Mirka Henninger

Under review (first-author only)

Henninger, M., Vanhasbroeck, N. & Tuerlinckx, F. (under review). Affect dynamics or response bias? The relationship between extreme response style and affect dynamics in a controlled experiment. OSF. Preprint.

Henninger, M., Radek, J., Sengewald, M.-A. & Strobl, C. (under review). Partial credit trees meet the partial gamma coefficient for quantifying DIF and DSF in polytomous items. OSF. Preprint.

Henninger, M. & Strobl, C. (under review). Local interpretation techniques for machine learning methods: Theoretical background, pitfalls and interpretation of LIME and Shapley values. OSF. Preprint.

Peer-reviewed publications (selected)

Strobl, C., Rothacher, Y., Theiler, S., & Henninger, M. (in press). Detecting interactions with random forests: A comment on Gries’ words of caution and suggestions for improvement. Corpus Linguistics and Linguistic Theory.

Fokkema, M., Henninger, M., & Strobl., C (in press). One model may not fit all: Subgroup detection using model-based recursive partitioning. Journal of School Psychology.

Ulitzsch, E., Henninger, M., & Meiser, T. (2024). Differences in response-scale usage are ubiquitous in cross-country comparisons and a potential driver of elusive relationships. Nature scientific reports. https://rdcu.be/dHMPi. OSF

Zimmer, F., Henninger, M., & Debelak, R. (2023). Sample size planning for complex study designs: A tutorial for the mlpwr package. Behavior Research Methods. https://link.springer.com/article/10.3758/s13428-023-02269-0. Preprint

Henninger, M., Debelak R., Rothacher, Y., & Strobl, C. (2023). Interpretable machine learning for psychological research: Opportunities and pitfalls. Psychological Methods. doi: 10.1037/met0000560. Preprint

Henninger, M., Debelak, R., & Strobl, C. (2023). A new stopping criterion for Rasch trees based on the Mantel-Haenszel effect size measure for differential item functioning. Educational Psychological Measurement, 83, 181-212. doi:10.1177/ 00131644221077135. Preprint

Henninger, M. & Plieninger, H. (2021). Different styles, different times: How response times can inform our knowledge about the response process in rating scale measurement. Assessment, 28, 1301-1319. doi:10.1177/ 1073191119900003

Henninger, M. & Meiser, T. (2020). Different approaches to modeling response styles in Divide-by-Total Item Response Theory models (Part I): A model integration. Psychological Methods, 25, 560-576. doi: 10.1037/met0000249. Preprint

Henninger, M. & Meiser, T. (2020). Different approaches to modeling response styles in Divide-by-Total Item Response Theory models (Part II): Applications and novel extensions. Psychological Methods, 25, 577-595. doi: 10.1037/met0000268. Preprint

Books and book chapters (selected)

Strobl, C., Henninger, M., Rothacher, Y., & Debelak, R. (in press). Simulationsstudien in R: Design und praktische Durchführung. Springer.

Henninger, M. (2016). Resilienz. in D. Frey (Ed.), Psychologie der Werte: Von Achtsamkeit bis Zivilcourage - Basiswissen aus Psychologie und Philosophie (pp. 157-165). Berlin, Heidelberg: Springer Berlin Heidelberg. doi: 10.1007/ 978-3-662-48014-4_14

Detecting heterogeneity between persons using techniques from psychometrics, machine learning, and their intersection (Steffi Pohl, Chair of Methods and Evaluation, FU Berlin; 02 / 2024)

Interpretable machine learning: Shape, relevance, and interactions of predictor effects (Claudia Peus, Chair of Research and Science Management, Technical University Munich; 11 / 2023)

Detecting heterogeneity using methods from psychometrics, machine learning, and multilevel modeling (Eva Ceulemans, Quantitative Psychology and Interindividual Differences, KU Leuven; 05 / 2023)

Interpretable machine learning: Shape, relevance, and interactions of predictor effects (Ginette Lafit, Center for Contextual Psychiatry, KU Leuven; 05 / 2023)

Detecting heterogeneity between persons: Insights using techniques from psychometrics, machine learning methods, and their intersection (Invited speaker at “Advancing quantitative perspectives in education science: A Cambridge-Zurich exchange”, CAMZH; 12 / 2022)

Comparing machine learning based approaches for differential item functioning through illustrative, simulated examples (Christian Aßmann, Timo Gnambs, & Marie-Ann Sengewald, Leibniz Institute for Educational Trajectories, Bamberg; 12 / 2022)

Guest Lecture in “Statistical methods evaluation via advanced simulation techniques” (Benjamin Becker & Martin Hecht, Berlin University Alliance; 05 / 2021)

Teaching

I am teaching diagnostic assessment, research methods, and statistics in the Bachelor and Master Psychology program.

Statistics 1.1 (Lecture; Level: B.Sc.; Fall 2022, 2023, University of Zurich)

Multilevel modeling in psychological research (Level: M.Sc.; Spring 2020 – 2022 University of Zurich)

Latent variable models and multilevel modeling (Level: M.Sc.; Spring 2018, 2019, University of Mannheim)

Data analysis using SPSS and R (Level: B.Sc.; Fall 2016 – 2019, University of Mannheim)

Invited and Pre-Conference Workshops & Summer Schools (Selected)

Planning and Conducting Simulation Studies in R (Full-day pre-conference workshop at the International Meeting of the Psychometric Society, Prague, Czech Republic; July 2024

Machine learning and interpretable machine learning with R (Invited workshop at the Research Data Center (FDZ) of the Institute for Educational Quality Development (IQB) in Berlin, Germany; February 2024

Machine learning and interpretable machine learning with R (Full-day pre-conference workshop) at the International Meeting of the Psychometric Society, Bologna, Italy; July 2022 and the European Congress of Methodology, Ghent, Belgium; July 2023)

Modeling heterogeneity of response processes in item response theory (SMiP IOPS Summer School at the University of Mannheim, Germany; June 2023). More Information here

Student Supervision

Topics: Response biases, estimation precision in multilevel and structural equation modeling, timescales in longitudinal measurement, machine learning, interpretability, stability assessments (Level: M.Sc.; University of Mannheim, University of Zurich, University of Kassel)

Topics: Response biases, publication bias, sequential testing, interpretable machine learning, intensive longitudinal assessments (Level: B.Sc.; University of Mannheim, University of Zurich)

Topics: Item Response Theory, machine learning, statistical modeling, intensive longitudinal assessments (Research internships; University of Mannheim, University of Zurich, University of Basel)

I am engaged to foster equality, diversity and inclusion in the psychological research community.

Together with Marie-Ann Sengewald, Pia Bechtloff, and Veit Kubik, I assessed how researchers in psychology are affected from and deal with challenges that arise from care work. We aim at making these challenges more visible via panel discussions and discussion within the research community in Germany, Austria, and Switzerland, and through our report documenting the challenges in our research community:

Sengewald, M.-A., Henninger, M., Bechtloff, P, & Kubik, V. (2024). Familiengerechte Chancen für eine wissenschaftliche Karriere in der psychologischen Forschung? Eine Bestandsaufnahme zur Vereinbarkeit beruflicher und familiärer Anforderungen im Fachbereich Psychologie mit zielgerichteten Unterstützungsmaßnahmen. [Family-friendly opportunities for a scientific career in psychological research? An inventory of the compatibility of professional and family requirements in the field of psychology with targeted support measures]. OSF. Link.

For more details on our activities, please visit the homepage of the German Psychological Association.