Research ArticleBEHAVIORAL PSYCHOLOGY

Risk preference shares the psychometric structure of major psychological traits

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Science Advances  04 Oct 2017:
Vol. 3, no. 10, e1701381
DOI: 10.1126/sciadv.1701381
  • Fig. 1 Network plot showing the correlations between risk-taking measures (only correlations exceeding an absolute value of 0.1; n = 1507).

    The full names of the measures are provided in Table 1. The panels on the right show the empirical rank orders across the measures of each tradition (participants sorted by their mean rank plotted against their actual mean rank). Each panel also displays two benchmarks resulting from simulated ranking: The blue curves depict the rank order assuming perfect consistency across measures (these ranks do not form entirely straight lines because some of the measures comprise a finite number of possible response values, thus leading to tied ranks). The brown curves depict the rank order assuming no consistency across measures (that is, random ranks).

  • Fig. 2 Bifactor model (n = 1507) with all risk-taking measures, grouped by measurement tradition (Table 1).

    R reflects a general factor of risk preference, and F1 to F7 reflect a series of specific factors. The specific factors were formed by selecting all measures that loaded ≥0.25 on at least one factor in a preceding EFA with bifactor rotation. The stacked bars indicate the proportion of variance in each of the measures explained by the factors. Negative loadings are represented by dotted lines.

  • Fig. 3 Test–retest reliability and coefficient of variation across participants (that is, a standardized measure of dispersion that allows the amount of variance captured by different measures to be compared; n = 109).

    Note that we do not report the coefficients of variation for the extracted factors because the factor values were determined on the basis of standardized measures (making a comparison of the variance futile).

  • Table 1 Risk-taking measures used in the Basel-Berlin Risk Study.

    DV, dependent variable. All measures were coded such that higher values indicate more risk taking, except for “DFEss” (a larger sample size may reflect stronger uncertainty reduction and thus less risk taking).

    MeasureSubscale/DVAbbreviation
    Propensity measures
      Socioeconomic panel (90)General risk takingSOEP
    FinancialSOEPfin
    HealthSOEPhea
    RecreationalSOEPrec
    OccupationalSOEPocc
    SocialSOEPsoc
    DrivingSOEPdri
      Domain-specific risk-attitude scale (4)InvestmentDinv
    GamblingDgam
    HealthDhea
    RecreationalDrec
    EthicalDeth
    SocialDsoc
      Gambling Attitude and Beliefs Survey (91)Total scoreGABS
      Personal Risk Inventory (92)Total scorePRI
      Sensation Seeking Scale (93)Thrill and adventure seekingSStas
    Experience seekingSSexp
    DisinhibitionSSdis
    Boredom susceptibilitySSbor
      Barratt’s Impulsivity Scale (94)AttentionalBISa
    MotorBISm
    Nonplanning behaviorBISn
    Behavioral measures
      Balloon Analogue Risk Task (95)Number of pumpsBART
      Decisions from experience (72)Sample sizeDFEss
    % Risky choicesDFEre
      Decisions from description (72)% Risky choicesDFD
      Adaptive lotteries (96)% Risky choicesLOT
      Multiple price list (97)Switching point (inverted)MPL
      Columbia Card Task (98)Number of cardsCCT
      Marbles task (99)% Risky choicesMT
      Vienna Risk-Taking Test Traffic (100)Reaction latencyVRTTT
    Frequency measures
      Alcohol use disorders identification test (22)Total scoreAUDIT
      Fagerström test for nicotine dependence (23)Total scoreFTND
      Pathological gambling (24)Total scorePG
      Drug Abuse Screening Test (101)Total scoreDAST
      Encounters with risky situations (102)Aggressive behaviorCAREa
    Sexual behaviorCAREs
    Behavior at workCAREw
      Risky behaviors in the past month (4)Total scoreDm

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/10/e1701381/DC1

    Methods and DVs of behavioral tasks

    fig. S1. Distributions of scores on risk-taking measures (n = 1507).

    fig. S2. Network plot showing the disattenuated correlations between risk-taking measures (only correlations exceeding an absolute value of 0.1; n = 1507).

    fig. S3. Reduced bifactor model (n = 1507).

    table S1. Sociodemographic and related variables.

    table S2. Correlations between all risk-taking measures and extracted factors.

    table S3. Disattenuated correlations between all risk-taking measures and extracted factors.

    table S4. Factor loadings resulting from the EFA across all risk-taking measures.

    table S5. Bayesian regression analyses: Individual measures as dependent variables.

    table S6. Bayesian regression analyses: Psychometric factors as dependent variables.

    table S7. Decision problems used in the MPL task.

    table S8. Initial lotteries used in the LOT task.

  • Supplementary Materials

    This PDF file includes:

    • Methods and DVs of behavioral tasks
    • fig. S1. Distributions of scores on risk-taking measures (n = 1507).
    • fig. S2. Network plot showing the disattenuated correlations between risk-taking measures (only correlations exceeding an absolute value of 0.1; n = 1507).
    • fig. S3. Reduced bifactor model (n = 1507).
    • table S1. Sociodemographic and related variables.
    • table S2. Correlations between all risk-taking measures and extracted factors.
    • table S3. Disattenuated correlations between all risk-taking measures and extracted factors.
    • table S4. Factor loadings resulting from the EFA across all risk-taking measures.
    • table S5. Bayesian regression analyses: Individual measures as dependent variables.
    • table S6. Bayesian regression analyses: Psychometric factors as dependent variables.
    • table S7. Decision problems used in the MPL task.
    • table S8. Initial lotteries used in the LOT task.

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