@article{RodriguezEntrenaSchuberthGelhard2018,
  author    = {Rodr{\´i}guez-Entrena, Macario and Schuberth, Florian and Gelhard, Carsten},
  title     = {Assessing statistical differences between parameters estimates in Partial Least Squares path modeling},
  series = {Quality \& Quantity},
  volume    = {52},
  journal   = {Quality \& Quantity},
  number    = {1},
  doi       = {10.1007/s11135-016-0400-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-226403},
  pages     = {57-69},
  year      = {2018},
  abstract  = {Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess differences between parameters that are estimated for different subpopulations, the study at hand introduces a technique that allows to also assess whether two parameter estimates that are derived from the same sample are statistically different. To illustrate this advancement to PLS-SEM, we particularly refer to a reduced version of the well-established technology acceptance model.},
  language  = {en}
}