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Updated: Dec 27, 2025
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
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Identifying robust correlates of risk preference: A systematic approach using specification curve analysis.
Renato Frey1, David Richter2, Jürgen Schupp2
1Center for Cognitive and Decision Sciences, Department of Psychology, University of Basel.
Journal of Personality and Social Psychology
|March 3, 2020
Summary
Risk preference associations depend heavily on measurement methods. While age and sex show consistent links, self-reported measures capture more correlations than behavioral ones, highlighting the importance of operationalization in psychological research.
Area of Science:
- Psychology
- Behavioral Economics
- Decision Science
Background:
- Risk preferences are crucial for real-life decisions.
- Previous research on risk preference correlates lacks robustness due to varied measurements and analytical limits.
- Identifying reliable correlates of risk preference is essential for psychological theories.
Purpose of the Study:
- To investigate robust correlates of risk preference using diverse operationalizations and advanced analytical techniques.
- To examine the influence of measurement methods (self-report vs. behavioral) on identified correlates.
- To clarify the associations between demographic and cognitive factors and risk preference.
Main Methods:
- Employed specification curve analysis, an exhaustive modeling approach.
- Collected data from a diverse German population sample (N = 916).
- Utilized multiple operationalizations of risk preference and analyzed six candidate correlates: income, sex, age, fluid intelligence, crystallized intelligence, and education.
Main Results:
- Sex and age demonstrated robust and consistent associations with risk preference.
- Other correlates, including income, education, and fluid intelligence, showed weaker or domain-specific associations.
- Crystallized intelligence exhibited no robust associations with risk preference.
- Self-reported risk propensity measures revealed more associations with correlates than incentivized behavioral measures.
Conclusions:
- The measurement operationalization of risk preference significantly impacts identified correlates.
- Behavioral measures of risk preference were less associated with demographic and cognitive factors compared to self-report measures.
- Findings underscore the critical role of measurement in understanding risk preference and its correlates, informing theoretical development in personality research.
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