About

About

This was done over a break in summer 2022 while starting to take my kids on college visits, then updated over winter break 2022-3 and tweaked to update with new data; as I mention on the home page, while there are lots of “College Ranking” websites, they use some odd factors to rank (such as college administrators ranking each other, standardized testing scores, how many students they reject, and more for US News and World Report) while omitting things like tenure track faculty numbers, library resources, climate, and whether the state safe for LGBTQ+ students. The US federal data have information on lots of schools: I chose to present info on all the schools that are still open rather than filter for the schools just of interest to my family (my kids are not going to seminary for a Masters degree or to a school for funeral service directors any time soon, but this might be of interest to students, staff, or faculty considering those schools, so I am including them). Some schools, especially smaller ones (some have just a few dozen students) do not include all information and so these school pages might be sparse.

The New York Times’ Ron Lieber wrote an article featuring this site and others that may be helpful for you.

If you have feedback on the site (corrections, good data sources, etc.) please go here (and thank you!).

There are two main types of pages on this site: one page for each institution (over 4,000 of them) and pages of degrees offered by various groupings of schools.

  • The institutions have their individual data as well as comparisons with “similar” schools, where similar is based on athletic conference (for example, small liberal arts schools in Western Massachusetts), the type of the school (i.e., is it a research intensive doctoral institution). For metrics where one direction is better (lower cost is good; more faculty is good) there is a ranking versus schools in the comparison group. There are also line plots showing trends over time. If a trend is “significant” (i.e., it is not likely to be due to random chance, as assessed by fitting a linear model (and not correcting for multiple comparisons)) then there is a column indicating the slope of the trend.
  • The degrees pages show degrees awarded in different fields in the most recent year available. There are many, many specific majors, so the main groups are listed at [fields_overview.html] and you can then drill down into the subfields.
  • This site historically has included social factors: gun control laws, abortion laws, and more. Many students choose to go in state, but a substantial number who consider out of state do look at factors such as these when deciding where to go to school. This site is set up to be neutral, just presenting information on state laws or other policies. However, as of this moment (end of 2024) there is substantial uncertainty as to what will happen nationally – I am concerned that any information here about state laws will quickly become out of date as federal laws or policies change. I have thus, for now, removed these factors so as not to inadvertently mislead anyone – please go to advocacy groups for issues that matter to you to help learn about policies in states you are considering.

Details of the metrics

  • Acceptance: Percentage of undergraduate applicants who are offered admission.

  • Yield: Percentage of people who, upon receiving the offer of admission, choose to attend.

  • First year retention: Percentage of undergraduates who re-enroll in their second year. This can indicate they adequately like the school and are receiving support they need to be successful.

  • Transfer: Percentage of students who transfer out from their bachelors program.

  • Graduation: Percentage of undergraduates who graduate with a degree. Note that not all students enter an instiution with this intent: someone could take a welding class at a community college just to learn the skill without intending to get a degree in it. That said, higher graduation rate is typically a good thing.

  • Undergrad enrollment: The number of undergraduate students (typically those seeking an associates or bachelors degree).

  • Graduate enrollment: The number of students enrolled seeking a masters, PhD, or similar degree.

  • Degrees (of various kinds) awarded: How many students got each degree from this college in a single year.

  • Undergrads per instructor: Undergraduate enrollment divided by number of instructors. This does not mean the typical class size: some instructors might teach four classes, others just one; one class may have 300 students, and most others have just 10. But generally one wants a lower number so that students have more access to instructors.

  • Undergrads per tenure-track professor: Undergraduate enrollment divided by the number of faculty on the tenure track (both tenured and pre-tenure). Tenure helps protect academic freedom: someone with tenure can be fired if they do not fulfill their duties, commit misconduct, and so forth, but they cannot (easily) be fired because the content of what they teach disagrees with what their boss thinks, their research topic annoys a major donor, and so forth. A lower number is better; the exceptions may be institutions where skilled professionals who practice a subject (a musician, an actor, a painter, and so forth) comes in to teach for a semester or a few years but with the intent for it to be short term.

  • Average net price for students with grants/scholarships: This uses federal data for what students who are getting some sort of grant or scholarship pay on average. There can be huge variation in this: some schools charge far less for in-state than out-of-state students, some with large endowments might charge students from less wealthy families nothing but charge a lot to others, etc. It is possible that an Ivy league private university could be cheaper for some students than their local government-supported college (but far more expensive for other students), but the average price does not reflect this. Thus, this may be among the least useful items of information since it will not do a good job predicting what your family will pay. This is only reported for colleges that accept some federal support for students. Federal support comes with various requirements (prohibition of some kinds of discrimination, guidelines about handling of some kinds of alleged misconduct, and more), so a handful of colleges reject all federal support so they are not subject to one or more of these requirements. I have included a comparison to Harvard, not because Harvard is particularly expensive (its large endowment lets it award a substantial amount of aid) but to help people realize that sticker price is not the same as actual cost of attendance.

  • Tuition and fees as a source of core revenues: What percentage of revenue comes from charging students. If an institution relies substantially on this, it can have problems if enrollment drops (it has fixed costs spread across fewer students, so it has to make cuts, and so it may attract fewer students…).

  • Investment return: Percentage of income from investments (stocks, bonds, and so forth). Provides a buffer, but not all institutions have had wealth to build upon.

  • State/Local appropriations: States, counties, cities may choose to invest in their residents by supporting institutions (more educated workforce, more practical skills, more understanding of our world and its history, etc.).

  • Government grants and contracts: Faculty often compete to get resources to support their work: a federal grant to study the effect of wildfire on deer numbers, a state grant to develop new algorithms to make self-driving cars more efficient, and so forth. These can pay for staff, support students, buy materials, cover travel, and more.

  • Private gifts, grants, and contracts: As above, but also just donations of money or other gifts.

  • Location: Various location statistics.

  • Diversity among the faculty or students: This uses data provided by the institutions, which is almost certainly based on individuals’ self-reporting. The federal data only allows the given racial/ethnic categories and only man or woman for gender. Most other axes of diversity are not readily available. Note that people who are not residents of the US fall into the “non-resident alien” for race/ethnicity.

  • Notes: Individual institution pages may display a note at the top of the page triggered by various conditions. They contain explanatory notes when they appear. They are not necessarily problems (for example, one thing that triggers is lack of data over several years – that does not mean the school is doing poorly).

Main sources for information

  • National Center for Education Statistics IPEDS: Source of much of the data on enrollment, composition of the student body and faculty, income sources, and more. Data come from mostly 2019-2020 (before the worst effects of the pandemic hit). Some data may have changed rapidly (reorganizing athletic conferences has become popular, for example).
  • Info on earnings comes from College Scorecard, which is a US government site that provides data on earnings of graduates, debt, and more. It only includes information if there are enough students in a given major the government has tax information about.
  • Information on censure of a university or university system’s administration comes from the American Association of University Professors, which will remove the censure once the problematic conditions are resolved.
  • Biome information comes from the World Wildlife Fund and is accessed using Alexander Zizka and Alexandre Antonelli’s speciesgeocodeR package (Töpel et al. 2017).
  • Map data comes from OpenStreetMap.
  • Distance to mountains comes from:
  • US Census data. Note that this product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

Made by Brian O’Meara, working as an individual parent (not as part of any employment, grant, etc.). It is hosted at https://github.com/bomeara/collegetables_source and made using the R packages knitr, tidyverse, tarchetypes, DT, usmap, ggplot2, ggrepel, plotly, leaflet, rmarkdown, htmlTable, ggbeeswarm, speciesgeocodeR, raster, viridisLite, graphics, and stats. The page is open source, so you can give suggestions (especially for new data sources) or even fork it; just make sure to cite the original data sources.