Code for data processing and clustering in the identification of personality types in:

M. Gerlach, B. Farb, W. Revelle, L.A.N. Amaral, A robust data-driven approach identifies four personality types across four large datasets, Nature Human Behaviour (2018).

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As part of the repository we provide the position of 145,388 respondents in the 5-dimensional trait space of the Five-Factor-Model obtained from factor analysis of the responses to 300 items (Neuroticism, Extraversion, Openness, Agreeableness, Conscientiousness; see the paper for more details). This is the starting point for the identification of personality-types using our clustering approach running the analysis_clustering* notebooks.

This step can be reproduced from the original data in data.tar.gz (simply extract as data/) amd running the preprocessing_* notebooks. The data can be downloaded from here: https://osf.io/tbmh5/

We are not allowed to share the other datasets. The BBC-data is publicly available (upon registration): http://doi.org/10.5255/UKDA-SN-7656-1 . The mypersonality data is not available anymore, see here: http://mypersonality.org/

analysis_clustering-01_number-of-clusters-BIC fits a Gaussian Mixture Model to the positions of the respondents in the 5D trait space varying the number of clusters. Using the Bayesian Information Criterion (BIC) we can determine the optimal number of cluster.

analysis_clustering-02_meaningful-clusters-kernel-density determines whether a cluster solution is spurious or meaningful. Fixing the number of clusters (e.g. the optimal number determined via the BIC), for each cluster center we determine the density at its position in the 5D-space and compare with the expected density from a randomized dataset. This yields two quantities: - p-value: indicating whether the true density is significantly larger than the randomized density (low p-value == significant) - enrichment: the ratio between the true density and the average of the different realizations of the randomized densities

preprocessing_01-filter-data reads Johnson's orginal data and puts it in a csv-file for easier usage, e.g. using pandas

preprocessing_02-questions-vs-domains creates a file storing association between item and trait

preprocessing_03-factor-analysis performs factor analysis and saves the factor scores as the positions of the 145,388 respondents in the 5-dimensional trait space. For this to work you have to specify the path to this folder in src/analysis/factor_analysis.py (path_project = '/DRIVE/REPOS/personality-types-shared/') -- to be fixed

preprocessing_04-demographics parses the csv file for extracting age and gender for follow-up analysis.

plotting_factor-loadings plots the factor loadings

plotting_factor-scores-mrginals plots the 1D and 2D marginals of the factor scores

plotting_gender-age plots gender and age distribution of the dataset

plotting_gender-age-per-type plots the relative frequency of gender and age in each clusters.

Clone the repository

git clone https://github.com/amarallab/personality-types.git

Install all required packages (here via conda):

• conda create -n personality-types python=3.6 creates an environment
• source activate personality-types activates the environment
• conda install numpy scikit-learn matplotlib pandas openpyxl jupyter installs all the packages
• python -m ipykernel install --user --name personality-types --display-name "personality-types" makes the environemnt available in the jupyter notebooks
• jupyter notebook starts jupyter notebooks

For factor rotation we use the code from https://github.com/mvds314/factor_rotation which is in src/external/