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Wednesday, September 4, 2013

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.


Nurulhuda Ibrahim said...

Dear Dr Kock,

I found your statement of estimating the minimum sample size as:

N > ( 2.48 / Abs(bm) ) ^ 2

Could you please point out on how to get the Abs(bm) index in WarpPLS. Is it the smallest value of significant path coefficient in the model?

I compare 2 equal sample of 109 each group using the Factor based PLS type CFM1, warp3, and stable 3 algorithms.

The lowest path coefficient is 0.191 with the effect size index of 0.101. My concern is that the result is not significant due to small sample.

I appreciate your kind advice :)

Thank you so much.

Best regards,

Ned Kock said...

Hi Nurul. Abs(bm) represents the path coefficient with the minimum absolute magnitude in the model.