Human Data Science (HDS)



Kamalabad, M. S., Leenders, R., & Mulder, J. (2023). What is the Point of Change? Change Point Detection in Relational Event Models, Social Networks, 74, 166-181.

Ohme, J., Araujo, T., Boeschoten, L., Freelon, D., Ram, N., Reeves, B. B., & Robinson, T. N. (2023). Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking. Communication Methods and Measures, 1-18.

Yazdi, A. A., Kamalabad, M. S., Oberski, D. L., & Grzegorczyk, M. (2023). Bayesian multivariate control charts for multivariate profiles monitoring, Quality Technology & Quantitative Management, 1-36.


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 careFrontiers in Digital Health4.

Boeschoten, L., Ausloos, J., Möller, J. E., Araujo, T., & Oberski, D. L. (2022). A framework for privacy preserving digital trace data collection through data donationComputational Communication Research4(2), 388–423.

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).

Boeschoten, L., Scholtus, S., Daalmans, J., Vermunt, J.K. and de Waal, T. (2022). Using Multiple Imputation of Latent Classes to construct population census tables with data from multiple sourcesSurvey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 48, No. 1.

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.

Fang, Q., Giachanou, A., Bagheri, A., Boeschoten, L., van Kesteren, E. J., Kamalabad, M. S., & Oberski, D. L. (2022). On Text-based Personality Computing: Challenges and Future DirectionsarXiv preprint arXiv:2212.06711.

Fang, Q., Nguyen, D., & Oberski, D. L. (2022). Evaluating the Construct Validity of Text Embeddings with Application to Survey QuestionsEPJ Data Science11(1).

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 researchSociological Methods and Research.

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 networksEP Europace.

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).

van Driel, I. I.Giachanou, A.Pouwels, J. L.Boeschoten, L.Beyens, I., & Valkenburg, P. M. (2022). Promises and pitfalls of social media data donationsCommunication Methods and Measures, 16(4), 266282.

Zhang, G., Giachanou, A., & Rosso, P. (2022). SceneFND: Multimodal fake news detection by modelling scene context informationJournal of Information Science, 2022, 1-13.


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 ReportsJournal of Healthcare Engineering, 2021, 1–11.

Boeschoten, L., van Kesteren, E.-J., Bagheri, A., & Oberski, D. L. (2021). Achieving Fair Inference Using Error-Prone OutcomesInternational Journal of Interactive Multimedia and Artificial Intelligence, 6(5), 9.

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.

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 & Knowledge Engineering, 138, 101960.

Giachanou, A., Ghanem, B., & Rosso, P. (2021). Detection of conspiracy propagators using psycho-linguistic characteristicsJournal of Information Science, 1 – 15.

Giachanou, A., Rosso, P., & Crestani, F. (2021). The impact of emotional signals on credibility assessmentJournal of the Association for Information Science and Technology, 72, 1117 – 1132.

van Kesteren, E.-J., & Kievit, R. A. (2021). Exploratory factor analysis with structured residuals for brain network data.Network Neuroscience, 5(1), 1–27.

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

Ruffo, G., Semeraro, A., Giachanou, A., & Rosso, P. (2021). Surveying the research on fake news in social media: a tale of networks and languagearXiv preprint arXiv:2109.07909.

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.

Volker, T. B., & Vink, G. (2021). Anonymiced shareable data: Using mice to create and analyze multiply imputed synthetic datasets. Psych3(4), 703-716.


Arnold, M., Oberski, D. L., Brandmaier, A. M. & Voelkle, M. C. (03-07-2020). Identifying Heterogeneity in Dynamic Panel Models with Individual Parameter Contribution RegressionStructural 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 LettersProceedings 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 historyJournal of Intelligent Information Systems.

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

Boeschoten, L., Filipponi, D. & Varriale, R. (2020). Combining Multiple Imputation and Hidden Markov Modeling to Obtain Consistent Estimates of Employment StatusJournal 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 PackagesIn Companion Publication of the 2020 International Conference on Multimodal Interaction (pp. 523-523).

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 ConstraintsJournal 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 classesJournal 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 AnalysisJournal 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 NetworksThe Thirty-Second International Florida Artificial Intelligence Research Society Conference.

van Erp, S., Oberski, D. L., & Mulder, J. (2019). Shrinkage priors for Bayesian penalized regressionJournal 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 VariablesStructural 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 trialsStatistical 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-useStatistical 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 modelingPsychological 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 adulthoodJournal 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 globallyNature, 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 ModelsJournal 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 AnalysisStructural 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.