Using WarpPLS in E-Collaboration Studies

Author(s):  
Ned Kock

This is a follow-up on a previous article (Kock, 2010b) discussing the five main steps through which a nonlinear structural equation modeling analysis could be conducted with the software WarpPLS (warppls.com). Both this and the previous article use data from the same E-collaboration study as a basis for the discussion of important WarpPLS features. The focus of this article is on specific features related to saving and analyzing grouped descriptive statistics, viewing and changing analysis algorithm and resampling settings, and viewing and saving the various minor and major results of the analysis. Even though its focus is on an E-collaboration study, this article contributes to the broad literature on multivariate analysis methods, in addition to the more specific research literature on E-collaboration. The vast majority of relationships between variables, in investigations of both natural and behavioral phenomena, are nonlinear; usually taking the form of U and S curves. Structural equation modeling software tools, whether variancE- or covariancE-based, typically do not estimate coefficients of association based on nonlinear analysis algorithms. WarpPLS is an exception in this respect. Without taking nonlinearity into consideration, the results can be misleading; especially in complex and multi-factorial situations such as those stemming from E-collaboration in virtual teams.

2011 ◽  
Vol 7 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Ned Kock

This is a follow-up on a previous article (Kock, 2010b) discussing the five main steps through which a nonlinear structural equation modeling analysis could be conducted with the software WarpPLS (warppls.com). Both this and the previous article use data from the same e-collaboration study as a basis for the discussion of important WarpPLS features. The focus of this article is on specific features related to saving and analyzing grouped descriptive statistics, viewing and changing analysis algorithm and resampling settings, and viewing and saving the various minor and major results of the analysis. Even though its focus is on an e-collaboration study, this article contributes to the broad literature on multivariate analysis methods, in addition to the more specific research literature on e-collaboration. The vast majority of relationships between variables, in investigations of both natural and behavioral phenomena, are nonlinear; usually taking the form of U and S curves. Structural equation modeling software tools, whether variance- or covariance-based, typically do not estimate coefficients of association based on nonlinear analysis algorithms. WarpPLS is an exception in this respect. Without taking nonlinearity into consideration, the results can be misleading; especially in complex and multi-factorial situations such as those stemming from e-collaboration in virtual teams.


2011 ◽  
Vol 7 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Ned Kock

This is a follow-up on two previous articles on WarpPLS and e-collaboration. The first discussed the five main steps through which a variance-based nonlinear structural equation modeling analysis could be conducted with the software WarpPLS (Kock, 2010b). The second covered specific features related to grouped descriptive statistics, viewing and changing analysis algorithm and resampling settings, and viewing and saving various results (Kock, 2011). This and the previous articles use data from the same e-collaboration study as a basis for the discussion of important WarpPLS features. Unlike the previous articles, the focus here is on a brief discussion of more advanced issues, such as: testing the significance of mediating effects, including control variables in an analysis, using second order latent variables, choosing the right warping algorithm, and using bootstrapping and jackknifing in combination.


2010 ◽  
Vol 6 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Ned Kock

Most relationships between variables describing natural and behavioral phenomena are nonlinear, with U-curve and S-curve relationships being particularly common. Yet, structural equation modeling software tools do not estimate coefficients of association taking nonlinear relationships between latent variables into consideration. This can lead to misleading results, particularly in multivariate and complex phenomena like those related to e-collaboration. One notable exception is WarpPLS (available from: warppls.com), a new structural equation modeling software currently available in its first release. The discussion presented in this paper contributes to the literature on e-collaboration research methods by providing a description of the main features of WarpPLS in the context of an e-collaboration study. The focus of this discussion is on the software’s features and their use and not on e-collaboration study itself. Particular emphasis is placed on the five steps through which a structural equation modeling analysis is conducted through WarpPLS.


Author(s):  
Ned Kock

Most relationships between variables describing natural and behavioral phenomena are nonlinear, with U-curve and S-curve relationships being particularly common. Yet, structural equation modeling software tools do not estimate coefficients of association taking nonlinear relationships between latent variables into consideration. This can lead to misleading results, particularly in multivariate and complex phenomena like those related to e-collaboration. One notable exception is WarpPLS (available from: warppls.com), a new structural equation modeling software currently available in its first release. The discussion presented in this paper contributes to the literature on e-collaboration research methods by providing a description of the main features of WarpPLS in the context of an e-collaboration study. The focus of this discussion is on the software’s features and their use and not on e-collaboration study itself. Particular emphasis is placed on the five steps through which a structural equation modeling analysis is conducted through WarpPLS.


Author(s):  
Ned Kock

This is a follow-up on two previous articles on WarpPLS and E-collaboration. The first discussed the five main steps through which a variancE-based nonlinear structural equation modeling analysis could be conducted with the software WarpPLS (Kock, 2010b). The second covered specific features related to grouped descriptive statistics, viewing and changing analysis algorithm and resampling settings, and viewing and saving various results (Kock, 2011). This and the previous articles use data from the same E-collaboration study as a basis for the discussion of important WarpPLS features. Unlike the previous articles, the focus here is on a brief discussion of more advanced issues, such as: testing the significance of mediating effects, including control variables in an analysis, using second order latent variables, choosing the right warping algorithm, and using bootstrapping and jackknifing in combination.


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