Data labels

In WarpPLS data labels can be added through the menu options “Add data labels from clipboard” and “Add data labels from file”. Data labels are text identifiers that are entered by you through these options, one column at a time.

Like the original numeric dataset, the data labels are stored in a table. Each column of this table refers to one data label variable, and each row to the corresponding row of the original numeric dataset.

Data labels can be shown on graphs (as illustrated above), either next to each data point that they refer to, or as part of the legend for a graph. The short video linked below illustrates this.

https://youtu.be/i5-_WIMXVl4

Once they have been added, data labels can be viewed or saved using the “View or save data labels” option.

Data labels can also be used to discover moderating effects, as discussed in the blog post linked below. They can also be used in multi-level analyses.

http://warppls.blogspot.com/2014/02/using-data-labels-to-discover.html

The use of data labels to discover moderating effects can be done in conjunction with the “Explore full latent growth” option, which provides a powerful alternative for the identification of moderating effects:

https://warppls.blogspot.com/2017/10/full-latent-growth.html

Using WarpPLS in E-Collaboration Studies: What if I Have Only One Group and One Condition?

A new article discussing methodological issues based on WarpPLS is available. The article is titled “Using WarpPLS in E-Collaboration Studies: What if I Have Only One Group and One Condition?” A full text version of the article is available here as a PDF file. Below is the abstract of the article.

What if a researcher obtains empirical data by asking questions to gauge the effect of an e-collaboration technology on task performance, but does not obtain data on the extent to which the e-collaboration technology is used? This characterizes what is referred to here as a scenario with one group and one condition, where the researcher is essentially left with only one column of data to be analyzed. When this happens, often researchers do not know how to analyze the data, or analyze the data making incorrect assumptions and using unsuitable techniques. Some of WarpPLS’s features make it particularly useful in this type of scenario, such as its support for small samples and the use of data that does not meet parametric assumptions. The main goal of this paper is to help e-collaboration researchers use WarpPLS to analyze data in this type of scenario, where only one group and one condition are available. Two other scenarios are also discussed – a typical scenario, and a scenario with one group and two before-after technology introduction conditions. While the focus here is on e-collaboration, the recommendations apply to many other fields.