Equivalently, an odds ratio of 1.65 (or 0.61 for negative association), represents a large effect for a positive association, an odds ratio of 1.35 (or 0.74) represents a medium-sized size effect, and an odds ratio of 1.15 (or 0.87) represents a small effect.
The analysis found that psychosocial interventions had medium-size effects on both pain severity and interference, and suggested that such interventions should be a part of a multimodal approach to the management of pain in patients with cancer [ 16].
From a clinical perspective, effect sizes of 0.56 – 1.2 can be interpreted as large, while effect sizes of 0.33 – 0.55 are of medium size, and effects of 0 – 0.32 are small [ 45].
There was one potential size-dependent consequence of nano-Se on scavenging free radicals: small size and medium size had similar effects and were both better than the large size [27].
A d = 0.2, in absolute value, was considered to be a small effect size, 0.5 a medium effect size, and 0.8 a large effect size.
The effect sizes (w) indicate that the effects are at least of a medium size (>|0.3|) and most are large effects (>|0.5|).
A Cohen's d value of 0.20 indicates a small effect size, 0.50 a medium effect size and 0.80 indicates a large effect size [ 25].
The authors propose that an effect smaller than.10 is considered small, an effect close to.30 should be considered medium size or moderate, and an effect of.50 or larger should be considered a large effect.
We used Cohen's categories to evaluate the magnitude of the effect size, calculated by standardized mean difference (SMD), with g >0.2 to 0.5, small effect size; g >0.5 to 0.8, medium effect size; and g >0.8, large effect size.
Effect sizes (eta squared) were interpreted using Cohen's guidelines with > 0.2 being a small effect size, > 0.5 a medium effect size and > 0.8 a large effect size.
