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International Comparative Surveys in Risk Perception Research: Data Sets, Construction of Questionnaires, and Analytical Dimensions

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Researching Risk and Uncertainty

Part of the book series: Critical Studies in Risk and Uncertainty ((CRSTRU))

Abstract

This chapter focuses on the construction and measurement of risk perception items in international comparative surveys (e.g. International Social Survey Programme (ISSP), Gallup World Polls, Eurobarometer) and provides a comprehensive review of available open access data sets. The chapter reviews and discusses how risk perception is worded in different questionnaires, what measurement scales are used, and how this relates to more general methodological concerns about the construction of rating scales.

A number of examples of studies using international surveys to investigate risk perceptions are provided in this chapter to illustrate the variety of analytical dimensions that can be used to analyse risk perceptions comparatively, the challenges faced by methodological differences across international surveys are analysed, and a checklist is provided to support researchers interested in comparative analysis of risk perception.

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Notes

  1. 1.

    http://ec.europa.eu/COMMFrontOffice/publicopinion/index.cfm

  2. 2.

    Eurobarometer Data Service, http://www.gesis.org/eurobarometer-data-service/survey-series/standard-special-eb/

  3. 3.

    Normal distribution in the case of continuous data is a symetrical distribution with a bell shape (Russo 2003:26)

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Resources of International Survey Data

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Correspondence to Aistė Balžekienė .

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Appendices

Appendix I: Resources

Summary of items related to environmental and technological risk in different international surveys

Survey

Year

Topic

Selected references

International Social Survey Programme

ISSP “Environment”

1993; 2000; 2010

Environmental risk (climate change, genetically modified organisms, air pollution)

Technological risk (nuclear power)

Tjernström and Tietenberg (2008) (climate change)

Lima et al. (2005) (technological risks)

Adeola (2007) (environmental risks)

Lo and Chow (2015) (climate change)

Eurobarometer

Special EB 354; 238

2010; 2006

Food-related risks

Gaskell et al. (2016)

Hohl and Gaskell (2008)

Special EB 416; 365; 295; 217; 180; 131; 88; 66

2014; 2011; 2008; 2005;

2003; 1999; 1995; 1993

Environmental risk

Climate change

Raska (2015)

Lorenzoni and Pidgeon (2006)

Special EB 314, 246

2010; 2006

Risk perception of potential health hazards

Bennie et al. (2013)

Special EB 324; 271

2010; 2007

Nuclear safety

Goodfellow et al. 2011

Special EB 340, 224; 154

2010; 2005; 2001

Technology in general

Rerimassie et al. 2015

Special EB 341; 244b; 177; 134; 108; 80

2010; 2006; 2003; 2000;

1997; 1993

Biotechnology

Connor and Siegrist (2016)

Gaskell et al. (2004)

Gallup World Poll

2007/2008; 2010

Climate change

Knight (2016)

Appendix II: Summary of Selected Statistics for Non-normal Distributions

Hypothesis

Type of measure

Non-parametric statistics

Correlation

Interval, ordinal

Spearman’s rho

Association

Nominal, ordinal

Phi (tables 2X2);

Contingency C (larger tables)

Cramer’s V (equal number of categories in dependent and independent variable)

Equality of means for two independent samples

Scale or interval as dependent variable/nominal or ordinal as independent

Mann–Whitney U test

Equality of means for K independent samples

Scale or interval as dependent variable/nominal or ordinal as independent

Kruskal–Wallis test

Equality of means for two paired samples

Scale or interval as dependent variable/nominal or ordinal as independent

Wilcoxon test

Equality of means for K paired samples

Scale or interval as dependent variable/nominal or ordinal as independent

Friedman test

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Balžekienė, A. (2019). International Comparative Surveys in Risk Perception Research: Data Sets, Construction of Questionnaires, and Analytical Dimensions. In: Olofsson, A., Zinn, J.O. (eds) Researching Risk and Uncertainty. Critical Studies in Risk and Uncertainty. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-95852-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-95852-1_10

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