Effect Size Thresholds in Early Literacy: Defining Benchmarks for Phonemic Awareness Research
Bibliographic record
Abstract
Effect size (ES) helps assess intervention effectiveness, often interpreted using Cohen’s thresholds for small (0.20), medium (0.50), and large effects (0.80). However, these thresholds must be contextualized. This study derived new ES thresholds in early literacy research, focusing on phonemic awareness (PA). Data from three recent meta-analyses on the effects of PA instruction and intervention on PA and reading outcomes in pre-K through Grade 1 children were used. We extracted Hedges’ g data and calculated an ES distribution, deriving thresholds at the 25th, 50th, and 75th percentiles (small, medium, and large effects). Additional thresholds were derived for various subgroups (risk status, use of letters, outcome measures, alignment between PA skills taught and measured, group size, interventionist). Research Findings: From 199 ESs on PA outcomes and 119 ESs on reading outcomes, the ES thresholds were at 0.262, 0.507, 0.817, and −0.014, 0.361, 0.670 for small, medium, and large effects, respectively, with no evidence of publication bias. ES distributions across subgroups were similar to overall results, though differences were found across PA outcomes measuring decoding-proximal vs. decoding-distal PA skills and interventionists. Practice or Policy: Cohen’s benchmarks are generally representative of current PA research on PA outcomes but are overestimated for reading outcomes by around 0.2 standard deviations.
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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 | 0.513 | 0.743 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".