Linear indices in nonlinear structural equation models: best fitting proper indices and other composites

By Theo K. Dijkstra, Jörg Henseler. 2011

86 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bafb547960f0156c8cb36420448ad3daefdf5e74

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In Artificial Intelligence

Citations in the corpus

On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations (2017, methodology): “Researchers who intend to estimate a factor model should use CB-SEM, while researchers intending to estimate a model of composites should use a composite-based method like PLS-SEM, GSCA, or another in that class (e.g., Dijkstra & Henseler, 2011; Tenenhaus & Tenenhaus, 2011).”
How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research (2020, background): “(composites) as proxies for the theoretical concepts ([31] [32],).”
A new criterion for assessing discriminant validity in variance-based structural equation modeling (2015, methodology): “Variance-based SEM methods—such as partial least squares path modeling (PLS; Lohmöller 1989; Wold 1982), generalized structured component analysis (GSCA; Henseler 2012; Hwang and Takane 2004), regularized generalized canonical correlation analysis (Tenenhaus and Tenenhaus 2011), and best fitting pro”
Using PLS path modeling in new technology research: updated guidelines (2016, background): “…interaction effects and quadratic effects can be easily analyzed by means of some rudimentary extensions to the standard PLS path modeling setup (Dijkstra and Henseler, 2011; Henseler and Fassott, 2010; Henseler et al., 2012; Henseler and Chin, 2010; Dijkstra and Schermelleh-Engel, 2014).”
Testing measurement invariance of composites using partial least squares (2016, background): “Others understand these models as a prescription for dimension reduction, where the aim is to condense the data so that it reflects a concept’s salient features adequately (Dijkstra and Henseler, 2011).”
Partial least squares path modeling: Quo vadis? (2018, methodology): “In particular, the PLS algorithm provides a prescription for dimension reduction (Dijkstra and Henseler 2011) i.e., it prescribes how variable values should be aggregated to proxy scores.”
PLS' Janus Face – Response to Professor Rigdon's ‘Rethinking Partial Least Squares Modeling: In Praise of Simple Methods’ (2014, methodology): “See also (Dijkstra & Henseler 2011) and (Dijkstra & Schermelleh-Engel 2012).”
Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling (2017, methodology): “Composites can be regarded as prescriptions for dimension reduction (Dijkstra and Henseler 2011) and generally go along with a loss of information. Analysts face a trade-off: Should they form the composite and accept the loss of information, or continue the analysis simply using the indicators? A ge”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, methodology): “^ PLS-based composites can also be applied as a method for dimension reduction, similar to principal components analysis, where the aim is to condense the measures so they adequately cover a conceptual variable’s salient features (Dijkstra and Henseler 2011).”