Methodenzentrum

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

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

  • Morelli, S., Faleh, R., & Brandt, H. (under review). RAPSEM: Identifying Latent Mediators Without Sequential Ignorability via a Rank-Preserving Structural Equation Model.
  • Faleh, R., Morelli, S., Andriamiarana, V., Roman, Z. J., Flückiger, C., & Brandt, H. (under review). Dynamic Latent Class Structural Equation Modeling: A Hands-On Tutorial for Modeling Intensive Longitudinal Data. Article Github Repository
  • 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, 71. doi: 10.3758/s13428-024-02589-9 Article
  • Brandt, H. (2024). Causal definitions vs. casual estimation: A reply to Valente, Rijnhart, and Miocevic (2022). Psychological Methods, 29, 589-602. doi: 10.1037/met0000544. Article
  • 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. Article Code on Github Empirical Data
  • Brandt, H., Chen, S. M., & Bauer, D. J. (2023). Bayesian penalty methods for evaluating measurement invariance in moderated nonlinear factor analysis. Psychological Methods. doi: 10.1037/met0000552 Article Code on Github
  • Andriamiarana, V., Kilian, P., Kelava, A., & Brandt, H. (2023). On the requirements of nonlinear dynamic latent class SEM: A simulation study with varying number of subjects and time points. Structural Equation Modeling, 30, 789-806. doi: 10.1080/10705511.2023.2169698 Article
  • Flückiger, C., Horvath, A. O., & Brandt, H. (2022). Understanding how patients evolve their concept of the alliance – A dynamic latent class structural equation modeling approach of the relation between alliance and symptoms. Journal of Counseling Psychology. Article
  • Kelava, A., Kilian, P., Glaesser, J., Merk, S., & Brandt, H. (2022). Forecasting intra-individual changes of affective states taking into account interindividual differences using intensive longitudinal data from a university student drop out study in math. Article
  • Roman, Z. J., Brandt, H., & Miller, J. M. (2022). Automated Bot Detection Using Bayesian Latent Class Models in Online Surveys. Frontiers in Psychology, 13: 789223. Article
  • Chen S. M., Bauer, D. J., Belzak, W. M., & Brandt, H. (2021). Advantages of spike and slab priors for detecting differential item functioning relative to other Bayesian regularizing priors and frequentist lasso. Structural Equation Modeling, 29, 122-139.. Article
  • Roman, Z. J. & Brandt, H. (2021). A latent auto-regressive approach for Bayesian structural equation modeling of spatially or socially dependent data. Multivariate Behavioral Research. Article
  • Chen, P.-Y., Wu, W., Brandt, H., & Jia, F. (2020). Addressing missing data in backward specification search in measurement invariance testing with Likert-type scale variables: a comparison of two approaches. Behavior Research Methods, 52, 2567-2587. Article
  • Brandt, H., Umbach, N., Kelava, A., & Bollen, K. A. (2020). Comparing estimators for latent interaction models under structural and distributional misspecifications. Psychological Methods, 25, 321-345. Article
  • Brandt, H. (2020). A more efficient causal mediator model without the no-unmeasured-confounder assumption. Multivariate Behavioral Research, 55, 531-552. Article
  • Kelava, A. & Brandt, H. (2019). Nonlinear Dynamic Latent Class Structural Equation Model. Structural Equation Modeling, 26,509-528. Article
  • Brandt, H., Cambria, J., & Kelava, A. (2018). An adaptive Bayesian lasso approach with spike-and-slab priors to identify linear and interaction effects in structural equation models. Structural Equation Modeling, 25, 956-960. Article
  • Umbach, N., Naumann, K., Brandt, H., & Kelava, A. (2017). Fitting nonlinear structural equation mixture models in R with package nlsem. Journal of Statistical Software, 7, 1–20. Article
  • 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, 6:1813. Article
  • Brandt, H. & Klein, A. G. (2015). A heterogeneous growth curve model for non-normal data. Multivariate Behavioral Research, 50, 416–435. Article
  • Kelava, A., Nagengast, B., & Brandt, H. (2014). A nonlinear structural equation mixture modeling approach for non-normally distributed latent predictor variables. Structural Equation Modeling, 21, 468-481. Article
  • 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 non-normality. Structural Equation Modeling, 21, 181-195. Article