WarpPLS explained
WarpPLS is a software with graphical user interface for variance-based and factor-based structural equation modeling (SEM) using the partial least squares and factor-based methods.[1] [2] The software can be used in empirical research to analyse collected data (e.g., from questionnaire surveys) and test hypothesized relationships. Since it runs on the MATLAB Compiler Runtime, it does not require the MATLAB software development application to be installed; and can be installed and used on various operating systems in addition to Windows, with virtual installations.
Main features
Among the main features of WarpPLS is its ability to identify and model non-linearity among variables in path models, whether these variables are measured as latent variables or not, yielding parameters that take the corresponding underlying heterogeneity into consideration.[3] [4] [5] [6] [7]
Other notable features are summarized:[8] [9] [10] [11]
- Guides SEM analysis flow via a step-by-step user interface guide.[12]
- Implements classic (composite-based) as well as factor-based PLS algorithms.
- Identifies nonlinear relationships, and estimates path coefficients accordingly.
- Also models linear relationships, using classic and factor-based PLS algorithms.
- Models reflective and formative variables, as well as moderating effects.
- Calculates P values, model fit and quality indices, and full collinearity coefficients.
- Calculates effect sizes and Q-squared predictive validity coefficients.
- Calculates indirect effects for paths with 2, 3 etc. segments; as well as total effects.
- Calculates several causality assessment coefficients.
- Provides zoomed 2D graphs and 3D graphs.
See also
Notes and References
- Kock, N., & Mayfield, M. (2015). PLS-based SEM algorithms: The good neighbor assumption, collinearity, and nonlinearity. Information Management and Business Review, 7(2), 113-130.
- Kock, N. (2015). A note on how to conduct a factor-based PLS-SEM analysis. International Journal of e-Collaboration, 11(3), 1-9.
- Gountas, S., & Gountas, J. (2016). How the ‘warped’ relationships between nurses' emotions, attitudes, social support and perceived organizational conditions impact customer orientation. Journal of Advanced Nursing, 72(2), 283-293.
- Guo, K.H., Yuan, Y., Archer, N.P., & Connelly, C.E. (2011). Understanding nonmalicious security violations in the workplace: A composite behavior model. Journal of Management Information Systems, 28(2), 203-236.
- Brewer, T.D., Cinner, J.E., Fisher, R., Green, A., & Wilson, S.K. (2012). Market access, population density, and socioeconomic development explain diversity and functional group biomass of coral reef fish assemblages. Global Environmental Change, 22(2), 399-406.
- Schmiedel, T., vom Brocke, J., & Recker, J. (2014). Development and validation of an instrument to measure organizational cultures’ support of business process management. Information & Management, 51(1), 43-56.
- Schmitz, K. W., Teng, J. T., & Webb, K. J. (2016). Capturing the complexity of malleable IT use: Adaptive structuration theory for individuals. Management Information Systems Quarterly, 40(3), 663-686.
- Memon, M. A., Ramayah, T., Cheah, J.-H., Ting, H., Chuah, F., & Cham, T. H. (2021). PLS-SEM statistical programs: A review. Journal of Applied Structural Equation Modeling, 5(1), i-xiii.
- Kock, N. (2019). Factor-based structural equation modeling with WarpPLS. Australasian Marketing Journal, 27(1), 57-63.
- Kock, N. (2019). From composites to factors: Bridging the gap between PLS and covariance‐based structural equation modeling. Information Systems Journal, 29(3), 674-706.
- Kock, N. (2011). Using WarpPLS in e-collaboration studies: Mediating effects, control and second order variables, and algorithm choices. International Journal of e-Collaboration, 7(3), 1-13.
- Web site: SEM Analysis with WarpPLS. www.youtube.com.
