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.