English Intern
Lehrstuhl für Ökonometrie



  • Schuberth, F., Hubona, G., Roemer, E., Zaza, S., Schamberger, T., Chuah, F., Cepeda-Carrión, G., Henseler, J. (2023). The choice of structural equation modeling technique matters: A commentary on Dash and Paul (2021). Technological Forecasting and Social Change, 194, 122665
  • Schamberger, T. (2023). Conducting Monte Carlo Simulations for PLS-PM and other variance-based estimators for structural equation modeling. Industrial Management & Data Systems, https://doi.org/10.1108/IMDS-07-2022-0418
  • Schuberth, F., Schamberger, T., Rönkkö, M., Liu, Y., Henseler, J. (2023). Premature Conclusions about the Signal-to-Noise Ratio in Structural Equation Modeling Research: A Commentary on Yuan and Fang (2023). British Journal of Mathematical and Statistical Psychology, http://doi.org/10.1111/bmsp.12304
  • Schamberger, T., Schuberth, F., & Henseler, J. (2023). Confirmatory composite analysis in human development research. International Journal of Behavioral Development, 47(1), 89–100. https://doi.org/10.1177/01650254221117506
  • Schuberth, F., Rademaker, M.E., Henseler, J. (accepted). Assessing the overall fit of composite models estimated by partial least squares path modeling. European Journal of Marketing, https://doi.org/10.1108/EJM-08-2020-0586
  • Schuberth, F., Rademaker, M.E., Henseler, J. (2020). Estimating and assessing second-order constructs using PLS-PM: the case of composites of composites. Industrial Management & Data Systems, 120(12), 2211-2241, doi.org/10.1108/IMDS-12-2019-0642.
  • Schamberger, T., Schuberth, F., Henseler, J., Dijkstra, T.K. (2020). Robust partial least squares path modeling. Behaviormetrika, 47, 307-334, https://doi.org/10.1007/s41237-019-00088-2
  • Schuberth, F., Henseler, J., Dijkstra, T.K. (2018). Partial least path modeling using ordinal categorical indicators. Quality & Quantity, 52(1), 9-35, https://doi.org/10.1007/s11135-016-0401-7
  • Rodríguez-Entrena, M., Schuberth, F., Gelhard, C. (2018). Assessing statistical differences between parameter estimates in Partial Least Squares path modeling. Quality & Quantity, 52(1), 57-69, https://doi.org/10.1007/s11135-016-0400-8


  • Schamberger, T., Cantaluppi, G., Schuberth, F. (accepted). Revisiting and Extending PLS for Ordinal Measurement and Prediction. In H. Latan, J. F. Hair, & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues, and applications (2nd ed.). Cham, Switzerland: Springer.
  • Schuberth, F., Cantaluppi, G. (2017). Ordinal consistent partial least squares. In: Latan, Hengky; Noonan, Richard (Eds.), Partial Least Squares Path Modeling Basic Concepts, Methodological Issues and Applications. Berlin, Heidelberg: Springer,  109-150



  • Schamberger, T. (2022). Methodological Advances in Composite-based Structural Equation Modeling. University of Twente/University of Würzburg. https://doi.org/10.3990/1.9789036553759
  • Rademaker, M.E. (2020). Composite-based Structural Equation Modeling. University of Würzburg. https://doi.org/10.25972/OPUS-21593
  • Schuberth, F. (2019). Composite-based Methods in Structural Equation Modeling. University of Würzburg. https://doi.org/10.25972/OPUS-15465
  • Dechert, A. (2014). Faktionale Integration und Kontegration in Theorie und Praxis. University of Würzburg.
  • Bayer, V. (2012). Multivariate Modellierung operationeller Risiken in Kreditinstituten. Gabler Verlag.
  • Kempf, S. (2007). Das Optionswertmodell zur Erklärung der Rentenentscheidung. University of Würzburg.