DS-22 · Predicting graduation, and deciding not to

Using institution-level higher-education data, model graduation rates from institutional characteristics. Then answer the harder question: what would go wrong if an admissions office used this model on individuals?

IPEDS institutional data (https://nces.ed.gov/ipeds/use-the-data). College Scorecard (https://collegescorecard.ed.gov/data/).

Institution-level patterns say nothing valid about individual students — the ecological fallacy again, and here it has direct human consequences. Selectivity and graduation rate are near-tautologically linked. Small institutions produce extreme rates from tiny cohorts.

An institution-level model with the tautology addressed, plus a written analysis of why applying it to individuals is invalid — and what a school would have to do differently to make an individual-level prediction defensible.

CT: the ecological fallacy, stated precisely. CR: separating selectivity from institutional effect. CO: peer debate on the deployment question. CM: a memo a counselor could use. CZ: predictive models in education already steer real students.

A higher-education researcher, admissions professional, or school counselor reviews the model at Checkpoint 1 and the deployment argument at Checkpoint 2. School-supervised.

R1: institution-level framing is explicit. R2: small-cohort institutions handled. R3: a selectivity-only baseline — and the student notices how strong it is. R4: residual analysis identifying over- and under-performing institutions. R5: a counselor-usable memo. R6: a clear, reasoned argument against individual-level use.

(a) The model is 90% selectivity and is presented as measuring institutional quality. (b) The student suggests the model could flag at-risk applicants — exactly the invalid inference the brief is built to prevent.