@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}
}