Written by Diamond Andy, Lily Gates, Mia Leandri, and Colin Thompsom

University of Maryland, Spring 2025

Exploring the differences between American research colleges and universities through data visualization.

1. Descriptive and Comparative Question

How do different tiered American research colleges and universities compare in terms of location, admissions, academic offerings, doctoral degrees awarded, and other institutional characteristics?

2. Analytical and Inferential Question

How do other institutional characteristics, besides financial expenditures, predict or correlate with research designation tiers among American colleges and universities?

Cleaned CSV versions of the "Carnegie Classification of Institutions of Higher Education" https://carnegieclassifications.acenet.edu/

1. carnegie_classification_stats

Original data from: https://carnegieclassifications.acenet.edu/institutions/?inst=&basic2021__bc%5B%5D=21

Variables:

• School information
  • Contains columns that seperates schools by flagship name and seperate campuses
    • e.g., University of Maryland, College Park has the "core name" University of Maryland and the "specific name" College Park
• Public or private (non-profit, for-profit)
• Description of types of degree programs offered
• Enrollment information
• Research classification from 2025

2. carnegie_spending_doctorate_awards

Original data from: https://carnegieclassifications.acenet.edu/carnegie-classification/research-designations/

Variables:

• School information
• Research classification from 2021 and 2025
• Expenses (2021, 2022, 2023)
• Research Doctorates Awarded (2020, 2021, 2022)

• classification_df is from the carnegie_classification_stats
• spending_df is from the carnegie_spending_doctorate_awards

Basic School Information

NAME

• unitid - unique ID number
• name.x - name of school
  • orig from classification_df
  • NOTE: is later renamed to name while name.y is dropped
• name.y - name
  • orig from spending_df
• core_name - name of the main university (e.g., "University of Maryland")
  • orig from the classification_df
• spec_name - name of the specific campus/satellite (e.g., "College Park")
  • orig from the classification_df

SCHOOL TYPE

• public_private_profit - public, private (for-profit, non-profit)
  • orig from classification_df

SCHOOL LOCATION

• city - city
• state - state abbreviation
• size_setting - size, 4-year vs. graduate/professional, how residential the school is
  • orig from classification_df

Enrollment and Admissions

• level - four or more years as opposed to associate degree 2-year programs such as community colleges
  • orig from classification_df
• enrollment_profile - distribution of undergrad vs. graduate students
  • orig from classification_df
• size_setting - student body population
  • orig from classification_df
    • size:
      • very small (less than 999)
      • small (1000 to 2999)
      • medium (3000 to 9999)
      • large (10000 or greater)
    • residential: percentage of living on-campus students
      • primarily non-res: less than 25% FT students
      • primarily residential: 25 to 50% FT students
      • highly residential: more than 50% FT students
• undergrad_profile - for 4-year universities
  • orig from classification_df
    • full/part-time:
      • higher part-time (over 40% PT)
      • medium-full-time (21-39% PT)
      • full-time (less than 20% PT)
    • selective:
      • inclusive (ACT equivalent <19)
      • more selective (ACT equivalent 19 to 23)
      • selective (ACT equivalent over 23)
    • transfer in:
      • lower (<20%)
      • higher (greater than 20%)

Research Classification Ranking

• research_tier_2025 - 2025 tier ranking
  • orig from classification_df
• classific_2021 - 2021 tier ranking
  • orig from spending_df
• classific_2025 - 2025 tier ranking
  • orig from spending_df

Finances
orig from spending_df

• herd_fy21 - 2021 fiscal year expenses on research
• herd_fy22 - 2022 fiscal year expenses on research
• herd_fy23 - 2023 fiscal year expenses on research
• herd_avg_fy21_to_fy23 - Average expenses on research from 2021 to 2023

Types of Academic Programs and Degrees Offered
orig from classficiation_df

• degree_focuses - degree level and academic focus overview
  • top degree level offered: bacc, masters, doctoral, special, tribal
  • focus: arts and science, diverse, engineering and tech, medical, other health, special focus, research, or NULL
  • size: of program (only for Masters Universities)
  • research activity: only for Doctoral Universities
• undergrad_program - distribution of undergrad degrees in certain academic fields
  • dominant subj: "arts and sciences" OR "professions" OR NULL
  • balanced vs plus vs focus: measured by the percentage of bachelor's degrees awarded in arts and sciences (rather than professions)
  • graduate degree coexistence: percentage of graduate degrees in undergraduate fields
    • e.g., a BS in Computer Science AND a MS or PHD in Computer Science is also offered at the same school
    • measured by "none" (0%) OR "some" (0 to 50%) or "high" (greater than 50%)
• grad_program - distribution of grad degrees in certain academic fields
  • program level: undergrad only, postbac, research doctorate, NULL
  • single or comprehensive: multiple subject fields or single subject
  • focus: education, business, humanities, STEM, professional

Research Doctorates Awarded
orig from spending_df

Number of doctoral research degrees conferred for...

• num_doc_degrees_2020_2021 - the 2020-2021 school year
• num_doc_degrees_2021_2022 - the 2021-2022 school year
• num_doc_degrees_2022_2023 - the 2022-2023 school year

Miscellaneous
orig from classification_df
Not important, used for Carnegie's specific engagement and purposes, special program with Carnegie

• community_engage - whether they were classified by Carnegie
• leadership_for_public_prax - whether they were classified by Carnegie

Core Tidyverse & Data Manipulation

• tidyverse: A collection of R packages for data science, including ggplot2, dplyr, tidyr, readr, etc.
• readr: For reading CSV files.
• tidyr: For reshaping data.
• dplyr: For data manipulation.
• stringr: For string operations.
• lubridate: For working with dates and times.

Visualization

• ggplot2: For static visualizations.
• plotly: For interactive graphs.
• ggbeeswarm: For beeswarm-style plots.
• patchwork: For combining multiple ggplot objects.
• RColorBrewer: For advanced color palettes.
• scales: For transforming and formatting axes.

Development & Utility

• devtools: For development tools and installing custom packages.

Looking ahead, our analysis could be extended through longitudinal tracking and predictive modeling. As updated Carnegie Classification data for the 2024–2025 cycle becomes available, we plan to compare it with our current findings to validate patterns and assess institutional shifts in research designation tiers.

Additionally, by applying machine learning models, we aim to forecast which colleges and universities are most likely to ascend in research tier rankings based on current institutional characteristics. Further work could also include clustering institutions by multidimensional features—such as degree focus, doctoral output, and enrollment type—to reveal nuanced typologies not captured by traditional tier labels.

Lastly, building an interactive dashboard would allow stakeholders to explore these trends dynamically and inform strategic decisions in higher education policy and planning.