The milestones in the movement of the field away from emphasizing p values, and toward emphasizing effect size reporting, are reviewed. The primer also briefly introduces the effect size types and recommends a few effect size usage and interpretation guidelines that journals and authors should follow.
Mark B. Andersen, Penny McCullagh and Gabriel J. Wilson
Many of the measurements used in sport psychology research are arbitrary metrics, and researchers often cannot make the jump from scores on paper-and-pencil tests to what those scores actually mean in terms of real-world behaviors. Effect sizes for behavioral data are often interpretable, but the meaning of a small, medium, or large effect for an arbitrary metric is elusive. We reviewed all the issues in the 2005 volumes of the Journal of Sport and Exercise Psychology, The Sport Psychologist, and the Journal of Applied Sport Psychology to determine whether the arbitrary metrics used in sport psychology research were interpreted, or calibrated, against real-world variables. Of the 54 studies that used quantitative methods, 25 reported only paper-and-pencil arbitrary metrics with no connections to behavior or other real-world variables. Also, 44 of the 54 studies reported effect sizes, but only 7 studies, using both arbitrary and behavioral metrics, had calculated effect indicators and interpreted them in terms of real-world meaning.
Philippe Terrier and Fabienne Reynard
Local dynamic stability (stability) quantifies how a system responds to small perturbations. Several experimental and clinical findings have highlighted the association between gait stability and fall risk. Walking without shoes is known to slightly modify gait parameters. Barefoot walking may cause unusual sensory feedback to individuals accustomed to shod walking, and this may affect stability. The objective was therefore to compare the stability of shod and barefoot walking in healthy individuals and to analyze the intrasession repeatability. Forty participants traversed a 70 m indoor corridor wearing normal shoes in one trial and walking barefoot in a second trial. Trunk accelerations were recorded with a 3D-accelerometer attached to the lower back. The stability was computed using the finite-time maximal Lyapunov exponent method. Absolute agreement between the forward and backward paths was estimated with the intraclass correlation coefficient (ICC). Barefoot walking did not significantly modify the stability as compared with shod walking (average standardized effect size: +0.11). The intrasession repeatability was high (ICC: 0.73–0.81) and slightly higher in barefoot walking condition (ICC: 0.81–0.87). Therefore, it seems that barefoot walking can be used to evaluate stability without introducing a bias as compared with shod walking, and with a sufficient reliability.
Catarina Vasques, Pedro Magalhães, António Cortinhas, Paula Mota, José Leitão and Vitor Pires Lopes
This meta-analysis study aims to assess the efficacy of school-based and after-school intervention programs on the BMIs of child and adolescents, addressing the correlation between some moderating variables.
We analyzed 52 studies (N = 28,236) published between 2000–2011.
The overall effect size was 0.068 (P < .001), school (r = .069) and after-school intervention (r = .065). Programs conducted with children aged between 15–19 years were the most effective (r = .133). Interventions programs with boys and girls show better effect sizes (r = .110) than programs that included just girls (r = .073). There were no significant differences between the programs implemented in school and after-school (P = .770). The effect size was higher in interventions lasting 1 year (r = .095), with physical activity and nutritional education (r = .148), and that included 3–5 sessions of physical activity per week (r = .080). The effect size also increased as the level of parental involvement increased.
Although of low magnitude (r = .068), the intervention programs had a positive effect in prevention and decreasing obesity in children. This effect seems to be higher in older children’s, involving interventions with physical activity and nutritional education combined, with parent’s participation and with 1-year duration. School or after-school interventions had a similar effect.
Michele A. Parker and Bruce M. Gansneder
Column-editor : Michael G. Dolan
Leanne Sawle, Jennifer Freeman and Jonathan Marsden
) presence of adverse events (4) effect size estimate (5) feasibility of using the outcome measures (6) effectiveness of the blinding strategy (7) practicality of the protocol Method Sampling and Recruitment Strategy A convenience sample of volunteers was recruited from UK-based sports clubs over 1 year. The
Talita Molinari, Tainara Steffens, Cristian Roncada, Rodrigo Rodrigues and Caroline P. Dias
difference rate (bivariate), a 95% confidence interval, and heterogeneity ( I 2 ) with a significance level of p < .05 for the maximal strength assessments in knee extensor and leg press exercises between pre- and posttraining and between training groups. The magnitude of the effect size was calculated
Kevin M. Carroll, Jake R. Bernards, Caleb D. Bazyler, Christopher B. Taber, Charles A. Stuart, Brad H. DeWeese, Kimitake Sato and Michael H. Stone
– 31 Statistical analyses were performed on a commercially available statistics software (JASP version 0.8.1.1) and Microsoft Excel 2016 (Microsoft Corp, Redmond, WA). To assess practical significance, the effect size using Hedge’s g was calculated for pre–post measures. 32 Within-group effect
Hiroshi Takasaki, Yu Okubo and Shun Okuyama
(criterion D), and (5) appropriate statistical assessment with effect size calculation (repeated-measures analysis of variance with effect size calculation for parametric analysis and Mann–Whitney U test using predifference to postdifference with effect size calculation; criterion E). These criteria have
Megan N. Houston, Johanna M. Hoch and Matthew C. Hoch
occurred. Descriptive statistics for FABQ scores were reported as median and interquartile range. Alpha was set at P ≤ .05 for all analyses. Nonparametric effect sizes (ESs) were estimated using z values and interpreted as small (0.10–0.29), medium (0.30–0.49), and large (≥0.50). SPSS software (version