Testing the Efficacy of Educational Interventions on Matched Student Samples: A Primer for Propensity Score Matching in R
Bibliographic record
Abstract
In many educational intervention programs, it is not possible to randomly assign students to an experimental and control condition. For example, in our research we wanted to compare students who were enrolled in a biomedical pathway program to students who were not in such a program. However, students select their academic pathway program and a randomized controlled trial cannot be conducted. Propensity score matching (PSM) is a valuable statistical technique in areas of research when randomized control trials are not always possible. It can be widely used to mimic the process of randomization by creating comparable groups based on key covariates while increasing causal inference and reducing bias. The aim of this article is to provide guidance for science education researchers to make informed decisions about the selection of matching methods and implementation of PSM using the MatchIt package (Ho et al., 2011) in R. In this article, we 1) discuss the utility of using PSM for research involving educational interventions, 2) provide a comprehensive guide for conducting PSM with educational data and provide a detailed step-by-step guide on conducting PSM for nearest neighbor matching using R, and 3) apply it to a National Institutes of Health (NIH)-funded high school education program.
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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 | 0.217 | 0.537 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".