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Assessment of Psychological Zone of Optimal Performance among Professional Athletes: EGA and Item Response Theory Analysis

Author

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  • Bing Li

    (Virtual Laboratory of Sports and Health, Southwest University, Chongqing 402460, China
    Sports Psychology and Education Research Center, Southwest University, Chongqing 402460, China)

  • Cody Ding

    (Education Sciences and Professional Programs, University of Missouri-St. Louis, St. Louis, MO 63121, USA)

  • Huiying Shi

    (Virtual Laboratory of Sports and Health, Southwest University, Chongqing 402460, China
    Sports Psychology and Education Research Center, Southwest University, Chongqing 402460, China
    Faculty of Psychology, Southwest University, Chongqing 402460, China)

  • Fenghui Fan

    (Virtual Laboratory of Sports and Health, Southwest University, Chongqing 402460, China
    Sports Psychology and Education Research Center, Southwest University, Chongqing 402460, China
    Faculty of Psychology, Southwest University, Chongqing 402460, China)

  • Liya Guo

    (Virtual Laboratory of Sports and Health, Southwest University, Chongqing 402460, China
    Sports Psychology and Education Research Center, Southwest University, Chongqing 402460, China)

Abstract

Sport psychology researchers have been investigating athletes’ ideal performance levels for a long time. Key areas of investigation in this field involve determining if there is an optimal performance zone and how to evaluate it. To advance this line of research, the current research aimed to create a short but reliable tool for assessing the psychological state of professional athletes during their peak performance, known as the “optimal performance zone”. After developing an initial item pool, the final 10-item scale was retained and validated using factor analytical models and item response theory analysis based on 357 Chinese professional athletes in 12 different sports types. The average age of the participants was 19.4 years ( SD = 3.67), and 54% were male. Experience in the sport ranged from 2 to 15 years, with a mean of 5.82 years ( SD = 3.65). The brief scale was found to form a one-factor solution, with factor loading ranging from 0.55 to 0.77. The IRT-based marginal reliability of this scale is 0.90, and the scale showed predictive validity in predicting an athlete’s professional ranking (χ 2 (3) = 8.34, p = 0.039). The brief scale can quickly screen for a psychological zone of optimal performance among professional athletes, and implications are discussed.

Suggested Citation

  • Bing Li & Cody Ding & Huiying Shi & Fenghui Fan & Liya Guo, 2023. "Assessment of Psychological Zone of Optimal Performance among Professional Athletes: EGA and Item Response Theory Analysis," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7904-:d:1144899
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    References listed on IDEAS

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    1. Hudson F Golino & Sacha Epskamp, 2017. "Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-26, June.
    2. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    3. Sacha Epskamp & Mijke Rhemtulla & Denny Borsboom, 2017. "Generalized Network Psychometrics: Combining Network and Latent Variable Models," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 904-927, December.
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