Using Data Labels to Discover Moderating Effects in PLS-Based Structural Equation Modeling

Using Data Labels to Discover Moderating Effects in PLS-Based Structural Equation Modeling

Ned Kock (Texas A&M, USA)
DOI: 10.4018/978-1-5225-0196-1.ch064
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PLS-based structural equation modeling is extensively used in e-collaboration research, as well as in many other fields of research. Two main types of exploratory analyses are frequently employed in the context of PLS-based structural equation modeling: covariance (or correlation) analyses, where the covariances (or correlations) among all variables are inspected; and model-driven exploratory analyses, where one or more variations of theory-supported models are built and adjusted associations among variables are inspected. These analyses, while useful, provide limited insights about the possible presence of moderating effects. We discuss a general approach through which researchers can employ data labels, implemented as symbols that are displayed together with legends on graphs, to uncover moderating relationships among variables. The discussion is illustrated with the software WarpPLS version 4.0. While the approach is illustrated in the context of e-collaboration research, it arguably applies to any field where PLS-based structural equation modeling can be used.
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Data Used In The Analysis

We created 300 rows of data, equivalent to 300 returned questionnaires, for several latent variables and indicators based on a Monte Carlo simulation (Robert & Casella, 2005; Paxton et al., 2001). Using this method we departed from a “true” model, which is a model for which we know the nature and magnitude of all of the relationships among variables beforehand. The true model was based on an actual study of the effects of e-collaboration technology use on team-based project success, which was previously used by Kock & Lynn (2012) to illustrate their discussion of vertical and lateral collinearity. At the time of this writing this data set was publicly available from the WarpPLS web site:

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