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Component-based structural equation modeling for the assessment of psycho-social aspects and performance of athletes


  • Rosa Fabbricatore

    (University of Naples Federico II)

  • Maria Iannario

    (University of Naples Federico II)

  • Rosaria Romano

    (University of Naples Federico II)

  • Domenico Vistocco

    (University of Naples Federico II)


Recent studies have pointed out the effect of personality traits on athletes’ performance and success; however, fewer analyses have focused the relation among these features and specific athletic behaviors, skills, and strategies to enhance performance. To fill this void, the present paper provides evidence on what personality traits mostly affect athletes’ mental skills and, in turn, their effect on the performance of a sample of elite swimmers. The main findings were obtained by exploiting a component-based structural equation modeling which allows to analyze the relationships among some psychological constructs, measuring personality traits and mental skills, and a construct measuring sports performance. The partial least squares path modeling was employed, as it is the most recognized method among the component-based approaches. The introduced method simultaneously encompasses latent and emergent variables. Rather than focusing only on objective behaviors or game/race outcomes, such an approach evaluates variables not directly observable related to sport performance, such as cognition and affect, considering measurement error and measurement invariance, as well as the validity and reliability of the obtained latent constructs. The obtained results could be an asset to design strategies and interventions both for coaches and swimmers establishing an innovative use of statistical methods for maximizing athletes’ performance and well-being.

Suggested Citation

  • Rosa Fabbricatore & Maria Iannario & Rosaria Romano & Domenico Vistocco, 2023. "Component-based structural equation modeling for the assessment of psycho-social aspects and performance of athletes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 343-367, March.
  • Handle: RePEc:spr:alstar:v:107:y:2023:i:1:d:10.1007_s10182-021-00417-5
    DOI: 10.1007/s10182-021-00417-5

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    References listed on IDEAS

    1. Monecke, Armin & Leisch, Friedrich, 2012. "semPLS: Structural Equation Modeling Using Partial Least Squares," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i03).
    2. Tim McGarry, 2009. "Applied and theoretical perspectives of performance analysis in sport: Scientific issues and challenges," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 9(1), pages 128-140, April.
    3. Henseler, Jörg & Schuberth, Florian, 2020. "Using confirmatory composite analysis to assess emergent variables in business research," Journal of Business Research, Elsevier, vol. 120(C), pages 147-156.
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