Multilevel and longitudinal modeling

Multilevel modeling, also known as hierarchical linear or mixed effects modeling, plays a pivotal role in many disciplines, including psychology, sociology, economics, and epidemiology, among others. Its significance is rooted in its capacity to effectively capture the intricate structures and complexities inherent in data that are organized into multiple levels.

Within this broader framework, longitudinal modeling stands out as a vital statistical technique. It is specifically designed to analyze data collected from the same individuals at various time points, making it exceptionally valuable for understanding development and change over time. This approach seamlessly integrates with the multilevel modeling paradigm, harmonizing the study of intraindividual change, interindividual variability in such change, temporal sequences, causal relationships, and the intricate dynamics of processes that unfold over time.

In summary, both multilevel modeling and longitudinal modeling are indispensable tools for researchers seeking to unlock the nuanced patterns and dynamics within their data, whether it be in psychology or any other field where understanding hierarchical structures and temporal changes is essential.

Selected Publications

  • 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 
  • 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., & 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 
  • 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 
  • 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
  • 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., 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 
  • Hecht*, M., Walther*, J.-K., Arnold*, M., & Zitzmann, S. (2023). Finding the optimal number of persons (N) and time points (T) for maximal power in dynamic longitudinal models given a fixed budget. Structural Equation Modeling: A Multidisciplinary Journal. Advance online publication. https://doi.org/10.1080/10705511.2023.2230520 
  • Lohmann, J. F., Zitzmann, S., & Hecht, M. (2023). 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. Advance online publication. https://doi.org/10.1080/10705511.2023.2192889
  • 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 
  • Zitzmann, S. (2018). A computationally more efficient and more accurate stepwise approach for correcting for sampling error and measurement error. Multivariate Behavioral Research, 53, 612–632. https://doi.org/10.1080/00273171.2018.1469086
  • Zitzmann, S. (2021). Mehrebenenanalysen [Multilevel analyses]. In G. Weißeno & B. Ziegler (Eds.), Handbuch Geschichts- und Politikdidaktik (pp. 1–15). Wiesbaden: Springer.
  • Zitzmann, S. (2023). A cautionary note regarding multilevel factor score estimates from lavaan. Psych, 5, 38–49. https://doi.org/10.3390/psych5010004
  • Zitzmann, S. (2023). Einzelfallbezogene Veränderungsdiagnostik [Diagnostics of individual change]. In R. Dohrenbusch (Ed.), Psychologische Begutachtung (pp. 1–9). Wiesbaden: Springer.
  • Zitzmann, S., & Helm, C. (2021). Multilevel analysis of mediation, moderation, and nonlinear effects in small samples, using expected a posteriori estimates of factor scores. Structural Equation Modeling, 28, 529–546. https://doi.org/10.1080/10705511.2020.1855076
  • 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. https://doi.org/10.3389/fpsyg.2020.611267
  • Zitzmann, S., Lohmann, J. 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, 1–15. https://doi.org/10.3390/math10050842
  • Zitzmann, S., Lüdtke, O., & Robitzsch, A. (2015). A Bayesian approach to more stable estimates of group-level effects in contextual studies. Multivariate Behavioral Research, 50, 688–705. https://doi.org/10.1080/00273171.2015.1090899
  • Zitzmann, S., Lüdtke, O., Robitzsch, A., & Marsh, H. W. (2016). A Bayesian approach for estimating multilevel latent contextual models. Structural Equation Modeling, 23, 661–679. https://doi.org/10.1080/10705511.2016.1207179
  • 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., 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 
  • *Shared first authorship.
    Open Access

Machine Learning / Artificial Intelligence

Machine learning methods are of paramount importance in today's data-driven world. They enable the automation of complex tasks, such as pattern recognition and prediction, offering valuable insights from vast data that would be impractical for humans to analyze manually. As our reliance on data continues to grow, the ability to leverage machine learning techniques is a key driver of progress and competitiveness.

Selected Publications

  • Nedderhoff, A., Zitzmann, S., & Hecht, M. (Under 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/ 

Metascience

Metascience, the study of a science itself, is crucial for improving the quality of research. It helps identify and address issues like bias, replication, and transparency, thereby enhancing the overall credibility of findings. By critically examining the process of generating knowledge, metascience fosters self-correction within the scientific community, ultimately advancing innovation and knowledge. Its importance lies in ensuring that a science continues to be a robust and trustworthy endeavor that benefits society.

Selected Publications

  • 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, Article 19. https://doi.org/10.1007/s10648-024-09862-5 
  • 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(6) .https://doi.org/10.1002/jrsm.1762
  • 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
  • Zitzmann, S., & Loreth, L. (2021). Regarding an "almost anything goes" attitude toward methods in psychology. Frontiers in Psychology, 12, 1–4. https://doi.org/10.3389/fpsyg.2021.612570

Causal Inference

Causal inference is pivotal in research because it enables identification and understanding of causal relationships in complex systems. It allows us to go beyond mere correlation and determine whether a given factor truly influences an outcome. This capability is vital for informed decision-making in practice, whether in business, healthcare, or social policy, because it helps us discern which actions are most likely to produce the desired results, leading to more effective strategies and improved outcomes.

Selected Publications

  • Hübner, N., Wagner, W., Zitzmann, S., & Nagengast, B. (2023). How strong is the evidence for a causal reciprocal effect? Contrasting traditional and new methods to investigate the reciprocal effects model of self-concept and achievement. Educational Psychology Review, Advance online publication. https://doi.org/10.1007/s10648-023-09724-6

Bayesian Statistics

Bayesian Statistics and modeling offer a powerful framework for reasoning under uncertainty, making them invaluable in various fields. By incorporating prior knowledge or beliefs into the analysis, Bayesian approaches provide a coherent and flexible way to update our understanding as new data become available. This Bayesian perspective not only allows for reducing uncertainty and cumulative knowledge building but also facilitates more informed decision-making, particularly in situations with limited data, making it an essential tool in research and practice.

Selected Publications

  • 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., 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
  • 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. (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 
  • 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., 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., 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., Lüdtke, O., & Robitzsch, A. (2015). A Bayesian approach to more stable estimates of group-level effects in contextual studies. Multivariate Behavioral Research, 50, 688–705. https://doi.org/10.1080/00273171.2015.1090899
  • 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., & 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
  • 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., 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 
  • Open Access

Optimal Design

The optimal design of studies is paramount in research for several reasons. First and foremost, it ensures that the study can effectively address the research questions or hypotheses, maximizing the chances of obtaining meaningful results. Second, a well-designed study can minimize bias and confounding variables, enhancing the internal validity of the findings. Third, it enables efficient resource allocation by reducing the need for unnecessary data collection and analysis, ultimately saving time and resources. Fourth, a thoughtfully designed study allows generalization of findings, making them applicable to broader contexts and populations. In essence, optimal study design is the cornerstone of impactful research.

Selected Publications

  • Hecht*, M., Walther*, J.-K., Arnold*, M., & Zitzmann, S. (2023). Finding the optimal number of persons (N) and time points (T) for maximal power in dynamic longitudinal models given a fixed budget. Structural Equation Modeling: A Multidisciplinary Journal. Advance online publication. https://doi.org/10.1080/10705511.2023.2230520 
  • 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 
  • *Shared first authorship.
    Open Access

Optimal Estimation

Techniques for optimal estimation, including regularization methods, hold significant importance across various fields. They enable the striking of a balance between model complexity and overfitting, ensuring that results from these models generalize to new data. Regularization enhances model stability and predictive accuracy, thereby fostering the development of useful models. As a related point, regularization plays a pivotal role in facilitating model convergence, particularly in complex models or when working with limited data. In a world marked by increasingly intricate data and modeling tasks, optimal estimation techniques are instrumental in enhancing the robust understanding of phenomena in diverse fields.

Selected Publications

  • 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

Computational Aspects

The era of big data is characterized by an exponential growth in data volume. Unlocking the potential of these data demands the deployment of powerful computational tools and algorithms. Central to this endeavor is computational efficiency. Shorter runtimes empower analysts to delve swiftly into diverse hypotheses and models, enabling a more thorough exploration of the data landscape and increasing the probability of discovering valuable insights and patterns. Moreover, improved computational efficiency not only facilitates the use of commonly applied techniques, such as Markov chain Monte Carlo, but also contributes to sustainable research, as these methods allow for the conservation of energy by reducing computational workload.

Selected Publications

  • 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  
  • 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., 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  
  • 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 
  • 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. (2018). A computationally more efficient and more accurate stepwise approach for correcting for sampling error and measurement error. Multivariate Behavioral Research, 53, 612–632. https://doi.org/10.1080/00273171.2018.1469086
  • Zitzmann, S., & Hecht, M. (2019). Going beyond convergence in Bayesian estimation: Why precision matters too and how to assess it. Structural Equation Modeling, 26, 646–661. https://doi.org/10.1080/10705511.2018.1545232
  • 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
  • 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
  • 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 
  • Open Access

Individual Diagnostics

Individual diagnostics are indispensable across multiple fields. In clinical settings, they empower clinicians to customize medical interventions, medications, and therapies according to each patient's unique makeup, optimizing outcomes while reducing potential side effects. In the age of precision medicine (also termed personalized medicine), individual diagnostics spearhead the transformation of healthcare into a more patient-centric and effective system. Similarly, in education, individual diagnostics aid educators in comprehending students' learning styles and strengths, facilitating tailored teaching and curriculum adaptations. In psychology, individual diagnostics deepen our understanding of mental health, personality, cognitive abilities, thereby enhancing the effectiveness of individual therapy and counseling.

Selected Publications

  • 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
  • Zitzmann, S. (2023). Einzelfallbezogene Veränderungsdiagnostik [Diagnostics of individual change]. In R. Dohrenbusch (Ed.), Psychologische Begutachtung (pp. 1–9). Wiesbaden: Springer.

Psychological Measurement

Psychological measurement and large-scale assessments play a crucial role in understanding human behavior and cognitive processes. They provide standardized tools for quantifying and evaluating traits and other constructs, enabling researchers to systematically compare individuals or groups. These methods are fundamental in medical, psychological, and educational research, helping to assess the effects of interventions, advance our understanding of humankind, and inform policy decisions.

Selected Publications

  • 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
  • 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
  • Machts, N., Zitzmann, S., & Möller, J. (2020). Dimensionality of teacher judgments on a competency-based report card in elementary school. Learning and Instruction, 67, 1–10. https://doi.org/10.1016/j.learninstruc.2020.101328
  • 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
  • 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 
  • 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 
  • Zitzmann, S. (2018). Einzelfallbezogene Veränderungsdiagnostik [Diagnostics of individual change]. In R. Dohrenbusch (Ed.), Psychologische Begutachtung (pp. 1–9). Wiesbaden: Springer.
  • 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
  • Open Access

Substantive Research

In addition to our work in methods and statistical research, we actively engage in substantive research projects, often serving as statistics experts, methods consultants, and data analysts. Examples of these projects include the CovSocial Project.

Selected Publications

  • 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
  • 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
  • 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
  • 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
  • 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 
  • Helm, F., Wolff, F., Möller, J., Zitzmann, S., Marsh, H. W., & Dicke, T. (2023). Individualized teacher frame of reference and student self-concept within and between school subjects. Journal of Educational Psychology, 115, 309–239. https://doi.org/10.1037/edu0000737
  • Hübner, N., Wagner, W., Zitzmann, S., & Nagengast, B. (2023). How strong is the evidence for a causal reciprocal effect? Contrasting traditional and new methods to investigate the reciprocal effects model of self-concept and achievement. Educational Psychology Review, Advance online publication. https://doi.org/10.1007/s10648-023-09724-6
  • 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 
  • Knops, A., Zitzmann, S., & McCrink, K. (2013). Examining the presence and determinants of operational momentum in childhood. Frontiers in Psychology, 4, 1–14. https://doi.org/10.3389/fpsyg.2013.00325
  • Lindner, C., Zitzmann, S., Klusmann, U., & Zimmermann, F. (in press). From procrastination to frustration – How delaying tasks can affect study satisfaction and dropout intentions over the course of university studies. Learning and Individual Differences.
  • Machts, N., Zitzmann, S., & Möller, J. (2020). Dimensionality of teacher judgments on a competency-based report card in elementary school. Learning and Instruction, 67, 1–10. https://doi.org/10.1016/j.learninstruc.2020.101328
  • 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, Article 570. https://doi.org/10.3390/ijerph21050570 
  • Möller, J., Zitzmann, S., Helm, F., Machts, N., & Wolff, F. (2020). A meta-analysis of relations between achievement and self-concept. Review of Educational Research, 90, 376–419. https://doi.org/10.3102/0034654320919354
  • Orona, G. A., Eccles, J. S., Zitzmann, S., Fischer, C., & Arum, R. (2023). Cognitive development in undergraduate emerging adults: How course-taking breadth supports skill formation. Contemporary Educational Psychology, Advance online publication. https://doi.org/10.1016/j.cedpsych.2023.102206
  • Orona, G. A., Pritchard, D., Arum, R., Eccles, J., Dang, Q.-V., Copp, D., . . . Zitzmann, S. (2023). Epistemic virtue in higher education: Testing the mechanisms of intellectual character development. Current Psychology, Advance online publication. https://doi.org/10.1007/s12144-023-05005-1
  • Parrisius, C., Gaspard, H., Zitzmann, S., Trautwein, U., & Nagengast, B. (2022). The "situative nature" of competence and value beliefs and the predictive power of autonomy support: A multilevel investigation of repeated observations. Journal of Educational Psychology, 114, 791–814. https://doi.org/10.1037/edu0000680
  • Preusler, S., Zitzmann, S., Baumert, J., & Möller, J. (2022). Development of German reading comprehension in two-way immersive primary schools. Learning and Instruction, 79, 1–10. https://doi.org/10.1016/j.learninstruc.2022.101598
  • Preusler, S., Zitzmann, S., Paulick, I., Baumert, J., & Möller, J. (2019). Ready to read in two languages? Testing the native language hypothesis and the majority language hypothesis in two-way immersion students. Learning and Instruction, 64, 1–8. https://doi.org/10.1016/j.learninstruc.2019.101247
  • Reininger, K. M., Biel, H. M., Hennig, T., Zitzmann, S., Weigel, A., Spitzer, C., . . . Löwe, B. (2023). Beliefs about emotions predict psychological stress related to somatic symptoms. British Journal of Clinical Psychology, Advance online publication. https://doi.org/0.1111/bjc.12438
  • Reininger, K. M., Schaefer, C. D., Zitzmann, S., & Simon, B. (2020). Dynamics of respect: Evidence from two different national and political contexts. Journal of Social and Political Psychology, 8, 542–559. https://doi.org/10.5964/jspp.v8i2.1199
  • Schaefer, C. D., Zitzmann, S., Loreth, L., Paffrath, J., Grabow, H., Loewy, M., & Simon, B. (2021). The meaning of respect under varying context conditions. Journal of Social and Political Psychology, 9, 536–552. https://doi.org/10.5964/jspp.7313
  • 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
  • Schmerse, D., & Zitzmann, S. (2021). Early school adjustment: Do social integration and persistence mediate the effects of school-entry skills on later achievement? Learning and Instruction, 71, 1–12. https://doi.org/10.1016/j.learninstruc.2020.101374
  • 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 
  • 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 
  • Simon, B., Eschert, S., Schaefer, C. D., Reininger, K. M., Zitzmann, S., & Smith, H. J. (2019). Disapproved, but tolerated: The role of respect in outgroup tolerance. Personality and Social Psychology Bulletin, 45, 406–415. https://doi.org/10.1177/0146167218787810
  • Simon, B., Reininger, K. M., Schaefer, C. D., Zitzmann, S., & Krys, S. (2019). Politicization as an antecedent of polarization: Evidence from two different political and national contexts. British Journal of Social Psychology, 58, 769–785. https://doi.org/10.1111/bjso.12307
  • Teerling, A., Zitzmann, S., Igler, J., Schlitter, T., Ohle-Peters, A., McElvany, N., & Köller, O. (2020). Kommunikative Rahmenbedingungen beim Change Manage-ment in der Schule [Communicative conditions in change management within schools]. Zeitschrift für Arbeits- und Organisationspsychologie, 64, 249–262. https://doi.org/10.1026/0932-4089/a000326
  • Wolff, F., Zitzmann, S., & Möller, J. (2021). Moderators of dimensional comparison effects: A comprehensive replication study putting prior findings on five moderators to the test and going beyond. Journal of Educational Psychology, 113, 621–640. https://doi.org/10.1037/edu0000505
  • Open Access