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Dyadic analysis for multi-block data in sport surveys analytics

Author

Listed:
  • Maria Iannario

    (University of Naples Federico II)

  • Rosaria Romano

    (University of Naples Federico II)

  • Domenico Vistocco

    (University of Naples Federico II)

Abstract

Analyzing sports data has become a challenging issue as it involves not standard data structures coming from several sources and with different formats, being often high dimensional and complex. This paper deals with a dyadic structure (athletes/coaches), characterized by a large number of manifest and latent variables. Data were collected in a survey administered within a joint project of University of Naples Federico II and Italian Swimmer Federation. The survey gathers information about psychosocial aspects influencing swimmers’ performance. The paper introduces a data processing method for dyadic data by presenting an alternative approach with respect to the current used models and provides an analysis of psychological factors affecting the actor/partner interdependence by means of a quantile regression. The obtained results could be an asset to design strategies and actions both for coaches and swimmers establishing an original use of statistical methods for analysing athletes psychological behaviour.

Suggested Citation

  • Maria Iannario & Rosaria Romano & Domenico Vistocco, 2023. "Dyadic analysis for multi-block data in sport surveys analytics," Annals of Operations Research, Springer, vol. 325(1), pages 701-714, June.
    • Handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-022-04864-4
    DOI: 10.1007/s10479-022-04864-4
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    References listed on IDEAS

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    1. Cristina Davino & Rosaria Romano & Domenico Vistocco, 2020. "On the use of quantile regression to deal with heterogeneity: the case of multi-block data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 771-784, December.
    2. J. Gower, 1975. "Generalized procrustes analysis," Psychometrika, Springer;The Psychometric Society, vol. 40(1), pages 33-51, March.
    3. Peres-Neto, Pedro R. & Jackson, Donald A. & Somers, Keith M., 2005. "How many principal components? stopping rules for determining the number of non-trivial axes revisited," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 974-997, June.
    4. Goldine Gleser & Philip DuBois, 1951. "A successive approximation method of maximizing test validity," Psychometrika, Springer;The Psychometric Society, vol. 16(1), pages 129-139, March.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. Koenker, Roger, 2000. "Galton, Edgeworth, Frisch, and prospects for quantile regression in econometrics," Journal of Econometrics, Elsevier, vol. 95(2), pages 347-374, April.
    7. Johannes Forkman & Julie Josse & Hans-Peter Piepho, 2019. "Hypothesis Tests for Principal Component Analysis When Variables are Standardized," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 289-308, June.
    8. Josse, Julie & Husson, François, 2012. "Selecting the number of components in principal component analysis using cross-validation approximations," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1869-1879.
    9. Arnold Wollenberg, 1977. "Redundancy analysis an alternative for canonical correlation analysis," Psychometrika, Springer;The Psychometric Society, vol. 42(2), pages 207-219, June.
    10. 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.
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