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
2021
Wetenschappelijke publicaties
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, [104181]. https://doi.org/10.1016/j.landurbplan.2021.104181
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
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
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), [37]. https://doi.org/10.1038/s41746-021-00404-9
van Kesteren, E-J. (Author), Vida, L. J. (Author), de Bruin, J. (Author), & Oberski, D. (Author). (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
2020
Wetenschappelijke publicaties
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
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
de Schoot, R. V., 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., Hindriks, S., Tummers, L., & Oberski, D. L. (2020). Open source software for efficient and transparent reviews. Nature Machine Intelligence.
Ferdinands, G., Schram, R., Bruin, J. D., Bagheri, A., Oberski, D. L., Tummers, L., & Schoot, R. V. D. (2020, Sep 16). Active learning for screening prioritization in systematic reviews - A simulation study. OSFPREPRINTS. https://doi.org/10.31219/osf.io/w6qbg
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
van de Schoot, R. (Author), de Bruin, J. (Author), Schram, R. (Author), Zahedi, P. (Author), de Boer, J. (Author), Weijdema, F. (Author), Kramer, B. (Author), Huijts, M. (Author), Ferdinands, G. (Author), Harkema, A. (Author), Fang, Q. (Author), & Oberski, D. L. (Author). (2020). Extension for COVID-19 related datasets in ASReview.. Software, Zenodo. https://doi.org/10.5281/zenodo.3764749
van de Schoot, R. (Author), de Bruin, J. (Author), Schram, R. (Author), Zahedi, P. (Author), de Boer, J. (Author), Weijdema, F. (Author), Kramer, B. (Author), Huijts, M. (Author), Hoogerwerf, M. (Author), Ferdinands, G. (Author), Harkema, A. (Author), Willemsen, J. (Author), Ma, Y. (Author), Fang, Q. (Author), Tummers, L. (Author), & Oberski, D. L. (Author). (2020). ASReview: Active learning for systematic reviews.. Software https://doi.org/10.5281/zenodo.3345592
Boeschoten, L., van Kesteren, E., Bagheri, A., & Oberski, D. L. (2020). Fair inference on error-prone outcomes. arXiv, 1-14. https://arxiv.org/abs/2003.07621
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
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
Bagheri, A., Sammani, A., Oberski, D. L., & Asselbergs, F. W. (Accepted/In press). Multi-label ICD Classification of Dutch Hospital Discharge Letters. https://clin30.sites.uu.nl/accepted-submissions/
2019
Wetenschappelijke publicaties
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
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
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
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
2018
Wetenschappelijke publicaties
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, Oxford. https://doi.org/10.1093/oxfordhb/9780190213299.013.21
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
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
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
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
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.
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). John Wiley & Sons Inc.. https://doi.org/10.1002/9781118884997.ch41
https://dspace.library.uu.nl/bitstream/handle/1874/394050/Approximate_Measurement_Invariance.pdf?sequence=1
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
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
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
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
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
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
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
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
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
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
2015
Wetenschappelijke publicaties
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. [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
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). VS Verlag fur 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 New York LLC. 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