Adding high-quality WarpPLS graphs into Word files

To add high-quality WarpPLS graphs to Word papers, try the following steps:

- Copy the graph to the clipboard.

- Open with Paint. 

- Crop and save as .png.

- Import into Word. 

The .png can also be edited prior to importing, or used in .ppt or .pptx drawings. 

The paper linked below illustrates the final results, which are arguably very good - see figs. 1 and 2.

Kock, N. (2016). Advantages of nonlinear over segmentation analyses in path models. International Journal of e-Collaboration, 12(4), 1-6.

Advantages of nonlinear over segmentation analyses in path models

Nonlinear analyses employing the software WarpPLS allow for the identification of linear segments emerging from a nonlinear analysis, but without the need to generate subsamples. A new article is available demonstrating the advantages of nonlinear over data segmentation analyses. These include a larger overall sample size for calculation of P values, and the ability to uncover very high segment-specific path coefficients. Its reference, abstract, and link to full text are available below.

Kock, N. (2016). Advantages of nonlinear over segmentation analyses in path models. International Journal of e-Collaboration, 12(4), 1-6.

The recent availability of software tools for nonlinear path analyses, such as WarpPLS, enables e-collaboration researchers to take nonlinearity into consideration when estimating coefficients of association among linked variables. Nonlinear path analyses can be applied to models with or without latent variables, and provide advantages over data segmentation analyses, including those employing finite mixture segmentation techniques (a.k.a. FIMIX). The latter assume that data can be successfully segmented into subsamples, which are then analyzed with linear algorithms. Nonlinear analyses employing WarpPLS also allow for the identification of linear segments mirroring underlying nonlinear relationships, but without the need to generate subsamples. We demonstrate the advantages of nonlinear over data segmentation analyses.

Among other things this article shows that identification of linear segments emerging from a nonlinear analysis with WarpPLS allows for: (a) a larger overall sample size for calculation of P values, which enables researchers to uncover actual segment-specific effects that could otherwise be rendered non-significant due to a combination of underestimated path coefficients and small subsample sizes; and (b) the ability to uncover very high segment-specific path coefficients, which could otherwise be grossly underestimated.

Enjoy!

Nonlinear analyses versus data segmentation in PLS-SEM

Those who conduct PLS-SEM analyses employing software other than WarpPLS and data segmentation approaches such as FIMIX-PLS may want to also conduct their analyses with WarpPLS, using a nonlinear algorithm, and compare the results against those obtained with data segmentation.

Data segmentation assumes the presence of underlying heterogeneity, which is also assumed (and accounted for) in a nonlinear analysis. The differences are that a nonlinear analysis assumes that the heterogeneity is somewhat uniform (a more reasonable assumption than that of “fragmented” heterogeneity), and that the heterogeneity can be described by nonlinear functions.

In WarpPLS users can define a main general type of nonlinear function for each structural link in their models.

Additionally, the “View focused relationship graphs with segments” options of WarpPLS allow users to view graphs that focus on the best-fitting line or curve, that exclude data points to provide the effect of zooming in on the best-fitting line or curve area, and that show curves as linear segments. The segments are shown with their respective beta coefficients and with or without P values (see figure below).

The options available are: “View focused multivariate relationship graph with segments (standardized scales)”, “View focused multivariate relationship graph with segments (standardized scales, P values)”, “View focused multivariate relationship graph with segments (unstandardized scales)”, “View focused bivariate relationship graph with segments (standardized scales)”, “View focused bivariate relationship graph with segments (standardized scales, P values)”, and “View focused bivariate relationship graph with segments (unstandardized scales)”.

The number of segments shown in the graphs above depends on the absolute effect segmentation delta chosen by the user using the “Settings” menu option. This absolute effect segmentation delta is the change (or delta) threshold in the first derivative of the nonlinear function depicting the relationship before a new segment is started.

For example, a delta of 0.1 means that in each segment the first derivative of the nonlinear function depicting the relationship does not vary more than 0.1. Since the first derivative does not change in linear relationships, segmentation only occurs in nonlinear relationships.

This graph segmentation option allows for the identification of unobserved heterogeneity without a corresponding reduction in sample size, providing a convenient alternative in this respect to data segmentation approaches such as FIMIX-PLS.

See the latest version of the User Manual for more details. The User Manual is available from the web site below.

http://warppls.com/