Mitch Barton

Implementing Policies to Enhance Physical Education and Physical Activity in Schools

The purpose of this commentary is to provide an overview of national physical activity recommendations and policies (e.g., from the Institute of Medicine, National Physical Activity Plan, and Centers for Disease Control and Prevention) and to discuss how these important initiatives can be implemented in local schools. Successful policies are illustrated. Specific strategies and ideas are shared regarding how physical educators can assert themselves and impart their knowledge in an effort to build support for policy implementations that enhance the delivery of physical education and physical activity in their schools and communities.

Moving Beyond Univariate Post-Hoc Testing in Exercise Science: A Primer on Descriptive Discriminate Analysis

There has been a recent call to improve data reporting in kinesiology journals, including the appropriate use of univariate and multivariate analysis techniques. For example, a multivariate analysis of variance (MANOVA) with univariate post hocs and a Bonferroni correction is frequently used to investigate group differences on multiple dependent variables. However, this univariate approach decreases power, increases the risk for Type 1 error, and contradicts the rationale for conducting multivariate tests in the first place. Purpose: The purpose of this study was to provide a user-friendly primer on conducting descriptive discriminant analysis (DDA), which is a post-hoc strategy to MANOVA that takes into account the complex relationships among multiple dependent variables. Method: A real-world example using the Statistical Package for the Social Sciences syntax and data from 1,095 middle school students on their body composition and body image are provided to explain and interpret the results from DDA. Results: While univariate post hocs increased the risk for Type 1 error to 76%, the DDA identified which dependent variables contributed to group differences and which groups were different from each other. For example, students in the very lean and Healthy Fitness Zone categories for body mass index experienced less pressure to lose weight, more satisfaction with their body, and higher physical self-concept than the Needs Improvement Zone groups. However, perceived pressure to gain weight did not contribute to group differences because it was a suppressor variable. Conclusion: Researchers are encouraged to use DDA when investigating group differences on multiple correlated dependent variables to determine which variables contributed to group differences.

The Use of Structure Coefficients to Address Multicollinearity in Sport and Exercise Science

ABSTRACT A common practice in general linear model (GLM) analyses is to interpret regression coefficients (e.g., standardized β weights) as indicators of variable importance. However, focusing solely on standardized beta weights may provide limited or erroneous information. For example, β weights become increasingly unreliable when predictor variables are correlated, which is often the case in the social sciences. To address this issue, structure coefficients, which are simply the bivariate correlation between a predictor and the synthetic Ŷ variable, should also be interpreted. By examining β weights and structure coefficients in conjunction, the predictive worth of each independent variable can be more accurately judged. Despite this benefit, researchers in the field of sport and exercise science have rarely reported structure coefficients when conducting multiple regression analysis. Thus, the purpose of the present article is to discuss problems associated with the sole interpretation of β weights and to demonstrate how structure coefficients can be incorporated to improve accuracy of interpretation. Additionally, a content analysis was conducted to examine current trends in reporting multiple regression results within sport and exercise science research.