TY - JOUR
T1 - Towards a typology of risk preference
T2 - Four risk profiles describe two-thirds of individuals in a large sample of the U.S. population
AU - Frey, Renato
AU - Duncan, Shannon M.
AU - Weber, Elke U.
N1 - Funding Information:
We thank the members of the Center for Cognitive and Decision Sciences (University of Basel) for valuable input and Laura Wiles for proofreading the manuscript.
Funding Information:
Open access funding provided by University of Zurich. The present work was supported by two grants of the Swiss National Science Foundation to Renato Frey (#174042 and #194540). The Center for Decision Sciences at Columbia Business School funded the data collection.
Publisher Copyright:
© 2022, The Author(s).
PY - 2023/2
Y1 - 2023/2
N2 - It has been a longstanding goal of the behavioral sciences to measure and model people’s risk preferences. In this article, we adopt a novel theoretical perspective of doing so and test to what extent specific types of individuals share similar risk profiles (i.e., configurations of multidimensional risk preferences). To this end, we analyzed data of a U.S. sample (N = 3,123) in a comprehensive and rigorous way, resulting in a twofold contribution. First, based on data from the Domain-Specific Risk-Taking scale (DOSPERT) and using a cross-validation procedure, we established a multidimensional trait space including general and domain-specific dimensions of risk preference. Second, we employed model-based cluster analyses in this multidimensional trait space, finding that 66% of participants can be described well with four basic risk profiles. In sum, the typological perspective proposed in this article has important implications for current theories of risk preference and the measurement of individual differences therein.
AB - It has been a longstanding goal of the behavioral sciences to measure and model people’s risk preferences. In this article, we adopt a novel theoretical perspective of doing so and test to what extent specific types of individuals share similar risk profiles (i.e., configurations of multidimensional risk preferences). To this end, we analyzed data of a U.S. sample (N = 3,123) in a comprehensive and rigorous way, resulting in a twofold contribution. First, based on data from the Domain-Specific Risk-Taking scale (DOSPERT) and using a cross-validation procedure, we established a multidimensional trait space including general and domain-specific dimensions of risk preference. Second, we employed model-based cluster analyses in this multidimensional trait space, finding that 66% of participants can be described well with four basic risk profiles. In sum, the typological perspective proposed in this article has important implications for current theories of risk preference and the measurement of individual differences therein.
KW - Bayesian latent profile analyses
KW - DOSPERT
KW - Psychometric modeling
KW - Risk preference
UR - http://www.scopus.com/inward/record.url?scp=85144722745&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144722745&partnerID=8YFLogxK
U2 - 10.1007/s11166-022-09398-5
DO - 10.1007/s11166-022-09398-5
M3 - Article
AN - SCOPUS:85144722745
SN - 0895-5646
VL - 66
SP - 1
EP - 17
JO - Journal of Risk and Uncertainty
JF - Journal of Risk and Uncertainty
IS - 1
ER -