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Simultaneous Analysis of Individual and Aggregate Responses in Psychometric Data Using Multilevel Modeling

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  • Ian H. Langford
  • Claire Marris
  • Annë‐Lise McDonald
  • Harvey Goldstein
  • Jon Rasbash
  • Tim O'Riordan

Abstract

Psychometric data on risk perceptions are often collected using the method developed by Slovic, Fischhoff, and Lichtenstein, where an array of risk issues are evaluated with respect to a number of risk characteristics, such as how dreadful, catastrophic or involuntary exposure to each risk is. The analysis of these data has often been carried out at an aggregate level, where mean scores for all respondents are compared between risk issues. However, this approach may conceal important variation between individuals, and individual analyses have also been performed for single risk issues. This paper presents a new methodological approach using a technique called multilevel modelling for analysing individual and aggregated responses simultaneously, to produce unconditional and unbiased results at both individual and aggregate levels of the data. Two examples are given using previously published data sets on risk perceptions collected by the authors, and results between the traditional and new approaches compared. The discussion focuses on the implications of and possibilities provided by the new methodology.

Suggested Citation

  • Ian H. Langford & Claire Marris & Annë‐Lise McDonald & Harvey Goldstein & Jon Rasbash & Tim O'Riordan, 1999. "Simultaneous Analysis of Individual and Aggregate Responses in Psychometric Data Using Multilevel Modeling," Risk Analysis, John Wiley & Sons, vol. 19(4), pages 675-683, August.
    • Handle: RePEc:wly:riskan:v:19:y:1999:i:4:p:675-683
    DOI: 10.1111/j.1539-6924.1999.tb00437.x
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    References listed on IDEAS

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    Cited by:

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    3. Peng Gu & Hao Zhang & Zeheng Liang & Dazhi Zhang, 2022. "Impact of Public Risk Perception in China on the Intention to Use Sports APPs during COVID-19 Pandemic," IJERPH, MDPI, vol. 19(19), pages 1-12, September.
    4. Busby, J.S. & Onggo, B.S.S. & Liu, Y., 2016. "Agent-based computational modelling of social risk responses," European Journal of Operational Research, Elsevier, vol. 251(3), pages 1029-1042.
    5. Lingyi Zhou & Yixin Dai, 2019. "The Influencing Factors of Haze Tolerance in China," IJERPH, MDPI, vol. 16(2), pages 1-23, January.
    6. Seda Erdem & Dan Rigby, 2013. "Investigating Heterogeneity in the Characterization of Risks Using Best Worst Scaling," Risk Analysis, John Wiley & Sons, vol. 33(9), pages 1728-1748, September.
    7. Henry H. Willis & Michael L. DeKay & Baruch Fischhoff & M. Granger Morgan, 2005. "Aggregate, Disaggregate, and Hybrid Analyses of Ecological Risk Perceptions," Risk Analysis, John Wiley & Sons, vol. 25(2), pages 405-428, April.
    8. B Ding & M Stevenson & J.S. Busby, 2017. "The relationship between risk control imperative and perceived causation: the case of product counterfeiting in China," Journal of Risk Research, Taylor & Francis Journals, vol. 20(6), pages 800-826, June.
    9. Kai Zhang & Xuejiao Chen, 2022. "Research on the Influencing Mechanism via Which Security Perception of Personal Information Affects Tourist Happiness: A Moderated Mediation Model," Sustainability, MDPI, vol. 14(22), pages 1-23, November.
    10. Craig W. Trumbo & Katherine A. McComas & John C. Besley, 2008. "Individual‐ and Community‐Level Effects on Risk Perception in Cancer Cluster Investigations," Risk Analysis, John Wiley & Sons, vol. 28(1), pages 161-178, February.
    11. Michael Siegrist & Carmen Keller & Henk A. L. Kiers, 2005. "A New Look at the Psychometric Paradigm of Perception of Hazards," Risk Analysis, John Wiley & Sons, vol. 25(1), pages 211-222, February.

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