Meta-Analytic Findings of the Self-Controlled Motor Learning Literature: Underpowered, Biased, and Lacking Evidential Value
DOI:
https://doi.org/10.15626/MP.2021.2803Keywords:
Motor Learning, Retention, Choice, OPTIMAL Theory, Meta-Analysis, p-curve, Publication BiasAbstract
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status was added to the model as a potential moderator, revealing a significant difference between published and unpublished findings, with only the former reporting a benefit of self-controlled practice.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
Published
Issue
Section
License
Copyright (c) 2022 Brad McKay, Zachary D. Yantha, Julia Hussien, Michael J. Carter, Diane M. Ste-Marie
This work is licensed under a Creative Commons Attribution 4.0 International License.