Predicting (un)healthy behavior: A comparison of risk-taking propensity measures
Journal Title: Judgment and Decision Making - Year 2012, Vol 7, Issue 6
Abstract
We compare four different risk-taking propensity measures on their ability to describe and to predict actual risky behavior in the domain of health. The risk-taking propensity measures we compare are: (1) a general measure of risk-taking propensity derived from a one-item survey question (Dohmen et al., 2011), (2) a risk aversion index calculated from a set of incentivized monetary gambles (Holt & Laury, 2002), (3) a measure of risk taking derived from an incentive compatible behavioral task—the Balloon Analog Risk Task (Lejuez et al., 2002), and (4) a composite score of risk-taking likelihood in the health domain from the Domain-Specific Risk Taking (DOSPERT) scale (Weber et al., 2002). Study participants are 351 clients of health centers around Witbank, South Africa. Our findings suggest that the one-item general measure is the best predictor of risky health behavior in our population, predicting two out of four behaviors at the 5% level and the remaining two behaviors at the 10% level. The DOSPERT score in the health domain performs well, predicting one out of four behaviors at the 1% significance level and two out of four behaviors at the 10% level, but only if the DOSPERT instrument contains a hypothetical risk-taking item that is similar to the actual risky behavior being predicted. Incentivized monetary gambles and the behavioral task were unrelated to actual health behaviors; they were unable to predict any of the risky health behaviors at the 10% level. We provide evidence that this is not because the participants had trouble understanding the monetary trade-off questions or performed poorly in the behavioral task. We conclude by urging researchers to further test the usefulness of the one-item general measure, both in explaining health related risk-taking behavior and in other contexts.
Authors and Affiliations
Helena Szrek, Li-Wei Chao, Shandir Ramlagan and Karl Peltzer
Which grades are better, A’s and C’s, or all B’s? Effects of variability in grades on mock college admissions decisions
Students may need to decide whether to invest limited resources evenly across all courses and thus end with moderate grades in all, or focus on some of the courses and thus end with variable grades. This study examined w...
Forecasting elections with mere recognition from small, lousy samples: A comparison of collective recognition, wisdom of crowds, and representative polls
We investigated the extent to which the human capacity for recognition helps to forecast political elections: We compared naïve recognition-based election forecasts computed from convenience samples of citizens’ recognit...
Subjective but not objective numeracy influences willingness to pay for BRCA1/2 genetic testing
A positive test result for BRCA1/2 gene mutation is a substantial risk factor for breast and ovarian cancer. However, testing is not always covered by insurance, even for high risk women. Variables affecting willingness...
The average laboratory samples a population of 7,300 Amazon Mechanical Turk workers
Using capture-recapture analysis we estimate the effective size of the active Amazon Mechanical Turk (MTurk) population that a typical laboratory can access to be about 7,300 workers. We also estimate that the time taken...
Compassion fade and the challenge of environmental conservation
Compassion shown towards victims often decreases as the number of individuals in need of aid increases, identifiability of the victims decreases, and the proportion of victims helped shrinks. Such “compassion fade” may h...