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Thursday, June 17, 2010

Multi-group analysis with WarpPLS: Comparing means of two or more groups

Comparing means of two groups is something that behavioral researchers often do. In fact, this is the most widely used quantitative analysis approach. This is also a form of multi-group analysis, where the number of groups is two. Common tests for comparing means are the t and and one-way ANOVA tests.

There is an arguably much better way of doing comparison of means test, with WarpPLS. Follow the steps below. This is a two-group test. Multi-group tests can be done in a similar way, through multiple two-group tests where conditions (i.e., groups) are compared pair by pair.

- Create a dummy variable (G) with numbers associated with each of the two groups - e.g., 0 for one group, and 1 for the other group. This dummy variable should be implemented as a LV with 1 indicator.

- Define your dependent (or criterion) construct (T) as you would normally do; in this case, I think that would be as a reflective LV with 3 indicators.

- Create a link between G and T, with G pointing at T.

- Estimate the model parameters with WarpPLS; this will calculate the beta and P values for the link. The P value is analogous to the P value you would obtain with a t test.

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.

This type of WarpPLS test has a number of advantages over a standard t test or a one-way ANOVA test (which are essentially the same thing). For example, it allows for the use of LVs as dependent variables; and it is a robust test, which does not require that the dependent variables be normally distributed.

Multi-group tests, with more than two groups, can also be conducted by assigning different values to each of the groups. The key here is to decide what values to assign to each group. This choice is often somewhat arbitrary in exploratory analyses. The “Save grouped descriptive statistics into a tab-delimited .txt file” option may be helpful in this respect. This is a special option that allows you to save descriptive statistics (means and standard deviations) organized by groups defined based on certain parameters. For more details, see the user manual for WarpPLS, which is available from

Starting in version 6.0 of WarpPLS, the menu options “Explore multi-group analyses” and “Explore measurement invariance” allow you to conduct analyses where the data is segmented in various groups, all possible combinations of pairs of groups are generated, and each pair of groups is compared. In multi-group analyses normally path coefficients are compared, whereas in measurement invariance assessment the foci of comparison are loadings and/or weights. The grouping variables can be unstandardized indicators, standardized indicators, and labels. These types of analyzes can now also be conducted via the new menu option “Explore full latent growth”, which presents several advantages (as discussed in the WarpPLS User Manual).

Related YouTube videos:

Explore Multi-Group Analyses in WarpPLS

Explore Measurement Invariance in WarpPLS


Nurulhuda Ibrahim said...

Dear Dr Kock,
I compared two groups using this technique. Before this I used Mann Whitney U test to compare the same groups. My data distribution were not normal. However, with Mann Whitney U, only 3 LVs show significant differences. With this technique, all LVs appear to have significant differences.
Do you mind to point a reference so that I can justify the difference?

Ned Kock said...

Hi Nurulhuda. I think this is what you are looking for:

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.