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Just How Different Are Regional Public Universities (RPUs) From Non-RPUs?

ByBlake Decker

Over the course of the past few blog posts, we have presented data pertaining to the new 2025 Carnegie Classification system, including the last two posts that introduced the designations for Regional Public Universities (RPUs) and Rural-Serving Institutions (RSIs). As previously discussed, RSI status was one of more than 125 variables that the Alliance for Research on Regional Colleges (ARRC) used in their cluster analysis to identify 474 institutions (out of 589, or 80.5%) that were classified as RPUs. In Texas, 26 of the 35 (74.3%) public universities in the national IPEDS data system were classified as RPUs (see “TX RPUs” in the visualization tab below). While we provided a high-level overview of several distinctive features of RPUs in a previous blog post, a deeper analysis of the underlying data revealed what many in higher education intuitively understand: RPUs and non-RPUs are fundamentally different types of institutions. The following analysis will begin to unpack the degree to which this statement is true.

quantifying the differences between rpu and non-rpu institutions

Using data published by ARRC, we reviewed 115 numerical variables that were used in their RPU and RSI analyses that, in part, determined RPU status. We conducted a pairwise comparison analysis across these variables that included features such as enrollment, percent of full-time, first-time undergraduates awarded Pell grants, SAT composite score at 75th percentile, institutional support expenses as percent of total core expenses, percent of all employees who are part-time faculty, etc. We also placed each variable into thematic categories for further analysis, as discussed below.

Broadly speaking…Across all 589 public institutions in the pairwise analysis, more than 85% of the 115 numerical variables showed a meaningful difference (Cohen’s d ≥ 0.2) between RPU and non-RPU institutions, with 60% of those variables showing large effect sizes (Cohen’s d ≥ 0.8). These “large effects” (at least 8/10ths of a difference in pooled standard deviations) indicated there were substantial differences between RPUs and non-RPUs across the majority of variables.

Beyond statistics, large effect sizes also typically indicate that the differences between groups are meaningful in real-world contexts. If we take the variables with the largest effect sizes in the pairwise analysis, there were 17 variables that had Cohen’s d absolute values that were greater than 2.0, indicating the mean difference between the two types of institutions was more than 2 pooled standard deviations apart. All but one of these variables (percentage of total expenses spent on research) fell into three thematic categories: “Academic Selectivity”, “Institutional Scale/Total Enrollment”, and “Institutional Scale/Financial Resources”. The “Variable Comparisons” visualization below shows a selection of variables across these categories to highlight some of the largest differences between RPUs and Non-RPUs.

  • Academic Selectivity: Instead of showing all 8 variables pertaining to incoming students’ average standardized test scores (SAT and ACT), we provide the average “SAT Composite” scores at the 25th and 75th percentiles by RPU status as a representation. As shown above, students at Non-RPUs have an average SAT Composite score at the 75th percentile that is 182 points (16%) higher than students at RPU institutions. At the 25th percentile, incoming students at RPUs scored, on average, 177 points (18%) below their counterparts at Non-RPU institutions.
  • Total Enrollment: In terms of Total Enrollment, Non-RPU institutions collectively had an average total enrollment of 31,237 students in the ARRC data, as compared to 9,199 students at RPU institutions. This means that the average Non-RPU institution has 2.4-times the number of students when compared to the average RPU institution.
  • Institutional Scale/Financial Resources: Out of the 17 variables with Cohen’s d values greater than 2.0, eight of those variables were within the Financial Resources category. In terms of numerical differences, there is a $364.5M gap between Non-RPUs ($458.6M) and RPUs ($94.1M) in Non-Operating Expenses, a $356.4M gap ($427.7M for Non-RPUs versus $71.3M for RPUs) in Instructional Expenses, and a $316.4M gap ($374.1M for Non-RPUs versus $57.7M for RPUs) in Net Tuition. In terms of largest percentage gaps across these variables, Non-RPU institutions spend over 2,600% more in Research ($275.1M versus $10.1M), almost 700% more in Sales/Services of Auxiliary Enterprises ($136.9M versus $17.2M), and over 675% more in Auxiliary Enterprises Expenses ($171.1M versus $22.1M).

So What?

The variables above show just how different RPUs and Non-RPUs are in terms of institutional scale and financial resources. The fact that these variables are included in a set with the largest differences is likely not a surprise, although the magnitude of those differences might raise some eyebrows. While the collection of variables above represent the factors where the pairwise analysis showed the greatest differences (Cohen’s d ≥ 2.0), these are certainly not the only variables where RPUs and Non-RPUs have significant differences. In our next blog post, we will look specifically at variables related to the differences in student populations served by each institution type, including variables such as Pell-eligible students, part-time students, adult learners, gender, and race/ethnicity.