Translating Standardized Effects of Education Programs Into More Interpretable Metrics
Bibliographic Data
| ID | 9733194 |
|---|---|
| Authors | Matthew D Baird (0000-0003-3016-480X, RAND Corporation, Pittsburgh, PA, corresponding author), John F Pane (0000-0001-5155-2436, RAND Corporation, Pittsburgh, PA) |
| Year | 2019 |
| Volume | 48 |
| Issue | 4 |
| Pages | 217-228 |
| Publication date | 2019-05-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational Researcher (JOURNAL) |
| Journal identifiers | ISSN: 0013-189X • E-ISSN: 1935-102X |
| Publisher | American Educational Research Association (AERA) (PUBLISHER) |
| DOI | 10.3102/0013189x19848729 |
| OpenAlex | W2795655335 |
| Language | EN |
| Citations received | 24 |
| References cited | 12 |
Evaluators report effects of education initiatives as standardized effect sizes, a scale that has merits but obscures interpretation of the effects’ practical importance. Consequently, educators and policymakers seek more readily interpretable translations of evaluation results. One popular metric is the number of years of learning necessary to induce the effect. We compare years of learning to three other translation options: benchmarking against other effect sizes, converting to percentile growth, and estimating the probability of scoring above a proficiency threshold. After enumerating the desirable properties of translations, we examine each option’s strengths and weaknesses. We conclude that years of learning performs worst, and percentile gains performs best, making it our recommended choice for more interpretable translations of standardized effects
Benchmarking · Econometrics · Economics · Interpretation (philosophy · Machine learning · Mathematics education · Metric (unit · Operations management · Percentile · Standardized test · Statistics · Strengths and weaknesses · Artificial Intelligence · Computer Science · Educational Assessment and Improvement · Evaluation and Performance Assessment · Mathematics · Psychology · School Choice and Performance · Social Psychology
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| Citations per year | 3.43 |
|---|---|
| Citation span | 2019 - 2026 (8) |
| Highly cited | No |