Ponseti Francisco Javier, Almeida Pedro L, Lameiras Joao, Martins Bruno, Olmedilla Aurelio, López-Walle Jeanette, Reyes Orlando, Garcia-Mas Alexandre
Department of Pedagogy, University of the Balearic Islands, Palma, Spain.
Departamento de Psicologia Social e Organizacional, ISPA - Instituto Universitario, Lisbon, Portugal.
Front Psychol. 2019 Sep 6;10:1947. doi: 10.3389/fpsyg.2019.01947. eCollection 2019.
This study attempts to analyze the relationship between two key psychological variables associated with performance in sports - Self-Determined Motivation and Competitive Anxiety - through Bayesian Networks (BN) analysis. We analyzed 674 university students that are athletes from 44 universities that competed at the University Games in Mexico, with an average age of 21 years (SD = 2.07) and with a mean of 8.61 years' (SD = 5.15) experience in sports. Methods: Regarding the data analysis, firstly, classification using the CHAID algorithm was carried out to determine the dependence links between variables; Secondly, a BN was developed to reduce the uncertainty in the relationships between the two key psychological variables. The validation of the BN revealed AUC values ranging from 0.5 to 0.92. Subsequently, various instantiations were performed with hypothetical values applied to the "bottom" variables. Results showed two probability trees that have extrinsic motivation and amotivation at the top, while the anxiety/activation due to worries about performance was at the bottom of the probabilities. The instantiations carried out support the existence of these probabilistic relationships, demonstrating their scarce influence on anxiety about competition generated by the intrinsic motivation, and the complex probabilistic effect of introjected and identified regulation regarding the appearance of anxiety due to worry about performance.
本研究试图通过贝叶斯网络（BN）分析来剖析与运动表现相关的两个关键心理变量——自我决定动机和竞争焦虑之间的关系。我们分析了来自44所大学的674名大学生运动员，他们参加了在墨西哥举行的大学生运动会，平均年龄为21岁（标准差=2.07），平均运动经历为8.61年（标准差=5.15）。方法：在数据分析方面，首先，使用CHAID算法进行分类，以确定变量之间的依赖关系；其次，构建一个贝叶斯网络，以减少两个关键心理变量之间关系的不确定性。贝叶斯网络的验证显示AUC值在0.5到0.92之间。随后，对应用于“底层”变量的假设值进行了各种实例化。结果显示了两棵概率树，顶部是外部动机和无动机，而因担心表现而产生的焦虑/激活处于概率树的底部。所进行的实例化支持了这些概率关系的存在，表明它们对内在动机产生的竞争焦虑影响不大，以及关于因担心表现而出现焦虑的内摄调节和认同调节的复杂概率效应。
