Designing the Next Study for Maximum Impact

Abstract

Generalized knowledge comes from cumulating results across studies, a process known as meta-analysis. Efficiently increasing generalized knowledge in a defined area—estimates of price or advertising, for example—is one important goal for research. Because (1) most meta-analyses are based on highly inefficient and unbalanced natural experiments or designs and (2) additional studies are costly, carefully selecting the next study is important. The authors demonstrate that, rather than simply selecting a study that uses currently underrepresented design variables, a procedure that reduces collinearity among design variables will produce far superior improvements in knowledge.