Cumulative Evidence Synthesis and Consideration of “Research Waste” Using Bayesian Methods An example updating a previous meta-analysis of self-talk interventions for sport/motor performance
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Abstract
In the present paper we demonstrate the application of methods for cumulative evidence synthesis including Bayesian meta-analysis, and exploration of questionable research practices such as publication bias or p-hacking, in the sport and exercise sciences for the evaluation of experimental interventions. The use of such methods can aid in study planning and avoid “research waste”. In demonstrating and discussing these methods we use the example of self-talk interventions and their effects upon sport/motor performance given a quantitative evidence synthesis has not been conducted on this topic, to the best of our knowledge, since 2011 when Hatzigeorgiadis et al. (2011) conducted their systematic review and meta-analysis. As such, this topic is ripe to use in demonstrating cumulative methods such as Bayesian updating. Therefore, our aim was to conduct an updated systematic review and Bayesian meta-analysis replicating the search, inclusion, and models of Hatzigeorgiadis et al. (2011) and demonstrate the application of cumulative evidence synthesis methods including; consideration of the initial probability that a new study of the effects of self-talk interventions would shift our prior belief in their effectiveness, the application of priors taken from the previous meta-analysis to be updated by new studies identified to a new posterior estimate of effect, and consideration of other possible sources of research waste from questionable research practices such as publication bias and p-hacking. Such methods as those demonstrated here, when used prospectively, can aid researchers in determining whether further research of a particular experimental intervention is in fact warranted. Considering the limited resources and time for conducting research we hope that highlighting the application of these methods might help researchers in the field to avoid research waste and more productively direct their research efforts.
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