The Sensitivity of Impact Estimates to Data Sources Used: Analysis From an Access to Postsecondary Education Experiment
Bibliographic record
Abstract
Background: This article reports on the Future to Discover Project—a Canadian randomized controlled trial of two high school interventions— where data on key postsecondary enrollment outcomes were collected for two phases. During the initial phase, outcomes were recorded from administrative data and follow-up surveys. During the later phase, data came from administrative records only. Objectives: The article provides analyses that are informative about the consequences of a change from administrative-only data to survey-only data (and vice versa) for the estimation of impacts. Results: The change from administrative-only to survey-only data tended to produce apparent drops in postsecondary enrollment rates that varied by subgroup and education outcome. Nonetheless, levels and significance of impact with respect to postsecondary enrollment remained relatively stable. Conclusions: The findings of the article provide evidence that estimating education program impacts in the context of a randomized experiment can be relatively robust to the data sources chosen. They suggest that internal validity and conclusions for policy need not be affected by changing data sources even when the change produces marked changes in levels of the outcome of interest observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.468 | 0.568 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".