DS-04 · What actually predicts chronic absenteeism?

Virginia publishes chronic absenteeism rates for every school, alongside enrollment, demographics, per-pupil spending, and outcome measures. Which school-level factors actually associate with chronic absenteeism — and which of the ones people assume matter do not survive controls?

VDOE School Quality Profiles indicator downloads (https://schoolquality.virginia.gov/download-data). Census ACS tract or ZIP-level income and vehicle access, joined on school location (https://www.census.gov/data/developers/data-sets/acs-5year.html). Optionally NCES Common Core of Data membership/locale files (https://nces.ed.gov/ccd/files.asp).

This is school-level data, so every finding is ecological — a correlation across schools says nothing reliable about individual students, and students consistently write conclusions the unit of analysis does not support. Suppressed cells for small subgroups are not missing at random; dropping them removes exactly the schools most worth studying. Per-pupil spending is partly caused by need, so it will look like spending increases absenteeism. Post-2020 years are structurally different and should not be pooled with earlier ones without saying so.

A regression or tree model over school-level features predicting chronic absenteeism rate, with a stated baseline (state mean, or division mean), a clear statement of the ecological limitation, sensitivity analysis showing which findings survive adding division as a control, and one named variable the student expected to matter that did not.

CT: separating association from cause, and saying which is which in writing. CR: constructing a join between school location and neighborhood data that does not exist in either source. CO: a structured peer critique where a partner attacks the causal language specifically. CM: a two-page brief written for a school board member, not a statistician. CZ: absenteeism is used to allocate resources and to blame families — naming that tension is part of the work.

A district data analyst, education researcher, school counselor, or public-policy analyst reviews the modeling plan at Checkpoint 1 and reads the draft brief at Checkpoint 2, with a specific instruction: challenge every causal verb. School-supervised, both sessions, never one-to-one unsupervised.

R1: the question is answerable at school level and the student says so. R2: suppressed cells counted and their effect on the sample described. R3: a real baseline, and a model whose gain over it is explained. R4: sensitivity to controls shown, not just one model. R5: a board member could read it and not be misled. R6: the student explicitly declines at least one conclusion the data cannot support.

(a) The student finds that higher per-pupil spending predicts higher absenteeism and writes that spending does not help — having discovered reverse causation and reported it as policy. (b) The student drops every suppressed row for cleanliness, silently removing small and high-poverty schools, and reports a relationship that exists only in the schools large enough to be published.