Meta-Analytic Findings of the Self-Controlled Motor Learning Literature: Underpowered, Biased, and Lacking Evidential Value

Downloads

Authors

DOI:

https://doi.org/10.15626/MP.2021.2803

Keywords:

Motor Learning, Retention, Choice, OPTIMAL Theory, Meta-Analysis, p-curve, Publication Bias

Abstract

The self-controlled motor learning literature consists of experiments that compare a group of learners who are provided with a choice over an aspect of their practice environment to a group who are yoked to those choices. A qualitative review of the literature suggests an unambiguous benefit from self-controlled practice. A meta-analysis was conducted on the effects of self-controlled practice on retention test performance measures with a focus on assessing and potentially correcting for selection bias in the literature, such as publication bias and p-hacking. First, a naïve random effects model was fit to the data and a moderate benefit of self-controlled practice, g = .44 (k = 52, N = 2061, 95% CI [.31, .56]), was found. Second, publication status was added to the model as a potential moderator, revealing a significant difference between published and unpublished findings, with only the former reporting a benefit of self-controlled practice. Third, to investigate and adjust for the impact of selectively reporting statistically significant results, a weight-function model was fit to the data with a one-tailed p-value cutpoint of .025. The weight-function model revealed substantial selection bias and estimated the true average effect of self- controlled practice as g = .107 (95% CI [.047, .18]). P-curve analyses were conducted on the statistically significant results published in the literature and the outcome suggested a lack of evidential value. Fourth, a suite of sensitivity analyses were conducted to evaluate the robustness of these results, all of which converged on trivially small effect estimates. Overall, our results suggest the benefit of self-controlled practice on motor learning is small and not currently distinguishable from zero.

Metrics

Metrics Loading ...

Downloads

Published

2022-11-08

Issue

Section

Original articles