Of 259 predictors analyzed for enrollment prediction, four stand out as most important: prior cohort sizes, in-school suspensions, out-of-school suspensions, and absences¶
Claim · All claims
Evidence · 1 study · associationalr?·q2
Currency · newest study 2021 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2024: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Among 259 predictors analyzed, prior cohort sizes, in-school suspensions, out-of-school suspensions, and absences are identified as the four most important predictors of fall enrollment. → Sean Tanner 2021
Evidence¶
Sean Tanner 2021¶
Sean Tanner, Jenna Terrell, Emily Vislosky, Jonathan Gellar, Brian Gill. (2021). Predicting Early Fall Student Enrollment in the School District of Philadelphia. Regional Educational Laboratory Mid-Atlantic, Institute of Education Sciences. https://www.mathematica.org/publications/predicting-early-fall-student-enrollment-in-the-school-district-of-philadelphia
q2 · i? · associational · r?
Predictor-importance analysis across 259 candidate variables in SDP administrative data. The study states, "Of the 259 predictors analyzed, 4 stand out as the most important: prior cohort sizes, in-school suspensions, out-of-school suspensions, and absences."
"Of the 259 predictors analyzed, 4 stand out as the most important: prior cohort sizes, in-school suspensions, out-of-school suspensions, and absences."
Discussion¶
Related Claims¶
- Near-term academic problems targeted include absenteeism, suspensions, poor grades, and low state-test performance — related
- The report examines how in-school and out-of-school predictors relate individually to each type of near-term academic problem — related
- Applicants' enrollment decisions depend most on 'other financing sources' (typically loans), with feature importance 0.712, far exceeding federal and institutional aid features — related
- Four statistical techniques for predicting school-by-grade fall enrollment in SDP differ little in accuracy, with predicted cohort size off by roughly six students across all methods — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
sdp-enrollment-four-key-predictors |
title |
Of 259 predictors analyzed for enrollment prediction, four stand out as most important: prior cohort sizes, in-school suspensions, out-of-school suspensions, and absences |
description |
Of 259 predictors analyzed for enrollment prediction, four stand out as most important: prior cohort sizes, in-school suspensions, out-of-school suspensions, and absences |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2021 |
recheck_by |
2024 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — sean-tanner-2021 |
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