Analysis of data from the International Outcome Inventory for Hearing Aids (IOI-HA) using Bayesian Item Response Theory

Arne Leijon, Harvey Dillon, Louise Hickson, Martin Kinkel, Sophia E Kramer, Peter Nordqvist

    Research output: Contribution to journalArticle

    Abstract

    OBJECTIVE: IOI-HA response data are conventionally analysed assuming that the ordinal responses have interval-scale properties. This study critically considers this assumption and compares the conventional approach with a method using Item Response Theory (IRT).

    DESIGN: A Bayesian IRT analysis model was implemented and applied to several IOI-HA data sets.

    STUDY SAMPLE: Anonymised IOI-HA responses from 13273 adult users of one or two hearing aids in 11 data sets using the Australian English, Dutch, German and Swedish versions of the IOI-HA.

    RESULTS: The raw ordinal responses to IOI-HA items do not represent values on interval scales. Using the conventional rating sum as an overall score introduces a scale error corresponding to about 10 - 15% of the true standard deviation in the population. Some interesting and statistically credible differences were demonstrated among the included data sets.

    CONCLUSIONS: It is questionable to apply conventional statistical measures like mean, variance, t-tests, etc., on the raw IOI-HA ratings. It is recommended to apply only nonparametric statistical test methods for comparisons of IOI-HA results between groups. The scale error can sometimes cause incorrect conclusions when individual results are compared. The IRT approach is recommended for analysis of individual results.

    Original languageEnglish
    Number of pages8
    JournalInternational Journal of Audiology
    DOIs
    Publication statusE-pub ahead of print - 11 Sep 2020

    Keywords

    • hearing aids
    • IOI-HA
    • Item Response Theory
    • behavioural measures
    • Hearing aids

    Fingerprint Dive into the research topics of 'Analysis of data from the International Outcome Inventory for Hearing Aids (IOI-HA) using Bayesian Item Response Theory'. Together they form a unique fingerprint.

    Cite this