Methodenzentrum

Prof. Dr. Holger Brandt

Professor für Psychometrie

Büro
Methodenzentrum
Haußerstr. 11
72076 Tübingen
 +49 7071 29 74932
Fax: +49 7071 29 35264
holger.brandtspam prevention@uni-tuebingen.de

Für alle Anfragen zur Lehre, schicken Sie bitte eine Mail an lehrespam prevention@mz.uni-tuebingen.de.


Forschung

Schwerpunkte

  • Dynamische Modelle für intensive Längsschnittdaten
  • Bayesianische Schätzverfahren und Methoden des Machine Learnings
  • Kausale Mediatormodelle
  • Identifikation von unaufmerksamen Verhalten

Kurzvita

seit August 2021
Professor für Psychometrie

Methodenzentrum, Eberhard Karls Universität Tübingen

2019 bis 2021
Assistenzprofessor für Quantitative Methoden der Intervention und Evaluation

Psychologische Institut, Universität Zürich

2016 bis 2019
Assistenzprofessor für Quantitative Methoden der Psychologie

Department of Psychology, University of Kansas

2013 bis 2016
Postdoc

Hector-Institut für Empirische Bildungsforschung, Eberhard Karls Universität Tübingen

2013
Promotion

Institut für Psychologie, Goethe Universität Frankfurt


Publikationen

Ausgewählte Publikationen

  • Chen S. M., Bauer, D. J., Belzak, W. M., & Brandt, H. (in press). Advantages of spike and slab priors for detecting differential item functioning relative to other Bayesian regularizing priors and frequentist lasso. Structural Equation Modeling. Article
  • Roman, Z. J. & Brandt, H. (in press). A latent auto-regressive approach for Bayesian structural equation modeling of spatially or socially dependent data. Multivariate Behavioral Research. Article
  • Flückiger, C., Horvath, A. O., & Brandt, H. (in press). 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.
  • 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. (2020). A more efficient causal mediator model without the no-unmeasured-confounder assumption. Multivariate Behavioral Research, 55, 531-552. 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.
  • 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. gibt es hier