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An Application of Item Response Theory to Scoring Patient Safety Culture Survey Data

Author

Listed:
  • Heon-Jae Jeong

    (The Care Quality Research Group, 174 Toegye, Chuncheon, Gangwon 24450, Korea)

  • Hsun-Hsiang Liao

    (Joint Commission of Taiwan, No. 31, Sec. 2, Sanmin Rd., Banqiao Dist., New Taipei City 220, Taiwan)

  • Su Ha Han

    (Department of Nursing, Soon Chun Hyang University, 31 Soon Chun Hyang 6-gil, Dongnam-gu, Cheonan-si, Chungcheongnam-do 31151, Korea)

  • Wui-Chiang Lee

    (Department of Medical Affairs and Planning, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., Taipei City 112, Taiwan
    National Yang-Ming University School of Medicine, Institute of Hospital and Healthcare Administration, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei City 112, Taiwan)

Abstract

Patient safety culture is important in preventing medical errors. Thus, many instruments have been developed to measure it. Yet, few studies focus on the data processing step. This study, by analyzing the Chinese version of the Safety Attitudes Questionnaire dataset that contained 37,163 questionnaires collected in Taiwan, found critical issues related to the currently used mean scoring method: The instrument, like other popular ones, uses a 5-point Likert scale, and because it is an ordinal scale, the mean scores cannot be calculated. Instead, Item Response Theory (IRT) was applied. The construct validity was satisfactory and the item properties of the instrument were estimated from confirmatory factor analysis. The IRT-based domain scores and mean domain scores of each respondent were estimated and compared. As for resolution, the mean approach yielded only around 20 unique values on a 0 to 100 scale for each domain; the IRT method yielded at least 440 unique values. Meanwhile, IRT scores ranged widely at each unique mean score, meaning that the precision of the mean approach was less reliable. The theoretical soundness and empirical strength of IRT suggest that healthcare institutions should adopt IRT as a new scoring method, which is the core step of processing collected data.

Suggested Citation

  • Heon-Jae Jeong & Hsun-Hsiang Liao & Su Ha Han & Wui-Chiang Lee, 2020. "An Application of Item Response Theory to Scoring Patient Safety Culture Survey Data," IJERPH, MDPI, vol. 17(3), pages 1-10, January.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:854-:d:314301
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    References listed on IDEAS

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    2. Heon-Jae Jeong & Wui-Chiang Lee & Hsun-Hsiang Liao & Feng-Yuan Chu & Tzeng-Ji Chen & Pa-Chun Wang, 2019. "The Hospital Patient Safety Culture Survey: Reform of Analysis and Visualization Methods," IJERPH, MDPI, vol. 16(19), pages 1-7, September.
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    4. Li Cai, 2010. "Metropolis-Hastings Robbins-Monro Algorithm for Confirmatory Item Factor Analysis," Journal of Educational and Behavioral Statistics, , vol. 35(3), pages 307-335, June.
    5. Alan C. Acock, 2013. "Discovering Structural Equation Modeling Using Stata," Stata Press books, StataCorp LP, number dsemus, March.
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