Multigroup Analysis (MGA)

Abstract

The multigroup analysis allows to test if pre-defined data groups have significant differences in their group-specific parameter estimates (e.g., outer weights, outer loadings and path coefficients). SmartPLS provides outcomes of three different approaches that are based on bootstrapping results from every group.

Description

The multigroup analysis allows to test if pre-defined data groups have significant differences in their group-specific parameter estimates (e.g., outer weights, outer loadings and path coefficients). SmartPLS provides outcomes of three different approaches that are based on bootstrapping results from every group. Sarstedt et al. (2011) as well as Hair et al. (2018) describe the multigroup analysis methods in detail.

(1) Confidence Intervals (Bias Corrected)

This method computes the bias-corrected confidence intervals for the group specific estimations of parameters in the PLS path model. The group-specific results of a path coefficient are significantly different if the bias-corrected confidence intervals do not overlap.

(2) Partial Least Squares Multigroup Analysis (PLS-MGA)

This method is a non-parametric significance test for the difference of group-specific results that builds on PLS-SEM bootstrapping results. A result is significant at the 5% probability of error level, if the p-value is smaller than 0.05 or larger than 0.95 for a certain difference of group-specific path coefficients. Please note: The PLS-MGA method (Henseler et al., 2009), as implemented in SmartPLS, is an extension of the bootstrap-based MGA approach originally proposed for PLS-SEM (as described, for example, by Sarstedt et al., 2011).

(3) Parametric Test

This method is a parametric significance test for the difference of group-specific PLS-SEM results that assumes equal variances across groups.

(4) Welch-Satterthwait Test

This method is a parametric significance test for the difference of group-specific PLS-SEM results that assumes unequal variances across groups.

MGA Settings in SmartPLS

Select Groups: The selected groups will be assessed for significant differences in the parameter estimates (e.g., outer weights, outer loadings and path coefficients). All data groups selected under Group A will be compared against all data groups selected under Group B.

References

Please always cite the use of SmartPLS!

Ringle, Christian M., Wende, Sven, & Becker, Jan-Michael. (2015). SmartPLS 3. Boenningstedt: SmartPLS. Retrieved from https://www.smartpls.com