Martin Hecht

Full Professor for Psychological Methods

Helmut Schmidt University

Links: E-Mail, Google Scholar, GitHub

Selected Publications

    In Review

    • König, L., Zitzmann, S., & Hecht, M. (in review). Strategizing AI utilization for psychological literature screening: A comparative analysis of machine learning algorithms and key factors to consider. https://doi.org/10.31234/osf.io/nc8hs
    • Nedderhoff, A., Zitzmann, S., & Hecht, M. (in review). Advancing forecasting in psychology: A tutorial and illustration of a novel approach based on LSTM neural networks for analyzing longitudinal data. PsyArXiv. https://osf.io/ukqtc/.

    2025

    • Walther, J.-K., Hecht, M., Nagengast, B., & Zitzmann, S. (2025). Multilevel multigroup structural equation modeling in a single-level framework. Structural Equation Modeling. Advance online publication. https://doi.org/10.1080/10705511.2024.2434596
    • Walther, J.-K., Hecht, M., & Zitzmann, S. (2025). Shrinking small sample problems in multilevel structural equation modeling via regularization of the sample covariance matrix. Structural Equation Modeling, 32, 46-65. https://doi.org/10.1080/10705511.2024.2380919

    2024

    • Bailey, D. H., Hübner, N., Zitzmann, S., Hecht, M., & Murayama, K. (2024). Illusory traits: Wrong but sometimes useful. Psychological Review,. Advanced online publication. https://doi.org/10.1037/rev0000522
    • Bardach, L., Lohmann, J., Horstmann, K., Zitzmann, S., & Hecht, M. (2024). From intellectual investment trait theory to dynamic intellectual investment trait and state theory: Theory extension, methodological advancement, and empirical illustration. Journal of Research in Personality, 108. https://doi.org/10.1016/j.jrp.2023.104445
    • Campos, D. G., Fütterer, T., Gfrörer, T., Lavelle-Hill, R., Murayama, K., König, L., Hecht, M., Zitzmann, S., & Scherer, R. (2024). Screening smarter, not harder: A comparative analysis of machine learning screening algorithms and heuristic stopping criteria for systematic reviews in educational research. Educational Psychology Review, 36, 1- 30. https://doi.org/10.1007/s10648-024-09862-5
    • Godara, M., Hecht, M., & Singer, T. (2024). Training-related improvements in mental well-being through reduction in negative interpretation bias: A randomized trial of online socio-emotional dyadic and mindfulness interventions. Journal of Affective Disorders. Advance Online Publication. https://doi.org/10.1016/j.jad.2024.03.037
    • Hecht, M., Walther, J.-K., Arnold, M., & Zitzmann, S. (2024). Finding the optimal number of persons (N) and time points (T) for maximal power in dynamic longitudinal models. Structural Equation Modeling , 31 , 535–551. https://doi.org/10.1080/10705511.2023.2230520
    • König, L., Zitzmann, S., Fütterer, T., Campos, D. G., Scherer, R., & Hecht, M (2024). An evaluation of the performance of stopping rules in AI-aided screening for psychological meta-analytical research. Research Synthesis Methods, 15, 1120- 1146. https://doi.org/10.1002/jrsm.1762
    • Lohmann, J. F., Zitzmann, S., & Hecht, M. (2024). Studying between-subject differences in trends and dynamics: Introducing the random coefficients continuous-time latent curve model with structured residuals. Structural Equation Modeling: A Multidisciplinary Journal, 31, 151-164. https://doi.org/10.1080/10705511.2023.2192889
    • Matthaeus, H., Godara, M., Silveira, S., Hecht, M., Voelkle, M., & Singer, T. (2024). Reducing loneliness through the power of practicing together: A randomized controlled trial of online dyadic socio-emotional vs. mindfulness-based training. International Journal of Environmental Research and Public Health, 21, (5), 570. https://doi.org/10.3390/ijerph21050570
    • Wagner, W., Zitzmann, S., & Hecht, M. (2024). HBMIRT: A SAS macro for estimating uni- and multidimensional 1- and 2-parameter item response models in small (and large!) samples. Behavior Research Methods, 56, 4130-4161. https://doi.org/10.3758/s13428-024-02366-8
    • Walther, J. K., Hecht, M., Nagengast, B., & Zitzmann, S. (2024). To be long or to be wide: How data format influences convergence and estimation accuracy in multilevel structural equation modelling. Structural Equation Modeling, 31(5),759-774. https://doi.org/10.1080/10705511.2024.2320050
    • Zitzmann, S., Bardach, L., Horstmann, K. T., Ziegler, M., & Hecht, M. (2024). Quantifying individual personality change more accurately by regression-based change scores. Structural Equation Modeling, 31 909- 922.. https://doi.org/10.1080/10705511.2023.2274800
    • Zitzmann, S., & Lindner, C. & Hecht, M. (2024). A straightforward and valid correction to Nathoo et al.’s Bayesian within-subject credible interval. Journal of Mathematical Psychology, 122, 1-6. https://doi.org/10.1016/j.jmp.2024.102873
    • Zitzmann, S., Orona, G. A., Lohmann, J. F., König, C., Bardach, L., & Hecht, M. (2024). Novick meets bayes: Improving the assessment of individual students in educational practice and research by capitalizing on assessors’ prior beliefs. Educational and Psychological Measurement, 0(0). https://doi.org/10.1177/00131644241296139
    • Zitzmann, S., Wagner, W., Lavelle-Hill, R., Jung, A., Jach, H., Loreth, L., ... Hecht, M. (2024). On the role of variation in measures, the worth of underpowered studies, and the need for tolerance among researchers: Some more reflections on Leising et al. from a methodological, statistical, and social-psychological perspective. Personality Science, 5 1-13. https://doi.org/10.1177/27000710241257413

    2023

    • Crewther, B. T., Hecht, M., Grillot, R. L., Eisenbruch, A. B., Catena, T., Potts, N., Kilduff, L. P., Cook, C. J., Maestripieri, D., & Roney, J. R. (2023). Day-to-day coordination of the stress and reproductive axes: A continuous-time analysis of within-person testosterone and cortisol relationships in athletic and healthy men. Physiology & Behavior, 263, Article 114104. https://doi.org/10.1016/j.physbeh.2023.114104
    • Hecht*, M., Horstmann*, K. T., Arnold, M., Sherman, R. A., & Voelkle, M. C. (2023). Modeling dynamic personality theories in a continuous-time framework: An illustration. Journal of Personality, 91, 718–735. https://doi.org/10.1111/jopy.12769
    • Silveira, S., Hecht, M., Voelkle, M. C., & Singer, T. (2023). Tend-and-befriend and rally around the flag effects during the COVID-19 pandemic: Differential longitudinal change patterns in multiple aspects of social cohesion. European Journal of Social Psychology. Advance online publication. https://doi.org/10.1002/ejsp.2974
    • Wagner, W., Hecht, M., & Zitzmann, S. (2023). A SAS macro for automated stopping of Markov chain Monte Carlo estimation in Bayesian modeling with PROC MCMC. Psych, 5, 966–982. https://doi.org/10.3390/psych5030063
    • Zitzmann, S., Weirich, S., & Hecht, M. (2023). Accurate standard errors in multilevel modeling with heteroscedasticity: A computationally more efficient jackknife technique. Psych, 5, 757–769. https://doi.org/10.3390/psych5030049

    2022

    • Jindra, C., Sachse, K. A., & Hecht, M. (2022). Dynamics between reading and math proficiency over time in secondary education – observational evidence from continuous time models. Large-Scale Assessments in Education, 10, Article 22. https://doi.org/10.1186/s40536-022-00136-6
    • Lohmann, J. F., Zitzmann, S., Voelkle, M. C., & Hecht, M. (2022). A primer on continuous-time modeling in educational research: An exemplary application of a continuous-time latent curve model with structured residuals (CT-LCM-SR) to PISA Data. Large-Scale Assessments in Education, 10, Article 5. https://doi.org/10.1186/s40536-022-00126-8
    • Silveira, S., Hecht, M., Adli, M., Voelkle, M. C., & Singer, T. (2022). Exploring the structure and interrelations of time-stable psychological resilience, psychological vulnerability, and social cohesion. Frontiers in Psychiatry, 13, Article 804763. https://doi.org/10.3389/fpsyt.2022.804763
    • Silveira, S., Hecht, M., Matthaeus, H., Adli, M., Voelkle, M. C., & Singer, T. (2022). Coping with the COVID-19 pandemic: Perceived changes in psychological vulnerability, resilience and social cohesion before, during and after lockdown. International Journal of Environmental Research and Public Health, 19, Article 3290. https://doi.org/10.3390/ijerph19063290
    • Weirich*, S., Hecht*, M., Becker, B., & Zitzmann, S. (2022). Comparing group means with the total mean in random samples, surveys, and large-scale assessments: A tutorial and software illustration. Behavior Research Methods, 54, 1051–1062. https://doi.org/10.3758/s13428-021-01553-1
    • Zitzmann, S., Lohmann, Julian F., Krammer, G., Helm, C., Aydin, B., & Hecht, M. (2022). A Bayesian EAP-based nonlinear extension of Croon and van Veldhoven’s model for analyzing data from micro–macro multilevel designs. Mathematics, 10, Article 842. https://doi.org/10.3390/math10050842
    • Zitzmann, S., Wagner, W., Hecht, M., Helm, C., Fischer, C., Bardach, L., & Göllner, R. (2022). 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, 35, 511–536. https://doi.org/10.1007/s10648-021-09635-4
    • Zitzmann, S., Walther, J.-K., Hecht, M., & Nagengast, B. (2022). What is the Maximum Likelihood estimate when the initial solution to the optimization problem is inadmissible? The case of negatively estimated variances. Psych, 4, 343–356. https://doi.org/10.3390/psych4030029

    2021

    • 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. https://doi.org/10.1007/s40750-021-00162-8
    • 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, Article e0256323. https://doi.org/10.1371/journal.pone.0256323
    • Hecht, M., Voelkle, M. C. (2021). Continuous-time modeling in prevention research: An illustration. International Journal of Behavioral Development, 45, 19–27. https://doi.org/10.1177/0165025419885026
    • 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, 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: A Multidisciplinary Journal, 28, 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
    • 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, Article 611267. https://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. https://doi.org/10.1080/10705511.2020.1752216
    • 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

    2020

    • 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, Article 104866. https://doi.org/10.1016/j.yhbeh.2020.104866
    • 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. https://doi.org/10.1080/10705511.2019.1642755
    • 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. https://doi.org/10.1080/10705511.2019.1647432
    • 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. https://doi.org/10.1080/10705511.2020.1719107
    • 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. https://doi.org/10.1080/0142159X.2020.1811844
    • 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. https://doi.org/10.1007/s10459-019-09928-y

    2019

    • 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. https://doi.org/10.1080/10705511.2018.1517355
    • Hecht, M., Hardt, K., Driver, C. C., & Voelkle, M. C. (2019). Bayesian continuous-time Rasch models. Psychological Methods, 24, 516–537. https://doi.org/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. https://doi.org/10.1080/10705511.2018.1545232

    2018

    • 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. https://doi.org/10.1007/s10459-017-9771-4

    2017

    • 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. https://www.waxmann.com/index.php?eID=download&id_artikel=ART102889&uid=frei
    • 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, Article 946. https://doi.org/10.3389/fpsyg.2017.00946
    • 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, 50, 115–129. https://doi.org/10.1177/0146621616676791
    • Wellnitz, N., Hecht, M., Heitmann, P., Kauertz, A., Mayer, J., Sumfleth, E., & Walpuski, M. (2017). Modellierung des Kompetenzteilbereichs naturwissenschaftliche Untersuchungen. Zeitschrift für Erziehungswissenschaft, 20, 556–584. https://doi.org/10.1007/s11618-016-0721-3

    2016

    • 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. https://doi.org/10.1075/ssol.6.1.07git

    2015

    • 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. https://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. https://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. https://doi.org/10.1007/s10459-015-9584-2

    2014

    • 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. https://doi.org/10.1080/09500693.2014.962644
    • 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, Article 9. https://doi.org/10.1186/s40536-014-0009-0
    • Weirich, S., Hecht, M., & Böhme, K. (2014). Modeling item position effects using generalized linear mixed models. Applied Psychological Measurement, 38, 535–548. https://doi.org/10.1177/0146621614534955

    2013

    • 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. https://doi.org/10.1111/medu.12229

    *Shared first authorship.
    Open Access