BigTen: Big Ten Conference Institutional Data (1996-2023)

Description

A comprehensive dataset containing institutional characteristics, enrollment data, financial information, and demographic composition for all Big Ten Conference member institutions from 1996 to 2023. This dataset tracks the evolution of these universities over nearly three decades, including data for institutions that joined the conference at different times.

Usage

BigTen

Arguments

Format

A data frame with 504 rows and 38 variables:

name: Character. Institution name (e.g., "MSU", "Michigan", "Ohio State")
Landgrant.tf: Logical. Whether the institution is a land-grant university
Public.tf: Logical. Whether the institution is public (TRUE) or private (FALSE)
AAU.tf: Logical. Whether the institution is a member of the Association of American Universities
entry_term: Numeric. Year the data entry corresponds to (1996-2023)
UGDS: Integer. Total undergraduate enrollment
ADM_RATE: Numeric. Admission rate (proportion of applicants admitted)
C150_4: Numeric. Completion rate for first-time, full-time students (150% of expected time)
PCTPELL: Numeric. Percentage of undergraduates receiving Pell grants
n.pell: Integer. Number of undergraduates receiving Pell grants
TUITIONFEE_IN: Integer. In-state tuition and fees
TUITIONFEE_OUT: Integer. Out-of-state tuition and fees
TUITFTE: Integer. Net tuition revenue per full-time equivalent student
BOOKSUPPLY: Integer. Average cost of books and supplies
ROOMBOARD_ON: Integer. Average cost of room and board for on-campus students
OTHEREXPENSE_ON: Integer. Other expenses for on-campus students
OTHEREXPENSE_FAM: Integer. Other expenses for students living with family
CoA.inState: Integer. Total cost of attendance for in-state students
CoA.outState: Integer. Total cost of attendance for out-of-state students
otherCosts.tot: Integer. Total other costs
fte.tot: Integer. Total full-time equivalent tuition plus other costs
fte.in.rat: Numeric. Ratio of full-time equivalent tuition plus other costs to in-state to total costs
UGDS_WHITE: Numeric. Proportion of white undergraduate students
UGDS_BLACK: Numeric. Proportion of Black undergraduate students
UGDS_HISP: Numeric. Proportion of Hispanic undergraduate students
UGDS_ASIAN: Numeric. Proportion of Asian undergraduate students
UGDS_AIAN: Numeric. Proportion of American Indian/Alaska Native undergraduate students
UGDS_NHPI: Numeric. Proportion of Native Hawaiian/Pacific Islander undergraduate students
UGDS_2MOR: Numeric. Proportion of students of two or more races
UGDS_NRA: Numeric. Proportion of non-resident alien undergraduate students
UGDS_UNKN: Numeric. Proportion of students with unknown race/ethnicity
UGDS_WHITENH: Numeric. Proportion of white non-Hispanic undergraduate students
UGDS_BLACKNH: Numeric. Proportion of Black non-Hispanic undergraduate students
UGDS_API: Numeric. Proportion of Asian/Pacific Islander undergraduate students
UGDS_AIANOLD: Numeric. Proportion of American Indian/Alaska Native students (legacy coding)
UGDS_HISPOLD: Numeric. Proportion of Hispanic students (legacy coding)
UGDS_MEN: Numeric. Proportion of male undergraduate students
UGDS_WOMEN: Numeric. Proportion of female undergraduate students

Details

The dataset includes all 18 current Big Ten Conference members:

Original members: Illinois, Indiana, Iowa, Michigan, Minnesota, Northwestern, Ohio State, Purdue, Wisconsin
1990 addition: MSU (Michigan State University)
1993 addition: Penn State
2011 addition: Nebraska
2014 additions: Maryland, Rutgers
2024 additions: UCLA, USoCal (USC), Oregon, Washington

Data availability varies by year and institution. Earlier years (especially 1996-2000) may have limited data for some variables. Financial data and demographic breakdowns became more standardized and complete in later years.

The dataset is particularly useful for:

Longitudinal analysis of Big Ten institutions
Comparative studies across public vs. private institutions
Analysis of conference expansion impacts
Demographic trend analysis in higher education
Cost and accessibility studies

Examples

Run this code

# Load the dataset
data(BigTen)

# View structure
str(BigTen)

# Check institutions included
unique(BigTen$name)

# Examine enrollment trends over time
if (require(ggplot2)) {
  library(ggplot2)
  BigTen_recent <- subset(BigTen, entry_term >= 2010 & !is.na(UGDS))
  ggplot(BigTen_recent, aes(x = entry_term, y = UGDS, color = name)) +
    geom_line() +
    scale_color_bigten_d(palette = "primary") +
    theme_MSU() +
    labs(title = "Big Ten Undergraduate Enrollment Trends",
         x = "Year", y = "Undergraduate Enrollment")
}

# Compare public vs private institutions
table(unique(BigTen[c("name", "Public.tf")])$Public.tf)