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.
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.
Brandt, H. (2020). A more efficient causal mediator model without the no-unmeasured-confounder assumption. Multivariate Behavioral Research, 55, 531-552.
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). A Nonlinear Dynamic Latent Class Structural Equation Model. Structural Equation Modeling, 26, 509-528.
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, 946-960.
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.
Brandt, H. & Klein, A. G. (2015). A heterogeneous growth curve model for non-normal data. Multivariate Behavioral Research, 50, 416–435.
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), 6:1813.
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.
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, 468–481.
Kelava, A. & Brandt, H. (2009). Estimation of nonlinear SEM with the sem package. Review of Psychology, 16, 123–131.