Mean and Mean difference are the two key statistical measures used in the statistical analysis. Both are essential for meta-analysis as well. Mean and Mean difference are used for the interpretation of a large set of values into a single number which explains the heterogeneity and variation among the individual values. However, one of a common challenge in meta-analysis is the unavailability of this data (mean and standard deviation).

Q & A Forum

Meta Analysis

Q: How can Standardized Mean Differences (SMDs) be interpreted using rules of thumb?

Effect Size

SMDs, often referred to as effect sizes, can be interpreted using commonly accepted guidelines, primarily derived from social science research. One widely used scale (Cohen, 1988) suggests:

0.2 = Small effect

0.5 = Moderate effect

0.8 = Large effect

Other variations exist, such as:

< 0.40 = Small effect

0.40 to 0.70 = Moderate effect

> 0.70 = Large effect

Reference

Cochrane Handbook for Systematic Reviews of Interventions. (2011). 12.6.2 Re-expressing SMDs using rules of thumb for effect sizes. The Cochrane Collaboration. Retrieved from

https://handbook-5-1.cochrane.org/chapter_12/12_6_2_re_expressing_smds_using_rules_of_thumb_for_effect_sizes.htm

Q: How can Standardized Mean Differences (SMDs) be interpreted using rules of thumb?

Effect Size

Reference

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