Psychometric Properties of the Chinese Translated Athlete Burnout Questionnaire: Evidence From Chinese Collegiate Athletes and Elite Athletes.

The purpose of the study was to translate the athlete burnout questionnaire (ABQ) into Simplified Chinese and examine its psychometric properties in Chinese collegiate athletes and elite athletes. Firstly, the factor structure, internal consistency reliability and nomological validity of the Chinese translated ABQ was examined in a sample of Chinese collegiate athletes ( n = 214, 58.9% females). Secondly, abovementioned psychometric properties were examined in a sample of Chinese elite athletes ( n = 505, 52.7% females). Finally, measurement invariance of the Chinese translated ABQ was examined across the two samples. It was found that the 12-item three-correlated-factors model outperformed the one factor model and bi-factor model in collegiate athlete sample whereas the 12-item bi-factor model best represented the factor structure of the Chinese translated ABQ in elite athlete sample. Satisfactory internal consistency reliabilities of the Chinese translated ABQ were evidenced in the two samples. Nomological validity was also supported by the results of the two samples that the three subscales of the ABQ were significantly associated with its theoretically related variables. Results of multiple-group confirmatory factor analysis revealed that weak measurement invariance of the Chinese translated ABQ (three-correlated-factors model) was evidenced across the two samples. Collectively, results of this study indicated that the 12-item Chinese translated ABQ could be used for measuring burnout of Chinese collegiate and elite athletes. Significance and implication of the current study as well as recommendations for future study were discussed.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2022 Liu, Wang, Wu, Zou, Jiang, Gao, You, Hu and Liu.)