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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.


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.
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.
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.
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.
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.
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.
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.
Ahmadi Yazdi, A., Shafiee Kamalabad, M., Oberski, D. L., & Grzegorczyk, M. (2023). Bayesian multivariate control charts for multivariate profiles monitoring. Quality Technology & Quantitative Management.


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.
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.
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).
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).
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.
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.
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.
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).


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.
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).
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).
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.
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.
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.
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.
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.
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.
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).
van Kesteren, E., & Oberski, D. L. (2021). Flexible Extensions to Structural Equation Models Using Computation Graphs. Structural Equation Modeling: A Multidisciplinary Journal, 1–15.
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.


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.
Oberski, D. L., & Kreuter, F. (2020). Differential privacy and social science: an urgent puzzle. Harvard Data Science Review, 2(1).
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.
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.
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.
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).
Oberski, D. L. (2020). Human Data Science. Patterns, 1(4).


van Kesteren, E.-J., & Oberski, D. L. (2019). Exploratory mediation analysis with many potential mediators. Structural Equation Modeling.
van Erp, S., Oberski, D. L., & Mulder, J. (2019). Shrinkage priors for Bayesian penalized regression. Journal of Mathematical Psychology, 89, 31–50.
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.
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.


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.
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.
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.
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.
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.


Van Erp, S., Mulder, J., & Oberski, D. L. (2017). Prior sensitivity analysis in default bayesian structural equation modeling. Psychological Methods.
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.
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.
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).
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).
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.
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).


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.
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.
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.
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.
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.
Oberski, D. L. (2016). A review of “Latent Variable Modeling with R.” Journal of Educational and Behavioral Statistics, 41(2), 226–233.
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.
Molenaar, D., Oberski, D. L., Vermunt, J. K., & De Boeck, P. (2016). Hidden Markov IRT models for responses and response times. Multivariate Behavioral Research.
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.
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.
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).
Di Mari, R., Oberski, D. L., & Vermunt, J. K. (2016). Bias-adjusted three-step latent Markov modeling with covariates. Structural Equation Modeling.
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.
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).


Oberski, D. L., Hagenaars, J., & Saris, W. E. (2015). The latent class multitrait-multimethod model. Psychological Methods, 20(4), 422–443.
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.
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.
Nagelkerke, E., Oberski, D. L., & Vermunt, J. K. (2015). Goodness-of-fit of multilevel latent class models for categorical data. Sociological Methodology, 46(1).
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).
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.
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.


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.
Oberski, D. L. (2014). Evaluating sensitivity of parameters of interest to measurement invariance in latent variable models. Political Analysis, 22(1), 45–60.
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.
Alfes, K., Schantz, A., & Oberski, D. L. (2014). The perpetuity of overqualification and the modifying effects of age and gender. Academy of Management Meeting.


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).
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.
Oberski, D. L., & Satorra, A. (2013). Measurement error models with uncertainty about the error variance. Structural Equation Modeling, 20, 409–428.
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.
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.
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.


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.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.
Oberski, D. L. (2012). Comparability of survey measurements. In L. Gideon (Ed.), Handbook of survey methodology in social sciences. Springer.
Gallego, A., & Oberski, D. L. (2012). Personality and political participation: The mediation hypothesis. Political Behavior, 34(3), 425–451.


Oberski, D. L. (2011). Measurement error in comparative surveys [Tilburg University, The Netherlands].


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.
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).
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.₂008.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.ₐndₚolitics3.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.


Fang, Q., Zhou, Z., Barbieri, F., Liu, Y., Neves, L., Nguyen, D., Oberski, D. L., Bos, M. W., & Dotsch, R. (2023). Designing and Evaluating General-Purpose User Representations Based on Behavioral Logs from a Measurement Process Perspective: A Case Study with Snapchat (arXiv:2312.12111). arXiv.
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].
Pankowska, P., & Oberski, D. L. (submitted). The effect of measurement error on clustering algorithms. Preprint: ArXiv:2005.11743 [Stat.ML].
Oberski, D. L., & Vermunt, J. K. (submitted). The expected parameter change (EPC) for local dependence assessment in binary data latent class models. Psychometrika.
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.
Oberski, D. L. (submitted). Model-based variance estimation for aggregated covariance structure models.
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.