Statistical methods for analysing discrete and categorical data recorded in performance analysis.
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
MetadataShow full item record
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.
CitationJournal of Sports Sciences, 20(10): 829-44
PublisherTaylor & Francis
- [Meta-analysis of the Italian studies on short-term effects of air pollution].
- Authors: Biggeri A, Bellini P, Terracini B, Italian MISA Group.
- Issue date: 2001 Mar-Apr
- Parameter estimation and goodness-of-fit in log binomial regression.
- Authors: Blizzard L, Hosmer DW
- Issue date: 2006 Feb
- Count data distributions and their zero-modified equivalents as a framework for modelling microbial data with a relatively high occurrence of zero counts.
- Authors: Gonzales-Barron U, Kerr M, Sheridan JJ, Butler F
- Issue date: 2010 Jan 1
- Testing specific hypotheses by fitting underlying distributions to categorical data.
- Authors: Johnson WD, Elston RC, Wickremasinghe AR
- Issue date: 1994 Mar
- Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives.
- Authors: Brookes ST, Whitley E, Peters TJ, Mulheran PA, Egger M, Davey Smith G
- Issue date: 2001