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Leisha Strachan, Jean Côté, and Janice Deakin

The purpose of the current study was to examine two different trajectories of sport participation and explore any similarities or differences that may result regarding personal development and sport outcomes. Seventy-four youth athletes (40 “specializers” and 34 “samplers”) were recruited for the current study and four measures were employed to assess sport experiences and outcomes. Discriminant function analyses revealed no differences between groups in asset possession or sources of enjoyment however, differences were reported in sport experiences and burnout. The “samplers” reported more experiences regarding the integration of sport and family as well as linkages to the community. Although the “specializers” reported higher levels of physical/emotional exhaustion than did the “samplers,” they also reported more experiences related to diverse peer groups. The differences highlight the importance of examining specific pathways of development in sport to gain a deeper understanding of youths’ experiences in sport.

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Peter A. Hastie

This paper examines the literature within sport pedagogy that addresses early sport specialization. The paper is presented in two sections. First, research on a number of common sense assumptions about early specialization is examined from a pedagogical perspective: (a) Is limiting youths’ experiences to a single sport the best path to elite status? (b) Do early specializers receive better coaching? (c) Do coaches of early specializers have better sport content knowledge? (d) Do coaches of early specializers have better planning behaviors? (e) Do instructional climates differ between specialized and diversified coaching settings? Second, a research agenda from a pedagogical perspective is proposed for answering the questions posed in the first section, as well as the various assessments and protocols that would allow for these questions to be answered.

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Shelby Waldron, J.D. DeFreese, Brian Pietrosimone, Johna Register-Mihalik, and Nikki Barczak

late specializers, and then samplers. 2) Early specializers will self-report the highest levels of other retrospective maladaptive psychological outcomes (i.e., amotivation and perceived sport stress), and the lowest levels of adaptive psychological outcomes (i.e., intrinsic motivation and perceived

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Heather K. Larson, Bradley W. Young, Tara-Leigh F. McHugh, and Wendy M. Rodgers

, Strachan, Côté, and Deakin ( 2009 ) found minimal differences between “samplers” and “specializers” on a variety of measures. Although specializers scored a bit higher than samplers on emotional/physical exhaustion, they also reported more experiences related to diverse peer groups and did not differ from

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Matthew Ellis, Mark Noon, Tony Myers, and Neil Clarke

Hamiltonian Markov Chain Monte Carlo with a No-U-Turn Sampler. 22 All models were checked for convergence ( r ˆ = 1 ), and graphical posterior predictive checks were used by bayesplot (Gabry, Mahr, and Buerkner, bayesplot, version 1.5.0, 2018). Fifteen participants were included in the multilevel model but

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Kristen Lucas and E. Whitney G. Moore

 Caucasian 53  Mindfulness 7  Sophomore 11  African American 27  Boot Camp 5  Junior 26  Asian 8  Kickboxing 5  Senior 46  Mixed 8  Boxing Conditioning <5  Graduate Student or Other 5  Asian Indian 4  Kickball & Dodgeball <5  American Indian or Hawaiian 1  Personal Fitness Sampler <5 Major  Pilates <5

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Rosemary A. Arthur, Nichola Callow, Ross Roberts, and Freya Glendinning

with a Gibbs sampler and a fixed number of 100,000 iterations for two MCMC chains. This procedure allowed for the examination of model convergence. We assessed model convergence with the potential scale reduction factor (PSR). Model convergence is evident when the PSR value lies between 1.0 and 1.1 for

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Daniel Gould

groups (e.g., male and female participants, specializers vs. sport samplers) are examined over time ( Holt, Pankow, Tamminen et al., 2018 ). A lack of high-quality physical education and unstructured free-play opportunities is also linked to the access issue. For example, only 8% of elementary, 6.4% of

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Johan Ekengren, Natalia Stambulova, Urban Johnson, Andreas Ivarsson, and Robert J. Schinke

fatigue during the intervention period, a Bayesian two-level longitudinal model was specified using the Markov Chain Monte Carlo simulation procedures with a Gibbs sampler in Mplus ( Muthén & Muthén, 2018 ). More specifically, the specification of the model stress and fatigue were, on the within

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Isaac Estevan, Javier Molina-García, Gavin Abbott, Steve J. Bowe, Isabel Castillo, and Lisa M. Barnett

non-informative priors (termed Model 1 and 3) and informative priors (cross-loadings) with zero-means and small variances equal to 0.01 (Model 2 and 4) ( Fong & Ho, 2013 ) were tested. Model estimation was performed using the Markov chain Monte Carlo algorithm and Gibbs Sampler with 60,000 iterations