2024
Fang, Q., Zhou, Z., Barbieri, F., Liu, Y., Neves, L., Nguyen, D., Oberski, D.L., Bos, M., & Dotsch, R. (2024). General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2431–2436. https://doi.org/10.1145/3626772.3657908
Borja Jiménez, K. C., Kemmeren, P., Van Den Heuvel-Ebrink, M., De Krijger, R., Grootenhuis, M., Partanen, M., Graf, N., Wen, S., Leemans, A., Oberski, D. L., Schoot, R., & Merks, J. H. M. (2024). Clinical Use-Cases and Implementation Guidelines for the Development of Valuable AI. EJC Paediatric Oncology, 100187. https://doi.org/10.1016/j.ejcped.2024.100187
Batouré Bamana, A., Shafiee Kamalabad, M., & Oberski, D. L. (2024). A systematic literature review of time series methods applied to epidemic prediction. Informatics in Medicine Unlocked, 50, 101571. https://doi.org/10.1016/j.imu.2024.101571
Helberger, N., et al. (2024). The Amsterdam Paper: Recommendations for the technical finalisation of the regulation of GPAI in the AI Act – AI, Media & Democracy Lab. [Policy report] https://www.aim4dem.nl/the-amsterdam-paper-recommendations-for-the-technical-finalisation-of-the-regulation-of-gpai-in-the-ai-act/
Rojas-Velazquez, D., Kidwai, S., Kraneveld, A. D., Tonda, A., Oberski, D. L., Garssen, J., & Lopez-Rincon, A. (2024). Methodology for Biomarker Discovery with Reproducibility in Microbiome Data using Machine Learning. BMC Bioinformatics. https://doi.org/10.1186/s12859-024-05639-3
Anadria, D., Giachanou, A., Kernahan, J., Dobbe, R., & Oberski, D. L. (2024). Algorithmic Fairness in Clinical Natural Language Processing: Challenges and Opportunities. Proceedings of the 3rd European Workshop on Algorithmic Fairness, Mainz, Germany, July 1-3, 2024 (EWAF’24).
2023
Fonville, F., Heijden, P. G. M. V. D., Siebes, A. P. J. M., & Oberski, D. L. (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
Asselbergs, F. W., Denaxas, S., Oberski, D. L., & Moore, J. H. (Eds.). (2023). Clinical Applications of Artificial Intelligence in Real-World Data. New York: Springer. ISBN: 978-3031366772. https://link.springer.com/book/10.1007/978-3-031-36678-9
Lopez-Rincon, A., Kidwai, S., Barbiero, P., Meijerman, I., Tonda, A., Perez-Pardo, *, Lio, P., Zee, A.-H. M. D., Oberski, D. L., & Kraneveld, 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. http://dx.doi.org/10.1002/clt2.12306
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. Findings of ACL 2023. http://arxiv.org/abs/2212.06711.
López-Rincón, A., Rojas-Velazquez, D., Garssen, J., Sander W. van der Laan, Oberski, D. L., & Tonda, A. (2023). Bayesian Optimization for the Inverse Problem in Electrocardiography. IEEE Symposium Series on Computational Intelligence (SSCI 2023).
Altamirano, S., Jansen, M. P., Oberski, D. L., Eijkemans, R. J., Mastbergen, S. C., Lafeber, F. P., Spil, E. E. V., & Welsing, P. M. (2023). Identifying multivariate disease trajectories and potential phenotypes of early knee osteoarthritis in the CHECK cohort. PLoS One. https://doi.org/10.1371/journal.pone.0283717
Moazeni, M., Numan, L., Brons, M., Houtgraaf, J., Rutten, F. H., Oberski, D. L., Van Laake, L. W., 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. https://doi.org/10.1093/ehjdh/ztad049
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), 100. https://doi.org/10.1186/s13643-023-02257-7
Ahmadi Yazdi, A., Shafiee Kamalabad, M., Oberski, D. L., & Grzegorczyk, M. (2023). Bayesian multivariate control charts for multivariate profiles monitoring. Quality Technology & Quantitative Management. https://doi.org/10.1080/16843703.2023.2214386.
2022
Saris, W., Oberski, D. L., & Weber, W. (2022). The quality of survey questions for continuous latent variables: Your guide to the SQP database and predictions. Independently published (November 11, 2022). ISBN 979-8363237201.
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. (2022). Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram- based deep neural networks. EP Europace. https://doi.org/10.1093/europace/euac054
Meuleman, B., Żółtak, T., Pokropek, A., Davidov, E., Muthén, B., Oberski, D. L., Billiet, J., & Schmidt, P. (2022). Why measurement invariance is important in comparative research. Sociological Methods and Research. https://doi.org/10.1177/00491241221091755
Cernat, A., & Oberski, D. L. (2022). Estimating stochastic survey response errors using the multitrait-multierror model. Journal of the Royal Statistical Society, Series A, 185(1). https://doi.org/http://doi.org/10.1111/rssa.12733
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). https://doi.org/10.1016/j.patter.2022.100444
Bartels, R., Dudink, J., Haitjema, S., Oberski, D. L., & 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. https://doi.org/https://doi.org/10.3389/fdgth.2022.942588
Cernat, A., & Oberski, D. (2022). Estimating Measurement Error in Longitudinal Data Using the Longitudinal MultiTrait MultiError Approach. Structural Equation Modeling: A Multidisciplinary Journal, 1–12. https://doi.org/10.1080/10705511.2022.2145961
Boeschoten, L., Ausloos, J., Möller, J. E., Araujo, T., & Oberski, D. L. (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
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). https://doi.org/10.1140/epjds/s13688-022-00353-7
2021
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. (2021). An open source machine learning framework for efficient and transparent systematic reviews. Nature Machine Intelligence. https://doi.org/10.1038/s42256-020-00287-7
Sammani, A., Bagheri, A., der Heijden, P. V., te Riele, A. S., Baas, A. F., Oosters, C. A. J., Oberski, D. L., & Folkert Asselbergs. (2021). Automatic multilabel detection of ICD10 codes in cardiology discharge letters using neural networks. Npj Digital Medicine, 4(37). https://doi.org/https://doi.org/10.1038/s41746-021-00404-9
Pankowska, P., Pavlopoulos, D., Bakker, B., & Oberski, D. L. (2021). Dependent interviewing: a remedy or a curse for measurement error in surveys? Survey Research Methods, 15(2). https://doi.org/https://doi.org/10.18148/srm/2021.v15i2.7640
Pankowska, Paulina, Bakker, B., Pavlopoulos, D., & Oberski, D. L. (2021). Modelling error dependence in categorical longitudinal data. In A. Cernat & J. W. Sakshaug (Eds.), Measurement Error in Longitudinal Data. Oxford University Press.
Oberski, D. L. (2021). Rank-deficiencies in a reduced information latent variable model. In J. L. Helm (Ed.), Preprint: arXiv:1911.00770 [stat]. Routledge. http://arxiv.org/abs/1911.00770
Numan, L., Ramjankhan, F. Z., Oberski, D. L., Oerlemans, M. I. F., 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. C., 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 LVAD support. ESC Heart Failure. https://doi.org/10.1002/ehf2.13267
Helbich, M., Poppe, R., Oberski, D. L., van Emmichoven, M. Z., & 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. https://doi.org/https://doi.org/10.1016/j.landurbplan.2021.104181
Giachanou, A., Ghanem, B., Ríssola, E. A., Rosso, P., Crestani, F., & Oberski, D. (2021). The impact of psycholinguistic patterns in discriminating between fake news spreaders and fact checkers. Data and Knowledge Engineering, 101960. https://doi.org/https://doi.org/10.1016/j.datak.2021.101960
Felix, S. E. A., Bagheri, A., Ramjankhan, F. R., Spruit, M. R., Oberski, D. L., de Jonge, N., van Laake, L. W., Suyker, W. J. L., & F.W. Asselbergs. (2021). A data mining-based cross-industry process for predicting major bleeding in mechanical circulatory support. European Heart Journal – Digital Health. https://doi.org/https://doi.org/10.1093/ehjdh/ztab082
Chang, C., Jaki, T., Sadiq, S., Kuhlemeier, A., Feaster, D., Cole, N., Lamont, A., Oberski, D. L., Desai, Y., The Pooled Resource Open-Access ALS Clinical Trials Consortium, & Van Horn, M. L. (2021). A permutation test for assessing the presence of individual differences in treatment effects. Statistical Methods in Medical Research. https://doi.org/https://doi.org/10.1177/09622802211033640
Boeschoten, L., Van Kesteren, E.-J., Bagheri, A., & Oberski, D. L. (2021). Fair inference on error-prone outcomes. International Journal of Interactive Multimedia and Artificial Intelligence, Special Issue Extended Papers European Conference on Artificial Intelligence (ECAI), 6(5). https://doi.org/10.9781/ijimai.2021.02.007
van Kesteren, E., & Oberski, D. L. (2021). Flexible Extensions to Structural Equation Models Using Computation Graphs. Structural Equation Modeling: A Multidisciplinary Journal, 1–15. https://doi.org/10.1080/10705511.2021.1971527
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. https://doi.org/10.1155/2021/6663884
2020
Pankowska, P., Pavlopoulos, D., Bakker, B., & Oberski, D. L. (2020). Reconciliation of inconsistent data sources using hidden Markov models. Statistical Journal of the International Association for Official Statistics. https://doi.org/10.3233/SJI-190594
Oberski, D. L., & Kreuter, F. (2020). Differential privacy and social science: an urgent puzzle. Harvard Data Science Review, 2(1). https://hdsr.mitpress.mit.edu/pub/g9o4z8au
Boeschoten, L., Oberski, D. L., Pouwels, J. L., & van Driel, I. (2020). Instagram use and the well-being of adolescents: Using deep learning to link social scientific self-reports with instagram data download packages. ACM SIGCHI6 IT.
Bagheri., A., Sammani., A., Heijden., P. G. M. V. D., Asselbergs., F. W., & Oberski., D. L. (2020). Automatic ICD-10 classification of diseases from Dutch discharge letters. Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies – Volume 3 BIOINFORMATICS: C2C, 281–289. https://doi.org/10.5220/0009372602810289
Bagheri, A., Oberski, D. L., Van der Heijden, P., Sammani, A., & Asselbergs, F. (2020). ETM: Enrichment by topic modeling for automated clinical short text classification. Journal of Intelligent Information Systems. https://doi.org/https://doi.org/10.1007/s10844-020-00605-w
Bagheri, A., Groenhof, T. K. J., Veldhuis, W. B., de Jong, P. A., Asselbergs, F. W., & Oberski, D. L. (2020). Multimodal Learning for Cardiovascular Risk Prediction using EHR Data. ACM-BCB 2020. http://arxiv.org/abs/2008.11979
Arnold, M., Oberski, D. L., Brandmaier, A. M., & Voelkle, M. (2020). Identifying heterogeneity in dynamic panel models with Individual Parameter Contribution regression. Structural Equation Modeling, 27(4). https://doi.org/https://doi.org/10.1080/10705511.2019.1667240
Oberski, D. L. (2020). Human Data Science. Patterns, 1(4). https://doi.org/https://doi.org/10.1016/j.patter.2020.100069
2019
van Kesteren, E.-J., & Oberski, D. L. (2019). Exploratory mediation analysis with many potential mediators. Structural Equation Modeling. https://doi.org/10.1080/10705511.2019.1588124
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
Pankowska, P., Bakker, B., Oberski, D. L., & Pavlopoulos, D. (2019). How linkage error affects hidden Markov model estimates: A sensitivity analysis. Journal of Survey Statistics and Methodology. https://doi.org/10.1093/jssam/smz011
Cernat, A., & Oberski, D. L. (2019). Extending the within-persons experimental design: The multitrait-multierror (MTME) approach. In P. J. Lavrakas, M. W. Traugott, C. Kennedy, A. L. Holbrook, & E. de Leeuw (Eds.), Experimental methods in survey research: Techniques that combine random sampling with random assignment. John Wiley & Sons.
2018
Oberski, D. L. (2018). A research programme for dealing with most administrative data challenges: data linkage and latent variable modelling – Discussion on “Statistical challenges of administrative and transaction data” by David J Hand. Journal of the Royal Statistical Society: Series A. https://doi.org/10.1111/rssa.12315
Boeschoten, L., Croon, M. A., & Oberski, D. L. (2018). A note on applying the BCH method under linear equality and inequality constraints. Journal of Classification. https://doi.org/10.1007/s00357-018-9298-2
Boeschoten, L., Oberski, D. L., de Waal, T. A. G., & Vermunt, J. K. (2018). Updating latent class imputations with external auxiliary variables. Structural Equation Modeling. https://doi.org/10.1080/10705511.2018.1446834
Lek, K., Oberski, D. L., Davidov, E., Cieciuch, J., Seddig, D., & Schmidt, P. (2018). Approximate Measurement Invariance. In T. P. Johnson, B.-E. Pennell, I. A. L. Stoop, & B. Dorer (Eds.), Advances in Comparative Survey Methods (pp. 911–929). John Wiley & Sons, Inc. https://doi.org/10.1002/9781118884997.ch41
Araujo, T., de Vreese, C., Helberger, N., Kruikemeier, S., van Weert, J., Bol, N., Oberski, D. L., Pechenizkiy, M., Schaap, G., & Taylor, L. (2018). Automated Decision-Making Fairness in an AI-driven World: Public Perceptions, Hopes and Concerns. Amsterdam: Digital Communication Methods Lab. https://dare.uva.nl/search?identifier=369fdda8-69f1-4e28-b2c7-ed4ff2f70cf6
2017
Van Erp, S., Mulder, J., & Oberski, D. L. (2017). Prior sensitivity analysis in default bayesian structural equation modeling. Psychological Methods. https://doi.org/10.1037/met0000162
Pankowska, P., Bakker, B., Pavlopoulos, D., & Oberski, D. L. (2017). Reconciliation of two data sources by correction for measurement error: a feasibility study. Statistical Journal of the International Association for Official Statistics. https://doi.org/10.3233/SJI-170368
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
Oberski, D. L. (2017). Sensitivity analysis for measurement invariance testing. In E. Davidov, P. Schmidt, J. Billiet, & B. Meuleman (Eds.), Cross-cultural analysis: Methods and applications, second edition. Routledge.
Oberski, D. L. (2017). Sexy data science. STatOR, 18(1). https://dspace.library.uu.nl/bitstream/handle/1874/360431/sexy.pdf
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(7639). https://doi.org/10.1038/nature21027
Borghuis, J., Denissen, J. J. A., Oberski, D. L., Sijtsma, K., Meeus, W. H. J., Branje, S., Koot, H. M., & Bleidorn, W. (2017). Big five personality (co-)development in adolescent friendship and sibling dyads: A 13-Wave longitudinal study. Journal of Personality and Social Psychology. https://doi.org/10.1037/pspp0000138
Boeschoten, L., Oberski, D. L., & Waal, T. D. (2017). Estimating classification error under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC). Journal of Official Statistics, 33(4). https://doi.org/10.1515/jos-2017-0044
2016
Van Smeden, M., Oberski, D. L., Vermunt, J. K., Reitsma, H., de Groot, J., & Moons, C. (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
Oberski, D. L. (2016). Estimating error rates in an administrative register and survey questions using a latent class model. In P. P. Biemer, E. D. D. Leeuw, S. Eckman, B. Edwards, F. Kreuter, L. E. Lyberg, C. Tucker, & B. T. West (Eds.), Total survey error in practice : Improving quality in the era of big data. Wiley. https://daob.nl/wp-content/uploads/2015/03/Oberski-TSE15.pdf
Oberski, D. L. (2016). Questionnaire science. In R. M. Alvarez & L. R. Atkeson (Eds.), The oxford handbook of polling and polling methods. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190213299.013.21
Oberski, D. L. (2016). Mixture models: latent profile and latent class analysis. In J. Robertson & M. Kaptein (Eds.), Modern statistical methods for HCI: A modern look at data analysis for HCI research. Springer. https://doi.org/10.1007/978-3-319-26633-6_12
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
Oberski, D. L. (2016). A review of “Latent Variable Modeling with R.” Journal of Educational and Behavioral Statistics, 41(2), 226–233. https://doi.org/10.3102/1076998615621305
Nagelkerke, E., Oberski, D. L., & Vermunt, J. K. (2016). Power and type I error of local fit statistics in multilevel latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2016.1250639
Molenaar, D., Oberski, D. L., Vermunt, J. K., & De Boeck, P. (2016). Hidden Markov IRT models for responses and response times. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2016.1192983
Lamont, A.E., Lyons, M. D., Jaki, T., Stuart, E. A., Feaster, D. J., Tharmaratnam, K., Oberski, D. L., Ishwaran, H., Wilson, D. K., & Van Horn, M. L. (2016). Identification of predicted individual treatment effects in randomized clinical trials. Statistical Methods in Medical Research. https://doi.org/10.1177/0962280215623981
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
EFSA Panel on Animal Health Αnd Welfare (AHAW) HEALTHY-B working group. (2016). Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection. EFSA Journal, 14(10). https://doi.org/10.2903/j.efsa.2016.4578
Di Mari, R., Oberski, D. L., & Vermunt, J. K. (2016). Bias-adjusted three-step latent Markov modeling with covariates. Structural Equation Modeling. https://doi.org/10.1080/10705511.2016.1191015
Boeschoten, L., Oberski, D. L., & de Waal, T. (2016). Estimating classification error under edit restrictions in combined survey-register data [CBS Discussion paper 2016 | 12]. Statistics Netherlands. https://www.cbs.nl/-/media/_pdf/2016/38/estimating-classification-error-under-edit-restrictions.pdf
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: A Multidisciplinary Journal, 23(2). https://doi.org/10.1080/10705511.2015.1049698
2015
Oberski, D. L., Hagenaars, J., & Saris, W. E. (2015). The latent class multitrait-multimethod model. Psychological Methods, 20(4), 422–443. https://doi.org/10.1037/a0039783
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. 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, 08(03), 368–377. https://doi.org/10.1285/i20705948v8n3p368
Nagelkerke, E., Oberski, D. L., & Vermunt, J. K. (2015). Goodness-of-fit of multilevel latent class models for categorical data. Sociological Methodology, 46(1). https://doi.org/10.1177/0081175015581379
Meyers, C., Van Woerkom, M., De Reuver, R., Bakk, Zs., & Oberski, D. L. (2015). Enhancing psychological capital and personal growth initiative: Developing strengths or deficiencies? Journal of Counseling Psychology, 62(1). 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
Cernat, A., & Oberski, D. L. (2015). Separating systematic measurement error components using MTMM in longitudinal studies. In T. A. Baghal (Ed.), Understanding society innovation panel wave 7: Results from methodological experiments. https://www.understandingsociety.ac.uk/research/publications/working-paper/understanding-society/2015-03
2014
Oberski, D. L. (2014). lavaan.survey: An R package for complex survey analysis of structural equation models. Journal of Statistical Software, 57(1), 1–27. https://doi.org/10.18637/jss.v057.i01
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
Bakk, Z., Oberski, D. L., & Vermunt, J. K. (2014). Relating latent class assignments to external variables: standard errors for correct inference. Political Analysis, 22, 520–540. https://doi.org/10.1093/pan/mpu003
Alfes, K., Schantz, A., & Oberski, D. L. (2014). The perpetuity of overqualification and the modifying effects of age and gender. Academy of Management Meeting. https://doi.org/10.5465/AMBPP.2014.15125abstract
2013
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). https://doi.org/10.1007/s11634-013-0146-2
Oberski, D. L., & Vermunt, J. K. (2013). A model-based approach to goodness-of-fit evaluation in item response theory. Measurement: Interdisciplinary Research & Perspectives, 11, 117–122. https://doi.org/10.1080/15366367.2013.835195
Oberski, D. L., & Satorra, A. (2013). Measurement error models with uncertainty about the error variance. Structural Equation Modeling, 20, 409–428. https://doi.org/10.1080/10705511.2013.797820
Oberski, D. L. (2013). Conditional design effects for structural equation model estimates. In I. S. Institute (Ed.), Proceedings of the 59th world statistics congress 2013. https://daob.nl/wp-content/uploads/2013/04/hk-Oberski.pdf
Oberski, D. L. (2013). Local dependence in latent class models: application to voting in elections. In E. Brentari & M. Carpita (Eds.), Advances in latent variables [SIS 2013 conference proceedings]. Vita e Pensiero. https://daob.nl/wp-content/uploads/2013/05/Oberski-breschia.pdf
Bollen, K., Tueller, S., & Oberski, D. L. (2013). Issues in the structural equation modeling of complex survey data. In I. S. Institute (Ed.), Proceedings of the 59th world statistics congress 2013. http://www.statistics.gov.hk/wsc/STS010-P1-S.pdf
2012
Saris, W. E., Oberski, D. L., Révilla, M., Zavala, D., Lilleoja, L., Gallhofer, I., & Gruner, T. (2012). The development of the program SQP 2.0 for the prediction of the quality of survey questions. European Social Survey. http://www.upf.edu/survey/ₚdf/RECSMwp024.pdf
Oberski, D. L., Révilla, M., & Weber, W. K. (2012). The effect of individual characteristics on reports of socially desirable attitudes towards immigration. In S. Salzborn, E. Davidov, & J. Reinecke (Eds.), Methods, theories, and empirical applications in the social sciences: Festschrift for peter schmidt. Springer. https://doi.org/10.1007/978-3-531-18898-0_19
Oberski, D. L. (2012). Comparability of survey measurements. In L. Gideon (Ed.), Handbook of survey methodology in social sciences. Springer. https://doi.org/10.1007/978-1-4614-3876-2_27
Gallego, A., & Oberski, D. L. (2012). Personality and political participation: The mediation hypothesis. Political Behavior, 34(3), 425–451. https://doi.org/10.1007/s11109-011-9168-7
2011
Oberski, D. L. (2011). Measurement error in comparative surveys [Tilburg University, The Netherlands]. http://arno.uvt.nl/show.cgi?fid=114255
2007-2010
Oberski, D. L., Saris, W. E., & Hagenaars, J. A. P. (2010). Categorization errors and differences in the quality of questions in comparative surveys. In J. A. Harkness, M. Braun, B. Edwards, T. P. Johnson, L. Lyberg, P. P. Mohler, B.-E. Pennell, & T. W. Smith (Eds.), Survey methods in multinational, multiregional, and multicultural contexts (pp. 435–453). Wiley. https://doi.org/10.1002/9780470609927.ch23
Oberski, D. L. (2008). Self-selection bias versus nonresponse bias in the Perceptions of Mobility survey: a comparison using multiple imputation. The Netherlands Institute for Social Research (SCP). http://www.scp.nl/english/dsresource?objectid=22003&type=org
Coromina, L., Saris, W. E., & Oberski, D. L. (2008). The Quality of the Measurement of Interest in the Political Issues in the Media in the ESS. ASK Research & Methods. https://daob.nl/wp-content/uploads/2013/03/CorominaSarisOberskiIPIMedia₂008.pdf
Saris, W. E., & Oberski, D. L. (2007). Access panels: a blessing or a serious threat for political processes? In I. Stoop & M. Wittenberg (Eds.), Access panels and online research, panacea or pitfall? Aksant Academic Publishers. https://daob.nl/wp-content/uploads/2013/03/websurveyₐndₚolitics3.doc
Oberski, D. L., Saris, W. E., & Hagenaars, J. (2007). Why are there differences in measurement quality across countries? In G. Loosveldt & Swyngedouw (Eds.), Measuring meaningful data in social research. Acco. https://daob.nl/wp-content/uploads/2013/03/Oberski-Saris-Why-are-there-differences-in-measurement-quality-across-countries.pdf
submitted/unpublished
Fang, Q., Oberski, D. L., & Nguyen, D. (2024). PATCH — Psychometrics-AssisTed benCHmarking of Large Language Models: A Case Study of Mathematics Proficiency (arXiv:2404.01799). arXiv. https://doi.org/10.48550/arXiv.2404.01799
Van Kesteren, E.-J., Sun, C., Oberski, D. L., Dumontier, M., & Ippel, L. (submitted). Privacy-preserving generalized linear models using distributed block coordinate descent. Preprint: ArXiv:1911.03183 [Cs]. https://arxiv.org/abs/1911.03183
Pankowska, P., & Oberski, D. L. (submitted). The effect of measurement error on clustering algorithms. Preprint: ArXiv:2005.11743 [Stat.ML]. https://arxiv.org/abs/2005.11743
Oberski, D. L., & Vermunt, J. K. (submitted). The expected parameter change (EPC) for local dependence assessment in binary data latent class models. Psychometrika. https://daob.nl/wp-content/uploads/2014/07/lca-epc-revision2.pdf
Oberski, D. L., & DeCastellarnau, A. (submitted). Predicting measurement error variance in social surveys.
Oberski, D. L. (submitted). A flexible method to explain differences in structural equation model parameters over subgroups. https://daob.nl/wp-content/uploads/2013/06/SEM-IPC-manuscript-new.pdf
Oberski, D. L. (submitted). Model-based variance estimation for aggregated covariance structure models. https://daob.nl/wp-content/uploads/2013/08/Model-based-variance-estimation-for-aggregated-covariance-structure-models.pdf
Bagheri, A., Oberski, D. L., Van der Heijden, P., Sammani, A., & Asselbergs, F. (submitted). SALTClass: classifying clinical short notes using background knowledge from unlabeled data. Preprint: BioRxiv. https://doi.org/https://doi.org/10.1101/801944