Mirka Henninger

Under review

Henninger, M., Plieninger, H., & Meiser, T. (under review). The effect of response formats on response style strength: An experimental comparison. Preprint

Paersch, C., Recher, D. Schulz, A., Henninger, M., Schlup, B., Künzler, F., Homan, S., Kowatsch, T., Fisher, A., Horn, A., & Kleim, B. (under review). Self-efficacy predicts avoidance in anxiety disorder patients’ daily life and early treatment response during transdiagnostic cognitive behavior therapy.

Rohde, J., Marciniak, M., Henninger, M., Homann, S., Ries, A., Paersch, C., Friedman, O., Brown, A., & Kleim, B. (under review). Efficacy of a digital self-efficacy training in stressed university students: A randomized control trial. Preprint

Zimmer, F., Henninger, M., & Debelak, R. (under review). Sample size planning for complex study designs: A tutorial for the mlpwr package. Preprint

in press

Paz Castro, R., Henninger, M., Schaub, M. P., & Salis Gross, C. (in press). Changes in attitudes towards smoking during smoking cessation courses for Turkish- and Albanian-speaking migrants in Switzerland and its association with smoking behavior: A latent change score approach. Frontiers in Psychology, section Health Psychology.

Henninger, M., Debelak R., Rothacher, Y., & Strobl, C. (in press). Interpretable machine learning for psychological research: Opportunities and pitfalls. Psychological Methods. Preprint

2022

Henninger, M., Debelak, R., & Strobl, C. (2022). A new stopping criterion for Rasch trees based on the Mantel-Haenszel effect size measure for differential item functioning. doi:10.1177/ 00131644221077135

Henninger, M. & Meiser, T. (2022). Quality control: Response style modeling. Chapter invited for the International Encyclopedia of Education, 4th Edition.

2021

Henninger, M. (2021). A novel Partial Credit extension using varying thresholds to account for response styles. Journal of Educational Measurement, 58, 104-129 doi:10.1111/jedm.12268

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

2020

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

2019

Meiser, T., Plieninger, H., & Henninger, M. (2019). IRTree models with ordinal and multidimensional decision nodes for response styles and trait-based rating responses. British Journal of Mathematical and Statistical Psychology, 72, 201-216. doi:10.1111/bmsp.12158

2016

Frey, D., Henninger, M., Lübke, R., & Kluge, A. (2016). Einführung und konzeptionelle Klärung. In D. Frey (Ed.), Psychologie der Werte: Von Achtsamkeit bis Zivilcourage - Basiswissen aus Psychologie und Philosophie (pp. 1–12). Berlin, Heidelberg: Springer Berlin Heidelberg. doi: 10.1007/ 978-3-662-48014-4_1

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

2015

Ettlin, F., Bröder, A., & Henninger, M. (2015). A new task format for investigating information search and organization in multi-attribute decisions. Behavior Research Methods, 47, 506-518. doi:10.3758/s13428-014-0482-y

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)

A new stopping criterion for Rasch trees based on the Mantel-Haenszel effect size measure for differential item functioning (Oliver Lüdtke & Esther Ullitzsch, Leibniz Institute for Science and Mathematics Education, Kiel; 06 / 2022)

Using the Mantel-Haenszel odds ratio as a stopping criterion in a recursive partitioning procedure to detect differential item functioning in large-scale assessments (Christian Aßman, Timo Gnambs, & Marie-Ann Sengewald, Leibniz Institute for Educational Trajectories, Bamberg; 01 / 2022)

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

Interpretable machine learning methods: Opportunities and pitfalls (Eunike Wetzel, University of Koblenz-Landau; 11 / 2020)

Response Styles as threshold shifts in Divide-by-Total IRT model extensions (Markus Bühner, Ludwig-Maximilian-University Munich; 03 / 2019)

Response Styles IRT models as tools to investigate heterogeneous response scale use (Carolin Strobl, University of Zurich; 01 / 2019)

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, University of Zurich)

Statistics 1.1 (Supervision of Tutors; Level: B.Sc.; Fall 2020, University of Zurich)

Multilevel modeling in psychological research (Level: M.Sc.; Spring 2020, Spring 2021 & Spring 2022 University of Zurich)

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

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

Psychological testing and assessment (Level: B.Sc.; Spring 2016 & Spring 2017, University of Mannheim)

Invited and Pre-Conference Workshops

Machine learning and interpretable machine learning with R (Full-day pre-conference workshop at the International Meeting of the Psychometric Society; July 2022). More information here

Multilevel modeling using R (Zurich R Courses; April 2022)

Linear Mixed Models with crossed-random effects in experimental research (Juliane Degner, Chair for Social Psychology, University of Hamburg; Fall 2018)

Student Supervision

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

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

Topics: Item Response Theory, measurement theory, reliability (Research internships; University of Mannheim, University of Zurich)