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Data and analysis scripts for entitlement project.
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README.md

README.md

Entitlement data analysis

This repository contains the critical data and scripts to replicate the analysis in Trott & Rossano (Submitted).

Details on the data

Experiment 1

Found in exp1_critical.csv.

The main variables of interest are:

  • condition: high vs. low entitlement.
  • request: raw response produced by participant.
  • formatting: annotated formatting (M, EM, D, IS).
  • reason_final: final annotation for whether a reason was given.
  • Just2.ST: annotation for whether repayment was offered.

Additionally, the human-normed estimates:

  • mean_entitlement
  • mean_power
  • mean_imposition
  • mean_distance

With random effects:

  • subject (participant)
  • stimNum (item)

Experiment 1b

Found in exp1b_critical.csv.

The main variables of interest are:

  • condition: high vs. low entitlement.
  • answer: raw response produced by participant.
  • formatting: annotated formatting (M, EM, D, IS).
  • Reason2: final annotation for whether a reason was given.
  • Repayment2: annotation for whether repayment was offered.

Additionally, the human-normed estimates:

  • mean_entitlement
  • mean_power
  • mean_imposition
  • mean_distance

With random effects:

  • subject (participant)
  • stimNum (item)

Experiment 2

Found in exp2_critical.csv.

The main variables of interest are:

  • condition: high vs. low entitlement.
  • answer: response chosen by participant.
  • formatting: option chosen (D, M, EM, IS)

Additionally, the human-normed estimates:

  • mean_entitlement
  • mean_power
  • mean_imposition
  • mean_distance

With random effects:

  • subject (participant)
  • stimNum (item)

Experiment 3

Found in exp3_critical.csv.

The main variables of interest are:

  • condition: high vs. low entitlement.
  • answer: response chosen by participant.
  • formatting: formatting used by character (D, M, EM, IS)
  • judgment: centered answer (from Likert scale)

Additionally, the human-normed estimates:

  • mean_entitlement
  • mean_power
  • mean_imposition
  • mean_distance

With random effects:

  • subject (participant)
  • stimNum (item)
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