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Codebook for Data in Jarecki & Rieskamp (2020)
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Codebook for Data in Jarecki & Rieskamp (2020)

Codebook for Data in Jarecki & Rieskamp (2020)

This document describes the labels and content of variables in the data.

Codebook for Choice Data

Metadata

Description

Dataset name: Human risky choices with goals

Repeated binary choices by adult humans among a high- and a low-variance two-outcome lottery, who were given a point goal to reach after a certain number of choices

Metadata for search engines
  • Temporal Coverage: Dec 2018 – Jan 2019
  • Spatial Coverage: online
  • Citation: Jarecki, J. B., & Rieskamp, J. (2020). Prospect Theory and Optimal Risky Choices with Goals.
  • Date published: 2020-08-23
  • Creator:
name value
@type Person
givenName Jana B.
familyName Jarecki
email jj@janajarecki.com
affiliation Organization , University of Basel, Switzerland
x
Incentivized online choice task
x
Center for Economic Psychology, University of Basel
x
online study
risky choice
risky gamble
risk-sensitive foraging
risk sensitivity
risk sensitive foraging
energy budget rule
decisions under quota
psychology
Prolific Academic
cognition
cognitive modeling

Codebook table

plot of chunk items

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook
publicly on the web.

{
  "citation": "Jarecki, J. B., & Rieskamp, J. (2020). Prospect Theory and Optimal Risky Choices with Goals.",
  "creator": {
    "@type": "Person",
    "givenName": "Jana B.",
    "familyName": "Jarecki",
    "email": "jj@janajarecki.com",
    "affiliation": {
      "@type": "Organization",
      "name": "University of Basel, Switzerland"
    }
  },
  "spatialCoverage": "online",
  "temporalCoverage": "Dec 2018 - Jan 2019",
  "measurementTechnique": "Incentivized online choice task",
  "funder": "Center for Economic Psychology, University of Basel",
  "keywords": ["online study", "risky choice", "risky gamble", "risk-sensitive foraging", "risk sensitivity", "risk sensitive foraging", "energy budget rule", "decisions under quota", "psychology", "Prolific Academic", "cognition", "cognitive modeling"],
  "description": "Repeated binary choices by adult humans among a high- and a low-variance two-outcome lottery, who were given a point goal to reach after a certain number of choices\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "name": "Human risky choices with goals",
  "datePublished": "2020-08-23",
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "id",
      "description": "Participant id, after excluding participants",
      "@type": "propertyValue"
    },
    {
      "name": "phase",
      "description": "Phase of the experiment",
      "value": "1. familiarize,\n2. five,\n3. one",
      "@type": "propertyValue"
    },
    {
      "name": "block",
      "description": "Block number in a phase: 1 in familiarization, 7 in five-trial phase, 1 in one-shot phase.",
      "@type": "propertyValue"
    },
    {
      "name": "round",
      "description": "Round number, consecutive number, independent of block or phase.",
      "@type": "propertyValue"
    },
    {
      "name": "trial",
      "description": "Trial number in a round",
      "@type": "propertyValue"
    },
    {
      "name": "state",
      "description": "Number of points accumulated until this trial",
      "@type": "propertyValue"
    },
    {
      "name": "budget",
      "description": "Point requirement in this round",
      "@type": "propertyValue"
    },
    {
      "name": "stimulus0",
      "description": "Describes the first option (risky gamble) in this round in xpy notation: firstOutcome_prFirst_secondOutcome",
      "@type": "propertyValue"
    },
    {
      "name": "stimulus1",
      "description": "Describes the second option (risky gamble) in this round in xpy notation: firstOutcome_prFirst_secondOutcome",
      "@type": "propertyValue"
    },
    {
      "name": "terminal_state",
      "description": "Total points by the end of this round",
      "@type": "propertyValue"
    },
    {
      "name": "choice_1isHighVar",
      "description": "Choice in this trial",
      "value": "0. Low-variance option,\n1. High-variance option",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "success",
      "description": "Did the terminal_state (total points) exceed the budget (requirement) by the end of this round?",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "successes",
      "description": "Number of rounds with a success up to this round",
      "@type": "propertyValue"
    },
    {
      "name": "rt_ms",
      "description": "Reaction time in this trial in milliseconds",
      "@type": "propertyValue"
    },
    {
      "name": "xh",
      "description": "Outcome of the high-variance option in this round",
      "@type": "propertyValue"
    },
    {
      "name": "ph",
      "description": "Probability of x_h",
      "@type": "propertyValue"
    },
    {
      "name": "yh",
      "description": "Second outcome of high-variance option, pr(y_h) = 1 - p_h",
      "@type": "propertyValue"
    },
    {
      "name": "xl",
      "description": "Outcome of the low-variance option in this round",
      "@type": "propertyValue"
    },
    {
      "name": "pl",
      "description": "Probability of x_l",
      "@type": "propertyValue"
    },
    {
      "name": "yl",
      "description": "Second outcome of low-variance option, pr(y_l) = 1 - p_l",
      "@type": "propertyValue"
    },
    {
      "name": "gneezy_potter",
      "description": "One-shot choice (asked once after the choice phase): Which part of the 100 pennys do you wish to invest in the lottery? With probability 67% the lottery pays zero. You earn the amount that you did not invest. With probability 33% the lottery pays 2.5X. You earn +2.5 x X plus the amount that you did not invest.",
      "@type": "propertyValue"
    },
    {
      "name": "layout_featurecolor",
      "description": "Which feature is shown in which color in this round. The colors are dark grey (RGB #D9D9D9, grey85) or light grey (RGB #737373, gray45)",
      "value": "1. Outcome x colored dark,\n2. Outcome x colored light",
      "@type": "propertyValue"
    },
    {
      "name": "layout_stimulusposition_01",
      "description": "In which order stimulus0 and stimulus 1 are shown in this round (left or right on screen)",
      "value": "1. stimulus0 shown left,\n2. stimulus1 shown left",
      "@type": "propertyValue"
    }
  ]
}`

Codebook for Demographic Data

Metadata

Description

Dataset name: results

The dataset has N=60 rows and 17 columns.
0 rows have no missing values on any column.

Metadata for search engines
  • Temporal Coverage: Dec 2018 – Jan 2019
  • Spatial Coverage: online
  • Citation: Jarecki, J. B., & Rieskamp, J. (2020). Prospect Theory and Optimal Risky Choices with Goals.
  • Date published: 2020-08-23
  • Creator:
name value
@type Person
givenName Jana B.
familyName Jarecki
email jj@janajarecki.com
affiliation Organization , University of Basel, Switzerland
x
Incentivized online choice task
x
Center for Economic Psychology, University of Basel
x
online study
risky choice
risky gamble
risk-sensitive foraging
risk sensitivity
risk sensitive foraging
energy budget rule
decisions under quota
psychology
Prolific Academic
cognition
cognitive modeling

Codebook table

plot of chunk items-32

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook
publicly on the web.

{
  "citation": "Jarecki, J. B., & Rieskamp, J. (2020). Prospect Theory and Optimal Risky Choices with Goals.",
  "creator": {
    "@type": "Person",
    "givenName": "Jana B.",
    "familyName": "Jarecki",
    "email": "jj@janajarecki.com",
    "affiliation": {
      "@type": "Organization",
      "name": "University of Basel, Switzerland"
    }
  },
  "spatialCoverage": "online",
  "temporalCoverage": "Dec 2018 - Jan 2019",
  "measurementTechnique": "Incentivized online choice task",
  "funder": "Center for Economic Psychology, University of Basel",
  "keywords": ["online study", "risky choice", "risky gamble", "risk-sensitive foraging", "risk sensitivity", "risk sensitive foraging", "energy budget rule", "decisions under quota", "psychology", "Prolific Academic", "cognition", "cognitive modeling"],
  "name": "results",
  "datePublished": "2020-08-23",
  "description": "The dataset has N=60 rows and 17 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                  |label                                                                                      | n_missing|\n|:---------------------|:------------------------------------------------------------------------------------------|---------:|\n|id                    |Participant id                                                                             |         0|\n|age                   |How old are you?                                                                           |         0|\n|gender                |NA                                                                                         |         0|\n|language_english      |NA                                                                                         |         0|\n|dataquality           |Is the data you just generated of sufficient quality to be useful for scientific research? |         0|\n|income                |Which category does your monthly income after tax fall into?                               |         0|\n|strategy              |Can you describe how you made the decision which of the two options to pick?               |         0|\n|task_clear            |Was it clear to you what your task was during this study?                                  |         0|\n|open_text             |Is there anything you would like us to know?                                               |         0|\n|created               |Time started UTC                                                                           |         0|\n|ended                 |Time ended UTC                                                                             |         0|\n|excl                  |If participant is excluded, the reason for exclusion                                       |         0|\n|bonus_riskychoice_GBP |Bonus from 5 risky choice rounds in British Pounds                                         |         0|\n|bonus_gneezy_GBP      |Bonus from Gneezy & Potter task in British Pounds                                          |         0|\n|session               |NA                                                                                         |         0|\n|modified              |NA                                                                                         |         0|\n|expired               |NA                                                                                         |        60|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "id",
      "description": "Participant id",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "How old are you?",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "value": "1. Female,\n2. Male,\n3. Prefer not to state",
      "@type": "propertyValue"
    },
    {
      "name": "language_english",
      "value": "1. No,\n2. Yes",
      "@type": "propertyValue"
    },
    {
      "name": "dataquality",
      "description": "Is the data you just generated of sufficient quality to be useful for scientific research?",
      "value": "0. Not useful at all,\n1. Partly useful,\n2. Mostly useful,\n3. Completely useful",
      "maxValue": 3,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "income",
      "description": "Which category does your monthly income after tax fall into?",
      "value": "0. up to 1000,\n1. 1001 - 2000,\n2. 2001 - 3000,\n3. 3001 - 4000,\n4. 4001 - or more,\n99. Do not want to answer",
      "maxValue": 99,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "strategy",
      "description": "Can you describe how you made the decision which of the two options to pick?",
      "@type": "propertyValue"
    },
    {
      "name": "task_clear",
      "description": "Was it clear to you what your task was during this study?",
      "value": "0. Not clear,\n1. Mostly not clear,\n2. Mostly clear,\n3. Completely clear",
      "maxValue": 3,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "open_text",
      "description": "Is there anything you would like us to know?",
      "@type": "propertyValue"
    },
    {
      "name": "created",
      "description": "Time started UTC",
      "@type": "propertyValue"
    },
    {
      "name": "ended",
      "description": "Time ended UTC",
      "@type": "propertyValue"
    },
    {
      "name": "excl",
      "description": "If participant is excluded, the reason for exclusion",
      "value": ". Included,\nany non-empty value. Excluded",
      "maxValue": "any non-empty value",
      "minValue": "",
      "@type": "propertyValue"
    },
    {
      "name": "bonus_riskychoice_GBP",
      "description": "Bonus from 5 risky choice rounds in British Pounds",
      "@type": "propertyValue"
    },
    {
      "name": "bonus_gneezy_GBP",
      "description": "Bonus from Gneezy & Potter task in British Pounds",
      "@type": "propertyValue"
    },
    {
      "name": "session",
      "@type": "propertyValue"
    },
    {
      "name": "modified",
      "@type": "propertyValue"
    },
    {
      "name": "expired",
      "@type": "propertyValue"
    }
  ]
}`