Publications: Statistical Methods (KEM)
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Handbook of Sharing Confidential Data
Drechsler, J., Kifer, D., Reiter, J. & Slavković, A. (eds.) (2025): Handbook of Sharing Confidential Data. Differential Privacy, Secure Multiparty Computation, and Synthetic Data. (Chapman & Hall/CRC Handbooks of Modern Statistical Methods), Boca Raton: CRC Press, 342 p.
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Bridging Between Different BeH Industry Classifications via Imputation
Drechsler, J. & Ludsteck, J. (2024): Bridging Between Different BeH Industry Classifications via Imputation. (FDZ-Methodenreport 04/2024 (en)), Nürnberg, 17 p. DOI:10.5164/IAB.FDZM.2404.en.v1
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An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity
Fössing, E. & Drechsler, J. (2024): An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, p. 178-193. DOI:10.1007/978-3-031-69651-0_12
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Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator
Latner, J., Neunhoeffer, M. & Drechsler, J. (2024): Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, p. 115-128. DOI:10.1007/978-3-031-69651-0_8
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Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling
Oganian, A., Drechsler, J. & Iqbal, M. (2024): Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, p. 129-143. DOI:10.1007/978-3-031-69651-0_9
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Rallying around the leader in times of crises: The opposing effects of perceived threat and anxiety
Lehrer, R., Bahnsen, O., Müller, K., Neunhoeffer, M., Gschwend, T. & Juhl, S. (2025): Rallying around the leader in times of crises: The opposing effects of perceived threat and anxiety. In: European journal of political research, Vol. 64, No. 2, p. 697-718. DOI:10.1111/1475-6765.12717
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The Complexities of Differential Privacy for Survey Data
Drechsler, J. & Bailie, J. (2024): The Complexities of Differential Privacy for Survey Data. (NBER working paper / National Bureau of Economic Research 32905), Cambridge, Mass, 18 p.
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The Impact of Mail, Web, and Mixed-Mode Data Collection on Participation in Establishment Surveys
Küfner, B., Sakshaug, J., Zins, S. & Globisch, C. (2025): The Impact of Mail, Web, and Mixed-Mode Data Collection on Participation in Establishment Surveys. In: Journal of survey statistics and methodology, Vol. 13, No. 1, p. 66-99. DOI:10.1093/jssam/smae033
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OnJoB: Die Online-Jobcenter-Befragung Bürgergeld
Bernhard, S., Nützel, U., Osiander, C., Ramos Lobato, P. & Zins, S. (2024): OnJoB: Die Online-Jobcenter-Befragung Bürgergeld. (IAB-Forschungsbericht 17/2024), Nürnberg, 63 p. DOI:10.48720/IAB.FB.2417
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Collecting Hair Samples in Online Panel Surveys: Participation Rates, Selective Participation, and Effects on Attrition
Lawes, M., Hetschko, C., Sakshaug, J. & Eid, M. (2024): Collecting Hair Samples in Online Panel Surveys: Participation Rates, Selective Participation, and Effects on Attrition. In: Survey research methods, Vol. 18, No. 2, p. 167-185. DOI:10.18148/srm/2024.v18i2.8170
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The effect of transitioning into temporary employment on wages is not negative: A comparative study in eight countries
Latner, J. (2024): The effect of transitioning into temporary employment on wages is not negative: A comparative study in eight countries. In: Research in Social Stratification and Mobility, Vol. 92. DOI:10.1016/j.rssm.2024.100957
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Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections
Bun, M., Gaboardi, M., Neunhoeffer, M. & Zhang, W. (2024): Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections. In: Proceedings of the ACM on Management of Data, Vol. 2, No. 2, p. 1-26. DOI:10.1145/3651595
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Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys
Bailie, J. & Drechsler, J. (2024): Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys. In: National Bureau of Economic Research (2024): Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, Spring 2024, Washington, p. 1-33.
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On the Formal Privacy Guarantees of Synthetic Data
Neunhoeffer, M., Latner, J. & Drechsler, J. (2024): On the Formal Privacy Guarantees of Synthetic Data. In: National Bureau of Economic Research (2024): Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, Spring 2024, Washington, p. 1-16.
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30 Years of Synthetic Data
Drechsler, J. & Haensch, A. (2024): 30 Years of Synthetic Data. In: Statistical Science, Vol. 39, No. 2, p. 221-242. DOI:10.1214/24-STS927
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Dokumentation und Codebuch für das Hochfrequente Online Personen Panel "Leben und Erwerbstätigkeit in Zeiten von Corona" (IAB-HOPP, Welle 1–9)
Volkert, M., Haas, G., Zins, S., Altschul, S., Bellmann, L., Dummert, S., Haensch, A., Hensgen, S., Heusler, A., Ludsteck, J., Müller, B., Müller, D., Osiander, C., Schmidtke, J., Stephan, G., Trahms, A. & Wayment, H. (2024): Dokumentation und Codebuch für das Hochfrequente Online Personen Panel "Leben und Erwerbstätigkeit in Zeiten von Corona" (IAB-HOPP, Welle 1–9). (FDZ-Datenreport 01/2024 (de)), Nürnberg, 39 p. DOI:10.5164/IAB.FDZD.2401.de.v1
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Regional Labor Market Forecasts 2024: Weak Dynamics Persist Across Regional Labor Markets
Heining, J., Jahn, D., Körner, K., Rossen, A., Teichert, C. & Weyh, A. (2024): Regionale Arbeitsmarktprognosen 2024: Schwache Dynamik auf den regionalen Arbeitsmärkten hält an. (IAB-Kurzbericht 08/2024), Nürnberg, 8 p. DOI:10.48720/IAB.KB.2408
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Evaluation des Teilhabechancengesetzes - Abschlussbericht
Achatz, J., Bauer, F., Bennett, J., Bömmel, N., Coban, M., Dietz, M., Englert, K., Fuchs, P., Gellermann, J., Globisch, C., Hülle, S., Kasrin, Z., Kupka, P., Nivorozhkin, A., Osiander, C., Pohlan, L., Promberger, M., Raab, M., Ramos Lobato, P., Schels, B., Schiele, M., Trappmann, M., Tübbicke, S., Wenzig, C., Wolff, J., Zins, S. & Zabel, C. (2024): Evaluation des Teilhabechancengesetzes - Abschlussbericht. (IAB-Forschungsbericht 04/2024), Nürnberg, 331 p. DOI:10.48720/IAB.FB.2404
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A data-driven approach to understanding non-response and restoring sample representativeness in the UK Next Steps cohort
Silverwood, R., Calderwood, L., Henderson, M., Sakshaug, J. & Ploubidis, G. (2024): A data-driven approach to understanding non-response and restoring sample representativeness in the UK Next Steps cohort. In: Longitudinal and life course studies, Vol. 15, No. 2, p. 227-250. DOI:10.1332/17579597y2024d000000010
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The Role of Hyperparameters in Machine Learning Models and How to Tune them
Arnold, C., Biedebach, L., Küpfer, A. & Neunhoeffer, M. (2024): The Role of Hyperparameters in Machine Learning Models and How to Tune them. In: Political Science Research and Methods, Vol. 12, No. 4, p. 841-848. DOI:10.1017/psrm.2023.61