ISSP Position Stand: To Sample or to Specialize? Seven Postulates About Youth Sport Activities that Lead to Continued Participation and Elite Performance

A comprehensive approach to sport expertise should consider the entire situation that is comprised of the person, the task, the environment, and the complex interplay of these components (Hackfort, 1986). Accordingly, the Developmental Model of Sport Participation (Côté, Baker, & Abernethy, 2007; Côté & Fraser-Thomas, 2007) provides a comprehensive framework for sport expertise that outlines different pathways of involvement in sport. In pathways one and two, early sampling serves as the foundation for both elite and recreational sport participation. Early sampling is based on two main elements of childhood sport participation: 1) involvement in various sports and 2) participation in deliberate play. In contrast, pathway three shows the course to elite performance through early specialization in one sport. Early specialization implies a focused involvement on one sport and a large number of deliberate practice activities with the goal of improving sport skills and performance during childhood. This paper proposes seven postulates regarding the role that sampling and deliberate play, as opposed to specialization and deliberate practice, can have during childhood in promoting continued participation and elite performance in sport.

Estimation of Sample Size and Power For Quantile Regression

Quantile regression (QR) was first introduced by Roger Koenker and Gilbert Bassett in 1978. It is robust to outliers which affect least squares estimator on a large scale in linear regression. Instead of modeling mean of the response, QR provides an alternative way to model the relationship between quantiles of the response and covariates. Therefore, QR can be widely used to solve problems in econometrics, environmental sciences and health sciences. Sample size is an important factor in the planning stage of experimental design and observational studies. In ordinary linear regression, sample size may be determined based on either precision analysis or power analysis with closed form formulas. There are also methods that calculate sample size based on precision analysis for QR like C.Jennen-Steinmetz and S.Wellek (2005). A method to estimate sample size for QR based on power analysis was proposed by Shao and Wang (2009). In this paper, a new method is proposed to calculate sample size based on power analysis under hypothesis test of covariate effects. Even though error distribution assumption is not necessary for QR analysis itself, researchers have to make assumptions of error distribution and covariate structure in the planning stage of a study to obtain a reasonable estimate of sample size. In this project, both parametric and nonparametric methods are provided to estimate error distribution. Since the method proposed can be implemented in R, user is able to choose either parametric distribution or nonparametric kernel density estimation for error distribution. User also needs to specify the covariate structure and effect size to carry out sample size and power calculation. The performance of the method proposed is further evaluated using numerical simulation. The results suggest that the sample sizes obtained from our method provide empirical powers that are closed to the nominal power level, for example, 80%.