The Evolution of Statistical Methods in Speech, Language, and Hearing Sciences.

Jacob J Oleson, Grant D Brown, Ryan McCreery
Author Information
  1. Jacob J Oleson: Department of Biostatistics, University of Iowa, Iowa City.
  2. Grant D Brown: Department of Biostatistics, University of Iowa, Iowa City.
  3. Ryan McCreery: Boys Town National Research Hospital, Omaha, NE.

Abstract

Purpose Scientists in the speech, language, and hearing sciences rely on statistical analyses to help reveal complex relationships and patterns in the data collected from their research studies. However, data from studies in the fields of communication sciences and disorders rarely conform to the underlying assumptions of many traditional statistical methods. Fortunately, the field of statistics provides many mature statistical techniques that can be used to meet today's challenges involving complex studies of behavioral data from humans. In this review article, we highlight several techniques and general approaches with promising application to analyses in the speech and hearing sciences. Method The goal of this review article is to provide an overview of potentially underutilized statistical methods with promising application in the speech, language, and hearing sciences. Results We offer suggestions to identify when alternative statistical approaches might be advantageous when analyzing proportion data and repeated measures data. We also introduce the Bayesian paradigm and statistical learning and offer suggestions for when a scientist might consider those methods. Conclusion Modern statistical techniques provide more flexibility and enable scientists to ask more direct and informative research questions.

References

  1. Ear Hear. 2015 Nov-Dec;36 Suppl 1:24S-37S [PMID: 26731156]
  2. Ecology. 2011 Jan;92(1):3-10 [PMID: 21560670]
  3. J Speech Hear Res. 1985 Sep;28(3):455-62 [PMID: 4046587]
  4. J Speech Lang Hear Res. 2019 Mar 25;62(3):577-586 [PMID: 30950731]
  5. Ear Hear. 2014 Mar-Apr;35(2):148-60 [PMID: 24231628]
  6. J Speech Lang Hear Res. 2017 Oct 17;60(10):2891-2905 [PMID: 28980007]
  7. J Speech Lang Hear Res. 2019 Mar 25;62(3):489-497 [PMID: 30950745]
  8. Stat Methods Med Res. 2016 Dec;25(6):2925-2938 [PMID: 24821002]
  9. Ear Hear. 2015 Nov-Dec;36 Suppl 1:76S-91S [PMID: 26731161]
  10. J Acoust Soc Am. 2016 Dec;140(6):4367 [PMID: 28040030]
  11. Laryngoscope. 2018 Feb;128(2):473-481 [PMID: 28543270]
  12. JAMA Otolaryngol Head Neck Surg. 2014 May;140(5):403-9 [PMID: 24700303]
  13. J Speech Lang Hear Res. 2018 Mar 15;61(3):675-689 [PMID: 29484363]
  14. Ear Hear. 2016 Jan-Feb;37(1):55-63 [PMID: 26226605]

Grants

  1. R01 DC013591/NIDCD NIH HHS

MeSH Term

Biomedical Research
Data Interpretation, Statistical
Hearing Disorders
Humans
Language Disorders
Speech Disorders
Statistics as Topic

Word Cloud

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