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A force profile analysis comparison between functional data analysis, statistical parametric mapping and statistical non-parametric mapping in on-water single sculling

Warmenhoven, J, Harrison, A, Robinson, MA, Vanrenterghem, J, Bargary, N, Smith, R, Cobley, S, Draper, C, Donnelly, C and Pataky, T (2018) A force profile analysis comparison between functional data analysis, statistical parametric mapping and statistical non-parametric mapping in on-water single sculling. Journal of Science and Medicine in Sport. ISSN 1440-2440

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Abstract

Objectives: To examine whether the Functional Data Analysis (FDA), Statistical Parametric Mapping (SPM) and Statistical non-Parametric Mapping (SnPM) hypothesis testing techniques differ in their ability to draw inferences in the context of a single, simple experimental design.
Design: The sample data used is cross-sectional (two-sample gender comparison) and evaluation of differences between statistical techniques used a combination of descriptive and qualitative assessments.
Methods: FDA, SPM and SnPM t-tests were applied to sample data of twenty highly skilled male and female rowers, rowing at 32 strokes per minute in a single scull boat. Statistical differences for gender were assessed by applying two t-tests (one for each side of the boat).
Results: The t-statistic values were identical for all three methods (with the FDA t-statistic presented as an absolute measure). The critical t-statistics (tcrit) were very similar between the techniques, with SPM tcrit providing a marginally higher tcrit than the FDA and SnPM tcrit values (which were identical). All techniques were successful in identifying consistent sections of the force waveform, where male and female rowers were shown to differ significantly (p < 0.05).
Conclusions: This is the first study to show that FDA, SPM and SnPM t-tests provide consistent results when applied to sports biomechanics data. Though the results were similar, selection of one technique over another by applied researchers and practitioners should be based on the underlying parametric assumption of SPM, as well as contextual factors related to the type of waveform data to be analysed and the experimental research question of interest.

Item Type: Article
Uncontrolled Keywords: 1106 Human Movement And Sports Science, 1117 Public Health And Health Services
Subjects: H Social Sciences > HA Statistics
Divisions: Sport & Exercise Sciences
Publisher: Elsevier
Date Deposited: 11 Apr 2018 11:01
Last Modified: 03 Nov 2018 14:32
DOI or Identification number: 10.1016/j.jsams.2018.03.009
URI: http://researchonline.ljmu.ac.uk/id/eprint/8459

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