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A Polytomous Item Response Theory Model for Measuring Near-Miss Appraisal as a Psychological Trait

Author

Listed:
  • Jinshu Cui

    (Department of Psychology, and Center for Risk and Economic Analysis of Terrorism Events, University of Southern California, Los Angeles, California 90089)

  • Heather Rosoff

    (Sol Price School of Public Policy, Center for Risk and Economic Analysis of Terrorism Events, University of Southern California, Los Angeles, California 90089)

  • Richard S. John

    (Department of Psychology, and Center for Risk and Economic Analysis of Terrorism Events, University of Southern California, Los Angeles, California 90089)

Abstract

Near-miss experiences have been identified as a contributing factor in responses to risk of disaster events. Researchers have found that specific characteristics of a near-miss event could lead individuals to interpret the risk as either “vulnerable” or “resilient.” Moreover, these interpretations can lead to quite different decisions regarding future protective behavior. We developed the Near-Miss Appraisal Scale (NMAS) to assess an individual’s tendency to interpret near misses as vulnerable (or resilient). We developed an initial item pool of 21 items and recruited a sample of 298 respondents through Amazon Mechanical Turk. The final version of the NMAS is based on 10 of these items, following psychometric analysis for dimensionality, scale reliability, and item functioning. We establish discriminant validity of the NMAS by correlating the NMAS with scales of locus of control and risk taking, and predictive validity by using the NMAS to predict individual responses to a near-miss disaster scenario. The current study demonstrates that responses to near misses are not only determined by the nature of the event itself, but also related to decision makers' near-miss appraisal tendencies.

Suggested Citation

  • Jinshu Cui & Heather Rosoff & Richard S. John, 2017. "A Polytomous Item Response Theory Model for Measuring Near-Miss Appraisal as a Psychological Trait," Decision Analysis, INFORMS, vol. 14(2), pages 75-86, June.
    • Handle: RePEc:inm:ordeca:v:14:y:2017:i:2:p:75-86
    DOI: 10.1287/deca.2017.0345
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    References listed on IDEAS

    as
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    Cited by:

    1. John Patrick Lalor & Pedro Rodriguez, 2023. "py-irt : A Scalable Item Response Theory Library for Python," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 5-13, January.

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