Introduction
ATTRITION
Over the last decade, crowdsourcing marketplaces such as Amazon Mechanical Turk (MTurk) and Prolific have provided participants for up to half of published studies in psychology and other social sciences [Despite the many benefits of using crowdsourcing in psychological research, one general limitation of such online research is attrition. Attrition can occur for many reasons–participants might drop out of a study due to an experimental condition (e.g., punishment versus no punishment [Research suggests that attrition rates are similar across studies conducted using both Prolific and MTurk. For example, Palan and Schitter reported ~25% attrition after two weeks for inexperienced Prolific participants–i.e., participants who had completed only one study on that platform [A few studies, however, examined possible remedies for attrition in online research. For example, Zhou and Fishbach examined the effects of Increasing pay is a second empirically validated method for reducing attrition in MTurk studies [The purpose of the present study was to extend findings on effects of (1) higher pay and (2) additional instructions on attrition to Prolific participants. Given the benefits of some of this platform’s features for psychological researchers, we sought only to test these manipulations in novel ways within the Prolific platform. For example, we evaluated effects of pay and instruction manipulations alone and in combination, a novel contribution to this research area. We hypothesized that both higher pay and additional instructions would reduce attrition, with greater reductions in attrition when combining these manipulations.