The difference between subjective response scores for at-risk and not-at-risk groups was the effect size of interest. Because they reflect different risk factors with potentially differing etiologies, we estimated effect sizes for the two comparison types (i.e., FH and typical consumption) separately. We computed Hedge’s g, the bias-corrected standardized mean difference, using Comprehensive Meta-Analysis (Borenstein et al., 2005). Hedge’s g is interpreted as the standardized mean difference and is more conservative than Cohen’s d in small samples (Borenstein et al., 2009). Conventional benchmarks for small, medium, and large effects are g = 0.2, 0.5, and 0.8, respectively (Cohen, 1988). Where reported, means, standard deviations (or standard errors), and sample sizes for at-risk and not-at risk groups were used to calculate g. When these were not available, we estimated means and standard deviations using figures. When statistics for continuous variable associations were reported (e.g., for associations between subjective response and a measure of typical alcohol consumption), we recorded correlation coefficients and sample sizes. These were converted to Hedge’s g in Comprehensive Meta-Analysis (Borenstein et al., 2005).
