N. (Nikola) Sekulovski MSc
- Nieuwe Achtergracht 129
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Postbus 15906
1001 NK Amsterdam
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Profile
I am a PhD candidate at the Department of Psychological Methods at the University of Amsterdam, working in the Bayesian Graphical Modeling Lab. My current project focuses on the further development of Bayesian methods for the analysis of graphical models, mainly for cross-sectional psychological data. My research interests lie in the areas of Bayesian hypothesis testing and network psychometrics.
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Research
Research methods
- Network Psychometrics
- Bayesian Statistics
- Bayesian Hypothesis Testing
Current research projects
Given the unknown structure of the graphical models used in network psychometrics, the Bayesian approach helps deal with uncertainty by expressing the plausibility of different network structures, testing hypotheses for edge inclusion/exclusion, and providing robust prediction intervals for network parameters based on empirical data. The goal of my Ph.D. project is to further extend the functionality of this methodology by adding additional features as well as making it more accessible to applied researchers.
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Teaching & PhD Supervision
BA
- Bayesian Statistics
MA
- Bayesian Hypothesis Testing in Practice
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Publications
2024
Hoogeveen, S., Borsboom, D., Kucharský, Š., Marsman, M., Molenaar, D., de Ron, J., Sekulovski, N., Visser, I., van Elk, M., & Wagenmakers, E.-J. (2024). Prevalence, patterns and predictors of paranormal beliefs in The Netherlands: a several-analysts approach. Royal Society Open Science, 11(9), Article 240049. https://doi.org/10.1098/rsos.240049 [details]- Huth, K. B. S., Keetelaar, S., Sekulovski, N., van den Bergh, D., & Marsman, M. (2024). Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package. advances.in/psychology, Article e66366. Advance online publication. https://doi.org/10.56296/aip00010
- Keetelaar, S., Sekulovski, N., Borsboom, D., & Marsman, M. (2024). Comparing maximum likelihood and maximum pseudolikelihood estimators for the Ising model. Advances.in/psychology, 2, Article e25745. https://doi.org/10.56296/aip00013 [details]
- Sekulovski, N., Keetelaar, S., Haslbeck, J., & Marsman, M. (2024). Sensitivity analysis of prior distributions in Bayesian graphical modeling: Guiding informed prior choices for conditional independence testing. advances.in/psychology, 2, Article e92355. https://doi.org/10.56296/aip00016 [details]
- Sekulovski, N., Keetelaar, S., Huth, K., Wagenmakers, E.-J., van Bork, R., van den Bergh, D., & Marsman, M. (2024). Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods. Multivariate Behavioral Research, 59(5), 913-933. https://doi.org/10.1080/00273171.2024.2345915 [details]
- Sekulovski, N., Marsman, M., & Wagenmakers, E.-J. (2024). A Good check on the Bayes factor. Behavior Research Methods, 56(8), 8552–8566. https://doi.org/10.3758/s13428-024-02491-4 [details]
2023
- Sekulovski, N., & Hoijtink, H. (2023). A Default Bayes Factor for Testing Null Hypotheses About the Fixed Effects of Linear Two-Level Models. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000573
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Ancillary activities
No ancillary activities
Hoogeveen, S., Borsboom, D., Kucharský, Š., Marsman, M., Molenaar, D., de Ron, J., Sekulovski, N., Visser, I., van Elk, M., & Wagenmakers, E.-J. (2024). Prevalence, patterns and predictors of paranormal beliefs in The Netherlands: a several-analysts approach. Royal Society Open Science, 11(9), Article 240049.