Methods Center

Prof. Dr. Holger Brandt

Professor for Psychometry

Methods Center
Haußerstr. 11
72076 Tübingen
 +49 7071 29 74932
Fax: +49 7071 29 35264


Please send all inquiries concerning academic issues (degrees, classes, grading) to teachingspam


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

Curriculum Vitae

since 2021
Professor for Psychometry

Methods Center, University of Tübingen

2019 to 2021
Assistant Professor for Quantitative Methods of Intervention and Evaluation

Department of Psychology, University of Zürich

2016 to 2019
Assistant Professor for Quantitative Methods

Department of Psychology, University of Kansas

2013 to 2016

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


Department of Psychology, University of Frankfurt


Selected journal articles

  • Brandt, H., Chen, S. M., & Bauer, D. J. (in press). Bayesian penalty methods for evaluating measurement invariance in moderated nonlinear factor analysis. Psychological Methods.
  • Brandt, H. (in press). Causal definitions vs. casual estimation: A reply to Valente, Rijnhart, and Miocevic (2022). Psychological Methods.
  • 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

Appendices etc. can be found here