Psychological Pertubation

This dataset contains data from 30 participants who completed the same questionnaire on meat consumption 12 times. The participant’s opinion was perturbed on each of the 11 items and measured to what extent this changed the participant’s scores on the questionnaire. It is a unique dataset that can be used for several purposes (Hoekstra et al., 2018).

Task: The dataset can be used to study causal discovery algorithms as Waldorp et al. (2021).

Summary: 

• Size of dataset: 360 x 11
• Task: Causal Discovery Problem
• Data Type: Discrete Data
• Dataset Scope: Standalone Dataset
• Ground Truth: Unknown Graph
• Temporal Structure: Static Data
• License: CC BY 4.0 (see https://openpsychologydata.metajnl.com/articles/10.5334/jopd.37#dataset-description)
• Missing Values: No Missing Values

Missingness Statement: There are no missing values.

Features: Each measurement is a a six-level factor with levels 1 (completely disagree) to 6 (completely agree)

• moral: Eating meat is morally wrong
• nutr: Meat contains important nutrients for your body
• envir: The production of meat if harmful for the environment
• infer: Animals are inferior to people
• suff: By consuming meat you contribute to animal suffering
• tax: There should be a tax on meat
• taste: I like the taste of meat
• death: Meat reminds me of death and suffering of animals
• sad: If I had to stop eating meat I would feel sad
• guilty: If I eat meat I feel guilty
• disg: If I eat meat I feel disgust

Files: 

• Data.csv: dateset
• Waldorp2021_estimated_CD.csv: Estimated causal directions by Waldorp et al. (2021) via a conditional invariant prediction method.