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

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

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

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

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

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

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.