Analysis of Human behavior in times of high-impact events

Primary supervisor

Benjamin Tag

This is a research-heavy project, looking for students with an interest in human behaviour and data analysis.

This project aims at an extensive analysis of a social science dataset that was collected through the collaborative COVIDiSTRESS Global Survey (and potentially its follow-up survey from 2022) – an open science project aiming at improving the understanding of the human experiences of the 2020 COVID-19 pandemic. A group of researchers from 44 countries collaborated to design and translate the survey including variables of various interests. The survey was globally distributed and collected data from 173,426 participants in 47 languages and dialects.


The dataset allows for different studies, e.g., cross-cultural psychological and behavioural responses to the Coronavirus pandemic and associated government measures like lockdown orders implemented in many countries. The dataset contains demographic background information as well as measures of perceived stress (PSS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with government interventions, and compliance with preventive measures, along with a large set of exploratory variables and written experiences.


This analysis can focus on different aspects of the survey and is not limited to the above-mentioned. Through this project, we aim at extending the knowledge of human behaviour and mental states in times of extended stress and large-impact events.


The richness of the dataset allows for the project to be of different sizes and scopes, including the possibility of impactful publications. The research questions ideally focus on an aspect related to Information Technology.

Student cohort

Single Semester
Double Semester

Aim/outline

The project scope includes:

  • familiarization with a large dataset
  • defining clear research questions
  • cleaning and filtering data
  • data analysis
  • visualisation of data

URLs/references

Recommended Literature:

  • Yamada, Y., Ćepulić, DB., Coll-Martín, T. et al. COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak. Sci Data 8, 3 (2021). https://doi.org/10.1038/s41597-020-00784-9Yamada, Y., Ćepulić, DB., Coll-Martín, T. et al. COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak. Sci Data 8, 3 (2021). https://doi.org/10.1038/s41597-020-00784-9
  • Lieberoth, A., Lin, S.-Y., Stöckli, S., Han, H., Kowal, M., Gelpi, R., Chrona, S., Tran, T. P., Jeftić, A., Rasmussen, J., Cakal, H., Milfont, T. L., Lieberoth, A., Yamada, Y., Han, H., Rasmussen, J., Amin, R., Debove, S., Gelpí, R., … Dubrov, D. (2021). Stress and worry in the 2020 coronavirus pandemic: relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey. Royal Society Open Science, 8(2), 200589. https://doi.org/10.1098/rsos.200589
  • Blackburn, A. M., Vestergren, S., Blackburn, A. M., Vestergren, S., Tran, T. P., Stöckli, S., Griffin, S. M., Ntontis, E., Jeftic, A., Chrona, S., Ikizer, G., Han, H., Milfont, T. L., Parry, D., Byrne, G., Gómez-López, M., Acosta, A., Kowal, M., de Leon, G., … Zoletic, E. (2022). COVIDiSTRESS diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemic. Scientific Data, 9(1), 331. https://doi.org/10.1038/s41597-022-01383-6

Required knowledge

Skills required:

  • Data cleaning
  • Data analysis (R, Python, SPSS, etc.)
  • Interest in Human Psychology