Model Comparison
Purpose
Researchers often have alternative ways of theorizing their models. The PLS-SEM model comparison enables the comparison of two distinct models by assessing them against model selection criteria and statistical tests. THe results offer a foundation for informed decision-making in selecting the most suitable model.
Description
Create two alternative models within a SmartPLS project (e.g. Model 1 and Model 2). Select the first model and open it in the modeling view. Under "Calculate" in the menu bar you will find the option "Model comparison". Within the "Model comparison" start dialog, you can select the second model with which the currently open model is to be compared. After starting the algorithm, SmartPLS generates the following model comparison results:
- PLSpredict (Shmueli et al. 2016; Shmueli et al. 2019),
- Cross validated ablility test (CVPAT; Liengard et al., 2022; Sharma et al., 2023), and
- Bayesian information criterion (BIC) for predictive model selection (Sharma et al., 2019; Sharma et al., 2021) and Akaike weights (Danks et al., 2020; Rigdon et al., 2023).
Based on these results, researchers and practitioners can decide which of the two model alternatives is advantageous, for example with regard to predictive power.
References
- Danks, N. P., Sharma, P. N., & Sarstedt, M. (2020). Model Selection Uncertainty and Multimodel Inference in Partial Least Squares Structural Equation Modeling (PLS-SEM). Journal of Business Research, 113, 13-24.
- Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, Yet Forsaken? Introducing a Cross-validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362-392.
- Rigdon, E., Sarstedt, M., & Moisescu, O.-I. (2023). Quantifying Model Selection Uncertainty via Bootstrapping and Akaike Weights. International Journal of Consumer Studies, 47(4), 1596-1608.
- Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2022). Predictive Model Assessment and Selection in Composite-based Modeling Using PLS-SEM: Extensions and Guidelines for Using CVPAT. European Journal of Marketing, 57(6), 1662-1677.
- Sharma, P. N., Sarstedt, M., Shmueli, G., Kim, K.H, and Thiele, K. O. (2019). PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research, Journal of the Association for Information Systems, 20(4), 346-397.
- Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N., and Ray, S. (2021). Prediction-oriented Model Selection in Partial Least Squares Path Modeling, Decision Sciences, 52(3), 567-607.
- Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016). The Elephant in the Room: Predictive Performance of PLS Models. Journal of Business Research, 69(10), 4552-4564.
- Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., 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.
- More literature ...
Cite correctly
Please always cite the use of SmartPLS!
Ringle, Christian M., Wende, Sven, & Becker, Jan-Michael. (2024). SmartPLS 4. Bönningstedt: SmartPLS. Retrieved from https://www.smartpls.com
