Dr. Tamara Schamberger

Titel of the dissertation: Methodological advances in composite-based structural equation modeling

Fields of research

General research field: Structural equation modeling (SEM)

Dissertation

Schamberger, T. (2022) Methodological Advances in Composite-based Structural Equation Modeling. University of Würzburg, University of Twente, https://doi.org/10.3990/1.9789036553759

Publications

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, 123(6), 1789-1813, 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., 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.
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
Schamberger, T., Schuberth, F., Henseler, J., Dijkstra T. K. (2020). Robust partial least squares path modeling. Behaviormetrika, https://doi.org/10.1007/s41237-019-00088-2