Dyadic analysis for multi-block data in sport surveys analytics

Maria Iannario (), Rosaria Romano () and Domenico Vistocco
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Maria Iannario: University of Naples Federico II
Rosaria Romano: University of Naples Federico II

Annals of Operations Research, 2023, vol. 325, issue 1, No 30, 714 pages

Abstract: 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.

Keywords: Athletes’/coaches’ perception; Dyadic analysis; Complex data structures; Quantile regression (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-022-04864-4

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