Dr. Marie-Ann Sengewald

Research Data Center, Methods Development | Scaling and Test Design

Vita

Research interests

  • Causal Inference
  • latent Variable Models
  • Replication research
  • Evaluation Research

    • Publications

    2025

    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. Advanced online publication. https://doi.org/10.1007/s41237-024-00252-3
    Kiefer, C., & Sengewald, M.-A. (2025). Mining exceptional Rasch models. Behaviormetrika. Advance online publication. https://doi.org/10.1007/s41237-024-00251-4

    2024

    Aßmann, C., Gnambs, T., Sengewald, M.-A., Kutscher, T., & Carstensen, C. H. (Eds.). (2024). The national educational panel study (NEPS) and methodological innovations in longitudinal large-scale assessments [Special issue]. Large-scale Assessments in Education. Springer Nature. https://largescaleassessmentsineducation.springeropen.com/neps
    Hahn, I., & Sengewald, M.-A. (2024). NEPS technical report for science: Scaling results of Starting Cohort 1 for nine-year-old children (NEPS Survey Paper No. 116). Leibniz Institute for Educational Trajectories, National Educational Panel Study. https://doi.org/10.5157/NEPS:SP116:1.0
    Hahn, I., & Sengewald, M.-A. (2024). NEPS technical report for science: Scaling results of Starting Cohort 1 for seven-year-old children (NEPS Survey Paper No. 115). Leibniz Institute for Educational Trajectories, National Educational Panel Study. https://doi.org/10.5157/NEPS:SP115:1.0
    Heyne, N., Gnambs, T., & Sengewald, M.-A. (2024). Participation rates, characteristics, and differential effects on reading literacy of extracurricular tutoring in a German large-scale assessment. Large-scale Assessments in Education, 12, Article 27. https://doi.org/10.1186/s40536-024-00216-9
    Kutscher, T., Sengewald, M.-A., Gnambs, T., Carstensen, C. H., & Aßmann, C. (2024). Editorial: The national educational panel study (NEPS) and methodological innovations in longitudinal large-scale assessments. Large-scale Assessments in Education, 12, Article 31. https://doi.org/10.1186/s40536-024-00221-y
    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. Advance online publication. https://doi.org/10.1080/00273171.2024.2412682
    Sengewald, M.-A., Henninger, M., Bechtloff, P., & Kubik, V. (2024). Familiengerechte Karrieremöglichkeiten in der psychologischen Forschung? Psychologische Rundschau, 75(3), 234-248. https://doi.org/10.1026/0033-3042/a000682

    2023

    Erhardt, T., Gnambs, T., & Sengewald, M.-A. (2023). Studying item effects and their correlation patterns with multi-construct multi-state models. PLOS ONE, 18(8), Article e0288711. https://doi.org/10.1371/journal.pone.0288711
    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., Erhardt, T., & 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.org/10.1177/10731911221149949
    Sengewald, M.-A., Hahn, I., & Kähler, J. (2023). NEPS Technical Report for science: Scaling results of Starting Cohorts 4 and 6 (Wave 14) (NEPS Survey Paper No. 109). Leibniz Institute for Educational Trajectories, National Educational Panel Study. https://doi.org/10.5157/NEPS:SP109:1.0

    2022

    Sengewald, M.-A., & Mayer, A. (2022). Causal effect analysis in nonrandomized data with latent variables and categorical indicators: The implementation and benefits of EffectLiteR. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000489

    2019

    Sengewald, M.-A. (2019). Latent covariates.
    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., & 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

    2017

    Desirée, T., 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

    2016

    Anne, G., 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. (2016). Adjustment when Covariates are Fallible. In W. Wiedermann, & A. von Eye (Eds.), Statistics and Causality: Methods for Applied Empirical Research (pp. 363-384). Hoboken, NJ: Wiley.

    Selected presentations and lectures

    2024

    Sengewald, M.-A. (2024, April 18). Causal interpretations of effect heterogeneity in replication research [Paper presentation]. Society & Choice Research Seminar, Basel, Switzerland. https://psychologie.unibas.ch/de/forschung/sed-research-seminar/
    Sengewald, M.-A., & Henninger, M. (13. - 15. November 2024). Welche Schlussfolgerungen ermöglichen Machine Learning Methoden zur Detektion von Differential Item Functioning? [Vortrag]. 29. Workshop der Angewandten Klassifikationsanalyse (AKA), Erlangen, Deutschland.
    Sengewald, M.-A., Hoffmann, J., Twardawski, M., Gast, A., Höhs, J., Kondzic, D., & Pohl, S. (2024, March 10 - 15). A causal view on replication research: Methodological challenges for explaining effect heterogeneity between studies [Paper presentation]. Colloquium on methods of empirical educational research, Frankfurt, Germany.

    2023

    Sengewald, M.-A. (2023, July 3). Planned replication designs [Paper presentation]. Colloquium of the Department of Psychological Methods with Interdisciplinary Orientation, Frankfurt am Main, Deutschland.
    Sengewald, M.-A. (2023, March 6 - 10). Identifying relevant predictors for differential item-functioning: A comparison of two machine learning based approaches [Paper presentation]. Kolloquium Methoden der empirischen Bildungsforschung, Frankfurt am Main, Deutschland.
    Sengewald, M.-A. (2023, May 4). The impact of measurement error on causal inference [Paper presentation]. Colloquium of the working group Psychological Methodology, Leipzig, Germany.
    Sengewald, M.-A., & Welling, J. (2023, July 10 - 14). Methods for covariate-adjusted group comparisons [Other]. 2nd COORDINATE Summer School, Bamberg, Germany.
    Sengewald, M.-A., Henninger, M., Bechtloff, P., & Kubik, V. (12. Juni 2023). Vereinbarkeit beruflicher und familiärer Anforderungen [Vortrag]. 17. Plenarversammlung des Fakultätentages Psychologie, Berlin, Deutschland. https://fakultaetentag-psychologie.de/