Bißantz, S., Frick, S., Melinscak, F., Iliescu, D., & Wetzel, E. (2024). The Potential of Machine Learning Methods in Psychological Assessment and Test Construction. European Journal of Psychological Assessment, 1015–5759/a000817. https://doi.org/10.1027/1015-5759/a000817
Doebler*, P., Frick*, S., & Doebler, A. (2024). Beta-Binomial Meta-Analysis of Individual Differences Based on Sample Means and Standard Deviations: Studying Reliability of Sum Scores of Binary Items. Psychological Methods, Advance online publication. https://doi.org/10.1037/met0000649
Frick, S. (2022). Modeling Faking in the Multidimensional Forced-Choice Format - The Faking Mixture Model. Psychometrika, 87, 773–794. https://doi.org/10.1007/s11336-021-09818-6
Frick, S. (2023). Estimating and Using Block Information in the Thurstonian IRT Model. Psychometrika, 88(4), 1556–1589. https://doi.org/10.1007/s11336-023-09931-8
Frick, S., Brown, A., & Wetzel, E. (2023). Investigating the normativity of trait estimates from multidimensional forced-choice data. Multivariate Behavioral Research, 58(1), 1–29. https://doi.org/10.1080/00273171.2021.1938960
Frick**, S., Krivošija**, A., & Munteanu**, A. (2024). Scalable Learning of Item Response Theory Models from Large Data. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:1234–1242. Retrieved from https://proceedings.mlr.press/v238/frick24a.html
Wetzel, E., Frick, S., & Brown, A. (2021). Does multidimensional forced-choice prevent faking? Comparing the susceptibility of the multidimensional forced-choice format and the rating scale format to faking. Psychological Assessment, 33(2), 156–170. https://doi.org/10.1037/pas0000971
* = shared first authorship
** = authors listed in alphabetical order
