Introducing the Numbers Needed for Change (NNC): an effect size that connects research to practice

Abstract

Effect size indices are valuable to research in health psychology, but generic measures (e.g. Cohen’s d or point-biserial correlation) are limited in their ability to convey practical information about intervention effects. Researchers rely on concepts such as ‘standardized mean difference’ or ‘proportion explained variance’ to express information about effect size. Practitioners, policymakers, and lay-people use concepts such as counts or percentages. Partial solutions provided to this discrepancy are offered by rules-of-thumb (e.g. Cohen’s categories of ‘small’, ‘moderate’ and ‘large’ effects), but such categories are somewhat arbitrary and of little nuance. More importantly, a ‘small’ intervention effect in terms of Cohen’s d does not imply that an intervention is practically meaningless, and conversely, a ‘large’ d value does not imply that an intervention is meaningful. To arrive at conclusions about the practical significance of an intervention, effect size estimates have to include population characteristics; specifically, the prevalence of undesired behavior prior to intervention. We introduce the Numbers Needed for Change (NNC), an effect size that fills this communicative gap between research and practice, and is particularly suited to provide information about health intervention effectiveness in the ‘real world’. The measure is an analogue to the Numbers Needed to Treat (NNT) index which is popular in the medical literature. We adapt and extent the index into the NNC to suit health psychology research purposes, and argue that the measure can strengthen the translation of intervention research to practice. The procedure to estimate the NNC is explained and illustrated with examples.