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Short Forms and Computerized Adaptive Tests With Monosyllabic Words Can Efficiently Measure Speech Recognition.

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Title: Short Forms and Computerized Adaptive Tests With Monosyllabic Words Can Efficiently Measure Speech Recognition.
Authors: Seamon, Bryant A.1,2 seamon@musc.edu, Salvador, Craig3, Mathews, Lois J.3, Velozo, Craig A.4, Dubno, Judy R.3, McRackan, Theodore R.3
Source: American Journal of Audiology. Jun2026, Vol. 35 Issue 2, p518-530. 13p.
Subject Terms: *Computer adaptive testing, *Auditory perception testing, *Data analysis, *Retrospective studies, *Verbal behavior testing, *Longitudinal method, *Research methodology, *Speech perception, *Auditory perception, *Factor analysis, *Theory, *Articulation (Speech), Hearing disorder diagnosis, Cross-sectional method, Pearson correlation (Statistics), Secondary analysis, Research funding, Research evaluation, Statistical sampling, Fisher exact test, Descriptive statistics, Statistics, Mathematical models, Medical records, Acquisition of data, Accuracy, Confidence intervals, Data analysis software, Sensitivity & specificity (Statistics)
Geographic Terms: South Carolina
Abstract: Purpose: Construct validity of the Northwestern University Auditory Test No. 6 (NU-6) monosyllabic word lists or shortened versions have not been examined using the Rasch measurement theory. The study purposes were to test the fit of the Rasch measurement model to monosyllabic word lists and whether short forms and computerized adaptive testing can measure speech recognition. Method: A cross-sectional study design with 50 persons (average age = 71 years; 35 female, 15 male) with mild-to-moderate hearing loss was used to test the fit of NU-6 Word Lists 1 and 2 to the Rasch measurement model. Pearson's correlations quantified the accuracy of person measures from short forms or computer-adaptive testing simulations with measures from the full lists. Average standard error of person measures quantified measurement precision. Results: Word lists were unidimensional, had negligible misfit, and had high person reliability. Nineteen- and 11-word short forms were made per list. Person measures from 19-word short forms had a high linear association with person measures from the full word lists (List 1: r = .92, p < .0001, SE = 0.76; List 2: r = .91, p < .0001, SE = 0.74) compared to moderate association for the 11-word short form (List 1: r = .84, p < .0001, SE = 0.99; List 2: r = .81, p < .0001, SE = 0.97). Person measures from computerized adaptive testing simulation reached a correlation threshold, r > .90, after 15-20 words were administered for both lists. A precision-based stopping rule used an average of 18 ( List 1) or 20 (List 2) words. Conclusion: Short forms with 19 words and computerized adaptive testing may accurately and precisely measure speech recognition. [ABSTRACT FROM AUTHOR]
Copyright of American Journal of Audiology is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Purpose: Construct validity of the Northwestern University Auditory Test No. 6 (NU-6) monosyllabic word lists or shortened versions have not been examined using the Rasch measurement theory. The study purposes were to test the fit of the Rasch measurement model to monosyllabic word lists and whether short forms and computerized adaptive testing can measure speech recognition. Method: A cross-sectional study design with 50 persons (average age = 71 years; 35 female, 15 male) with mild-to-moderate hearing loss was used to test the fit of NU-6 Word Lists 1 and 2 to the Rasch measurement model. Pearson&#39;s correlations quantified the accuracy of person measures from short forms or computer-adaptive testing simulations with measures from the full lists. Average standard error of person measures quantified measurement precision. Results: Word lists were unidimensional, had negligible misfit, and had high person reliability. Nineteen- and 11-word short forms were made per list. Person measures from 19-word short forms had a high linear association with person measures from the full word lists (List 1: r = .92, p &lt; .0001, SE = 0.76; List 2: r = .91, p &lt; .0001, SE = 0.74) compared to moderate association for the 11-word short form (List 1: r = .84, p &lt; .0001, SE = 0.99; List 2: r = .81, p &lt; .0001, SE = 0.97). Person measures from computerized adaptive testing simulation reached a correlation threshold, r &gt; .90, after 15-20 words were administered for both lists. A precision-based stopping rule used an average of 18 ( List 1) or 20 (List 2) words. Conclusion: Short forms with 19 words and computerized adaptive testing may accurately and precisely measure speech recognition. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of American Journal of Audiology is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1044/2025_AJA-24-00240
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      – Code: eng
        Text: English
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        StartPage: 518
    Subjects:
      – SubjectFull: Computer adaptive testing
        Type: general
      – SubjectFull: Auditory perception testing
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Retrospective studies
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      – SubjectFull: Verbal behavior testing
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      – SubjectFull: Longitudinal method
        Type: general
      – SubjectFull: Research methodology
        Type: general
      – SubjectFull: Speech perception
        Type: general
      – SubjectFull: Auditory perception
        Type: general
      – SubjectFull: Factor analysis
        Type: general
      – SubjectFull: Theory
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      – SubjectFull: Articulation (Speech)
        Type: general
      – SubjectFull: Hearing disorder diagnosis
        Type: general
      – SubjectFull: Cross-sectional method
        Type: general
      – SubjectFull: Pearson correlation (Statistics)
        Type: general
      – SubjectFull: Secondary analysis
        Type: general
      – SubjectFull: Research funding
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      – SubjectFull: Medical records
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      – SubjectFull: Sensitivity & specificity (Statistics)
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      – SubjectFull: South Carolina
        Type: general
    Titles:
      – TitleFull: Short Forms and Computerized Adaptive Tests With Monosyllabic Words Can Efficiently Measure Speech Recognition.
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            – D: 01
              M: 06
              Text: Jun2026
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