@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}
}
@article{RademakerSchuberthDijkstra2019,
  author    = {Rademaker, Manuel E. and Schuberth, Florian and Dijkstra, Theo K.},
  title     = {Measurement error correlation within blocks of indicators in consistent partial least squares : Issues and remedies},
  series = {Internet Research},
  volume    = {29},
  journal   = {Internet Research},
  number    = {3},
  doi       = {10.1108/IntR-12-2017-0525},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-224901},
  pages     = {448-463},
  year      = {2019},
  abstract  = {Purpose The purpose of this paper is to enhance consistent partial least squares (PLSc) to yield consistent parameter estimates for population models whose indicator blocks contain a subset of correlated measurement errors. Design/methodology/approach Correction for attenuation as originally applied by PLSc is modified to include a priori assumptions on the structure of the measurement error correlations within blocks of indicators. To assess the efficacy of the modification, a Monte Carlo simulation is conducted. Findings In the presence of population measurement error correlation, estimated parameter bias is generally small for original and modified PLSc, with the latter outperforming the former for large sample sizes. In terms of the root mean squared error, the results are virtually identical for both original and modified PLSc. Only for relatively large sample sizes, high population measurement error correlation, and low population composite reliability are the increased standard errors associated with the modification outweighed by a smaller bias. These findings are regarded as initial evidence that original PLSc is comparatively robust with respect to misspecification of the structure of measurement error correlations within blocks of indicators. Originality/value Introducing and investigating a new approach to address measurement error correlation within blocks of indicators in PLSc, this paper contributes to the ongoing development and assessment of recent advancements in partial least squares path modeling.},
  language  = {en}
}
@article{SchuberthHenselerDijkstra2016,
  author    = {Schuberth, Florian and Henseler, J{\"o}rg and Dijkstra, Theo K.},
  title     = {Partial least squares path modeling using ordinal categorical indicators},
  series = {Quality \& Quantity},
  journal   = {Quality \& Quantity},
  doi       = {10.1007/s11135-016-0401-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144016},
  year      = {2016},
  abstract  = {This article introduces a new consistent variance-based estimator called ordinal consistent partial least squares (OrdPLSc). OrdPLSc completes the family of variance-based estimators consisting of PLS, PLSc, and OrdPLS and permits to estimate structural equation models of composites and common factors if some or all indicators are measured on an ordinal categorical scale. A Monte Carlo simulation (N =500) with different population models shows that OrdPLSc provides almost unbiased estimates. If all constructs are modeled as common factors, OrdPLSc yields estimates close to those of its covariance-based counterpart, WLSMV, but is less efficient. If some constructs are modeled as composites, OrdPLSc is virtually without competition.},
  language  = {en}
}