Brandt, Holger, Prof. Dr. | Universität Tübingen

Prof. Dr. Holger Brandt

Professor for Psychometrics

Office
Methods Center
Haußerstr. 11
72076 Tübingen
+49 7071 29 74939
Fax: +49 7071 29 35264

holger.brandtspam prevention@uni-tuebingen.de

Key Reseach Topics

Dynamic models for intensive longitudinal data
Bayesian estimation and machine learning
Causal mediator models
Identification of inattentive behavior

I'm a Member (Link) of the Cluster of Excellence "Machine Learning for Science" (Link).

Research Projects

Causal Inference and Machine Learning for mediator variables (Current)
Detection of Careless Responding (Completed)
Estimation of nonlinear Effects (Completed)

Profile

since 2021

Professor for Psychometry

Methods Center, University of Tübingen

2019 - 2021

Assistant Professor for Quantitative Methods of Intervention and Evaluation

Department of Psychology, University of Zürich

2016 - 2019

Assistant Professor for Quantitative Methods

Department of Psychology, University of Kansas

2013 - 2016

Postdoc

Hector Research Institute of Education Sciences and Psychology, University of Tübingen

2013

PhD in Psychology

University of Frankfurt

2010

Diploma in Psychology

University of Frankfurt

Publications

Faleh, R., Morelli, S., Andriamiarana, V., Roman, Z. J., Flückiger, C., & Brandt, H. (2026). Dynamic latent class structural equation modeling: A hands-on tutorial for modeling intensive longitudinal data. Psychological Methods. Advance online publication. Code.
Faleh, R., Morelli, S., & Brandt, H. (2026). Representation learning for semiparametric causal mediation analysis under no essential heterogeneity. arXiv preprint, arXiv:2607.10540. Manuscript under review.
Andriamiarana, V., Kilian, P., Brandt, H., & Kelava, A. (2025). Are Bayesian regularization methods a must for multilevel dynamic latent variables models? Behavior Research Methods, 57(2), Article 71.
Aydin, S. B., & Brandt, H. (2025). Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning. arXiv preprint, arXiv:2512.16489. Manuscript in revision.
Berg, M., Suchotzki, K., Zimmermann, J., Merz, C. J., Szota, K., Brandt, H., Hartwigsen, G., Lincoln, T. M., Mokros, A., Gade, M., Niessen, C., Rauthmann, J., Hoehl, S., Kubiak, T., Franke, T., Frischlich, L., Degner, J., Matthies, E., Sparfeldt, J. R., Rief, W., Haberkamp, A., & the PsyChange Network (2025). A hitchhiker’s guide to translation: Ideas for fostering and disseminating (clinical) translational psychology. European Psychologist, 30(4), 219–241.
Brandt, H., Chen, S. M., & Bauer, D. J. (2025). Bayesian penalty methods for evaluating measurement invariance in moderated nonlinear factor analysis. Psychological Methods, 30(3), 482–512. Code.
Morelli, S., Faleh, R., & Brandt, H. (2025). RAPSEM: Identifying latent mediators without sequential ignorability via a rank-preserving structural equation model. arXiv preprint, arXiv:2509.23935. Conditionally accepted by Psychometrika.
Okuyama, K., Schaffland, T. F., Kilian, P., Brandt, H., & Kelava, A. (2025). Frequentist forecasting in regime-switching models with extended Hamilton filter. arXiv preprint, arXiv:2512.18149. Revised manuscript under review at Psychometrika. Code.
Brandt, H. (2024). Causal definitions versus casual estimation: Reply to Valente et al. (2022). Psychological Methods, 29(3), 589–602.
Brandt, H., Henninger, M., Ulitzsch, E., Kleinke, K., & Schäfer, T. (2024). Responsible research assessment in the area of quantitative methods research: A comment on Gärtner et al.. Meta-Psychology, 8.
Roman, Z. J., Schmidt, P., Miller, J. M., & Brandt, H. (2024). Identifying dynamic shifts to careless and insufficient effort behavior in questionnaire responses: A novel approach and experimental validation. Structural Equation Modeling: A Multidisciplinary Journal, 31(5), 775–793. Code · Empirical data.
Andriamiarana, V., Kilian, P., Kelava, A., & Brandt, H. (2023). On the requirements of non-linear dynamic latent class SEM: A simulation study with varying numbers of subjects and time points. Structural Equation Modeling: A Multidisciplinary Journal, 30(5), 789–806.
Flückiger, C., Horvath, A. O., & Brandt, H. (2022). The evolution of patients’ concept of the alliance and its relation to outcome: A dynamic latent-class structural equation modeling approach. Journal of Counseling Psychology, 69(1), 51–62.
Kelava, A., Kilian, P., Glaesser, J., Merk, S., & Brandt, H. (2022). Forecasting intra-individual changes of affective states taking into account inter-individual differences using intensive longitudinal data from a university student dropout study in math. Psychometrika, 87(2), 533–558.
Roman, Z. J., Brandt, H., & Miller, J. M. (2022). Automated bot detection using Bayesian latent class models in online surveys. Frontiers in Psychology, 13, Article 789223.
Chen, S. M., Bauer, D. J., Belzak, W. M., & Brandt, H. (2022). Advantages of spike and slab priors for detecting differential item functioning relative to other Bayesian regularizing priors and frequentist lasso. Structural Equation Modeling: A Multidisciplinary Journal, 29(1), 122–139.
Roman, Z. J., & Brandt, H. (2023). A latent auto-regressive approach for Bayesian structural equation modeling of spatially or socially dependent data. Multivariate Behavioral Research, 58(1), 90–114.
Chen, P.-Y., Wu, W., Brandt, H., & Jia, F. (2020). Addressing missing data in specification search in measurement invariance testing with Likert-type scale variables: A comparison of two approaches. Behavior Research Methods, 52(6), 2567–2587.
Brandt, H., Umbach, N., Kelava, A., & Bollen, K. A. (2020). Comparing estimators for latent interaction models under structural and distributional misspecifications. Psychological Methods, 25(3), 321–345.
Brandt, H. (2020). A more efficient causal mediator model without the no-unmeasured-confounder assumption. Multivariate Behavioral Research, 55(4), 531–552.
Kelava, A., & Brandt, H. (2019). A nonlinear dynamic latent class structural equation model. Structural Equation Modeling: A Multidisciplinary Journal, 26(4), 509–528.
Brandt, H., Cambria, J., & Kelava, A. (2018). An adaptive Bayesian lasso approach with spike-and-slab priors to identify multiple linear and nonlinear effects in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(6), 946–960.
Umbach, N., Naumann, K., Brandt, H., & Kelava, A. (2017). Fitting nonlinear structural equation models in R with package nlsem. Journal of Statistical Software, 77(7), 1–20.
Brandt, H., Umbach, N., & Kelava, A. (2015). The standardization of linear and nonlinear effects in direct and indirect applications of structural equation mixture models for normal and nonnormal data. Frontiers in Psychology, 6, Article 1813.
Brandt, H., & Klein, A. G. (2015). A heterogeneous growth curve model for nonnormal data. Multivariate Behavioral Research, 50(4), 416–435.
Kelava, A., Nagengast, B., & Brandt, H. (2014). A nonlinear structural equation mixture modeling approach for nonnormally distributed latent predictor variables. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 468–481.
Brandt, H., Kelava, A., & Klein, A. G. (2014). A simulation study comparing recent approaches for the estimation of nonlinear effects in SEM under the condition of nonnormality. Structural Equation Modeling: A Multidisciplinary Journal, 21(2), 181–195.