The included scaffolding studies show small-study effects, which the Bayesian approach addresses by shrinking overweighted effect sizes¶

Claim · All claims
Evidence · 1 study · quant-synthesis r2 · q2
Currency · newest study 2018 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Egger's regression on the included outcomes rejected the null hypothesis of no small-study effects (p < 0.05), and around 10.6% of outcomes had small sample sizes (n < 10). → Nam Ju Kim 2018

Evidence¶

Nam Ju Kim 2018¶

Nam Ju Kim, Brian R. Belland, & Andrew E. Walker. (2018). Effectiveness of Computer-Based Scaffolding in the Context of Problem-Based Learning for STEM Education: Bayesian Meta-analysis. Educational Psychology Review. https://doi.org/10.1007/s10648-017-9419-1

q2 · i? · quant-synthesis · r2

Egger's regression test for small-study effects conducted on the meta-analytic dataset. The null hypothesis of no small-study effects was rejected at "p < 0.05"; no effect size is printed for this test. The article notes around 10.6% of outcomes had n < 10.

"The result shows that there are small-study effects since the null hypothesis was rejected, p < 0.05."

Discussion¶

Related Claims¶

• Publication bias analyses suggest possible small-sample bias for SCED studies, with trim-and-fill reducing the adjusted mean effect to 1.01 — related
• Publication bias is detected in the cross-sectional data set, and bias-corrected estimates are highly heterogeneous — related
• Egger's test suggests publication bias may be present, with the selection-model adjusted mean effect falling to 0.36 — related