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Adapting football to the child: an application of the logistic regression model in observational methodology

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Listed:
  • Javier Arana
  • Daniel Lapresa
  • María Anguera
  • Belén Garzón

Abstract

Logistic regression is included into the analysis techniques which are valid for observational methodology. However, its presence at the heart of this methodology, and more specifically in physical activity and sports studies, is scarce. With a view to highlighting the possibilities this technique offers within the scope of observational methodology applied to physical activity and sports, an application of the logistic regression model is presented. The model is applied in the context of an observational design which aims to determine, from the analysis of use of the playing area, which football discipline (7 a side football, 9 a side football or 11 a side football) is best adapted to the child’s possibilities. A multiple logistic regression model can provide an effective prognosis regarding the probability of a move being successful (reaching the opposing goal area) depending on the sector in which the move commenced and the football discipline which is being played. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Javier Arana & Daniel Lapresa & María Anguera & Belén Garzón, 2013. "Adapting football to the child: an application of the logistic regression model in observational methodology," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(6), pages 3473-3480, October.
    • Handle: RePEc:spr:qualqt:v:47:y:2013:i:6:p:3473-3480
    DOI: 10.1007/s11135-012-9734-z
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    References listed on IDEAS

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    1. Aura-Nidia Herrera & Juana Gómez, 2008. "Influence of equal or unequal comparison group sample sizes on the detection of differential item functioning using the Mantel–Haenszel and logistic regression techniques," Quality & Quantity: International Journal of Methodology, Springer, vol. 42(6), pages 739-755, December.
    2. M. Hidalgo & Juana Gómez, 2006. "Nonuniform DIF Detection using Discriminant Logistic Analysis and Multinomial Logistic Regression: A comparison for polytomous items," Quality & Quantity: International Journal of Methodology, Springer, vol. 40(5), pages 805-823, October.
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