scholarly journals A case against default effect sizes in sport and exercise science

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10314
Author(s):  
Aaron Caldwell ◽  
Andrew D. Vigotsky
Keyword(s):  
Repeated Measures ◽  
Effect Sizes ◽  
Exercise Science ◽  
Signal To Noise ◽  
Repeated Measures Data ◽  
Science Community ◽  
Philosophical Framework ◽  
Sport And Exercise ◽  
Mean Differences ◽  
High Level

Recent discussions in the sport and exercise science community have focused on the appropriate use and reporting of effect sizes. Sport and exercise scientists often analyze repeated-measures data, from which mean differences are reported. To aid the interpretation of these data, standardized mean differences (SMD) are commonly reported as a description of effect size. In this manuscript, we hope to alleviate some confusion. First, we provide a philosophical framework for conceptualizing SMDs; that is, by dichotomizing them into two groups: magnitude-based and signal-to-noise SMDs. Second, we describe the statistical properties of SMDs and their implications. Finally, we provide high-level recommendations for how sport and exercise scientists can thoughtfully report raw effect sizes, SMDs, or other effect sizes for their own studies. This conceptual framework provides sport and exercise scientists with the background necessary to make and justify their choice of an SMD.

scholarly journals Does One Effect Size Fit All? The Case Against Default Effect Sizes for Sport and Exercise Science

2020 ◽  
Author(s):  
Aaron R Caldwell ◽  
Andrew David Vigotsky
Keyword(s):  
Effect Size ◽  
Repeated Measures ◽  
Effect Sizes ◽  
Exercise Science ◽  
Repeated Measures Data ◽  
Science Community ◽  
Philosophical Framework ◽  
Sport And Exercise ◽  
Mean Differences ◽  
High Level

Recent discussions in the sport and exercise science community have focused on the appropriate use and reporting of effect sizes. Sport and exercise scientists often analyze repeated-measures data, from which mean differences are reported. To aid the interpretation of these data, standardized mean differences (SMD) are commonly reported as description of effect size. In this manuscript, we hope to alleviate some confusion. First, we provide a philosophical framework for conceptualizing SMDs; that is, by dichotomizing them into two groups: magnitude-based and signal-to-noise based SMDs. Second, we describe the statistical properties of SMDs and their implications. Finally, we provide high-level recommendations for how sport and exercise scientists can thoughtfully report raw effect sizes, SMDs, or other effect sizes for their own studies. This conceptual framework provides sport and exercise scientists with the background necessary to make and justify their choice of an SMD. The code to reproduce all analyses and figures within the manuscript can be found at the following link: https://www.doi.org/10.17605/OSF.IO/FC5XW.

scholarly journals Dilution Method of Menthol Solutions Affects Subsequent Perceptual Thermal Responses during Passive Heat Exposure in Non-Heat Acclimated Participants

Beverages ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 62
Author(s):  
Russ Best ◽  
Rachana Naicker ◽  
Peter Maulder ◽  
Nicolas Berger
Keyword(s):  
Thermal Comfort ◽  
Repeated Measures ◽  
Thermal Sensation ◽  
Heat Exposure ◽  
Tympanic Temperature ◽  
Dilution Method ◽  
Science Literature ◽  
Exercise Science ◽  
Repeated Measures Design ◽  
Sport And Exercise

Due to its volatility, the qualitative experience of menthol may be modulated by its preparation and combination with other compounds. One such method of preparation is dilution, with two dilution methods existing within the sport and exercise science literature, where menthol is used to impart feelings of oral cooling and improve thermal comfort and sensation during heat exposure. This study compared these two dilution methods; one using a solvent the other using temperature, via a randomized counterbalanced repeated measures design (n = 12; Height: 174.0 ± 8.5 cm Mass: 73.4 ± 13.3 kg Age: 28.7 ± 8.4 y; two exposures to each solution) to assess the effect of solution and heat exposure, upon thermal comfort, thermal sensation and associated physiological parameters in non-heat acclimated participants. Thermal comfort was significantly affected by solution (p = 0.041; η2 = 0.017) and time (p < 0.001; η2 = 0.228), whereas thermal sensation was significantly affected by time only (p = 0.012; η2 = 0.133), as was tympanic temperature (p < 0.001; η2 = 0.277). Small to moderate clear differences between solutions at matched time points were also observed. These trends and effects suggest that, depending upon the dilution method employed, the resultant perceptual effects are likely impacted; this also likely depends upon the timing of menthol administration within a heat exposure session.

scholarly journals A tribute to Professor Edward Winter

2020 ◽  
Author(s):  
Robert J. Copeland ◽  
Stuart Flint ◽  
Alan Nevill ◽  
Jonathan Wheat ◽  
Karon Breckon ◽  
...  
Keyword(s):  
Exercise Science ◽  
Late Professor ◽  
Science Community ◽  
Sport And Exercise

This is a tribute to the late Professor Edward Winter from colleagues across the sport and exercise science community

Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 157-164
Author(s):  
Karl Schweizer
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Processing ◽  
Latent Variables ◽  
Repeated Measures ◽  
Latent Variable ◽  
Model Fit ◽  
Repeated Measures Data ◽  
Confirmatory Factor ◽  
And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

Sport and Exercise Science (SES) case studies

2019 ◽  
pp. 7-8
Author(s):  
Jo Bowtell
Keyword(s):  
Case Studies ◽  
Exercise Science ◽  
Sport And Exercise
Sign in / Sign up

Export Citation Format

Share Document