Anti-RAMSing toolkit: annotated R scripts and worked example dataset on OSF¶
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Evidence · 4 claims (4 for) · 1 study (1 quant-synthesis),q3· 1 of 1 report an effect size · 4 claims rest on one study
Description¶
A publicly available toolkit by Bicakci et al. providing annotated R scripts and a worked dataset for structural mapping, bias-adjusted Bayesian meta-analysis, effect-size benchmarking, and moderator power analysis.
Components¶
- Anti-RAMSing toolkit: annotated R scripts and worked example dataset on OSF: The authors developed "a comprehensive toolkit, implemented as fully annotated R scripts and accompanied by a worked example dataset, which is publicly available on the Open Science Framework." Its workflow is explicitly sequential and integrates four components: structural mapping, robust Bayesian meta-analysis and meta-regression (RoBMA-PSMA and RoBMA-Regression), effect-size benchmarking, and prospective power analysis. Once a domain-specific dataset is constructed with the shared coding framework, the toolkit can reproduce the core analytic procedures and extend them to other fields; the structural mapping stage itself depends on systematic qualitative coding and cannot be automated in R. (Bicakci et al. (2026))
Claims¶
- The bias-adjusted mean moderator effect in STEM education meta-analyses is g = 0.27 with substantial heterogeneity, and the published record shows overwhelming evidence of publication-bias-type selection [+M]
- A five-level moderator-theme classification improves model fit and explains systematic variation in moderator effect sizes, with disciplinary focus yielding the largest and design/quality the smallest expected effects [+M]
- Bias-adjusted posterior-predictive benchmarks for STEM moderator effect sizes (0.18, 0.38, 0.64 at the 25th, 50th, 75th percentiles) fall below both Cohen's conventional thresholds and raw empirical quartiles [+M]
- Under bias-adjusted planning assumptions, approximately 14 independent moderator effect sizes per moderator level are needed for 80% power, and about 60.2% of corpus effect sizes fell below this threshold [+M]
Related Products and Programmes¶
Key Sources¶
- Bicakci, M., Heller, F., Stoeger, H., & Ziegler, A. (2026). From RAMSing to Rigor: Improving Moderator Analysis in STEM Education Meta-Research. Educational Psychology Review. https://doi.org/10.1007/s10648-026-10172-1
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
anti-ramsing-toolkit-annotated-r-scripts-and-worked-example-dataset-on-osf |
title |
Anti-RAMSing toolkit: annotated R scripts and worked example dataset on OSF |
description |
A publicly available toolkit by Bicakci et al. |
status |
review |
product_kind |
software |
generated |
by process:settle-candidates on 2026-10-21 |
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unverified — no one has confirmed this page's content |
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