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Confounding dynamic risk taking propensity with a momentum prognostic strategy: the case of the Columbia Card Task (CCT).

Markiewicz Ł, Kubińska E, Tyszka T - Front Psychol (2015)

Bottom Line: We argue that the HOT version confounds an individual's willingness to accept risk with their beliefs in trend continuation vs. trend reversal in a prognostic task.However, this is not the case in the COLD version.We speculate that other dynamic risk taking measures can also be biased by a momentum strategy.

View Article: PubMed Central - PubMed

Affiliation: Centre for Economic Psychology and Decision Sciences, Kozminski University Warsaw, Poland.

ABSTRACT
Figner et al. (2009) developed the Columbia Card Task (CCT) to measure risk-taking attitudes. This tool consists of two versions: in the COLD version the decision maker needs to state in advance how many cards (out of 32) they want to turn over (so called static risk taking), in the HOT version they have the possibility of turning over all 32 cards one-by-one until they decide to finish (dynamic risk taking). We argue that the HOT version confounds an individual's willingness to accept risk with their beliefs in trend continuation vs. trend reversal in a prognostic task. In two experimental studies we show that people believing in trend continuation (momentum subjects) turn over more cards than those believing in trend reversal (contrarians) in the HOT version of the task. However, this is not the case in the COLD version. Thus, we provide evidence that, when considered as a dynamic risk propensity measure, the number of turned over cards in the HOT version of the CCT is a contaminated measure and reflects two phenomena: (1) risk preference and (2) the decision-maker's belief in trend continuation. We speculate that other dynamic risk taking measures can also be biased by a momentum strategy.

No MeSH data available.


Study 1: the number of turned over cards in the HOT and COLD conditions separately for momentum and contrarian participants.
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Figure 2: Study 1: the number of turned over cards in the HOT and COLD conditions separately for momentum and contrarian participants.

Mentions: On average, respondents taking part in the HOT task3 disclosed more cards (M = 26.95; SD = 3.17) than those taking part in the COLD task (M = 13.24; SD = 4.59), as revealed by t-test, t(223.669) = 27.743, p = 0.001; d = 3.48. To test the hypothesis (H1) that prognostic strategy, as measured externally for the CCT, influences the number of turned over cards in the HOT condition, a two-way independent factorial ANOVA was conducted with the results presented in Figure 2. We demonstrated a significant main effect of CCT task on the number of turned over cards, F(1,154) = 506.71, p = 0.001, η2 = 0.767. There was also a significant interaction effect of CCT task and prognostic strategy on the number of turned over cards, F(1,154) = 5.18, p = 0.024; η2 = 0.033. Prognostic strategy influenced the number of turned over cards differently in the COLD and HOT tasks. The observed interaction contributes to better understanding of the CCT HOT task, since such interactions are nowadays considered to provide major contributions to judgment and decision making studies (Appelt et al., 2011).


Confounding dynamic risk taking propensity with a momentum prognostic strategy: the case of the Columbia Card Task (CCT).

Markiewicz Ł, Kubińska E, Tyszka T - Front Psychol (2015)

Study 1: the number of turned over cards in the HOT and COLD conditions separately for momentum and contrarian participants.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528171&req=5

Figure 2: Study 1: the number of turned over cards in the HOT and COLD conditions separately for momentum and contrarian participants.
Mentions: On average, respondents taking part in the HOT task3 disclosed more cards (M = 26.95; SD = 3.17) than those taking part in the COLD task (M = 13.24; SD = 4.59), as revealed by t-test, t(223.669) = 27.743, p = 0.001; d = 3.48. To test the hypothesis (H1) that prognostic strategy, as measured externally for the CCT, influences the number of turned over cards in the HOT condition, a two-way independent factorial ANOVA was conducted with the results presented in Figure 2. We demonstrated a significant main effect of CCT task on the number of turned over cards, F(1,154) = 506.71, p = 0.001, η2 = 0.767. There was also a significant interaction effect of CCT task and prognostic strategy on the number of turned over cards, F(1,154) = 5.18, p = 0.024; η2 = 0.033. Prognostic strategy influenced the number of turned over cards differently in the COLD and HOT tasks. The observed interaction contributes to better understanding of the CCT HOT task, since such interactions are nowadays considered to provide major contributions to judgment and decision making studies (Appelt et al., 2011).

Bottom Line: We argue that the HOT version confounds an individual's willingness to accept risk with their beliefs in trend continuation vs. trend reversal in a prognostic task.However, this is not the case in the COLD version.We speculate that other dynamic risk taking measures can also be biased by a momentum strategy.

View Article: PubMed Central - PubMed

Affiliation: Centre for Economic Psychology and Decision Sciences, Kozminski University Warsaw, Poland.

ABSTRACT
Figner et al. (2009) developed the Columbia Card Task (CCT) to measure risk-taking attitudes. This tool consists of two versions: in the COLD version the decision maker needs to state in advance how many cards (out of 32) they want to turn over (so called static risk taking), in the HOT version they have the possibility of turning over all 32 cards one-by-one until they decide to finish (dynamic risk taking). We argue that the HOT version confounds an individual's willingness to accept risk with their beliefs in trend continuation vs. trend reversal in a prognostic task. In two experimental studies we show that people believing in trend continuation (momentum subjects) turn over more cards than those believing in trend reversal (contrarians) in the HOT version of the task. However, this is not the case in the COLD version. Thus, we provide evidence that, when considered as a dynamic risk propensity measure, the number of turned over cards in the HOT version of the CCT is a contaminated measure and reflects two phenomena: (1) risk preference and (2) the decision-maker's belief in trend continuation. We speculate that other dynamic risk taking measures can also be biased by a momentum strategy.

No MeSH data available.