Statistical methods for analysing discrete and categorical data recorded in performance analysis.

2.50
Hdl Handle:
http://hdl.handle.net/2436/19654
Title:
Statistical methods for analysing discrete and categorical data recorded in performance analysis.
Authors:
Nevill, Alan M.; Atkinson, Greg; Hughes, Mike D.; Cooper, Stephen-Mark
Abstract:
In this paper, we identify appropriate statistical methods for analysing categorical differences in discrete variables or 'performance indicators' resulting from performance analysis. The random mechanisms associated with discrete events do not follow a normal distribution; that is, the normal distribution is a continuous not a discrete probability distribution. We propose appropriate statistical methods based on two key discrete probability distributions, the Poisson and binomial distributions. Two approaches are proposed and compared using examples from notational analysis. The first approach is based on the classic chi-square test of significance (both the goodness-of-fit test and the test of independence). The second approach adopts a more contemporary method based on log-linear and logit models fitted using the statistical software GLIM. Provided relatively simple one-way and two-way comparisons in categorical data are required, both of these approaches result in very similar conclusions. However, as soon as more complex models or higher-order comparisons are required, the approach based on log-linear and logit models is shown to be more effective. Indeed, when investigating those factors and categorical differences associated with binomial or binary response variables, such as the proportion of winners when attempting decisive shots in squash or the proportion of goals scored from all shots in association football, logit models become the only realistic method available. By applying log-linear and logit models to discrete events resulting from notational analysis, greater insight into the underlying mechanisms associated with sport performance can be achieved.
Citation:
Journal of Sports Sciences, 20(10): 829-44
Publisher:
Taylor & Francis
Issue Date:
2002
URI:
http://hdl.handle.net/2436/19654
DOI:
10.1080/026404102320675666
PubMed ID:
12363298
Additional Links:
http://www.ingentaconnect.com/content/tandf/rjsp/2002/00000020/00000010/art00008
Type:
Article
Language:
en
Description:
Metadata only
ISSN:
0264-0414
Appears in Collections:
Sport, Exercise and Health Research Group; Sport Performance; Learning and Teaching in Sport, Exercise and Performance

Full metadata record

DC FieldValue Language
dc.contributor.authorNevill, Alan M.-
dc.contributor.authorAtkinson, Greg-
dc.contributor.authorHughes, Mike D.-
dc.contributor.authorCooper, Stephen-Mark-
dc.date.accessioned2008-03-04T13:49:17Z-
dc.date.available2008-03-04T13:49:17Z-
dc.date.issued2002-
dc.identifier.citationJournal of Sports Sciences, 20(10): 829-44en
dc.identifier.issn0264-0414-
dc.identifier.pmid12363298-
dc.identifier.doi10.1080/026404102320675666-
dc.identifier.urihttp://hdl.handle.net/2436/19654-
dc.descriptionMetadata onlyen
dc.description.abstractIn this paper, we identify appropriate statistical methods for analysing categorical differences in discrete variables or 'performance indicators' resulting from performance analysis. The random mechanisms associated with discrete events do not follow a normal distribution; that is, the normal distribution is a continuous not a discrete probability distribution. We propose appropriate statistical methods based on two key discrete probability distributions, the Poisson and binomial distributions. Two approaches are proposed and compared using examples from notational analysis. The first approach is based on the classic chi-square test of significance (both the goodness-of-fit test and the test of independence). The second approach adopts a more contemporary method based on log-linear and logit models fitted using the statistical software GLIM. Provided relatively simple one-way and two-way comparisons in categorical data are required, both of these approaches result in very similar conclusions. However, as soon as more complex models or higher-order comparisons are required, the approach based on log-linear and logit models is shown to be more effective. Indeed, when investigating those factors and categorical differences associated with binomial or binary response variables, such as the proportion of winners when attempting decisive shots in squash or the proportion of goals scored from all shots in association football, logit models become the only realistic method available. By applying log-linear and logit models to discrete events resulting from notational analysis, greater insight into the underlying mechanisms associated with sport performance can be achieved.en
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.relation.urlhttp://www.ingentaconnect.com/content/tandf/rjsp/2002/00000020/00000010/art00008en
dc.subjectChi-square tests of significanceen
dc.subjectDiscrete eventsen
dc.subjectLog-linear modelsen
dc.subjectPerformance indicatorsen
dc.subject.meshHumansen
dc.subject.meshPsychomotor Performanceen
dc.subject.meshSportsen
dc.subject.meshStatistics as Topicen
dc.subject.meshTask Performance and Analysisen
dc.titleStatistical methods for analysing discrete and categorical data recorded in performance analysis.en
dc.typeArticleen

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