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Thursday, January 28, 2010

Saving and using grouped descriptive statistics in WarpPLS


When the “Save grouped descriptive statistics into a tab-delimited .txt file” option is selected, a data entry window is displayed. There you can choose a grouping variable, number of groups, and the variables to be grouped. This option is useful if one wants to conduct a comparison of means analysis using the software, where one variable (the grouping variable) is the predictor, and one or more variables are the criteria (the variables to be grouped).

The figure below (click on it to enlarge) shows the grouped statistics data saved through the “Save grouped descriptive statistics into a tab-delimited .txt file” option. The tab-delimited .txt file was opened with a spreadsheet program, and contained the data on the left part of the figure.




That data on the left part of the figure was organized as shown above the bar chart; next the bar chart was created using the spreadsheet program’s charting feature. If a simple comparison of means analysis using this software had been conducted in which the grouping variable (in this case, an indicator called “ECU1”) was the predictor, and the criterion was the indicator called “Effe1”, those two variables would have been connected through a path in a simple path model with only one path. Assuming that the path coefficient was statistically significant, the bar chart displayed in the figure, or a similar bar chart, could be added to a report describing the analysis.

The following article goes into some detail about this procedure, contrasting it with other approaches:

Kock, N. (2013). Using WarpPLS in e-collaboration studies: What if I have only one group and one condition? International Journal of e-Collaboration, 9(3), 1-12.

Some may think that it is an overkill to conduct a comparison of means analysis using an SEM software package such as this, but there are advantages in doing so. One of those advantages is that this software calculates P values using a nonparametric class of estimation techniques, namely resampling estimation techniques. (These are sometimes referred to as bootstrapping techniques, which may lead to confusion since bootstrapping is also the name of a type of resampling technique.) Nonparametric estimation techniques do not require the data to be normally distributed, which is a requirement of other comparison of means techniques (e.g., ANOVA).

Another advantage of conducting a comparison of means analysis using this software is that the analysis can be significantly more elaborate. For example, the analysis may include control variables (or covariates), which would make it equivalent to an ANCOVA test. Finally, the comparison of means analysis may include latent variables, as either predictors or criteria. This is not usually possible with ANOVA or commonly used nonparametric comparison of means tests (e.g., the Mann-Whitney U test).

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