Guidelines for Statistical Analysis of Percentage of Syllables Stuttered Data Purpose The purpose of this study was to develop guidelines for the statistical analysis of percentage of syllables stuttered (%SS) data in stuttering research. Method Data on %SS from various independent sources were used to develop a statistical model to describe this type of data. On the basis ... Research Article
Research Article  |   November 14, 2016
Guidelines for Statistical Analysis of Percentage of Syllables Stuttered Data
 
Author Affiliations & Notes
  • Mark Jones
    School of Population Health, University of Queensland, Brisbane, Australia
  • Mark Onslow
    Australian Stuttering Research Centre, The University of Sydney, Sydney, Australia
  • Ann Packman
    Australian Stuttering Research Centre, The University of Sydney, Sydney, Australia
  • Val Gebski
    National Health and Medical Research Council Clinical Trials Centre, The University of Sydney
  • Contact author: Mark Onslow, Australian Stuttering Research Centre, The University of Sydney, Cumberland Campus, Lidcombe, New South Wales 1825, Australia. E-mail: m.onslow@fhs.usyd.edu.au
Article Information
Speech, Voice & Prosodic Disorders / Fluency Disorders / Research Issues, Methods & Evidence-Based Practice / Speech, Voice & Prosody / Speech / Research Articles
Research Article   |   November 14, 2016
Guidelines for Statistical Analysis of Percentage of Syllables Stuttered Data
Journal of Speech, Language, and Hearing Research, November 2016, Vol. 49, 867-878. doi:10.1044/1092-4388(2006/062)
History: Received December 8, 2004 , Revised May 27, 2005 , Accepted November 21, 2005
 
Journal of Speech, Language, and Hearing Research, November 2016, Vol. 49, 867-878. doi:10.1044/1092-4388(2006/062)
History: Received December 8, 2004; Revised May 27, 2005; Accepted November 21, 2005

Purpose The purpose of this study was to develop guidelines for the statistical analysis of percentage of syllables stuttered (%SS) data in stuttering research.

Method Data on %SS from various independent sources were used to develop a statistical model to describe this type of data. On the basis of this model, %SS data were simulated with varying means, standard deviations, and sample sizes. Four methods for analyzing %SS were compared.

Results Results suggested that %SS data can be adequately modeled with a gamma distribution. Simulations based on a gamma distribution showed that all 4 analysis techniques performed favorably with respect to Type I error except for F. E. Satterthwaite’s (1946)  t test, which had increased Type I error on two occasions. Power was generally lower for the Wilcoxon–Mann–Whitney test compared with the other methods. Analysis of variance (ANOVA) performed on square-root-transformed data performed adequately under all scenarios, but ANOVA performed on nontransformed data and Satterthwaite’s t test performed poorly when sample sizes were small or when sample sizes and variances of the groups were markedly different.

Conclusions Standard techniques such as t test and ANOVA are appropriate for most analysis scenarios with %SS data. Two occasions when this is not the case are when sample size is small, with fewer than 20 in each group, or when sample sizes and variances of the groups are markedly different. Under these circumstances, analyses should be based on standard methods, with a suitable transformation performed prior to analysis.

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