Prelude # 4

  • Date/time:
    Wednesday, April 27, 2022, 3.00pm to 5.00pm CET (Berlin time zone); via Zoom 
  • Title:
    Predictive model evaluation and comparison in PLS-SEM
  • Instructors:
    Nicholas P. Danks (Trinity College Dublin)
    Benjamin Liengaard (Aarhus University)


CONTENTS 

Methodological research on Partial Least Squares Structural Equation Models (PLS-SEM) is increasingly focused on the ability of the method to evaluate out-of-sample predictive power of models (Legate et al. 2021; Liengaard et al. 2020; Shmueli et al. 2019). Such developments are beneficial for establishing validity, practical utility and generalizability of models. However, further improvement is still needed to strengthen PLS as a causal-predictive method. 

 

This workshop will contain three main parts: 1) a general introduction to prediction and predictive analyses in PLS-SEM (Hair et al., 2021). 2) Guidelines to estimating and evaluating overfit at the construct level and identifying possible sources of overfitting (Danks et al., 2021). 3) A set of out-of-sample prediction tests that compares a given PLS-SEM model to either a naïve benchmark model or linear benchmark model (Sharma et al. 2020).

 

On completion of this workshop, you obtain a general understanding of predictive evaluations in PLS-SEM and will be on par with some of the newest techniques within this area. 

TIME & PLACE 

Wednesday, April 27, 2022, 3.00pm to 5.00pm CET (Berlin time zone); via Zoom

REGISTRATION

Please register before April 2, 2022.


Nicholas P. Danks

Trinity Business School, Trinity College Dublin

https://scholar.google.com/citations?hl=en&user=D7nnR8gAAAAJ

 

Benjamin Liengaard

Department of Economics and Business Economics, Aarhus University

https://pure.au.dk/portal/en/persons/benjamin-liengaard(20b26e34-52de-46c0-bfd7-1fce386755bf).html

REFERENCES

 

  • Danks, N.P., Ray, S., Shmueli, G. (2021). The Composite Overfit Analysis Framework: Assessing the Out-of-sample Generalizability of Construct-based Models Using Predictive Deviance, Deviance Trees, and Unstable Paths. Working Paper.
  • Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., Danks, N.P., Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
  • Springer Nature (open access). https://link.springer.com/book/10.1007/978-3-030-80519-7
  • Legate, A. E., Hair Jr, J. F., Chretien, J. L., & Risher, J. J. (2021). PLS‐SEM: Prediction‐oriented solutions for HRD researchers. Human Resource Development Quarterly.
  • Liengaard, B., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2020). Prediction: Coveted, Yet Forsaken? Introducing a Cross-validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, Advance online publication.
  • Sharma, P. N., Liengaard, B. D., Hair, J.F., Sarstedt, M., & Ringle, C. M. (2021). Predictive Model Assessment and Selection in Composite-based Modeling Using PLS-SEM: Extensions and Guidelines for Using CVPAT. Working Paper
  • Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict. European Journal of Marketing, 53(11), 2322-2347.