Friday, February 28, 2014
Using data labels to discover moderating effects in PLS-based structural equation modeling
How can one discover moderating effects with data labels? This question is addressed through the article below:
Kock, N. (2014). Using data labels to discover moderating effects in PLS-based structural equation modeling. International Journal of e-Collaboration, 10(4), 1-14.
http://cits.tamiu.edu/kock/pubs/journals/2014JournalIJeC2/Kock_2014_IJeC_UsingDataLabelsMod.pdf
This publication refers to a sample dataset, with data and data labels, illustrating a moderating effect. This dataset is linked below as a .xlsx file. The data was created based on a Monte Carlo simulation.
http://www.scriptwarp.com/warppls/data/Kock_2014_ECollabModStudyData.xlsx
Another approach to discover moderating effects is a full latent growth analysis.
Sometimes the actual inclusion of moderating variables and corresponding links in a model leads to problems; e.g., increases in collinearity levels, and the emergence of instances of Simpson’s paradox. The WarpPLS menu option “Explore full latent growth”, available starting in version 6.0, allows you to completely avoid these problems, and estimate the effects of a latent variable or indicator on all of the links in a model (all at once), without actually including the variable in the model. Moreover, growth in coefficients associated with links among different latent variables and between a latent variable and its indicators, can be estimated; allowing for measurement invariance tests applied to loadings and/or weights.
Related YouTube video:
Explore Full Latent Growth in WarpPLS
http://youtu.be/x_2e8DVyRhE
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5 comments:
Hi Ned
I'd like to know the default setting for moderating effect analysis in WarpPLS, i.e., either it's median split or mean split.
The above article demonstrates pre-determine data split which is later used by the software to analyse the moderating effect.
I'm using WarpPLS 4.0.
Appreciate your response. Cheers.
Saiyidi
Hi Saiyidi.
The graph in Fig. 11 employs a median split, for display only, as do the 2D graphs of moderating relationships in WarpPLS.
There is no splitting of the dataset for the calculation of moderating effect coefficients, nor for the generation of the 3D graphs available in version 5.0 of WarpPLS:
http://youtu.be/XEC2a3paJ98
Thanks Ned.
Greatly appreciated.
Saiyidi
Dear Professor Kock,
I am testing the moderating influence of trade shows participation on the link between informational barriers and export propensity. The results show that while the link between barriers and propensity to export is negative, the moderating influence is negative too. I am I right to say that trade shows decrease the negative influence of barriers on export propensity? meaning that trade shows are a good thing for fostering propensity to export? The plots seem to confirm my assumption. Is my conclusion right or is it the other way?
Many thanks
Hi Yacine. It is a good idea to take a look at the graphs in cases like this, maybe even create and use labels, as discussed in the blog post above, to highlight possible moderating effects. What do the moderating effects graphs tell you? Also, are the results consistent with a corresponding full latent growth test?
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