plspm (version 0.2-2)

plspm.groups: Group Comparison in PLS-PM

Description

This function performs a group comparison test for comparing path coefficients between two groups

Usage

plspm.groups(pls, g, Y = NULL, method = "bootstrap", reps = NULL)

Arguments

pls

An object of class "plspm" returned by the function plspm

g

A factor with 2 levels indicating the groups to be compared

Y

Optional dataset (matrix or data frame) used when argument dataset=NULL inside pls.

method

The method to be used in the test. Possible values are "bootstrap" or "permutation"

reps

An integer indicating the number of either bootstrap resamples or number of permutations. If NULL then reps=100

Value

• An object of class "plspm.groups", basically a list with the following elements:
• test
  Table with the results of the applied test. Includes: path coefficients of the global model, path coeffs of group1, path coeffs of group2, (absolute) difference of path coeffs between groups, and the test results with the p-value.
• global
  List with results of the inner model for the global model
• group1
  List with results of the inner model for group1
• group2
  List with results of the inner model for group2

Details

The function plspm.groups performs a two groups comparison test in PLS-PM for comparing path coefficients between two groups. For the moment, only two methods are available: 1) bootstrap, and 2) permutation. The bootstrap test is an adapted t-test based on bootstrap standard errors. The permutation test is a randomization test which provides a non-parametric option. The null and alternative hypotheses to be tested are: H0: path coefficients are not significantly different H1: path coefficients are significantly different When the object pls does not contain a data matrix (i.e. pls$data=NULL), the user must provide the data matrix or data frame in Y.

References

Chin, W.W. (2003) A permutation procedure for multi-group comparison of PLS models. In: Vilares, M., Tenenhaus, M., Coelho, P., Esposito Vinzi, V., Morineau, A. (Eds.) PLS and Related Methods - Proceedings of the International Symposium PLS03. Decisia, pp. 33-43. Chin, W.W. (2000) Frequently Asked Questions, Partial Least Squares PLS-Graph. Available from: http://disc-nt.cba.uh.edu/chin/plsfaq/multigroup.htm

See Also

print.plspm.groups, plot.plspm.groups

Examples

## example with customer satisfaction analysis
  ## group comparison based on the segmentation variable "gender"
  data(satisfaction)
  IMAG <- c(0,0,0,0,0,0)
  EXPE <- c(1,0,0,0,0,0)
  QUAL <- c(0,1,0,0,0,0)
  VAL  <- c(0,1,1,0,0,0)
  SAT  <- c(1,1,1,1,0,0) 
  LOY  <- c(1,0,0,0,1,0)
  sat.inner <- rbind(IMAG, EXPE, QUAL, VAL, SAT, LOY)
  sat.outer <- list(1:5,6:10,11:15,16:19,20:23,24:27)
  sat.mod <- rep("A",6)   ## reflective indicators
  pls <- plspm(satisfaction, sat.inner, sat.outer, sat.mod, scheme="factor", 
                scaled=FALSE)
  ## permutation test with 100 permutations
  res.group <- plspm.groups(pls, satisfaction$gender, 
               method="permutation", reps=100)
  res.group
  plot(res.group)