Meng L, Liang X, Zhang B, Liang J. Development of a scale for the impact of emotion management on young athletes' training efficiency.
Heliyon 2024;
10:e30069. [PMID:
38699037 PMCID:
PMC11064430 DOI:
10.1016/j.heliyon.2024.e30069]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/11/2024] [Accepted: 04/18/2024] [Indexed: 05/05/2024] Open
Abstract
In this study, we developed a scale to evaluate emotion management and its benefits for young athletes in China, and to analyze the impact of emotion management on their training efficiency. Following an extensive literature review, we used AMOS structural equation model software to develop a scale for evaluating the effects and benefits of emotion management on young athletes' training efficiency. Results showed that young athletes' emotion management training and its benefits can be divided into five dimensions: benefit evaluation, emotional cognition, emotion influence, emotion control, and emotion regulation. The internal consistency reliability of the formal scale was 0.895, and the internal consistency reliability of each subscale was between 0.734 and 0.901. The split-half reliability was 0.769, and the split-half reliability of each subscale was between 0.623 and 0.864. The KMO value was 0.904, P = 0.00 (p < 0.05), and the cumulative interpretation rate was 61.782 % of the total variance. The lowest factor load of a scale item was 0.436, and the highest factor load was 0.846. The common degree of all items was between 0.402 and 0.762, indicating that the scale has good validity. A SEM model verified that the scale has good construct validity. Significant correlational differences were observed among the levels. The results of the SEM structural equation model analysis showed that the model's NC = 2.660 (1 < NC < 3 indicates that the model has a simple fit), PGFI = 0.722, PNFI = 0.699, IFI = 0.851, PRA = 0.927, RMR = 0.006, and RMSEA = 0.07, thus, these indexes reached the standard of excellent model fitting. The strongest correlation was found between emotional cognition and benefit evaluation (R = 0.690), and the weakest correlation was found between emotion influence and benefit evaluation (R = 0.079). These findings provide a basis for measuring the effect of emotion management on training efficiency in the training process of young athletes and offer a theoretical reference for their emotional development while in training.
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