humboldt State University              
## # … with 29 more rows
ncaa_schools %>%
  filter(Overall == "Fail") %>%
  arrange(desc(`Total Enrollment`)) %>%
  count(Overall, sort = TRUE)
## # A tibble: 1 × 2
##   Overall     n
##   <chr>   <int>
## 1 Fail     1040
ncaa_schools %>%
  filter(Overall == "Fail") %>%
  arrange(desc(`Total Enrollment`)) %>%
  select(Institution)
## # A tibble: 1,040 × 1
##    Institution                               
##    <chr>                                     
##  1 Pennsylvania State University-Main Campus 
##  2 Texas A & M University-College Station    
##  3 Ohio State University-Main Campus         
##  4 University of Central Florida             
##  5 Arizona State University-Tempe            
##  6 The University of Texas at Austin         
##  7 Michigan State University                 
##  8 Rutgers University-New Brunswick          
##  9 Purdue University-Main Campus             
## 10 University of Illinois at Urbana-Champaign
## # … with 1,030 more rows

It can be seen here that almost all schools, and most notably the largest ones, are failing to meet Title IX standards.

Enrollment Demographics

The next step was sorting the states by total enrollment of college students and using Tableau to map out the student demographics in each state.

ncaa_schools %>% 
  group_by(State) %>% 
  summarize(total = sum(`Total Enrollment`)) %>% 
  arrange(desc(total)) 
## # A tibble: 53 × 2
##    State  total
##    <chr>  <dbl>
##  1 CA    691267
##  2 NY    478339
##  3 TX    465993
##  4 PA    364589
##  5 FL    271852
##  6 OH    249594
##  7 NC    220376
##  8 VA    213239
##  9 MA    207094
## 10 IL    192482
## # … with 43 more rows

Student enrollment demographic percentages by state

This graph ranks each state by their total scholarship gap while also comparing the states total enrollment to show which areas of the country have the largest disparity. Another map was included to show the geographic disparity and the trends among regions of the United States that are seeing larger scholarship gaps.

Scholarship Gap by State

Conclusion

Overall, this study has shown that the vast majority of institutions are not meeting the standards that are expected of them when it comes to Title IX. Title IX was created to help even the playing field for gender equality in education and yet the largest schools in the country are receiving a failing grade. As of 2022, no NCAA member institution has faced any repercussions by the NCAA or by the United States government for not meeting these standards, despite there being laws in place on the matter. This study has also concluded that the southern part of the United States has scholarship gaps that are much larger in comparison to states with similar student enrollment in other parts of the country. For example, Texas and New York have similar enrollment overall, yet Texas has almost double the total scholarship gap between men and women than New York does. With 39 passing schools and 1040 failing schools, it is safe to say that Title IX standards are not doing enough to enforce clear results with fair gender representation in sports.