Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Fundamental frequency range and other acoustic factors that might contribute to the clear-speech benefit.

The Journal of the Acoustical Society of America·2021
Same author

Diagnostic Precision of Open-Set Versus Closed-Set Word Recognition Testing.

Journal of speech, language, and hearing research : JSLHR·2019
Same author

Backward masking of tones and speech in people who do and do not stutter.

Journal of fluency disorders·2018
Same author

Clinical Strategies for Sampling Word Recognition Performance.

Journal of speech, language, and hearing research : JSLHR·2018
Same author

Building a developmental toxicity ontology.

Birth defects research·2018
Same author

Pure-Tone-Spondee Threshold Relationships in Functional Hearing Loss: A Test of Loudness Contribution.

Journal of speech, language, and hearing research : JSLHR·2016

Related Experiment Video

Updated: Jul 9, 2026

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

A multinomial model for identifying significant pure-tone threshold shifts.

Robert S Schlauch1, Edward Carney

  • 1Department of Speech-Language-Hearing Sciences, University of Minnesota, 115 Shevlin Hall, 164 Pillsbury Drive, SE, Minneapolis, MN 55455, USA. schla001@umn.edu

Journal of Speech, Language, and Hearing Research : JSLHR
|December 7, 2007
PubMed
Summary

A new multinomial probability model provides a statistically sound method for evaluating hearing threshold changes on retest. This tool helps identify significant audiogram shifts in clinical audiology practice.

More Related Videos

Pupillometry to Assess Auditory Sensation in Guinea Pigs
09:25

Pupillometry to Assess Auditory Sensation in Guinea Pigs

Published on: January 6, 2023

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Related Experiment Videos

Last Updated: Jul 9, 2026

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

Pupillometry to Assess Auditory Sensation in Guinea Pigs
09:25

Pupillometry to Assess Auditory Sensation in Guinea Pigs

Published on: January 6, 2023

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Area of Science:

  • Audiology
  • Statistical modeling
  • Hearing science

Background:

  • Current methods for assessing hearing threshold changes on retest often use arbitrary rules.
  • These traditional methods do not account for the inherent probability of observing specific audiogram patterns.

Purpose of the Study:

  • To develop a statistically rigorous method for evaluating test-retest differences in pure-tone audiometry.
  • To address the limitations of ad hoc rules in identifying significant hearing threshold shifts.

Main Methods:

  • A general solution was developed using multinomial probabilities.
  • The model incorporates the standard deviation of inter-test differences and categories of threshold change.
  • Probabilities of observing retest threshold patterns are calculated.

Main Results:

  • The multinomial model was compared against existing ad hoc methods for identifying threshold shifts.
  • The model demonstrated comparable performance to traditional methods in identifying significant audiograms in individuals exposed to high sound pressure levels.

Conclusions:

  • Tables derived from the multinomial model offer a clinical tool for audiogram evaluation.
  • This approach enhances the identification of statistically significant test-retest hearing threshold differences.