CROSS-COMPARISON OF THE PERFORMANCE OF DISCRETE, PHASE AND FUNCTIONAL DATA ANALYSIS TO DESCRIBE A DEPENDENT VARIABLE

  • Chris Richter
  • Leonardo Gualano
  • Noel E O'Connor
  • Kieran Moran
Keywords: discrete point analysis, functional principal component analysis, analysis of characterizing phases, dependent variable

Abstract

The aim of this study was to assess and contrast the ability of discrete point, functional principal component analysis (fPCA) and analysis of characterizing phases (ACP) to describe a dependent variable (jump height) from vertical ground reaction force curves captured during the propulsion phase of a countermovement jump. A stepwise multiple regression analysis was used to assess the ability of each data analysis technique. The order of effectiveness (high to low) was ACP, fPCA and discrete point analysis. Discrete point analysis was not able to generate strong predictors and detected also erroneous variables. FPCA and ACP detected similar factors to describe jump height. However, ACP performed better than fPCA because it considers the time and magnitude domain separately and in combination and it examines key-phases, without the influence of non-key-phases.
Published
2013-08-29