Abstract
Sports Coaching research continues to develop, although with a narrow spread of publication, mainly within Sports Psychology, and small impact across Sports Science journals. Nevertheless, Sports Coaching research potentially investigates an array of basic and applied research questions. Hence, there is an opportunity for improvement. Moreover, there is an increased awareness in several scientific areas, including Sports Science, about several problems pertaining to design, transparency, replicability, and trust of research practices. Particularly in Sports Coaching research, these problems include limited or inadequate validation of surrogate outcomes and lack of multidisciplinary designs, lack of longitudinal and replication studies, inappropriate data analysis and reporting, limited reporting of null or trivial results, and insufficient scientific transparency. In this chapter, we initially discuss the trends of publication in Sports Coaching, highlighting research problems as they pertain to their treatment in other disciplines, namely psychology. Lastly, we illustrate an example applied to Sport Coaching research with a repeated measures design and an interdisciplinary approach as a recommendation to promote transparency, replicability, and trust in Sports Coach research.
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References
Amrhein, V., & Greenland, S. (2018). Remove, rather than redefine, statistical significance. Nature Human Behaviour, 2(1), 4. https://doi.org/10.1038/s41562-017-0224-0
Amrhein, V., Greenland, S., & McShane, B. (2019a). Scientists rise up against statistical significance. Nature, 567(7748), 305–307. https://doi.org/10.1038/d41586-019-00857-9
Amrhein, V., Greenland, S., & McShane, B. B. (2019b). Statistical significance gives bias a free pass. European Journal of Clinical Investigation, 49(12), e13176. https://doi.org/10.1111/eci.13176
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Bartell, S. M. (2019). Understanding and mitigating the replication crisis, for environmental epidemiologists. Current Environmental Health Reports, 6(1), 8–15. https://doi.org/10.1007/s40572-019-0225-4
Batterham, A. M., & Hopkins, W. G. (2006). Making meaningful inferences about magnitudes. International Journal of Sports Physiology Performance, 1(1), 50–57.
Begley, C. G., & Ioannidis, J. P. (2015). Reproducibility in science. Circulation Research, 116(1), 116–126. https://doi.org/10.1161/CIRCRESAHA.114.303819
Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80, 1–28.
Burwitz, L., Moore, P. M., & Wilkinson, D. M. (1994). Future directions for performance-related sports science research: An interdisciplinary approach. Journal of Sports Science, 12(1), 93–109. https://doi.org/10.1080/02640419408732159
Caldwell, A. R., Vigotsky, A. D., Tenan, M. S., Radel, R., Mellor, D. T., Kreutzer, A., … Boisgontier, M. P. (2020). Moving sport and exercise science forward: A call for the adoption of more transparent research practices. Sports Medicine, 50(3), 449–459. https://doi.org/10.1007/s40279-019-01227-1
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., … Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1), 32. https://doi.org/10.18637/jss.v076.i01
Chambers, C. (2017). The seven deadly sins of psychology: A manifesto for reforming the culture of scientific practice. Princeton, NJ: Princeton University Press.
Gelman, A., & Geurts, H. M. (2017). The statistical crisis in science: How is it relevant to clinical neuropsychology? Clinical Neuropsychology, 31(6–7), 1000–1014. https://doi.org/10.1080/13854046.2016.1277557
Gelman, A., & Shalizi, C. R. (2013). Philosophy and the practice of Bayesian statistics. British Journal of Mathematical and Statistical Psychology, 66(1), 8–38. https://doi.org/10.1111/j.2044-8317.2011.02037.x
Gilbert, W. D., & Trudel, P. (2004). Analysis of coaching science research published from 1970–2001. Research Quarterly for Exercise & Sport, 75(4), 388–399. https://doi.org/10.1080/02701367.2004.10609172
Gonçalves, C. E., Carvalho, H. M., & Catarino, L. M. (2018). Body in movement: Better measurements for better coaching. In S. Pill (Ed.), Perspectives on athlete-centered coaching (pp. 116–126). Abingdon: Routledge.
Grecic, D., & Collins, D. (2013). The epistemological chain: Practical applications in sports. Quest, 65(2), 151–168. https://doi.org/10.1080/00336297.2013.773525
Griffo, J. M., Jensen, M., Anthony, C. C., Baghurst, T., & Kulinna, P. H. (2019). A decade of research literature in sport coaching (2005–2015). International Journal of Sports Science & Coaching, 14(2), 205–215. https://doi.org/10.1177/1747954118825058
Halperin, I., Vigotsky, A. D., Foster, C., & Pyne, D. B. (2018). Strengthening the practice of exercise and sport-science research. International Journal of Sports Physiology and Performance, 13(2), 127–134. https://doi.org/10.1123/ijspp.2017-0322
Jacobs, F., Claringbould, I., & Knoppers, A. (2016). Becoming a ‘good coach’. Sport, Education and Society, 21(3), 411–430. https://doi.org/10.1080/13573322.2014.927756
Kennedy, L., & Gelman, A. (2020). Know your population and know your model: Using model-based regression and poststratification to generalize findings beyond the observed sample. ArXiv e-prints, 1906.11323 (1906.11323 [stat.AP]).
Knudson, D. (2017). Confidence crisis of results in biomechanics research. Sports Biomechanics, 16(4), 425–433. https://doi.org/10.1080/14763141.2016.1246603
Lee, M. D., & Wagenmakers, E.-J. (2013). Bayesian cognitive modeling: A practical course. Cambridge: Cambridge University.
Leek, J. T., & Peng, R. D. (2015). Opinion: Reproducible research can still be wrong: Adopting a prevention approach. Proceedings of the National Academy of Sciences, 112(6), 1645. https://doi.org/10.1073/pnas.1421412111
McElreath, R. (2015). Statistical rethinking: A Bayesian course with examples in R and Stan. Boca Raton, FL: Chapman & Hall/CRC Press.
McShane, B. B., Gal, D., Gelman, A., Robert, C., & Tackett, J. L. (2019). Abandon statistical significance. The American Statistician, 73(suppl 1), 235–245. https://doi.org/10.1080/00031305.2018.1527253
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716
Pashler, H., & Wagenmakers, E. J. (2012). Editors’ introduction to the special section on replicability in psychological science: A crisis of confidence? Perspectives on Psychological Science, 7(6), 528–530. https://doi.org/10.1177/1745691612465253
Piggott, B., Muller, S., Chivers, P., Papaluca, C., & Hoyne, G. (2018). Is sports science answering the call for interdisciplinary research? A systematic review. European Journal Sport Science, 19(2), 1–20. https://doi.org/10.1080/17461391.2018.1508506
Powers, S. M., & Hampton, S. E. (2019). Open science, reproducibility, and transparency in ecology. Ecological Applications, 29(1), e01822. https://doi.org/10.1002/eap.1822
R Core Team. (2018). R: A language and environment for statistical computing. Retrieved from http://www.R-project.org/
Sainani, K. L. (2018). The problem with “Magnitude-Based Inference”. Medicine Science Sports Exercise, 50(10), 2166–2176. https://doi.org/10.1249/MSS.0000000000001645
Schweizer, G., & Furley, P. (2016). Reproducible research in sport and exercise psychology: The role of sample sizes. Psychology of Sport and Exercise, 23, 114–122. https://doi.org/10.1016/j.psychsport.2015.11.005
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychology Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632
Welsh, A. H., & Knight, E. J. (2015). “Magnitude-based inference”: A statistical review. Medicine Science Sports Exercise, 47(4), 874–884. https://doi.org/10.1249/MSS.0000000000000451
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Carvalho, H.M., Gonçalves, C.E. (2020). Coaching in Sports: Implications for Researchers and Coaches. In: Resende, R., Gomes, A.R. (eds) Coaching for Human Development and Performance in Sports. Springer, Cham. https://doi.org/10.1007/978-3-030-63912-9_22
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DOI: https://doi.org/10.1007/978-3-030-63912-9_22
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