Publications

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

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

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

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.

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

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). Open access URL: https://hdl.handle.net/1887/69119. DOI: 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

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

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

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

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

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/RECSM_wp024.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

Oberski, D. L. (2011). Measurement error in comparative surveys [Tilburg University, The Netherlands]. http://arno.uvt.nl/show.cgi?fid=114255

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