PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Analysis of the Uncertainty in the relationship between Self-Determined Motivation and competitive Anxiety in dual-career students: Application of Information Theory and Bayesian Networks
Version 1
: Received: 9 October 2018 / Approved: 10 October 2018 / Online: 10 October 2018 (11:48:18 CEST)
How to cite:
Ponseti, X.; L. Almeida, P.; Lameiras, J.; Martins, B.; Olmedilla-Zafra, A.; López-Walle, J.; García-Mas, A. Analysis of the Uncertainty in the relationship between Self-Determined Motivation and competitive Anxiety in dual-career students: Application of Information Theory and Bayesian Networks. Preprints2018, 2018100219. https://doi.org/10.20944/preprints201810.0219.v1
Ponseti, X.; L. Almeida, P.; Lameiras, J.; Martins, B.; Olmedilla-Zafra, A.; López-Walle, J.; García-Mas, A. Analysis of the Uncertainty in the relationship between Self-Determined Motivation and competitive Anxiety in dual-career students: Application of Information Theory and Bayesian Networks. Preprints 2018, 2018100219. https://doi.org/10.20944/preprints201810.0219.v1
Ponseti, X.; L. Almeida, P.; Lameiras, J.; Martins, B.; Olmedilla-Zafra, A.; López-Walle, J.; García-Mas, A. Analysis of the Uncertainty in the relationship between Self-Determined Motivation and competitive Anxiety in dual-career students: Application of Information Theory and Bayesian Networks. Preprints2018, 2018100219. https://doi.org/10.20944/preprints201810.0219.v1
APA Style
Ponseti, X., L. Almeida, P., Lameiras, J., Martins, B., Olmedilla-Zafra, A., López-Walle, J., & García-Mas, A. (2018). Analysis of the Uncertainty in the relationship between Self-Determined Motivation and competitive Anxiety in dual-career students: Application of Information Theory and Bayesian Networks. Preprints. https://doi.org/10.20944/preprints201810.0219.v1
Chicago/Turabian Style
Ponseti, X., Jeannete López-Walle and Alexandre García-Mas. 2018 "Analysis of the Uncertainty in the relationship between Self-Determined Motivation and competitive Anxiety in dual-career students: Application of Information Theory and Bayesian Networks" Preprints. https://doi.org/10.20944/preprints201810.0219.v1
Abstract
This study is framed on the Information Theory as a constructive criterion to generate probabilistic distributions –through the elaboration of Bayesian Networks- and to reduce the uncertainty in the occurrence and relationship between two key psychological variables associated with the sports’ performance: Self-Determined Motivation and Competitive Anxiety. We analyzed 674 universitary students/athletes who competed in the 2017 Universitary Games (Universiade) in México, from 44 universities, with an average age of 21 years old (SD = 2.07), and with a sportive experience of 8.61 years of average (SD = 5.15). Methods: Regarding the data analysis, first of all a CHAID algorithm was carried out for to know the independence links among variables, and then two Bayesian networks (BN) were elaborated. The validation of the BN revealed AUC values ranging from 0.5 to 0.92. Subsequently, various instantations were carried out with hypothetical values applied to the “bottom” variables. Results showed two probability trees that have Extrinisic Motivation and Amotivation at the top, while the anxiety/activation due to the worry for performance was at the bottom of probabilities. The instantiations carried out support the existence of these probabilistic relationships, demonstrating the little influence on the competition anxiety generated by the intrinsic motivation. In conclusion, the reduction of the uncertainty made up by the restricted BN may aloe to re-introduce Information Theory principles in psychosocial studies, allowing authors to obtain useful probabilities values upon target psychological variables related with sportive performance.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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