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Prof. dr. Daniel Oberski

Hoogleraar 
Sociale Wetenschappen
Maatschappijwetenschappen
Methoden en Statistiek
Methoden en Statistiek
Sjoerd Groenmangebouw
Padualaan 14
Kamer C1.109
3584 CH Utrecht

Prof. dr. Daniel Oberski

Hoogleraar
Methoden en Statistiek
030 253 9039
d.l.oberski@uu.nl
Profiel CV Publicaties Activiteiten Prijzen In de media Onderzoek Onderwijs Nevenwerkzaamheden Contact
Profiel CV Publicaties Activiteiten Prijzen In de media Onderzoek Onderwijs Nevenwerkzaamheden Contact
Expertises
Onderzoeksthema's

This is a non-exhaustive list of peer-reviewed journal articles only. For my full list of publications, preprints, and reproduction materials, please see http://daob.nl/publications

Publicaties

2025

Wetenschappelijke publicaties

Kidwai, S., Rojas-Velazquez, D., Lopez-Rincon, A., Kraneveld, A. D., Oberski, D. L., & Meijerman, I. (2025). Keeping pace in the age of innovation: The perspective of Dutch pharmaceutical science students on the position of machine learning training in an undergraduate curriculum. Currents in Pharmacy Teaching and Learning, 17(2), Article 102231. https://doi.org/10.1016/j.cptl.2024.102231
https://research-portal.uu.nl/ws/files/245916609/1-s2.0-S1877129724002636-main.pdf

2024

Wetenschappelijke publicaties

Anadria, D., Giachanou, A., Kernahan, J., Dobbe, R., & Oberski, D. (2024). Algorithmic Fairness in Clinical Natural Language Processing: Challenges and Opportunities. Paper presented at Proceedings chair: European Workshop on Algorithmic Fairness (EWAF'24).
Bamana, A. B., Shafiee Kamalabad, M., & Oberski, D. (2024). A systematic literature review of time series methods applied to epidemic prediction. Informatics in Medicine Unlocked, 50, Article 101571. https://doi.org/10.1016/j.imu.2024.101571
https://research-portal.uu.nl/ws/files/248118733/1-s2.0-S2352914824001278-main.pdf
Fang, Q., Zhou, Z., Barbieri, F., Liu, Y., Nguyen, D., Oberski, D., Bos, M., & Dotsch, R. (2024). General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study. In SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2431-2436). (SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery. https://doi.org/10.1145/3626772.3657908
https://research-portal.uu.nl/ws/files/238951749/3626772.3657908.pdf
Rojas-Velazquez, D., Kidwai, S., Kraneveld, A. D., Tonda, A., Oberski, D., Garssen, J., & Lopez-Rincon, A. (2024). Methodology for biomarker discovery with reproducibility in microbiome data using machine learning. BMC Bioinformatics, 25(1), Article 26. https://doi.org/10.1186/s12859-024-05639-3
https://dspace.library.uu.nl/bitstream/handle/1874/435906/s12859-024-05639-3.pdf?sequence=1
Lopez-Rincon, A., Rojas-Velazquez, E., Garssen, J., Laan, S. W. V. D., Oberski, D., & Tonda, A. (2024). Bayesian Optimization for the Inverse Problem in Electrocardiography. In 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 (pp. 1593-1598). Article 10371791 (2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371791
https://dspace.library.uu.nl/bitstream/handle/1874/436483/Bayesian_Optimization_for_the_Inverse_Problem_in_Electrocardiography.pdf?sequence=1
Ahmadi Yazdi, A., Shafiee Kamalabad, M., Oberski, D., & Grzegorczyk, M. (2024). Bayesian multivariate control charts for multivariate profiles monitoring. Quality Technology and Quantitative Management, 21(3), 386-421. https://doi.org/10.1080/16843703.2023.2214386

2023

Wetenschappelijke publicaties

Cernat, A., & Oberski, D. (2023). Estimating Measurement Error in Longitudinal Data Using the Longitudinal MultiTrait MultiError Approach. Structural Equation Modeling, 30(4), 592-603. https://doi.org/10.1080/10705511.2022.2145961
https://dspace.library.uu.nl/bitstream/handle/1874/435954/Estimating_Measurement_Error_in_Longitudinal_Data_Using_the_Longitudinal_MultiTrait_MultiError_Approach.pdf?sequence=1
Fonville, F., Van der Heijden, P. G. M., Siebes, A., & Oberski, D. (2023). Understanding financial distress by using Markov random fields on linked administrative data. Statistical Journal of the IAOS, 39(4), 903-920. https://doi.org/10.3233/SJI-230028
Altamirano, S., Jansen, M. P., Oberski, D. L., Eijkemans, M. J. C., Mastbergen, S. C., Lafeber, F. P. J. G., van Spil, W. E., & Welsing, P. M. J. (2023). Identifying multivariate disease trajectories and potential phenotypes of early knee osteoarthritis in the CHECK cohort. PLoS One, 18(7 July), Article e0283717. https://doi.org/10.1371/journal.pone.0283717
https://dspace.library.uu.nl/bitstream/handle/1874/434945/journal.pone.0283717.pdf?sequence=1
Kidwai, S., Barbiero, P., Meijerman, I., Tonda, A., Perez-Pardo, P., Lio, P., van der Maitland-Zee, A. H., Oberski, D. L., Kraneveld, A. D., & Lopez-Rincon, A. (2023). A robust mRNA signature obtained via recursive ensemble feature selection predicts the responsiveness of omalizumab in moderate-to-severe asthma. Clinical and Translational Allergy, 13(11), 1-11. Article e12306. https://doi.org/10.1002/clt2.12306
https://dspace.library.uu.nl/bitstream/handle/1874/434887/Clinical_Translational_All_-_2023_-_Kidwai_-_A_robust_mRNA_signature_obtained_via_recursive_ensemble_feature_selection.pdf?sequence=1
Fang, Q., Giachanou, A., Bagheri, A., Boeschoten, L., van Kesteren, E. J., Kamalabad, M. S., & Oberski, D. L. (2023). On Text-based Personality Computing: Challenges and Future Directions. In Findings of the Association for Computational Linguistics, ACL 2023 (pp. 10861-10879). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-acl.691
https://dspace.library.uu.nl/bitstream/handle/1874/436397/2023.findings-acl.691.pdf?sequence=1
Moazeni, M., Numan, L., Brons, M., Houtgraaf, J., Rutten, F. H., Oberski, D. L., Laake, L. W. V., Asselbergs, F. W., & Aarts, E. (2023). Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure. European Heart Journal - Digital Health, 4(6), 455-463. https://doi.org/10.1093/ehjdh/ztad049
https://dspace.library.uu.nl/bitstream/handle/1874/434341/ztad049.pdf?sequence=1
Ferdinands, G., Schram, R., de Bruin, J., Bagheri, A., Oberski, D. L., Tummers, L., Teijema, J. J., & van de Schoot, R. (2023). Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records. Systematic Reviews , 12(1), Article 100. https://doi.org/10.1186/s13643-023-02257-7
https://dspace.library.uu.nl/bitstream/handle/1874/434228/s13643-023-02257-7.pdf?sequence=1
Meuleman, B., Żółtak, T., Pokropek, A., Davidov, E., Muthén, B., Oberski, D. L., Billiet, J., & Schmidt, P. (2023). Why Measurement Invariance is Important in Comparative Research. A Response to Welzel et al. (2021). Sociological Methods and Research, 52(3), 1401-1419. Article 00491241221091755. https://doi.org/10.1177/00491241221091755
https://dspace.library.uu.nl/bitstream/handle/1874/431341/meuleman-et-al-2022-why-measurement-invariance-is-important-in-comparative-research-a-response-to-welzel-et-al-2021.pdf?sequence=1

2022

Wetenschappelijke publicaties

Bartels, R., Dudink, J., Haitjema, S., Oberski, D., & van ‘t Veen, A. (2022). A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care. Frontiers in Digital Health, 4, Article 942588. https://doi.org/10.3389/fdgth.2022.942588
https://dspace.library.uu.nl/bitstream/handle/1874/437133/fdgth-04-942588.pdf?sequence=1
Fang, Q., Nguyen, D., & Oberski, D. L. (2022). Evaluating the construct validity of text embeddings with application to survey questions. EPJ Data Science, 11(1), 1-31. Article 39. https://doi.org/10.1140/epjds/s13688-022-00353-7
https://dspace.library.uu.nl/bitstream/handle/1874/425343/s13688_022_00353_7.pdf?sequence=1
Boeschoten, L., Ausloos, J., Möller, J., Araujo, T., & Oberski, D. (2022). A framework for privacy preserving digital trace data collection through data donation. Computational Communication Research, 4(2), 388-423. https://doi.org/10.5117/CCR2022.2.002.BOES
https://dspace.library.uu.nl/bitstream/handle/1874/425114/CCR2022.2.002.BOES.pdf?sequence=1
Sammani, A., van de Leur, R. R., Henkens, M. T. H. M., Meine, M., Loh, P., Hassink, R. J., Oberski, D. L., Heymans, S. R. B., Doevendans, P. A., Asselbergs, F. W., te Riele, A. S. J. M., & van Es, R. (2022). Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks. Europace, 24(10), 1645–1654. https://doi.org/10.1093/europace/euac054
https://dspace.library.uu.nl/bitstream/handle/1874/423654/euac054.pdf?sequence=1
Boeschoten, L., Mendrik, A., van der Veen, E., Vloothuis, J., Hu, H., Voorvaart, R., & Oberski, D. L. (2022). Privacy-preserving local analysis of digital trace data: A proof-of-concept. Patterns, 3(3), 1-11. Article 100444. https://doi.org/10.1016/j.patter.2022.100444
https://dspace.library.uu.nl/bitstream/handle/1874/423653/PIIS2666389922000174.pdf?sequence=1
Giachanou, A., Ghanem, B., Ríssola, E. A., Rosso, P., Crestani, F., & Oberski, D. (2022). The impact of psycholinguistic patterns in discriminating between fake news spreaders and fact checkers. Data and Knowledge Engineering, 138, 1-15. Article 101960. https://doi.org/10.1016/j.datak.2021.101960
https://dspace.library.uu.nl/bitstream/handle/1874/419112/1_s2.0_S0169023X21000835_main.pdf?sequence=1
Cernat, A., & Oberski, D. L. (2022). Estimating stochastic survey response errors using the multitrait‐multierror model. Journal of the Royal Statistical Society. Series A: Statistics in Society, 185(1), 134-155. https://doi.org/10.1111/rssa.12733
https://dspace.library.uu.nl/bitstream/handle/1874/422437/Royal_Stats_Society_Series_A_2021_Cernat_Estimating_stochastic_survey_response_errors_using_the_multitrait_multierror.pdf?sequence=1
Kesteren, EJ. V., & Oberski, D. L. (2022). Flexible Extensions to Structural Equation Models Using Computation Graphs. Structural Equation Modeling, 29(2), 233-247. https://doi.org/10.1080/10705511.2021.1971527
https://dspace.library.uu.nl/bitstream/handle/1874/422436/10705511.2021.pdf?sequence=1

2021

Wetenschappelijke publicaties

Numan, L., Ramjankhan, F. Z., Oberski, D. L., Oerlemans, M. I. F. J., Aarts, E., Gianoli, M., Van der Heijden, J. J., De Jonge, N., Van der Kaaij, N. P., Meuwese, C. L., Mohkles, M. M., Oppelaar, A. M., De Waal, E. E. C., Asselbergs, F. W., & Van Laake, L. W. (2021). Long-term outcome of patients on HeartWare and HeartMate 3 support in a single centre: a propensity score-based analysis. European Journal of Heart Failure, 23, 148-149.
Sammani, A., Leur, R. R., Meine, M., Loh, P., Hassink, R. J., Oberski, D. L., Henkens, M. T., Heymans, S., Doevendans, P., te Riele, A. S., van Es, R., & Asselbergs, F. W. (2021). Predicting Life-Threatening Ventricular Arrhythmias in Patients with Non-Ischemic Dilated Cardiomyopathy Using Electrocardiogram-Based Deep Neural Networks. Circulation, 144. https://doi.org/10.1161/circ.144.suppl_1.10161
Felix, S. E. A., Bagheri, A., Ramjankhan, F. R., Spruit, M. R., Oberski, D., De Jonge, N., Van Laake, L. W., Suyker, W. J. L., & Asselbergs, F. W. (2021). A data mining-based cross-industry process for predicting major bleeding in mechanical circulatory support. European Heart Journal - Digital Health, 2(4), 635-642. https://doi.org/10.1093/ehjdh/ztab082
https://dspace.library.uu.nl/bitstream/handle/1874/415924/ztab082.pdf?sequence=1
Boeschoten, L., Mendrik, A., Veen, E. V. D., Vloothuis, J., Hu, H., Voorvaart, R., & Oberski, D. (2021). Privacy preserving local analysis of digital trace data: A proof-of-concept. (pp. 1-14). arXiv. https://doi.org/10.48550/arXiv.2110.05154
https://dspace.library.uu.nl/bitstream/handle/1874/415166/2110.05154v1.pdf?sequence=1
Bagheri, A., Groenhof, T. K. J., Asselbergs, F. W., Haitjema, S., Bots, M. L., Veldhuis, W. B., De Jong, P. A., & Oberski, D. L. (2021). Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports. Journal of Healthcare Engineering, 2021, 1-11. Article 6663884. https://doi.org/10.1155/2021/6663884
https://dspace.library.uu.nl/bitstream/handle/1874/413665/6663884.pdf?sequence=1
Pavlopoulos, D., Pankowska, P., Bakker, B., & Oberski, D. (2021). Modelling error dependence in categorical longitudinal data. In A. Cernat, & J. W. Sakshaug (Eds.), Measurement Error in Longitudinal Data (pp. 173-194). Oxford University Press. https://doi.org/10.1093/oso/9780198859987.003.0008
https://dspace.library.uu.nl/bitstream/handle/1874/415050/Modelling_Error_Dependence_in_Categorical_Longitudinal_Data.pdf?sequence=1
Pankowska, P., Bakker, B., Oberski, D., & Pavlopoulos, D. (2021). Dependent interviewing: A remedy or a curse for measurement error in surveys? Survey Research Methods, 15(2), 135-146. https://doi.org/10.18148/srm/2021.v15i2.7640
https://dspace.library.uu.nl/bitstream/handle/1874/413664/7640_Article_Text_26828_2_10_20210803.pdf?sequence=1
The Pooled Resource Open-Access ALS Clinical Trials Consortium, Chang, C., Jaki, T., Sadiq, M. S., Kuhlemeier, A., Feaster, D., Cole, N., Lamont, A., Oberski, D., Desai, Y., & Lee Van Horn, M. (2021). A permutation test for assessing the presence of individual differences in treatment effects. Statistical Methods in Medical Research, 30(11), 2369-2381. https://doi.org/10.1177/09622802211033640
https://dspace.library.uu.nl/bitstream/handle/1874/418993/09622802211033640.pdf?sequence=1
Oberski, D. L. (2021). Rank-deficiencies in a reduced information latent variable model. In Advanced Multitrait-Multimethod Analyses for the Behavioral and Social Sciences (pp. 80-102). Taylor & Francis. https://doi.org/10.4324/9780429320989-5
https://dspace.library.uu.nl/bitstream/handle/1874/415049/10.4324_9780429320989_5_chapterpdf.pdf?sequence=1
Helbich, M., Poppe, R., Oberski, D., Zeylmans Van Emmichoven, M., & Schram, R. (2021). Can’t see the wood for the trees? An assessment of street view- and satellite-derived greenness measures in relation to mental health. Landscape and Urban Planning, 214, 1-10. Article 104181. https://doi.org/10.1016/j.landurbplan.2021.104181
https://dspace.library.uu.nl/bitstream/handle/1874/412301/1_s2.0_S0169204621001444_main.pdf?sequence=1
Boeschoten, L., van Kesteren, E.-J., Bagheri, A., & Oberski, D. L. (2021). Achieving Fair Inference Using Error-Prone Outcomes. International Journal of Interactive Multimedia and Artificial Intelligence, 6(5), 9-15. https://doi.org/10.9781/ijimai.2021.02.007
https://dspace.library.uu.nl/bitstream/handle/1874/411989/ijimai_6_5_1_0.pdf?sequence=1
Numan, L., Ramjankhan, F. Z., Oberski, D. L., Oerlemans, M. I. F. J., Aarts, E., Gianoli, M., Van Der Heijden, J. J., De Jonge, N., Van Der Kaaij, N. P., Meuwese, C. L., Mokhles, M. M., Oppelaar, A.-M., De Waal, E. E. C., Asselbergs, F. W., & Van Laake, L. W. (2021). Propensity score-based analysis of long-term outcome of patients on HeartWare and HeartMate 3 left ventricular assist device support. ESC heart failure, 8(2), 1596-1603. https://doi.org/10.1002/ehf2.13267
https://dspace.library.uu.nl/bitstream/handle/1874/411987/ehf2.13267.pdf?sequence=1
Sammani, A., Bagheri, A., van der Heijden, P. G. M., Te Riele, A. S. J. M., Baas, A. F., Oosters, C. A. J., Oberski, D., & Asselbergs, F. W. (2021). Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks. npj Digital Medicine, 4(1), 1-10. Article 37. https://doi.org/10.1038/s41746-021-00404-9
https://dspace.library.uu.nl/bitstream/handle/1874/411986/s41746_021_00404_9.pdf?sequence=1
van Kesteren, E.-J., Vida, L. J., de Bruin, J., & Oberski, D. (2021). osmenrich - Enrich sf Data with Geographic Features from OpenStreetMaps. Software https://doi.org/10.5281/ZENODO.4548774
Schoot, R. V. D., Bruin, J. D., Schram, R., Zahedi, P., Boer, J. D., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Hindriks, S., Tummers, L., & Oberski, D. (2021). An open source machine learning framework for efficient and transparent systematic reviews. Nature Machine Intelligence, 3(2), 125-133. https://doi.org/10.1038/s42256-020-00287-7
https://dspace.library.uu.nl/bitstream/handle/1874/411488/s42256_020_00287_7.pdf?sequence=1

2020

Wetenschappelijke publicaties

van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Tummers, L., & Oberski, D. (2020). ASReview: Open Source Software for Efficient and Transparent Active Learning for Systematic Reviews. https://www.researchgate.net/publication/342377295_ASReview_Open_Source_Software_for_Efficient_and_Transparent_Active_Learning_for_Systematic_Reviews
Boeschoten, L., van Kesteren, E., Bagheri, A., & Oberski, D. L. (2020). Fair inference on error-prone outcomes. (pp. 1-14). arXiv. https://doi.org/10.48550/arXiv.2003.07621
https://dspace.library.uu.nl/bitstream/handle/1874/410479/2003.07621.pdf?sequence=1
Pankowska, P., Bakker, B. F. M., Oberski, D. L., & Pavlopoulos, D. (2020). How linkage error affects hidden Markov model estimates: A sensitivity analysis. Journal of Survey Statistics and Methodology, 8(3), 483-512. https://doi.org/10.1093/jssam/smz011
https://dspace.library.uu.nl/bitstream/handle/1874/437220/smz011.pdf?sequence=1
Oberski, D. L. (2020). Human Data Science. Patterns, 1(4), Article 100069. https://doi.org/10.1016/j.patter.2020.100069
https://dspace.library.uu.nl/bitstream/handle/1874/437124/1-s2.0-S2666389920300891-main.pdf?sequence=1
Bagheri, A., Groenhof, T. K. J., Veldhuis, W. B., Jong, P. A. D., Asselbergs, F. W., & Oberski, D. L. (2020). Multimodal Learning for Cardiovascular Risk Prediction using EHR Data. (pp. 1-8). arXiv. https://doi.org/10.48550/arXiv.2008.11979
https://dspace.library.uu.nl/bitstream/handle/1874/419959/2008.11979v1.pdf?sequence=1
Pankowska, P., Bakker, B. F. M., Oberski, D. L., & Pavlopoulos, D. (2020). Corrigendum to: How Linkage Error Affects Hidden Markov Model Estimates: A Sensitivity Analysis. Journal of Survey Statistics and Methodology, 8(4), 817-819. https://doi.org/10.1093/jssam/smz035
https://dspace.library.uu.nl/bitstream/handle/1874/411988/smz035.pdf?sequence=1
Pankowska, P., Pavlopoulos, D., Bakker, B., & Oberski, D. L. (2020). Reconciliation of inconsistent data sources using hidden Markov models. Statistical Journal of the IAOS, 36(4), 1261-1279. https://doi.org/10.3233/SJI-190594
Ferdinands, G., Schram, R., Bruin, J. D., Bagheri, A., Oberski, D. L., Tummers, L., & Schoot, R. V. D. (2020, Sept 16). Active learning for screening prioritization in systematic reviews - A simulation study. OSFPREPRINTS. https://doi.org/10.31219/osf.io/w6qbg
https://dspace.library.uu.nl/bitstream/handle/1874/415518/manuscript_Ferdinands.pdf?sequence=1
Boeschoten, L., Van Driel, I., Oberski, D. L., & Pouwels, L. (2020). Instagram Use and the Well-Being of Adolescents: Using Deep Learning to Link Social Scientific Self-reports with Instagram Data Download Packages. In ICMI '20 Companion: Companion Publication of the 2020 International Conference on Multimodal Interaction (pp. 523). Association for Computing Machinery. https://doi.org/10.1145/3395035.3425185
https://dspace.library.uu.nl/bitstream/handle/1874/414890/3395035.3425185.pdf?sequence=1
van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Ferdinands, G., Harkema, A., Fang, Q., & Oberski, D. L. (2020). Extension for COVID-19 related datasets in ASReview.. Software, Zenodo. https://doi.org/10.5281/zenodo.3764749
van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Tummers, L., & Oberski, D. L. (2020). ASReview: Active learning for systematic reviews.. Software https://doi.org/10.5281/zenodo.3345592
Bagheri, A., Sammani, A., van der Heijden, P. G. M., Asselbergs, F. W., & Oberski, D. L. (2020). ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients’ disease history. Journal of Intelligent Information Systems, 55(2), 329-349. https://doi.org/10.1007/s10844-020-00605-w
https://dspace.library.uu.nl/bitstream/handle/1874/409890/Bagheri2020_Article_ETMEnrichmentByTopicModelingFo.pdf?sequence=1
Arnold, M., Oberski, D. L., Brandmaier, A. M., & Voelkle, M. C. (2020). Identifying Heterogeneity in Dynamic Panel Models with Individual Parameter Contribution Regression. Structural Equation Modeling, 27(4), 613-628. https://doi.org/10.1080/10705511.2019.1667240
https://dspace.library.uu.nl/bitstream/handle/1874/409340/Identifying_Heterogeneity_in_Dynamic_Panel_Models_with_Individual_Parameter_Contribution_Regression.pdf?sequence=1
Bagheri, A., Sammani, A., van der Heijden, P. G. M., Asselbergs, F. W., & Oberski, D. L. (2020). Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters. In In conjunction with the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSTEC 2020 https://www.insticc.org/node/TechnicalProgram/biostec/2020/presentationDetails/93726
https://dspace.library.uu.nl/bitstream/handle/1874/420196/Ayoub_ICDL1701.pdf?sequence=1
Bagheri, A., Sammani, A., Oberski, D. L., & Asselbergs, F. W. (2020). Multi-label ICD Classification of Dutch Hospital Discharge Letters. https://clin30.sites.uu.nl/accepted-submissions/

2019

Wetenschappelijke publicaties

van Kesteren, E.-J., Sun, C., Oberski, D. L., Dumontier, M., & Ippel, L. (2019). Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent. arXiv. https://doi.org/10.48550/arXiv.1911.03183
https://dspace.library.uu.nl/bitstream/handle/1874/437069/1911.03183.pdf?sequence=1
Oberski, D. L. (2019). Rank-deficiencies in a reduced information latent variable model. arXiv. https://doi.org/10.48550/arXiv.1911.00770
https://dspace.library.uu.nl/bitstream/handle/1874/437067/1911.00770.pdf?sequence=1
Cernat, A., & Oberski, D. L. (2019). Extending the within-persons experimental design: The multitrait-multierror (MTME) approach. In Experimental Methods in Survey Research: Techniques that Combine Random Sampling with Random Assignment (pp. 481-500). Wiley-Blackwell. https://doi.org/10.1002/9781119083771.ch24
https://dspace.library.uu.nl/bitstream/handle/1874/420268/Experimental_Methods_in_Survey_Research_2019_Lavrakas_Extending_the_Within_Persons_Experimental_Design_The.pdf?sequence=1
Bagheri, A., Oberski, D. L., Sammani, A., van der Heijden, P. G. M., & Asselbergs, F. W. (2019). SALTClass: classifying clinical short notes using background knowledge from unlabeled data. https://doi.org/10.1101/801944
https://dspace.library.uu.nl/bitstream/handle/1874/390877/801944v1.full.pdf?sequence=1
Van Erp, S., Oberski, D. L., & Mulder, J. (2019). Shrinkage priors for Bayesian penalized regression. Journal of Mathematical Psychology, 89, 31-50. https://doi.org/10.1016/j.jmp.2018.12.004
https://dspace.library.uu.nl/bitstream/handle/1874/390035/Bayesian.pdf?sequence=4
Boeschoten, L., Croon, M. A., & Oberski, D. L. (2019). A Note on Applying the BCH Method Under Linear Equality and Inequality Constraints. Journal of Classification, 36(3), 566-575. https://doi.org/10.1007/s00357-018-9298-2
https://dspace.library.uu.nl/bitstream/handle/1874/389395/Boeschoten2019_Article_ANoteOnApplyingTheBCHMethodUnd.pdf?sequence=1
Brinkman, L., & Oberski, D. L. (2019). Open Science in Bachelor education: 25 easy-to-implement teaching formats for Open Science.
van Kesteren, E. J., & Oberski, D. L. (2019). Exploratory Mediation Analysis with Many Potential Mediators. Structural Equation Modeling, 26(5), 710-723. https://doi.org/10.1080/10705511.2019.1588124
https://dspace.library.uu.nl/bitstream/handle/1874/389625/vankesteren.pdf?sequence=1

2018

Wetenschappelijke publicaties

Oberski, D. L. (2018). Sensitivity analysis. In Cross-Cultural Analysis: Methods and Applications, 2nd Edition (pp. 593-614). Taylor & Francis. https://doi.org/10.4324/9781315537078
https://dspace.library.uu.nl/bitstream/handle/1874/437123/10.4324_9781315537078-22_chapterpdf.pdf?sequence=1
Oberski, D. L. (2018). Questionnaire science. In L. R. Atkeson, & R. M. Alvarez (Eds.), The Oxford Handbook of Polling and Polling Methods (pp. 113-137). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190213299.013.21
https://dspace.library.uu.nl/bitstream/handle/1874/414656/oxfordhb_9780190213299_e_21.pdf?sequence=1
Lamont, A., Lyons, M. D., Jaki, T., Stuart, E., Feaster, D. J., Tharmaratnam, K., Oberski, D., Ishwaran, H., Wilson, D. K., & Van Horn, M. L. (2018). Identification of predicted individual treatment effects in randomized clinical trials. Statistical Methods in Medical Research, 27(1), 142-157. https://doi.org/10.1177/0962280215623981
https://dspace.library.uu.nl/bitstream/handle/1874/376755/0962280215623981.pdf?sequence=1
van Erp, S., Mulder, J., & Oberski, D. L. (2018). Prior sensitivity analysis in default Bayesian structural equation modeling. Psychological Methods, 23(2), 363-388. https://doi.org/10.1037/met0000162
https://research-portal.uu.nl/ws/files/248804373/Prior_Sensitivity_Analysis_in_Default_Bayesian_Structural_Equation_Modeling.pdf
Pankowska, P., Bakker, B., Oberski, D. L., & Pavlopoulos, D. (2018). Reconciliation of inconsistent data sources by correction for measurement error: The feasibility of parameter re-use. Statistical Journal of the IAOS, 34(3), 317-329. https://doi.org/10.3233/SJI-170368
https://research-portal.uu.nl/ws/files/248653120/pankowska-et-al-2018-reconciliation-of-inconsistent-data-sources-by-correction-for-measurement-error-the-feasibility-of.pdf
Boeschoten, L., Oberski, D. L., Waal, T. A. G. D., & Vermunt, J. K. (2018). Updating latent class imputations with external auxiliary variables. Structural Equation Modeling, 25(5), 750-761. https://doi.org/10.1080/10705511.2018.1446834
https://dspace.library.uu.nl/bitstream/handle/1874/394070/Updating_Latent_Class_Imputations_with_External_Auxiliary_Variables.pdf?sequence=1
Oberski, D. L. (2018). A research programme for dealing with most administrative data challenges: data linkage and latent variable modelling. Journal of the Royal Statistical Society. Series A: Statistics in Society, 181(3), 595-596. https://doi.org/10.1111/rssa.12315
Lek, K. M., Oberski, D. L., Davidov, E., Cieciuch, J., Seddig, D., & Schmidt, P. (2018). Approximate measurement invariance. In T. P. Johnson, B.-E. Pennell, I. Stoop, & B. Dorer (Eds.), Advances in Comparative Survey Methodology (pp. 911-929). Wiley. https://doi.org/10.1002/9781118884997.ch41
https://dspace.library.uu.nl/bitstream/handle/1874/394050/Approximate_Measurement_Invariance.pdf?sequence=1

2017

Wetenschappelijke publicaties

Oberski, D. L., Kirchner, A., Eckman, S., & Kreuter, F. (2017). Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models. Journal of the American Statistical Association, 112(520), 1477-1489. https://doi.org/10.1080/01621459.2017.1302338
https://dspace.library.uu.nl/bitstream/handle/1874/363296/Evaluating_the_Quality_of_Survey_and_Administrative_Data_with_Generalized_Multitrait_Multimethod_Models.pdf?sequence=1
Boeschoten, L., Oberski, D., & De Waal, T. (2017). Estimating Classification Errors under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC). Journal of Official Statistics, 33(4), 921-962. https://doi.org/10.1515/jos-2017-0044
https://dspace.library.uu.nl/bitstream/handle/1874/363295/_Journal_of_Official_Statistics_Estimating_Classification_Errors_Under_Edit_Restrictions_in_Composite_Survey_Register_Data_Using_Multiple_Imputation_Latent_Class_Modelling_MILC_.pdf?sequence=1
Borghuis, J., Denissen, J., Oberski, D. L., Sijtsma, K., Meeus, W. H. J., Branje, S. J. T., Koot, H. M., & Bleidorn, W. (2017). Big Five Personality Stability, Change, and Co-Development across Adolescence and Early Adulthood. Journal of Personality and Social Psychology, 113(4), 641-657. https://doi.org/10.1037/pspp0000138
https://dspace.library.uu.nl/bitstream/handle/1874/358188/Big.pdf?sequence=1
Mayor, J. R., Sanders, N. J., Classen, A. T., Bardgett, R. D., Clément, J.-C., Fajardo, A., Lavorel, S., Sundqvist, M. K., Bahn, M., Chisholm, C., Cieraad, E., Gedalof, Z., Grigulis, K., Kudo, G., Oberski, D. L., & Wardle, D. A. (2017). Elevation alters ecosystem properties across temperate treelines globally. Nature, 542, 91-95. https://doi.org/10.1038/nature21027
https://dspace.library.uu.nl/bitstream/handle/1874/394068/Eleveation_alters_ecosystem_properties.pdf?sequence=1
Nagelkerke, E., Oberski, D. L., & Vermunt, J. K. (2017). Power and Type I Error of Local Fit Statistics in Multilevel Latent Class Analysis. Structural Equation Modeling, 24(2), 216-229. https://doi.org/10.1080/10705511.2016.1250639
https://dspace.library.uu.nl/bitstream/handle/1874/346055/Power_and_Type_I_Error_of_Local_Fit_Statistics_in_Multilevel_Latent_Class_Analysis.pdf?sequence=3

Vakpublicaties

Oberski, D. L. (2017). Sexy Data Science. STAtOR, 18(1), 34-37. https://www.vvsor.nl/wp-content/uploads/2018/01/STAtOR-2017-1-LR-spreads.pdf
https://dspace.library.uu.nl/bitstream/handle/1874/360431/sexy.pdf?sequence=1

2016

Wetenschappelijke publicaties

Nagelkerke, E., Oberski, D. L., & Vermunt, J. K. (2016). Goodness-of-fit of multilevel latent class models for categorical data. Sociological Methodology, 46(1), 252-282. https://doi.org/10.1177/0081175015581379
Gallego, A., Buscha, F., Sturgis, P., & Oberski, D. (2016). Places and Preferences: A Longitudinal Analysis of Self-Selection and Contextual Effects. British Journal of Political Science, 46(3), 529-550. https://doi.org/10.1017/S0007123414000337
https://dspace.library.uu.nl/bitstream/handle/1874/342236/Places.pdf?sequence=1
Oberski, D. L. (2016). A review of latent variable modeling with R (vol 41, pg 226, 2016). Journal of Educational and Behavioral Statistics, 41(3), 355-355. https://doi.org/10.3102/1076998616645100
Oberski, D. L. (2016). Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis. Advances in Data Analysis and Classification, 10(2), 171-182. https://doi.org/10.1007/s11634-015-0211-0
https://dspace.library.uu.nl/bitstream/handle/1874/342234/Beyond.pdf?sequence=1
Bakk, Z., Oberski, D. L., & Vermunt, J. K. (2016). Relating Latent Class Membership to Continuous Distal Outcomes: Improving the LTB Approach and a Modified Three-Step Implementation. Structural Equation Modeling, 23(2), 278-289. https://doi.org/10.1080/10705511.2015.1049698
Van Smeden, M., Oberski, D. L., Reitsma, J. B., Vermunt, J. K., Moons, K. G. M., & De Groot, J. A. H. (2016). Problems in detecting misfit of latent class models in diagnostic research without a gold standard were shown. Journal of Clinical Epidemiology, 74, 158-166. https://doi.org/10.1016/j.jclinepi.2015.11.012
Molenaar, D., Oberski, D., Vermunt, J., & De Boeck, P. (2016). Hidden Markov Item Response Theory Models for Responses and Response Times. Multivariate Behavioral Research, 51(5), 606-626. https://doi.org/10.1080/00273171.2016.1192983
https://dspace.library.uu.nl/bitstream/handle/1874/342231/Hidden.pdf?sequence=1
Di Mari, R., Oberski, D. L., & Vermunt, J. K. (2016). Bias-Adjusted Three-Step Latent Markov Modeling With Covariates. Structural Equation Modeling, 23(5), 649-660. https://doi.org/10.1080/10705511.2016.1191015
https://dspace.library.uu.nl/bitstream/handle/1874/342230/Markov.pdf?sequence=1

2015

Wetenschappelijke publicaties

Oberski, D. L. (2015). Separating systematic measurement error components using MTMM in longitudinal studies. In Understanding Society Innovation Panel Wave 7: Results from Methodological Experiments
Oberski, D. L. (2015). Estimating error rates in an administrative register and survey questions using a latent class model. In Total Survey Error
Oberski, D. L., Hagenaars, J. A. P., & Saris, W. E. (2015). The Latent Class Multitrait-Multimethod Model. Psychological Methods, 20(4), 422-443. https://doi.org/10.1037/a0039783
Meyers, M. C., van Woerkom, M., de Reuver, R. S. M., Bakk, Z., & Oberski, D. L. (2015). Enhancing Psychological Capital and Personal Growth Initiative: Working on Strengths or Deficiencies. Journal of Counseling Psychology, 62(1), 50-62. https://doi.org/10.1037/cou0000050
Cieciuch, J., Davidov, E., Oberski, D. L., & Algesheimer, R. (2015). Testing for measurement invariance by detecting local misspecification and an illustration across online and paper-and-pencil samples. European Political Science, 14(4), 521-538. https://doi.org/10.1057/eps.2015.64
Oberski, D. L., Vermunt, J. K., & Moors, G. B. D. (2015). Evaluating Measurement Invariance in Categorical Data Latent Variable Models with the EPC-Interest. Political Analysis, 23(4), 550-563. Article mpv020. https://doi.org/10.1093/pan/mpv020
Oberski, D. L., & Vermunt, J. K. (2015). The relationship between cub and loglinear models with latent variables. Electronic Journal of Applied Statistical Analysis, 8(3), 368-377. https://doi.org/10.1285/i20705948v8n3p374
https://dspace.library.uu.nl/bitstream/handle/1874/342628/relationship.pdf?sequence=1

2014

Wetenschappelijke publicaties

Oberski, D. L. (2014). Evaluating Sensitivity of Parameters of Interest to Measurement Invariance in Latent Variable Models. Political Analysis, 22(1), 45-60. https://doi.org/10.1093/pan/mpt014
Oberski, D. (2014). lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models. Journal of Statistical Software, 57(1), 1-27.
Bakk, Z., Oberski, D. L., & Vermunt, J. K. (2014). Relating Latent Class Assignments to External Variables: Standard Errors for Correct Inference. Political Analysis, 22(4), 520-540. https://doi.org/10.1093/pan/mpu003

2013

Wetenschappelijke publicaties

Oberski, D. L., Weber, W., & Révilla, M. (2013). The effect of individual characteristics on reports of socially desirable attitudes toward immigration. In S. Salzborn, E. Davidov, & J. Reinecke (Eds.), Methods, theories, and empirical applications in the social sciences: Festschrift for Peter Schmidt (pp. 151-157). Verlag für Sozialwissenschaften. https://doi.org/10.1007/978-3-531-18898-0_19
Oberski, D. L., & Vermunt, J. K. (2013). A Model-Based Approach to Goodness-of-Fit Evaluation in Item Response Theory. Measurement, 11(3), 117-122. https://doi.org/10.1080/15366367.2013.835195
Oberski, D. L., van Kollenburg, G. H., & Vermunt, J. K. (2013). A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models. Advances in Data Analysis and Classification, 7(3), 267-279. https://doi.org/10.1007/s11634-013-0146-2
Oberski, D. L., & Satorra, A. (2013). Measurement Error Models With Uncertainty About the Error Variance. Structural Equation Modeling, 20(3), 409-428. https://doi.org/10.1080/10705511.2013.797820

2012

Wetenschappelijke publicaties

Oberski, D. L. (2012). Comparability of survey measurements. In L. Gideon (Ed.), Handbook of Survey Methodology for the Social Sciences (pp. 477-498). Springer. https://doi.org/10.1007/978-1-4614-3876-2_27
Gallego, A., & Oberski, D. (2012). Personality and Political Participation: The Mediation Hypothesis. Political Behavior, 34(3), 425-451. https://doi.org/10.1007/s11109-011-9168-7

2010

Wetenschappelijke publicaties

Oberski, D., Saris, W. E., & Hagenaars, J. A. (2010). Categorization Errors and Differences in the Quality of Questions in Comparative Surveys. In J. A. Harkness (Ed.), Survey methods in multicultural, multinational, and multiregional contexts (pp. 435-453). (Wiley series in survey methodology). Wiley-Blackwell. https://doi.org/10.1002/9780470609927.ch23

This is a non-exhaustive list of peer-reviewed journal articles only. For my full list of publications, preprints, and reproduction materials, please see http://daob.nl/publications

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