Reporting effect sizes in original psychological research: A discussion and tutorial.
Bibliographic record
Abstract
Statistical practice in psychological science is undergoing reform which is reflected in part by strong recommendations for reporting and interpreting effect sizes and their confidence intervals. We present principles and recommendations for research reporting and emphasize the variety of ways effect sizes can be reported. Additionally, we emphasize interpreting and reporting unstandardized effect sizes because of common misconceptions regarding standardized effect sizes which we elucidate. Effect sizes should directly answer their motivating research questions, be comprehensible to the average reader, and be based on meaningful metrics of their constituent variables. We illustrate our recommendations with empirical examples involving a One-way ANOVA, a categorical variable analysis, an interaction effect in linear regression, and a simple mediation model, emphasizing the interpretation of effect sizes. (PsycINFO Database Record
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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 | 0.294 | 0.596 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".