@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}
}
@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}
}
@phdthesis{Schuberth2019,
  author    = {Schuberth, Florian},
  title     = {Composite-based Methods in Structural Equation Modeling},
  doi       = {10.25972/OPUS-15465},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-154653},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {This dissertation deals with composite-based methods for structural equation models with latent variables and their enhancement. It comprises five chapters. Besides a brief introduction in the first chapter, the remaining chapters consisting of four essays cover the results of my PhD studies.Two of the essays have already been published in an international journal. The first essay considers an alternative way of construct modeling in structural equation modeling.While in social and behavioral sciences theoretical constructs are typically modeled as common factors, in other sciences the common factor model is an inadequate way construct modeling due to its assumptions. This essay introduces the confirmatory composite analysis (CCA) analogous to confirmatory factor analysis (CFA). In contrast to CFA, CCA models theoretical constructs as composites instead of common factors. Besides the theoretical presentation of CCA and its assumptions, a Monte Carlo simulation is conducted which demonstrates that misspecifications of the composite model can be detected by the introduced test for overall model fit. The second essay rises the question of how parameter differences can be assessed in the framework of partial least squares path modeling. Since the standard errors of the estimated parameters have no analytical closed-form, the t- and F-test known from regression analysis cannot be directly used to test for parameter differences. However, bootstrapping provides a solution to this problem. It can be employed to construct confidence intervals for the estimated parameter differences, which can be used for making inferences about the parameter difference in the population. To guide practitioners, guidelines were developed and demonstrated by means of empirical examples. The third essay answers the question of how ordinal categorical indicators can be dealt with in partial least squares path modeling. A new consistent estimator is developed which combines the polychoric correlation and partial least squares path modeling to appropriately deal with the qualitative character of ordinal categorical indicators. The new estimator named ordinal consistent partial least squares combines consistent partial least squares with ordinal partial least squares. Besides its derivation, a Monte Carlo simulation is conducted which shows that the new estimator performs well in finite samples. Moreover, for illustration, an empirical example is estimated by ordinal consistent partial least squares. The last essay introduces a new consistent estimator for polynomial factor models. Similarly to consistent partial least squares, weights are determined to build stand-ins for the latent variables, however a non-iterative approach is used. A Monte Carlo simulation shows that the new estimator behaves well in finite samples.},
  subject      = {Strukturgleichungsmodell},
  language  = {en}
}
@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}
}