Assessing heterogeneity

Once the overall effect size is computed, the next question is how much the individual studies actually agree with it. Heterogeneity across study effect sizes can be assessed through two statistics:

• Q statistic: used for establishing if there is significant heterogeneity across studies.
• I²: used to quantify heterogeneity; it estimates the proportion of observed variance that reflects real differences in effect sizes, conventionally read as low (~25%), moderate (~50%), or high (~75%).

The underlying between-study variance itself is denoted τ² (tau-squared) — I² simply expresses it as a percentage of total variance. Significant, large heterogeneity is common once a meta-analysis includes 10 or more studies that address the question in different ways (e.g., different age groups, national samples, or measures).

See Methods and formulas for the Q and I² formulas.

The result of this step is a judgment on whether heterogeneity is negligible, moderate, or high — and, if it's non-negligible, a reason to look for explanatory moderators, covered next in Testing moderators.