Team

• seit 04/2021  Eberhard Karls Universität Tübingen
  Nachwuchsgruppenleiter im Bereich Methodenforschung am Hector-Institut für Empirische Bildungsforschung
• WS 2018/19 Humboldt-Universität zu Berlin
  Gastdozent beauftragt mit den Aufgaben der Professur für Psychologische Methodenlehre (W3-Gastprofessur)
• WS 2017/18 Carl von Ossietzky Universität Oldenburg
  Gastwissenschaftler beauftragt mit den Aufgaben der Professur für Psychologische Methodenlehre (W2-Gastprofessur)
• 2015-2021 Humboldt-Universität zu Berlin
  Wissenschaftlicher Mitarbeiter (Post-Doc) am Institut für Psychologie (Psychologische Methodenlehre)
• 2010 – 2015 Humboldt-Universität zu Berlin
  Wissenschaftlicher Mitarbeiter (Doktorand) am Institut zur Qualitätsentwicklung im Bildungswesen (IQB),
  Abschluss: Dr. rer. nat. im Fach Psychologie
• 2009  Charité Berlin
  Studentischer Mitarbeiter
• 2009  Technische Universität Berlin
  Studentischer Mitarbeiter
• 2006 – 2008  Max-Planck-Institut für Bildungsforschung, Berlin
  Studentischer Mitarbeiter
• 2000–2007  Friedrich-Schiller-Universität Jena
  Studium der Psychologie, Abschluss: Diplom im Fach Psychologie
• 2006 – 2007  Fraunhofer Institut für Produktionsanlagen und Konstruktionstechnik, Berlin
  Diplomand
• 2005  Penn State University, USA
  Student & Research Assistant am Methodology Center
• 2003 – 2004   Uni Jena / Institut für Psychologie
  Studentischer Mitarbeiter im Bereich Methoden und Evaluationsforschung
• 2002–2003  Vrije Universiteit Amsterdam, Niederlande
  Student
• 2002  Uni Jena / Institut für Psychologie
  Studentischer Mitarbeiter im Bereich Methoden und Evaluationsforschung

newspaper article

• Zitzmann, S., Wagner, W., Hecht, M., Helm, C., Fischer, C., Bardach, L., & Göllner, R. (2021). How many classes and students should ideally be sampled when assessing the role of classroom climate via student ratings on a limited budget? An optimal design perspective. Educational Psychology Review. Advance online publication. https://doi.org/10.1007/s10648-021-09635-4
• Zitzmann, S., Weirich, S., & Hecht, M. (2021). Using the effective sample size as the stopping criterion in Markov chain Monte Carlo with the Bayes module in Mplus. Psych, 3, 336-347. https://doi.org/10.3390/psych3030025
• Weirich, S., Hecht, M. (shared first authorship), Becker, B., & Zitzmann, S. (2021). Comparing group means with the total mean in random samples, surveys, and large-scale assessments: A tutorial and software illustration. Behavior Research Methods. Advance online publication.  https://doi.org/10.3758/s13428-021-01553-1
• Godara, M., Silveira, S., Matthäus, H., Heim, C., Voelkle, M., Hecht, M., Binder, E. B., & Singer, T. (2021). Investigating differential effects of socio-emotional and mindfulness-based online interventions on mental health, resilience and social capacities during the COVID-19 pandemic: The study protocol. PLOS ONE, 16, e0256323.  https://doi.org/10.1371/journal.pone.0256323
• Hecht, M., Weirich, S., & Zitzmann, S. (2021). Comparing the MCMC efficiency of JAGS and Stan for the multi-level intercept-only model in the covariance- and mean-based and classic parametrization. Psych, 3(4), 751–779.  https://doi.org/10.3390/psych3040048
• Hecht, M., & Zitzmann, S. (2021). Exploring the unfolding of dynamic effects with continuous-time models: Recommendations concerning statistical power to detect peak cross-lagged effects. Structural Equation Modeling, 28(6), 894–902.  https://doi.org/10.1080/10705511.2021.1914627
• Hecht, M., & Zitzmann, S. (2021). Sample size recommendations for continuous-time models: Compensating shorter time-series with higher numbers of persons and vice versa. Structural Equation Modeling: A Multidisciplinary Journal, 28, 229–236. https://doi.org/10.1080/10705511.2020.1779069
• Crewther, B. T., Hecht, M., & Cook, C. J. (2021). Diurnal within-person coupling between testosterone and cortisol in healthy men: evidence of positive and bidirectional time-lagged associations using a continuous-time model. Adaptive Human Behavior and Physiology, 7, 89-104.  http://dx.doi.org/10.1007/s40750-021-00162-8
• Zitzmann, S., Helm, C., & Hecht, M. (2021). Prior specification for more stable Bayesian estimation of multilevel latent variable models in small samples: A comparative investigation of two different approaches. Frontiers in Psychology, 11, 1–11. http://dx.doi.org/10.3389/fpsyg.2020.611267
• Zitzmann, S., Lüdtke, O., Robitzsch, A., & Hecht, M. (2021). On the performance of Bayesian approaches in small samples: A Comment on Smid, McNeish, Miocevic, and van de Schoot (2020). Structural Equation Modeling: A Multidisciplinary Journal, 28, 40–50.  http://doi.org/10.1080/10705511.2020.1752216
• Hecht, M., Voelkle, M. C. (2021). Continuous-time modeling in prevention research: An illustration. International Journal of Behavioral Development, 45. 19–27.  http://dx.doi.org/10.1177/0165025419885026
• Crewther, B. T., Hecht, M., Potts, N., Kilduff, L. P., Drawer, S., Marshall, E., & Cook, C. J. (2020). A longitudinal investigation of bidirectional and time-dependent interrelationships between testosterone and training motivation in an elite rugby environment. Hormones and Behavior, 126, 1–8.  http://dx.doi.org/10.1016/j.yhbeh.2020.104866
• Schauber, S. K., & Hecht, M. (2020). How sure can we be that a student really failed? On the measurement precision of individual pass-fail decisions from the perspective of Item Response Theory. Medical Teacher, 42, 1374–1384. http://dx.doi.org/10.1080/0142159X.2020.1811844
• Hecht, M., & Zitzmann, S. (2020). A computationally more efficient Bayesian approach for estimating continuous-time models. Structural Equation Modeling: A Multidisciplinary Journal, 27, 829–840.  http://dx.doi.org/10.1080/10705511.2020.1719107
• Hecht, M., Gische, C., Vogel, D., & Zitzmann, S. (2020). Integrating out nuisance parameters for computationally more efficient Bayesian estimation – An illustration and tutorial. Structural Equation Modeling: A Multidisciplinary Journal, 27, 483– 493.  http://dx.doi.org/10.1080/10705511.2019.1647432
• Hardt, K., Hecht, M., & Voelkle, M. C. (2020). Robustness of individual score methods against model misspecification in autoregressive panel models. Structural Equation Modeling: A Multidisciplinary Journal, 27, 240–254. http://dx.doi.org/10.1080/10705511.2019.1642755
• Schüttpelz-Braun, K., Hecht, M., Hardt, K., Karay, Y., Zupanic, M., & Kämmer, J. (2020). Institutional strategies related to test-taking behavior in low stakes assessment. Advances in Health Sciences Education, 25, 321–335.  http://dx.doi.org/10.1007/s10459-019-09928-y
• Hecht, M., Hardt, K., Driver, C. C., & Voelkle, M. C. (2019). Bayesian continuous-time Rasch models. Psychological Methods, 24, 516–537.  doi:10.1037/met0000205
• Zitzmann, S., & Hecht, M. (2019). Going beyond convergence in Bayesian estimation: Why precision matters too and how to assess it. Structural Equation Modeling: A Multidisciplinary Journal, 26, 646–661.  http://dx.doi.org/10.1080/10705511.2018.1545232
• Hardt, K., Hecht, M., Oud, J. H. L., & Voelkle, M. C. (2019). Where have the persons gone? – An illustration of individual score methods in autoregressive panel models. Structural Equation Modeling: A Multidisciplinary Journal, 26, 310–323. http://dx.doi.org/10.1080/10705511.2018.1517355
• Schauber, S. K., & Hecht, M., & Nouns, Z. M. (2018). Why assessment in medical education needs a solid foundation in modern test theory. Advances in Health Sciences Education, 23, 217–232.  http://dx.doi.org/10.1007/s10459-017-9771-4
• Hecht, M., Siegle, T., & Weirich, S. (2017). A model for the estimation of testlet response time to optimize test assembly in paper-and-pencil large-scale assessments. Journal for Educational Research Online, 9, 32–51.
• Heitmann, P., Hecht, M., Scherer, R., & Schwanewedel, J. (2017). “Learning science is about facts and language learning is about being discursive”: An empirical investigation of students’ disciplinary beliefs in the context of argumentation. Frontiers in Psychology, 8, 1–16.  http://dx.doi.org/10.3389/fpsyg.2017.00946
• Wellnitz, N., Hecht, M., Heitmann, P., Kauertz, A., Mayer, J., Sumfleth, E., & Walpuski, M. (2017). Modellierung des Kompetenzteilbereichs naturwissenschaftliche Untersuchungen. Zeitschrift für Erziehungswissenschaft, 556–584. http://dx.doi.org/10.1007/s11618- 016-0721-3
• Weirich, S., Hecht, M., Penk, C., Roppelt, A., & Böhme, K. (2017). Item position effects are moderated by changes in test-taking effort. Applied Psychological Measurement, 115–129.  http://dx.doi.org/10.1177/0146621616676791
• Gittel, B., Deutschländer, R., & Hecht, M. (2016). Conveying moods and knowledge-what-it-is-like through lyric poetry: An empirical study of authors’ intentions and readers’ responses. Scientific Study of Literature, 6, 131–163.  http://dx.doi.org/10.1075/ssol.6.1.07git
• Hecht, M., Weirich, S., Siegle, T., & Frey, A. (2015). Effects of design properties on parameter estimation in large-scale assessments. Educational and Psychological Measurement, 75, 1021-1044.  http://dx.doi.org/10.1177/0013164415573311
• Hecht, M., Weirich, S., Siegle, T., & Frey, A. (2015). Modeling booklet effects for nonequivalent group designs in large-scale assessment. Educational and Psychological Measurement, 75, 568-584.  http://dx.doi.org/10.1177/0013164414554219
• Schauber, S. K., Hecht, M., Nouns, Z. M., Kuhlmey, A., & Dettmer, S. (2015). The role of environmental and individual characteristics in the development of student achievement: A comparison between a traditional and a problem-based-learning curriculum. Advances in Health Sciences Education, 20, 1033-1052.  http://dx.doi.org/10.1007/s10459-015-9584-2
• 2014 Weirich, S., Hecht, M., & Böhme, K. (2014). Modeling item position effects using generalized linear mixed models. Applied Psychological Measurement, 38, 535-548.  http://dx.doi.org/10.1177/0146621614534955
• Weirich, S., Haag, N., Hecht, M., Böhme, K., Siegle, T., & Lüdtke, O. (2014). Nested multiple imputation in large-scale assessments. Large-scale Assessments in Education, 2, 1-18.  http://dx.doi.org/10.1186/s40536-014-0009-0
• Heitmann, P., Hecht, M., Schwanewedel, J., & Schipolowski, S. (2014). Students’ argumentative writing skills in science and first-language education: Commonalities and differences. International Journal of Science Education, 36, 3148-3170.  http://dx.doi.org/10.1080/09500693.2014.962644
• Schauber, S. K., Hecht, M., Nouns, Z. M., & Dettmer, S. (2013). On the role of biomedical knowledge in the acquisition of clinical knowledge. Medical Education, 47, 1223–1235. http://dx.doi.org/10.1111/medu.12229

more publications

• Schauber, S. K., & Hecht, M. (2020). Reply to Jiang et al. Medical Teacher, 43, 608-609.  http://dx.doi.org/10.1080/0142159X.2020.1834932
• Voelkle, M. C., & Hecht, M. (2017). Longitudinal research designs. In V. Zeigler-Hill & T. K. Shackelford (Eds.), Encyclopedia of Personality and Individual Difference (pp. 1–6).  http://dx.doi.org/10.1007/978-3-319-28099-8_1323-1
• Voelkle, M. C., & Hecht, M. (2017). Cross-sectional research designs. In V. Zeigler- Hill & T. K. Shackelford (Eds.), Encyclopedia of Personality and Individual Differences (pp. 1–4).  http://dx.doi.org/10.1007/978-3-319-28099-8_1295-1
• Lenski, A. E., Hecht, M., Penk, C., Milles, F., Mezger, M., Heitmann, P., Stanat, P., & Pant, H. A. (2016). IQB-Ländervergleich 2012. Skalenhandbuch zur Dokumentation der Erhebungsinstrumente. Berlin: Humboldt-Universität zu Berlin, Institut zur Qualitätsentwicklung im Bildungswesen. http://dx.doi.org/10.20386/HUB-42547
• Hecht, M. (2015). Optimierung von Messinstrumenten im Large-scale Assessment (Doctoral Dissertation). Humboldt-Universität zu Berlin.  http://dx.doi.org/10.18452/17270
• Hecht, M., Roppelt, A. & Siegle, T. (2013). Testdesign und Auswertung des Ländervergleichs. In H. A. Pant, P. Stanat, U. Schroeders, A. Roppelt, T. Siegle, & C. Pöhlmann (Hrsg.), IQB-Ländervergleich 2012. Mathematische und naturwissenschaftliche Kompetenzen am Ende der Sekundarstufe I (S. 391-402). Münster: Waxmann.
• Schroeders, U., Hecht, M., Heitmann, P., Jansen, M., Kampa, N., Klebba, N., Lenski, A. E., & Siegle, T. (2013). Der Ländervergleich in den naturwissenschaftlichen Fächern. In H. A. Pant, P. Stanat, U. Schroeders, A. Roppelt, T. Siegle, & C. Pöhlmann (Hrsg.), IQB-Ländervergleich 2012. Mathematische und naturwissenschaftliche Kompetenzen am Ende der Sekundarstufe I (S. 141-158). Münster: Waxmann.
• Edele, A., Schotte, K., Hecht, M., & Stanat, P. (2012). Listening comprehension tests of immigrant students’ first languages (L1) Russian and Turkish in grade 9: Scaling procedure and results (NEPS Working Paper No. 13). Bamberg: Otto-Friedrich-Universität, Nationales Bildungspanel.
• Pohlmeyer, A. E., Hecht, M., & Blessing, L. (2009). User Experience Lifecycle Model ContinUE [Continuous User Experience]. In A. Lichtenstein, C. Stößel & C. Clemens (Hrsg.), Der Mensch im Mittelpunkt technischer Systeme. Fortschritt- Berichte VDI Reihe 22 Nr. 29 (pp. 314-317). Düsseldorf, Germany: VDI-Verlag

software/datasets

• Weirich, S., Hecht, M., Sachse, K., Becker, B., & Mahler, N. (2021). eatTools: Miscellaneous Functions for the Analysis of Educational Assessments (Version 0.5.0) [Computer software].  https://cran.r-project.org/package=eatTools
• Weirich, S., Hecht, M., Becker, B. (2021). eatRep: Educational assess- ment tools for replication methods (Version 0.13.5) [Computer software].  https://cran.r-project.org/package=eatRep
• Pant, H. A., Stanat, P., Hecht, M., Heitmann, P., Jansen, M., Lenski, A. E., Penk, C., Pöhlmann, C., Roppelt, A., Schroeders, U., & Siegle, T. (2017). IQB-Ländervergleich Mathematik und Naturwissenschaften 2012 (IQB-LV 2012) (Version 4) [Datensatz]. Berlin: IQB – Institut zur Qualitätsentwicklung im Bildungswesen.    http://dx.doi.org/10.5159/IQB_LV_2012_v4