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Department’s Research Grants

2021 – 2025 Research Lab for the Digital Social Sciences. About the use of digital footprints in studying social problems.
This project aims to advance methods to use joint digital footprints and survey to solve social problems (i.e., trust, attitudes towards migration, environment, and governments, misinformation, and polarization of attitudes). To do that, we propose a large methodologically oriented study that will utilize five sources of data: representative survey data, online trace data, data from a panel study (mixing survey and online trace data), experimental studies, and qualitative data. (The National Science Centre grant “Sonata Bis” 2020/38/E/HS6/00302).

2020 – 2024 Understanding response styles in self-report data: consequences, remedies and sources.
Self-report data, essential for research, politics, and commerce, face issues like non-comparability and biased responses due to different response styles and systematic tendencies to answer beyond the item content. Despite ample research, the effects, control methods, and reasons for response styles remain unclear, raising questions about their impact on data quality and individual and contextual causes. This project explores these questions using simulation studies, international database analyses, machine learning, and experimental studies to understand predictors like personality, motivation, and cognitive abilities (The National Science Centre grant “Opus”, 2019/17/HS6/00937)

Other Research and Activities

2022 – 2025 CSS is collaborating with The Centre for Advanced Study (CAS) at The Norwegian Academy of Science on a research grant: Disadvantaged Students Who Beat the Odds. Toward a New Generation of Research in Academic Resilience (#BeResilient project)

Ongoing The goal of the department is to develop and provide methods and tools for the social sciences to analyze digital behavioural data (produced in interactions with computers, phones and other devices) and to combine digital with traditional types of survey data to improve the analysis of a wide range of sociocultural phenomena. The department addresses these tasks using machine learning, natural language processing, network analysis, psychometrics and statistical modelling, combining various research and statistical techniques with social science expertise. The department engages in purely sociological research and multidisciplinary projects, applying the developed methods to various data types. It pays particular attention to the implementation nature of research, which can positively influence the socio-economic development of our country.

Recent Publications

Pokropek, A., Żółtak, T., & Muszyński, M. (2024). Identifying Careless Responding in Web-Based Surveys: Exploiting Sequence Data from Cursor Trajectories and Approximate Areas of Interest. Zeitschrift für Psychologie, 232(2), 95–108.

Cachia, R., Pokropek, A., & Giannoutsou, N. (2024). Supporting the monitoring of the digital capacity of schools through optimal shortening of the SELFIE tool. Computers & Education208, 104938.

Rogoza, R., Krammer, G., Jauk, E., Flakus, M., Baran, L., Di Sarno, M., … & Fatfouta, R. (2024). The peaks and valleys of narcissism: The factor structure of narcissistic states and their relations to trait measures. Psychological Assessment36(2), 147.

Pokropek, A., Żółtak, T., & Muszyński, M. (2023). Mouse chase: Detecting careless and unmotivated responders using cursor movements in web-based surveys. European Journal of Psychological Assessment, 39(4), 299–306.

Meuleman, B., Żółtak, T., Pokropek, A., Davidov, E., Muthén, B., Oberski, D. L., … & Schmidt, P. (2023). Why measurement invariance is important in comparative research. A response to Welzel et al.(2021). Sociological methods & research52(3), 1401-1419.

Zając, T. Z., Żółtak, T., Bożykowski, M., & Jasiński, M. (2023). All that glitters is not gold—Mixed early labour market outcomes of STEM graduates in Poland. European Journal of Education58(3), 477-497.

Pokropek, A., Khorramdel, L., & von Davier, M. (2023). Detecting and Differentiating Extreme and Midpoint Response Styles in Rating Scales using Tree-Based Item Response Models: Simulation Study and Empirical Evidence. Psychological Test and Assessment Modeling, 65(2), 220-258.

Kajdy, A., Sys, D., Pokropek, … Mind-COVID Collaborative Team (2023). Risk factors for anxiety and depression among pregnant women during the COVID-19 pandemic: Results of a web-based multinational cross-sectional study. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics160(1), 167–186.

Muszyński, M., Pokropek, A., Castaño-Muñoz, J., & Vuorikari, R. (2023). Can Overclaiming Technique Improve Self-Assessment Tools for Digital Competence? The Case of DigCompSat. Social Science Computer Review,  41 (6), 2318-2341