Holger Brandt

College of Liberal Arts and Sciences - Psychology
Assistant Professor
Primary office:
Fraser Hall
Room 521
University of Kansas
1415 Jayhawk Blvd.
Lawrence, KS 66045-7556


Ph.D., 2013, Goethe University - Frankfurt
Research Areas: Quantitative Psychology   

Education

Ph.D., Dr. phil. nat., Department of Psychology, Research Methods and Evaluation Section, and Department of Psychology, Goethe-University Frankfurt

Diploma, Psychology, Department of Psychology, Goethe-University Frankfurt

Research

- Latent variable models (approaches for the estimation of nonlinear effects, semi- and nonparametric latent models, mixture models, Bayesian analysis)

- Modeling of heterogeneous growth patterns

- Development of a test for the measurement of basic numerical skills

Selected Publications

Umbach, N. Naumann, K. Brandt, H. & Kelava, A. (in press). Fitting nonlinear structural equation mixture models in R with package nlsem. Journal of Statistical Software.

Brandt, H. & Klein, A. G. (2015). A heterogeneous growth curve model for non-normal data. Multivariate Behavioral Research, 50(4), 416-435.

Gerhard, C. Klein, A. G., Schermelleh-Engel, K. Moosbrugger, H. Gäde, J. & Brandt, H. (2015). On the performance of likelihood-based difference tests in nonlinear structural equation models. Structural Equation Modeling, 22(2), 276-287.

Brandt, H. Umbach, N. & Kelava, A. (2015). The standardization of nonlinear effects in direct and indirect applications of structural equation mixture models. Frontiers in Psychology (Quantitative Psychology and Measurement).

Kelava, A. & Brandt, H. (2014). A general nonlinear multilevel structural equation mixture model. Frontiers in Psychology (Quantitative Psychology and Measurement), 5, 748.

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(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 non-normality. Structural Equation Modeling, 21(2), 181-195.


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