IDEAS home Printed from
   My bibliography  Save this article

Dyadic analysis for multi-block data in sport surveys analytics


  • Maria Iannario

    (University of Naples Federico II)

  • Rosaria Romano

    (University of Naples Federico II)

  • Domenico Vistocco

    (University of Naples Federico II)


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

    Download full text from publisher

    File URL:
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL:
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    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. 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.
    5. 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.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Arnold Wollenberg, 1977. "Redundancy analysis an alternative for canonical correlation analysis," Psychometrika, Springer;The Psychometric Society, vol. 42(2), pages 207-219, June.
    8. 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.
    9. Koenker, Roger, 2000. "Galton, Edgeworth, Frisch, and prospects for quantile regression in econometrics," Journal of Econometrics, Elsevier, vol. 95(2), pages 347-374, April.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Greenacre & Patrick J. F Groenen & Trevor Hastie & Alfonso Iodice d’Enza & Angelos Markos & Elena Tuzhilina, 2023. "Principal component analysis," Economics Working Papers 1856, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    3. Koutsomanoli-Filippaki, Anastasia I. & Mamatzakis, Emmanuel C., 2011. "Efficiency under quantile regression: What is the relationship with risk in the EU banking industry?," Review of Financial Economics, Elsevier, vol. 20(2), pages 84-95, May.
    4. Chang, Hao-Wen & Lin, Chinho, 2023. "Currency portfolio behavior in seven major Asian markets," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 540-559.
    5. Chen, Mei-Ping & Lee, Chien-Chiang & Hsu, Yi-Chung, 2017. "Investor sentiment and country exchange traded funds: Does economic freedom matter?," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 285-299.
    6. Omid Ranjbar & Chien-Chiang Lee & Tsangyao Chang & Mei-Ping Chen, 2014. "Income Convergence in African Countries: Evidence from a Stationary Test With Multiple Structural Breaks," South African Journal of Economics, Economic Society of South Africa, vol. 82(3), pages 371-391, September.
    7. Simila, Timo, 2006. "Self-organizing map visualizing conditional quantile functions with multidimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 2097-2110, April.
    8. Li, Ming-Yuan Leon, 2009. "Value or volume strategy?," Finance Research Letters, Elsevier, vol. 6(4), pages 210-218, December.
    9. Manuel Landajo & Javier De Andrés & Pedro Lorca, 2008. "Measuring firm performance by using linear and non‐parametric quantile regressions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(2), pages 227-250, April.
    10. Cuadras, Carles M. & Greenacre, Michael, 2022. "A short history of statistical association: From correlation to correspondence analysis to copulas," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    11. Maria Letizia Giorgetti, 2001. "Quantile Regression in Lower Bound Estimation," STICERD - Economics of Industry Papers 29, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    12. Pietro Lovaglio & Giorgio Vittadini, 2013. "Multilevel dimensionality-reduction methods," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 183-207, June.
    13. Pereira, Pedro T. & Martins, Pedro S., 2000. "Does Education Reduce Wage Inequality? Quantile Regressions Evidence from Fifteen European Countries," IZA Discussion Papers 120, Institute of Labor Economics (IZA).
    14. Warren Gilchrist, 2008. "Regression Revisited," International Statistical Review, International Statistical Institute, vol. 76(3), pages 401-418, December.
    15. Adamska Agata & Dąbrowski Tomasz J. & Homa Magdalena & Mościbrodzka Monika & Tomaszewski Jacek, 2022. "Demutualization, Corporatization, and Sustainability Initiatives: Evidence from the European Stock Exchange Industry," Journal of Management and Business Administration. Central Europe, Sciendo, vol. 30(3), pages 2-35, September.
    16. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly, 2022. "Fast algorithms for the quantile regression process," Empirical Economics, Springer, vol. 62(1), pages 7-33, January.
    17. E. Mamatzakis, 2015. "Risk and efficiency in the Central and Eastern European banking industry under quantile analysis," Quantitative Finance, Taylor & Francis Journals, vol. 15(3), pages 553-567, March.
    18. Marcio Laurini, 2007. "A note on the use of quantile regression in beta convergence analysis," Economics Bulletin, AccessEcon, vol. 3(52), pages 1-8.
    19. Cristina Davino & Tormod Næs & Rosaria Romano & Domenico Vistocco, 2022. "A quantile regression perspective on external preference mapping," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 545-571, December.
    20. Richard Spady & Sami Stouli, 2020. "Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions," Papers 2011.06416,


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-022-04864-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.