Publications

2025

  • Achterhold, E., Mühlböck, M., Steiber, N., & Kern, C. (2025). Fairness in algorithmic profiling: The AMAS case. Minds and Machines, 35(9). https://doi.org/10.1007/s11023-024-09706-9
  • Arnold, C., & Neunhoeffer, M. (2025). Working with synthetic data: The do’s, dangers and don’ts. In Sage Research Methods: Research and Data Literacy. SAGE Publications.
  • Bach, R. L., & Kern, C. (2025). Fairness, justice, and social inequality in machine learning. SocArXiv. https://doi.org/10.31235/osf.io/39jcs_v1
  • Beck, J., Steinberg, A., Dimmelmeier, A., et al. (2025). Addressing data gaps in sustainability reporting: A benchmark dataset for greenhouse gas emission extraction. Scientific Data, 12, 1497. https://doi.org/10.1038/s41597-025-05664-8
  • Breznau, N., Rinke, E. M., Wuttke, A., & 163 others (including Neunhoeffer, M.). (2025). The reliability of replications: A study in computational reproductions. Royal Society Open Science, 12(3), 241038.
  • Classe, F., Debelak, R., & Kern, C. (2025). Score-based tests for parameter instability in ordinal factor models. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12392
  • Collins, J., & Kern, C. (2025). Pre-trained nonresponse prediction in panel surveys with machine learning. Survey Research Methods, 19(2), 123–137. https://doi.org/10.18148/srm/2025.v19i2.8473
  • Eckman, S., Ma, B., Kern, C., Chew, R., Plank, B., & Kreuter, F. (2025). Correcting annotator bias in training data: Population-aligned instance replication (PAIR). arXiv preprint arXiv:2501.06826.
  • Kern, C., Fischer-Abaigar, U., Schweisthal, J., Frauen, D., Ghani, R., Feuerriegel, S., van der Schaar, M., & Kreuter, F. (2025). Algorithms for reliable decision-making need causal reasoning. Nature Computational Science, 5, 356–360. https://doi.org/10.1038/s43588-025-00814-9
  • Kononykhina, O., Haensch, A.-C., & Kreuter, F. (2025). Mind the gap: Gender-based differences in occupational embeddings. In Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP) (pp. 83–91). Association for Computational Linguistics.
  • 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. European Journal of Political Research, 64(2), 697–718.
  • Ma, B., Huang, C. A., & Haensch, A.-C. (2025). Can large language models advance crosswalks? The case of Danish occupation codes. In Proceedings of the 2025 Conference of the North America Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop) (pp. 392–399). Association for Computational Linguistics.
  • Ma, B., Li, Y., Zhou, W., Gong, Z., Liu, Y. J., Jasinskaja, K., Friedrich, A., Hirschberg, J., Kreuter, F., & Plank, B. (2025). Pragmatics in the era of large language models: A survey on datasets, evaluation, opportunities and challenges. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 8679–8696). Association for Computational Linguistics.
  • Ma, B., Yoztyurk, B., Haensch, A.-C., Wang, X., Herklotz, M., Kreuter, F., Plank, B., & Aßenmacher, M. (2025). Algorithmic fidelity of large language models in generating synthetic German public opinions: A case study. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1785–1809). Association for Computational Linguistics.
  • Novotny, M., Weber, W., Kern, C., & Kreuter, F. (2025). Measuring public opinion towards artificial intelligence: Development and validation of a general AI attitude short scale. AI & Society. https://doi.org/10.1007/s00146-025-02478-5
  • Rittmann, O., Neunhoeffer, M., & Gschwend, T. (2025). How to improve the substantive interpretation of regression results when the dependent variable is logged. Political Science Research and Methods, 13(1), 203–211.
  • Schenk, P. O., Kern, C., & Buskirk, T. D. (2025). Fares on fairness: Using a total error framework to examine the role of measurement and representation in training data on model fairness and bias. In Proceedings of the Fourth European Workshop on Algorithmic Fairness (pp. 187–211). PMLR. https://proceedings.mlr.press/v294/schenk25a.html
  • Simson, J., Draxler, F., Mehr, S., & Kern, C. (2025). Less is more: Preventing harmful data practices by using participatory input to navigate the machine learning multiverse. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2025). https://doi.org/10.1145/3706598.3713482
  • Strasser Ceballos, C., & Kern, C. (2025). Location matching on shaky grounds: Re-evaluating algorithms for refugee allocation. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’25). https://dl.acm.org/doi/full/10.1145/3715275.3732149
  • Strasser Ceballos, C., Kern, C., Kappenberger, J., Gerdon, F., Szafran, D., Rupp, F., & Bach, R. (2025). A simulation framework for studying the social impacts of algorithm-based refugee matching. In Proceedings of the Fourth European Workshop on Algorithmic Fairness. PMLR. https://proceedings.mlr.press/v294/kern25a.html
  • Strasser Ceballos, C., Novotny, M., & Kern, C. (2025). Re-evaluating the role of refugee integration factors for building more equitable allocation algorithms. In Proceedings of the Fourth European Workshop on Algorithmic Fairness. PMLR. https://proceedings.mlr.press/v294/ceballos25a.html
  • von der Heyde, L., Haensch, A.-C., & Wenz, A. (2025). Vox populi, vox AI? Using large language models to estimate German vote choice. Social Science Computer Review. Advance online publication. https://doi.org/10.1177/08944393251337014
  • von der Heyde, L., Haensch, A.-C., Weiß, B., & Daikeler, J. (2025). AIn’t nothing but a survey? Using large language models for coding German open-ended survey responses on survey motivation. arXiv preprint arXiv:2506.14634. https://doi.org/10.48550/arXiv.2506.1463

2024

  • Arnold, C., Biedebach, L., Küpfer, A., & Neunhoeffer, M. (2024). The role of hyperparameters in machine learning models and how to tune them. Political Science Research and Methods, 12(4), 841–848.
  • Beck, J., Eckman, S., Ma, B., Chew, R., & Kreuter, F. (2024). Order effects in annotation tasks: Further evidence of annotation sensitivity. In Proceedings of the EACL Workshop on UncertaiNLP. https://aclanthology.org/2024.uncertainlp-1.8/
  • Classe, F., & Kern, C. (2024). Detecting differential item functioning in multidimensional graded response models with recursive partitioning. Applied Psychological Measurement. https://doi.org/10.1177/01466216241238743
  • Classe, F., & Kern, C. (2024). Latent variable forests for latent variable score estimation. Educational and Psychological Measurement. https://doi.org/10.1177/00131644241237502
  • Collins, J., & Kern, C. (2024). Longitudinal nonresponse prediction with time series machine learning. Journal of Survey Statistics and Methodology. https://doi.org/10.1093/jssam/smae037
  • Dimmelmeier, A. (2024, in press). The financial geography of sustainability data: A mapping exercise of the spatial dimension of the ESG information industry. Finance and Space. https://doi.org/10.1080/2833115X.2023.2296980
  • Dimmelmeier, A., Doll, H., Schierholz, M., Kormanyos, E., Fehr, M., Ma, B., Beck, J., Fraser, A., & Kreuter, F. (2024). Informing climate risk analysis using textual information: A research agenda. In Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024) (pp. 12–26). Association for Computational Linguistics.
  • Eckman, S., Plank, B., & Kreuter, F. (2024). Position: Insights from survey methodology can improve training data. In Proceedings of the 41st International Conference on Machine Learning (ICML 2024). PMLR. https://proceedings.mlr.press/v235/eckman24a.html
  • Fischer-Abaigar, U., Kern, C., & Kreuter, F. (2024). The missing link: Allocation performance in causal machine learning. In Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact (co-located with ICML 2024). https://arxiv.org/abs/2407.10779
  • Fischer-Abaigar, U., Kern, C., Barda, N., & Kreuter, F. (2024). Bridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector. Government Information Quarterly, 41(4). https://doi.org/10.1016/j.giq.2024.101976
  • Jaime, S., & Kern, C. (2024). Ethnic classifications in algorithmic fairness: Concepts, measures and implications in practice. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24) (pp. 237–253). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658902
  • Kern, C., Bach, R., Mautner, H., & Kreuter, F. (2024). When small decisions have big impact: Fairness implications of algorithmic profiling schemes. ACM Journal on Responsible Computing. https://doi.org/10.1145/3689485
  • Kern, C., Kim, M., & Zhou, A. (2024). Multi-accurate CATE is robust to unknown covariate shifts. Transactions on Machine Learning Research (TMLR). https://openreview.net/pdf?id=VOGlTb27ob
  • Kraus, E., & Kern, C. (2024). Measurement modeling of predictors and outcomes in algorithmic fairness. In Proceedings of the 3rd European Workshop on Algorithmic Fairness (EWAF ’24). https://ceur-ws.org/Vol-3908/
  • Latner, J., Neunhoeffer, M., & Drechsler, J. (2024). Generating synthetic data is complicated: Know your data and know your generator. In Privacy in Statistical Databases (pp. 115–128). Springer.
  • Ma, B. (2024). Evaluating lexical aspect with large language models. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp. 123–131). Association for Computational Linguistics.
  • Ma, B., Wang, X., Hu, T., Haensch, A.-C., Hedderich, M. A., Plank, B., & Kreuter, F. (2024). The potential and challenges of evaluating attitudes, opinions, and values in large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 8783–8805). Association for Computational Linguistics.
  • Schenk, P. O., & Kern, C. (2024). Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production. AStA Wirtschafts- und Sozialstatistisches Archiv. https://doi.org/10.1007/s11943-024-00344-2
  • Simson, J., Fabris, A., & Kern, C. (2024). Lazy data practices harm fairness research. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658931
  • Simson, J., Fabris, A., & Kern, C. (2024). Unveiling the blindspots: Examining availability and usage of protected attributes in fairness datasets. In Proceedings of the 3rd European Workshop on Algorithmic Fairness (EWAF ’24). https://ceur-ws.org/Vol-3908/
  • Simson, J., Pfisterer, F., & Kern, C. (2024). One model many scores: Using multiverse analysis to prevent fairness hacking and evaluate the influence of model design decisions. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658974
  • Sommer, F. (2024). Representations of landed property in statistics and state data architectures: (Family) relation versus (Business) ratio. In D. Schuck, H. Gibson, & S. B. Mancini (Eds.), Relating to Land (forthcoming).
  • Strasser Ceballos, C., & Kern, C. (2024). Deciding the future of refugees: Rolling the dice or algorithmic location assignment? In Proceedings of the 3rd European Workshop on Algorithmic Fairness (EWAF ’24). https://ceur-ws.org/Vol-3908/
  • von der Heyde, L., Haensch, A., Wenz, A., & Ma, B. (2024). United in diversity? Contextual biases in LLM-based predictions of the 2024 European Parliament elections. arXiv preprint. https://arxiv.org/abs/2409.09045

2023

  • Bach, R. L., Kern, C., Mautner, H., & Kreuter, F. (2023). The impact of modeling decisions in statistical profiling. Data & Policy, 5. https://doi.org/10.1017/dap.2023.29
  • Beck, J. (2023). Quality aspects of annotated data: A research synthesis. AStA Wirtschafts- und Sozialstatistisches Archiv, 1–23. https://doi.org/10.1007/s11943-023-00332-y
  • Classe, F. L., & Steyer, R. (2023). A probit multistate IRT model with latent item effect variables for graded responses. European Journal of Psychological Assessment. Advance online publication. https://doi.org/10.1027/1015-5759/a000751
  • Dimmelmeier, A. (2023). Dataset on environmental, social and governance information firms and their merger and acquisitions activities. Data in Brief. https://doi.org/10.1016/j.dib.2023.109457
  • Dimmelmeier, A. (2023). Expanding the politics of measurement in sustainable finance: Reconceptualizing environmental, social and governance information as infrastructure. Environment and Planning C: Politics and Space. https://doi.org/10.1177/23996544231209149
  • Dimmelmeier, A., & Egerer, E. (2023). Das Transformationspotential des deutschen Sustainable Finance Diskurses: Eine Einschätzung auf Basis von Logiken und Frames. DIW Berlin. https://doi.org/10.3790/vjh.92.1.11
  • Fischer-Abaigar, U., Kern, C., Barda, N., & Kreuter, F. (2023). Bridging the gap: Towards an expanded toolkit for ML-supported decision-making in the public sector. arXiv. https://doi.org/10.48550/arXiv.2310.19091
  • Gruber, C., Schenk, P. O., Schierholz, M., Kreuter, F., & Kauermann, G. (2023). Sources of uncertainty in machine learning: A statisticians’ view. arXiv. https://arxiv.org/abs/2305.16703
  • Kaufmann, T., Ball, S., Beck, J., Hüllermeier, E., & Kreuter, F. (2023). On the challenges and practices of reinforcement learning from real human feedback. In Proceedings of the ECML PKDD 2023 Workshop: Towards Hybrid Human-Machine Learning and Decision Making.
  • Kern, C., Eckman, S., Beck, J., Chew, R., Ma, B., & Kreuter, F. (2023). Annotation sensitivity: Training data collection methods affect model performance. In Findings of the Association for Computational Linguistics: EMNLP 2023. https://aclanthology.org/2023.findings-emnlp.992/
  • Kern, C., Weiss, B., & Kolb, J. P. (2023). Predicting nonresponse in future waves of a probability-based mixed-mode panel with machine learning. Journal of Survey Statistics and Methodology, 11(1), 100–123.
  • Mann, D., Huber, M., Schmitt, M., & Sommer, F. (2023). Spatial variations in heat or energy consumption and space use per person in Germany: A comparative analysis. In Proceedings of the Joint UEF5 and WPC59 Conference.
  • Neunhoeffer, M. (2023). Generative adversarial nets for social scientists [Doctoral dissertation, Universität Mannheim].
  • Sommer, F. (2023). Nachhaltigkeit codieren. Verfassungsblog.
  • Sommer, F. (2023). Verwaltungsordnungen als Wertordnungen: Die Datendimension in der Nachhaltigkeitsgovernance. zfwu – Zeitschrift für Wirtschafts- und Unternehmensethik, 24(3), 360–371. https://doi.org/10.5771/1439-880X-2023-3-360

2022

  • Beck, J., Eckman, S., Chew, R., & Kreuter, F. (2022). Improving labeling through social science insights: Results and research agenda. In International Conference on Human-Computer Interaction (pp. 245–261). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-21707-4_19
  • Breznau, N., Rinke, E. M., Wuttke, A., & 163 others (including Neunhoeffer, M.). (2022). Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. Proceedings of the National Academy of Sciences, 119(44). https://doi.org/10.1073/pnas.2203150119
  • Gerdon, F., Bach, R. L., Kern, C., & Kreuter, F. (2022). Social impacts of algorithmic decision-making: A research agenda for the social sciences. Big Data & Society. https://doi.org/10.1177/20539517221089305
  • Gschwend, T., Müller, K., Munzert, S., Neunhoeffer, M., & Stoetzer, L. F. (2022). The Zweitstimme model: A dynamic forecast of the 2021 German federal election. PS: Political Science & Politics, 55(1), 85–90. https://doi.org/10.1017/S1049096521001403
  • Kaiser, P., Kern, C., & Rügamer, D. (2022). Uncertainty-aware predictive modeling for fair data-driven decisions. In Trustworthy and Socially Responsible Machine Learning (TSRML 2022) (co-located with NeurIPS 2022). https://arxiv.org/abs/2211.02730
  • Kern, C., Gerdon, F., Bach, R. L., Keusch, F., & Kreuter, F. (2022). Humans versus machines: Who is perceived to decide fairer? Experimental evidence on attitudes toward automated decision-making. Patterns. https://doi.org/10.1016/j.patter.2022.100591
  • Kim, M. P., Kern, C., Goldwasser, S., Kreuter, F., & Reingold, O. (2022). Universal adaptability: Target-independent inference that competes with propensity scoring. Proceedings of the National Academy of Sciences, 119(4). https://doi.org/10.1073/pnas.2108097119
  • Kuppler, M., Kern, C., Bach, R. L., & Kreuter, F. (2022). From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making. Frontiers in Sociology. https://doi.org/10.3389/fsoc.2022.883999

2021

  • Achimescu, V., & Chachev, P. D. (2021). Raising the flag: Monitoring user-perceived disinformation on Reddit. Information, 12(4). https://www.mdpi.com/2078-2489/12/1/4
  • Gerdon, F., Nissenbaum, H., Bach, R. L., Kreuter, F., & Zins, S. (2021). Individual acceptance of using health data for private and public benefit: Changes during the COVID-19 pandemic. Harvard Data Science Review, Special Issue 1. https://doi.org/10.1162/99608f92.edf2fc97
  • Kern, C., Bach, R. L., Mautner, H., & Kreuter, F. (2021). Fairness in algorithmic profiling: A German case study. arXiv. https://arxiv.org/abs/2108.04134
  • Kuppler, M., Kern, C., Bach, R. L., & Kreuter, F. (2021). Distributive justice and fairness metrics in automated decision-making: How much overlap is there? arXiv. https://arxiv.org/abs/2105.01441
  • Neunhoeffer, M., Wu, Z. S., & Dwork, C. (2021). Private post-GAN boosting. In International Conference on Learning Representations (ICLR 2021). https://openreview.net/forum?id=3lI79ZrxgR

2020

  • Amaya, A., Bach, R. L., Kreuter, F., & Keusch, F. (2020). Measuring the strength of attitudes in social media data. In C. A. Hill (Ed.), Big data meets survey science: A collection of innovative methods (pp. 163–192). Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781118976357.ch5
  • Arnold, C., & Neunhoeffer, M. (2020). Really useful synthetic data: A framework to evaluate the quality of differentially private synthetic data. arXiv preprint arXiv:2004.07740.
  • Bach, R. L., & Wenz, A. (2020). Studying health-related internet and mobile device use using web logs and smartphone records. PLOS ONE, 15, e0234663. https://doi.org/10.1371/journal.pone.0234663
  • Bähr, S., Haas, G.-C., Keusch, F., Kreuter, F., & Trappmann, M. (2020). Missing data and other measurement quality issues in mobile geolocation sensor data. Social Science Computer Review, 1–24. https://doi.org/10.1177/0894439320944118
  • Cernat, A., & Keusch, F. (2020). Do surveys change behaviour? Insights from digital trace data. International Journal of Social Research Methodology. https://doi.org/10.1080/13645579.2020.1853878
  • Gerdon, F., Theil, C. K., Kern, C., Bach, R. L., Kreuter, F., Stuckenschmidt, H., & Eckert, K. (2020). Exploring impacts of artificial intelligence on urban societies with social simulations. In Proceedings of the 40th Congress of the German Sociological Association (DGS 2020), Online.
  • Haas, G.-C., Kreuter, F., Keusch, F., Trappmann, M., & Bähr, S. (2020). Effects of incentives in smartphone data collection. In C. A. Hill (Ed.), Big data meets survey science: A collection of innovative methods (pp. 387–414). Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781118976357.ch13
  • Haas, G.-C., Trappmann, M., Keusch, F., Bähr, S., & Kreuter, F. (2020). Using geofences to collect survey data: Lessons learned from the IAB-SMART study. Survey Methods: Insights from the Field, 2020(10/12/20), 1–12. https://doi.org/10.13094/SMIF-2020-00023
  • Kern, C., Li, Y., & Wang, L. (2020). Boosted kernel weighting: Using statistical learning to improve inference from nonprobability samples. Journal of Survey Statistics and Methodology. https://doi.org/10.1093/jssam/smaa028
  • Keusch, F., Struminskaya, B., Kreuter, F., & Weichbold, M. (2020). Combining active and passive mobile data collection: A survey of concerns. In C. A. Hill (Ed.), Big data meets survey science: A collection of innovative methods (pp. 657–682). Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781118976357.ch22
  • Keusch, F., Bähr, S., Haas, G.-C., Kreuter, F., & Trappmann, M. (2020). Coverage error in data collection combining mobile surveys with passive measurement using apps: Data from a German national survey. Sociological Methods & Research. https://doi.org/10.1177/0049124120914924
  • Schierholz, M., & Schonlau, M. (2020). Machine learning for occupation coding: A comparison study. Journal of Survey Statistics and Methodology. https://doi.org/10.1093/jssam/smaa023
  • Struminskaya, B., & Keusch, F. (2020). Editorial: From web surveys to mobile web to apps, sensors, and digital traces. Survey Methods: Insights from the Field, 2020(10/12/20), 1–7. https://doi.org/10.13094/SMIF-2020-00015
  • Struminskaya, B., Lugtig, P., Keusch, F., & Höhne, J. K. (2020). Augmenting surveys with data from sensors and apps: Opportunities and challenges. Social Science Computer Review. https://doi.org/10.1177/0894439320979951

2019

  • Amaya, A., Bach, R. L., Keusch, F., & Kreuter, F. (2019). New data sources in social science research: Things to know before working with Reddit data. Social Science Computer Review, 1–10. https://doi.org/10.1177/0894439319893305
  • Bach, R. L., Kern, C., Amaya, A., Keusch, F., Kreuter, F., Hecht, J., & Heinemann, J. (2019). Predicting voting behavior using digital trace data. Social Science Computer Review. https://doi.org/10.1177/0894439319882896
  • Bauer, P. C., Keusch, F., & Kreuter, F. (2019). Trust and cooperative behavior: Evidence from the realm of data-sharing. PLOS ONE, 14(8), e0220115. https://doi.org/10.1371/journal.pone.0220115
  • Keusch, F., Struminskaya, B., Antoun, C., Couper, M. P., & Kreuter, F. (2019). Willingness to participate in passive mobile data collection. Public Opinion Quarterly, 83(S1), 210–235. https://doi.org/10.1093/poq/nfz007
  • Kreuter, F., Haas, G.-C., Keusch, F., Bähr, S., & Trappmann, M. (2019). Collecting survey and smartphone sensor data with an app: Opportunities and challenges around privacy and informed consent. Social Science Computer Review, 38(5), 533–549. https://doi.org/10.1177/0894439318816389
  • Neunhoeffer, M., & Sternberg, S. (2019). How cross-validation can go wrong and what to do about it. Political Analysis, 27(1), 101–106. https://doi.org/10.1017/pan.2018.48
  • Schierholz, M. (2019). New methods for job and occupation classification (Doctoral dissertation, Universität Mannheim). https://madoc.bib.uni-mannheim.de/50617/
  • Stoetzer, L. F., Neunhoeffer, M., Gschwend, T., Munzert, S., & Sternberg, S. (2019). Forecasting elections in multiparty systems: A Bayesian approach combining polls and fundamentals. Political Analysis, 27(2), 255–262. https://doi.org/10.1017/pan.2018.57

2018

  • Bähr, S., Haas, G.-C., Keusch, F., Kreuter, F., & Trappmann, M. (2018, January 9). IAB-SMART-Studie: Mit dem Smartphone den Arbeitsmarkt erforschen. IAB-Forum: Das neue Onlinemagazin des Instituts für Arbeitsmarkt- und Berufsforschung.
  • Schierholz, M. (2018). Eine Hilfsklassifikation mit Tätigkeitsbeschreibungen für Zwecke der Berufskodierung. AStA Wirtschafts- und Sozialstatistisches Archiv, 12(3–4), 285–298. https://doi.org/10.1007/s11943-018-0231-2
  • Schierholz, M., Brenner, L., Cohausz, L., Damminger, L., Fast, L., Hörig, A.-K., Huber, A.-L., Ludwig, T., Petry, A., & Tschischka, L. (2018). Eine Hilfsklassifikation mit Tätigkeitsbeschreibungen für Zwecke der Berufskodierung: Leitgedanken und Dokumentation (IAB-Discussion Paper, 13/2018). Nürnberg: Institut für Arbeitsmarkt- und Berufsforschung.
  • Schierholz, M., Gensicke, M., Tschersich, N., & Kreuter, F. (2018). Occupation coding during the interview. Journal of the Royal Statistical Society: Series A (Statistics in Society), 181(2), 379–407. https://doi.org/10.1111/rssa.12297

2017

  • Munzert, S., Stötzer, L., Gschwend, T., Neunhoeffer, M., & Sternberg, S. (2017). Zweitstimme.org: Ein strukturell-dynamisches Vorhersagemodell für Bundestagswahlen. PVS Politische Vierteljahresschrift, 58(3), 418–441. https://doi.org/10.5771/0032-3470-2017-3-418

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