vdeni / rrr_blame-game

Materials for a replication attempt of the linguistic frame effect on perceived blame and financial liability

A preregistered direct replication of the linguistic frame effect on perceived blame and financial liability

Table of contents

• General information
• Data metadata
• Directory structure

General information

This repository contains the scripts and cleaned data files used in a replication attempt of: Fausey, C. M., Boroditsky, L. (2010). Subtle linguistic cues influence perceived blame and financial liability. Psychonomic Bulletin & Review 17, 644–650. https://doi.org/10.3758/PBR.17.5.644

The associated Open Science Framework project is: https://osf.io/jmknw/

Data metadata

Variable names are shared between files:

• respondent_id: SurveyMonkey's internal participant ID
• collector_id: SurveyMonkey's internal "data collector" ID
• start_date: the datetime at which the questionnaire was accessed
• end_date: the datetime at which the questionnaire was finished
• informed_consent: has the participant given an informed consent to participation. {TRUE, FALSE}. Should be TRUE for everyone.
• experimental_situation: specific to study_1X. whether the participant read the agentive or the nonagentive description of the event. {agentive, nonagentive, NA if an experimental situation wasn't assigned, i.e. if the participant quit}
• assess_blame: specific to study_1X. the participants' assessment of the blame of the subject of the described event. should be {1, 2, ..., 7, NA}
• assess_fine: the fine that the participants' assigned to the subject of the described event. lower bound should be 0, upper bound is defined by exclusion criteria outlined in the paper; NA if participant did not respond
• assess_active_role: the participants' assessment of the extent to which the subject of the described event played an active role in starting the fire. this is a manipulation check. should be {1, 2, ..., 7, NA}
• age: the participants stated age. lower bound is 0, upper bound was not defined
• gender: the participants stated gender. differs between studies (sorry!)
  • study_1a: {woman, man, I do not want to disclose, other, NA}
  • study_1b: {Female, Male, Other, NA}
  • study_2a & study_2b: {female, male, I prefer not to answer, other, NA}
• agency: specific to study_2X. has the participant read the agentive or the nonagentive description of the event. {nonagentive, agentive, NA if they quit}
• blame_level: specific to study_2X. the level of blame set for the subject of the described event. {1, 4, 7, NA if they quit}
• collector_number: specific to study_2a. SurveyMonkey's internal collector number.
• recently_participated_similar: specific to study_2a. whether the participant participated in a similar study recently. {1 is no, 2 is yes, NA if unanswered}

Directory structure

The directory structure is displayed below.

All files related to the analyses are in the analyses folder.

renv/ contains files needed by the R package {renv}.

helpers/ contains R scripts with helpers functions used for generating certain statistics, plots, and tables.

reports/ contains the .Rmd files for the methods and analyses sections of the paper.

study_XX/ folders contain the cleaned data files (files used in the analyses; data/clean/*.csv) and wrangling scripts (wrangling/*.R) for each of the four studies.

.
├── analyses
│   ├── helpers
│   │   ├── plots.R
│   │   ├── stats.R
│   │   └── tables.R
│   ├── Makefile
│   ├── renv
│   │   ├── activate.R
│   │   └── settings.dcf
│   ├── renv.lock
│   ├── reports
│   │   ├── analyses_plots_create.R
│   │   ├── analyses_plots_extract.sed
│   │   ├── analyses_plots_save.py
│   │   ├── analyses.Rmd
│   │   └── methods.Rmd
│   ├── study_1a
│   │   ├── data
│   │   │   ├── clean
│   │   │   │   └── study_1a.csv
│   │   │   └── raw
│   │   │       └── survey
│   │   │           └── study_1a_data.xlsx
│   │   └── wrangling
│   │       ├── study_1a_clean.R
│   │       └── study_1a_prepare-analysis-data.R
│   ├── study_1b
│   │   ├── data
│   │   │   ├── clean
│   │   │   │   └── study_1b.csv
│   │   │   └── raw
│   │   │       ├── prolific
│   │   │       │   ├── study_1b_prolific_pt1.csv
│   │   │       │   └── study_1b_prolific_pt2.csv
│   │   │       └── survey
│   │   │           ├── study_1b_data_pt1.xlsx
│   │   │           └── study_1b_data_pt2.xlsx
│   │   └── wrangling
│   │       ├── study_1b_clean.R
│   │       ├── study_1b_merge.R
│   │       └── study_1b_prepare-analysis-data.R
│   ├── study_2a
│   │   ├── data
│   │   │   ├── clean
│   │   │   │   └── study_2a.csv
│   │   │   └── raw
│   │   │       └── survey
│   │   │           └── study_2a_data.sav
│   │   └── wrangling
│   │       ├── study_2a_clean.R
│   │       └── study_2a_prepare-analysis-data.R
│   └── study_2b
│       ├── data
│       │   ├── clean
│       │   │   └── study_2b.csv
│       │   └── raw
│       │       ├── prolific
│       │       │   ├── study_2b_prolific_pt1.csv
│       │       │   └── study_2b_prolific_pt2.csv
│       │       └── survey
│       │           ├── study_2b_data_pt1.xlsx
│       │           └── study_2b_data_pt2.xlsx
│       └── wrangling
│           ├── study_2b_clean.R
│           ├── study_2b_merge.R
│           └── study_2b_prepare-analysis-data.R
└── README.md