Logo Utrecht University

Human Data Science (HDS)



Fang, Q., Burger, J., Meijers, R., & van Berkel, K. (2021). The Role of Time, Weather and Google Trends in Understanding and Predicting Web Survey Response. Survey Research Methods15(1), 1-25.


Arnold, M., Oberski, D. L., Brandmaier, A. M. & Voelkle, M. C. (03-07-2020). Identifying Heterogeneity in Dynamic Panel Models with Individual Parameter Contribution Regression. Structural Equation Modeling, 27 (4), (pp. 613-628) (16 p.).

Bagheri, A., Sammani, A., Van Der Heijden, P., Asselbergs, F., & Oberski, D. (2020). Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, 281–289.

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.

Boeschoten, L., Ausloos, J., Moeller, J., Araujo, T., & Oberski, D. L. (2020). Digital trace data collection through data donation. arXiv preprint arXiv:2011.09851.

Boeschoten, L., Filipponi, D. & Varriale, R. (2020). Combining Multiple Imputation and Hidden Markov Modeling to Obtain Consistent Estimates of Employment Status. Journal of Survey Statistics and Methodology, 0, (pp. 1-25).

Boeschoten, L., van Driel, I. I., Oberski, D. L., & Pouwels, L. J. (2020). Instagram Use and the Well-Being of Adolescents: Using Deep Learning to Link Social Scientific Self-reports with Instagram Data Download Packages. In Companion Publication of the 2020 International Conference on Multimodal Interaction (pp. 523-523).

Boeschoten, L., van Kesteren, E., Bagheri, A. & Oberski, D.L. (2020). Fair inference on error-prone outcomes. arXiv, (pp. 1-14).

Oberski, Daniel L., & Kreuter, F. (2020). Differential Privacy and Social Science: An Urgent PuzzleHarvard Data Science Review, 2(1).

Sosnovsky, S., Fang, Q., de Vries, B., Luehof, S. & Wiegant, F. (2020). Towards Adaptive Social Comparison for Education. In European Conference on Technology Enhanced Learning (pp. 421-426). Springer, Cham.


Arnold, M., Oberski, D. L., Brandmaier, A. M., & Voelkle, M. C. (2019). Identifying Heterogeneity in Dynamic Panel Models with Individual Parameter Contribution RegressionStructural Equation Modeling: A Multidisciplinary Journal, 1–16. 

Bagheri, A. (2019). Integrating word status for joint detection of sentiment and aspect in reviewsJournal of Information Science, 45(6), 736–755. 

Bagheri, A., Oberski, D., Sammani, A., van der Heijden, P. G. M., & Asselbergs, F. W. (2019). SALTClass: Classifying clinical short notes using background knowledge from unlabeled data [Preprint]. Bioinformatics.

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.

Boeschoten, Laura, Waal, T., & Vermunt, J. K. (2019). Estimating the number of serious road injuries per vehicle type in the Netherlands by using multiple imputation of latent classes. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(4), 1463–1486. 

Pankowska, P., Bakker, B. F. M., Oberski, D. L., & Pavlopoulos, D. (2019). How Linkage Error Affects Hidden Markov Model Estimates: A Sensitivity Analysis. Journal of Survey Statistics and Methodology, smz011. 

Sammani, A., Jansen, M., Linschoten, M., Bagheri, A., de Jonge, N., Kirkels, H., van Laake, L. W., Vink, A., van Tintelen, J. P., Dooijes, D., te Riele, A. S. J. M., Harakalova, M., Baas, A. F., & Asselbergs, F. W. (2019). UNRAVEL: Big data analytics research data platform to improve care of patients with cardiomyopathies using routine electronic health records and standardised biobankingNetherlands Heart Journal, 27(9), 426–434.

Sedighi, Z., Ebrahimpour-Komleh, H., Bagheri, A., & Kosseim, L. (2019). Opinion Spam Detection with Attention-Based Neural Networks. The Thirty-Second International Florida Artificial Intelligence Research Society Conference.

van Erp, S., Oberski, D. L., & Mulder, J. (2019). Shrinkage priors for Bayesian penalized regression. Journal of Mathematical Psychology, 89, 31–50. 

van Kesteren, E.-J., & Oberski, D. L. (2019). Exploratory Mediation Analysis with Many Potential MediatorsStructural Equation Modeling: A Multidisciplinary Journal, 26(5), 710–723.


Boeschoten, L., Oberski, D. L., De Waal, T., & Vermunt, J. K. (2018). Updating Latent Class Imputations with External Auxiliary Variables. Structural Equation Modeling: A Multidisciplinary Journal, 25(5), 750–761.

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.

Lek, K., Oberski, D., 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.

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. 

van Erp, S., Mulder, J., & Oberski, D. L. (2018). Prior sensitivity analysis in default Bayesian structural equation modeling. Psychological Methods, 23(2), 363–388. 


Boeschoten, L., Vink, G., & Hox, J. J. C. M. (2017). How to Obtain Valid Inference under Unit Nonresponse?Journal of Official Statistics, 33(4), 963–978. 

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. 

Borghuis, J., Denissen, J. J. A., Oberski, D., Sijtsma, K., Meeus, W. H. J., Branje, S., Koot, H. M., & Bleidorn, W. (2017). Big Five personality stability, change, and codevelopment across adolescence and early adulthood. Journal of Personality and Social Psychology, 113(4), 641–657.

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), 91–95. 

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. 

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: A Multidisciplinary Journal, 24(2), 216–229.


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), 278–289.

Di Mari, R., Oberski, D. L., & Vermunt, J. K. (2016). Bias-Adjusted Three-Step Latent Markov Modeling With Covariates. Structural Equation Modeling: A Multidisciplinary Journal, 23(5), 649–660.

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.

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.

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.

Oberski, D. (2016a). Mixture Models: Latent Profile and Latent Class Analysis. In J. Robertson & M. Kaptein (Eds.), Modern Statistical Methods for HCI (pp. 275–287). Springer International Publishing.

Oberski, D. (2016b). Questionnaire Science (L. R. Atkeson & R. M. Alvarez, Eds.; Vol. 1). Oxford University Press.

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. 

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.


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. 

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. 

Oberski, D. L., Hagenaars, J. A. P., & Saris, W. E. (2015). The latent class multitrait-multimethod model. Psychological Methods, 20(4), 422–443.

Oberski, D. L., & Vermunt, J. K. (2015). The relationship between CUB and loglinear models with latent variables[Data set]. University of Salento. 

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.


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.

Oberski, D. (2014).  lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models.. Journal of Statistical Software, 57(1). 

Oberski, D. L. (2014). Evaluating Sensitivity of Parameters of Interest to Measurement Invariance in Latent Variable Models. Political Analysis, 22(1), 45–60. 


Oberski, D. L., & Satorra, A. (2013). Measurement Error Models With Uncertainty About the Error Variance. Structural Equation Modeling: A Multidisciplinary Journal, 20(3), 409–428.

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.

Oberski, D. L., & Vermunt, J. K. (2013). A Model-Based Approach to Goodness-of-Fit Evaluation in Item Response Theory. Measurement: Interdisciplinary Research & Perspective, 11(3), 117–122.


Gallego, A., & Oberski, D. (2012). Personality and Political Participation: The Mediation Hypothesis. Political Behavior, 34(3), 425–451. 

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.

Oberski, D. L., Weber, W., & Révilla, M. (2012). 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 (pp. 151–157). VS Verlag für Sozialwissenschaften. 


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, M. Braun, B. Edwards, T. P. Johnson, L. Lyberg, P. Ph. Mohler, B.-E. Pennell, & T. W. Smith (Eds.), Survey Methods in Multinational, Multiregional, and Multicultural Contexts (pp. 435–453). John Wiley & Sons, Inc.