External Funding
01/2025 – 12/2025
Sigrid Blömeke Scholarship Award: Benefits of Process Data for Evaluating the Differential Effectiveness of App-Based Treatments
01/2022 – 12/2024
DFG-Project in the Priority Programm META-REP (SPP 2317): Conceptual Replications - Guidelines for implementation and factors influencing replicability across different fields in psychology (SE 3287/1-1)
07/2022 – 12/2022
Internal Cooperation Fund of the DGPs Method and Evaluation Section for the project: Identifying relevant predictors for differential item-functioning: A comparison of different machine learning based approaches (with Dr. Mirka Henninger)
08/2009 – 01/2010
DAAD scholarship for an ISAP semester abroad
Publications with peer review
(* indicates student co-authors under my supervision)
Edelsbrunner, P. A., Tetzlaff, L., Bach, K. M., Dumas, D., Hofer, S. I., Köhler, C., Kozlova, Z., Moeller, J., Reinhold, F., Roberts, G. J., Sengewald, M.-A., Bichler, S. (2025). Beyond linear regression: Statistically modeling aptitude-treatment interactions and the differential effectiveness of educational interventions. Learning and Individual Differences, 124, https://doi.org/10.1016/j.lindif.2025.102812.
Hoffmann*, J., Pohl, S., Twardawski, M., Gast, A., Höhs, J., & Sengewald, M.-A. (2025). Current practices for designing replications in social and cognitive psychology. Advances in Methods and Practices in Psychological Science, 8(2), 1-22. https://doi.org/10.1177/25152459251328273
Kiefer, C. & Sengewald, M.-A. (2025). Mining exceptional Rasch models. Behaviormetrika, 52, 361–391. https://doi.org/10.1007/s41237-024-00251-4
Henninger, M., Radek, J., Sengewald, M.-A., & Strobl, C. (2025). Partial credit trees meet the partial gamma coefficient for quantifying DIF and DSF in polytomous items. Behaviormetrika, 52, 221–257. https://doi.org/10.1007/s41237-024-00252-3
Sengewald, E., Hardt, K. & Sengewald, M.-A. (2024). A causal view on bias in missing data imputation: The impact of problematic auxiliary variables on the norming of test scores. Multivariate Behavioral Research, 1-17. https://doi.org/10.1080/00273171.2024.2412682
Heyne, N., Gnambs T. & Sengewald, M-A. (2024). Participation rates and differential effects of Extracurricular Tutoring Programs on Reading Literacy in a German Large-Scale Assessment. Large Scale Assessment in Education, 12, 27. https://doi.org/10.1186/s40536-024-00216-9
Sengewald, M.-A. & Mayer, A. (2024). Causal effect analysis in non-randomized data with latent variables and categorical indicators: The implementation and benefits of EffectLiteR. Psychological Methods, 29(2), 287-307. https://doi.org/10.1037/met0000489
Erhardt*, T. H., Gnambs, T. & Sengewald, M-A. (2023). Studying item-effect variables and their correlation patterns with multi-construct multi-state models. PLoS ONE, 18(8): e0288711. https://doi.org/10.1371/journal.pone.0288711
Sengewald, M.-A., Erhardt*, T. E., & Gnambs, T. (2023). The predictive validity of item-effect variables in the satisfaction with life scale for psychological and physical health. Assessment, 30(8), 2461-2475. https://doi/10.1177/10731911221149949
Gnambs, T., & Sengewald, M.-A. (2023). Meta-analytic structural equation modeling with fallible measurements. Zeitschrift für Psychologie. 231(1), 39-52. https://doi.org/10.1027/2151-2604/a000511
Sengewald, M.-A. & Pohl, S. (2019). Compensation and amplification of attenuation bias in causal effect estimates. Psychometrika, 84(2), 589-610. https://doi.org/10.1007/s11336-019-09665-6
Sengewald, M.-A., Steiner, P. M., & Pohl, S. (2019). When does measurement error in covariates impact causal effect estimates? - Analytical derivations of different scenarios and an empirical illustration. British Journal of Mathematical and Statistical Psychology, 72(2), 244-270. https://doi.org/10.1111/bmsp.12146
Thielemann*, D., Sengewald, M.-A., Kappler, G., & Steyer, R. (2017). A probit latent state IRT model with latent item-effect variables. European Journal of Psychological Assessment, 33(4), 271-284. https://doi.org/10.1027/1015-5759/a000417
Gast, A., Langer, S., & Sengewald, M.-A. (2016). Evaluative conditioning increases with temporal contiguity. The influence of stimulus order and stimulus interval on evaluative conditioning. Acta Psychologica, 170, 177-185. https://doi.org/10.1016/j.actpsy.2016.07.002
Pohl, S., Sengewald, M.-A., & Steyer, R. (2016). Adjustment when Covariates are Fallible. In W. Wiedermann & A. von Eye (Hrsg.), Statistics and Causality: Methods for Applied Empirical Research (pp. 363-384). Hoboken, NJ: Wiley4
Editorial
(# indicates shared first authorship)
Kutscher#, T., Sengewald#, M.-A., Gnambs, T., Carstensen, C. & Aßmann, C. (2024). The National Educational Panel Study (NEPS) and Methodological Innovations in Longitudinal Large-Scale Assessments. Large Scale Assessment in Education.
Preregistrations
(* indicates student co-authors under my supervision)
Twardawski, M., Hoffmann*, J., Kondzic, D., Pohl, S., Gast, A., Höhs, J., & Sengewald, M.-A. (2023). Conceptual replications of the imagined intergroup contact effect. https://doi.org/10.17605/OSF.IO/EU64T
Gast, A., Höhs, J., Hoffmann*, J., Kondzic, D., Pohl, S., Twardawski, M., & Sengewald, M.-A. (2023). Impact of online vs. lab setting on the Evaluative Conditioning Effect - identification of causal effects of replication factors. https://osf.io/rvms3
Hoffmann*, J., Pohl, S., Twardawski, M., Gast, A., Höhs, J., & Sengewald, M. (2022). Current practices for designing replications in social and cognitive psychology: A protocol for a systematic literature review and comparison.https://doi.org/10.17605/OSF.IO/YXGC8
Organized Symposia
Sengewald, M.-A. (2025, December). Response Behavior in Digital Learning and Assessments. 10th International NEPS Conference, Bamberg, Germany.
Sengewald, M.-A. & Ulitzsch, E. (2025, July). Advances in Investigating Response Behavior. EAM2025 - XI Conference of the European Association of Methodology, Tenerife, Spain.
Sengewald, M.-A. & Twardawski, M. (2024, October). Causal interpretations of effect Heterogeneity in Replication Research. META-REP 2024 Conference on Meta-science and Replicability, Munich, Germany.
Brandt, H., Hecht, M., Sengewald, M.-A., Frick, S., & Irmer, J. (2024, September). The use of machine learning for psychological research: What are its opportunities to answer substantive research questions? Joint congress of the German Psychological Society and Austrian Psychological Society, Vienna, Austria.
Koch, T. & Sengewald, M.-A. (2024, September). Improving psychological research through formal methodological frameworks: Illustrations for Measurement, Missing Data, Explanation, Replication. Joint congress of the German Psychological Society and Austrian Psychological Society, Vienna, Austria.
Pohl, S., Sengewald, M.-A., Steiner, P. & Wong, V. (2024, July). Using causal inference theory for designing and analyzing replication studies. International Meeting of the Psychometric Society, Prague, Czech Republic.
Wong, V., Pohl, S., Sengewald, M.-A., & Steiner, P. (2024, July). Quantifying replication success: Correspondence measures for replication studies. International Meeting of the Psychometric Society, Prague, Czech Republic.
Wetzel, E. & Sengewald, M.-A. (2023, September). Methodological developments in replication science: Insights from the META-REP priority program. 15th Conference of the Section Methods & Evaluation of the German Psychological Society, Konstanz, Germany.
Sengewald, M.-A., & Gnambs, T. (2022, March). Modeling multidimensionality in applied educational assessments. 9th Conference of the Society for Empirical Research in Education (GEBF), Leibniz Institute for Educational Trajectories, Bamberg.
Sengewald, M.-A., & Gnambs, T. (2020, September). Fair comparisons in educational large-scale assessments: Methodological challenges and novel solutions. Joint congress of the German Psychological Society and Austrian Psychological Society, Vienna, Austria.
Invited Talks
Sengewald, M.-A. (2024, April). Causal interpretations of effect heterogeneity in replication research (Host: Prof. Mirka Henninger, Society & Choice Research Seminar, University Basel).
Sengewald, M.-A. (2023, Juli). Planned replication designs (Host: Martin Schultze, Goethe Universität Frankfurt).
Sengewald M.-A., Henninger, M., Brechtloff, P. & Kubik, V. (2023, Juni). Vereinbarkeit beruflicher und familiärer Anforderungen (Host: Momme von Sydow, 17. Plenarversammlung des Fakultätentages Psychologie).
Sengewald, M.-A. & Henninger M. (2023, Juni). Using regularization approaches for DIF detection in polytomous IRT models (Host: Carolin Strobl, International Meeting on Detecting Differential Item Functioning in Polytomous IRT Models and / or Multiple Groups, Universität Zürich).
Sengewald, M.-A. (2023, Mai). The impact of measurement error on causal inference (Host: Jana Holtmann, Universität Leipzig).
Sengewald, M.-A. (2023, Februar). EffectLiteR: An R based tool for modeling differential effectiveness of treatments (Host: Peter Edelsbrunner, Modeling of Individual Differences in Education - MIDE Working Group, ETH Zürich).
Sengewald, M.-A. (2022, Mai). Kausale Effektschätzung mit Kontrolle für Messfehler: Modellierungsansätze und Beispiele für die praktische Relevanz (Host: Mario Gollwitzer, Ludwig-Maximilians-Universität München).
Sengewald, E., Hardt, K. & Sengewald, M.-A. (2022, April).Variablen-Selektion bei multipler Imputation und anschließender Normierung (Host: Dorothea Klinck, Bundesagentur für Arbeit).
Sengewald, M.-A. (2021, June). Causal effect analysis with latent covariates: Theoretical conditions and empirical benefits (Host: Tobias Koch, Friedrich-Schiller-Universität Jena).
Sengewald, M.-A. (2020, September). Causal effect estimation in non-randomized data with fallible covariates (Host: Axel Mayer, RWTH Aachen Universität).
Sengewald, M.-A. (2020, Juni). Kausale Inferenz ohne Randomisierung und mit unreliablen Kovariaten: Ein Beispiel aus der Bildungsforschung (Host: Steffi Pohl, Freie Universität Berlin).