• 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. (in press). Bayesian penalty methods for evaluating measurement invariance in moderated nonlinear factor analysis. Psychological Methods. Article Code on Github
• 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