Estimation issues with PLS and CBSEM: Where the bias lies!
Abstract
Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about the meaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective, we disentangle the confusion between the terminologies and develop a unifying framework. Results from a simulation study substantiate our conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data's nature is common factor- or composite-based.
Cite this
Marko Sarstedt, Joseph F. Hair, Christian M. Ringle, Kai Oliver Thiele, Siegfried Paul Gudergan (2016). Estimation issues with PLS and CBSEM: Where the bias lies!. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2016.06.007
@article{marko2016estimation,
title = {Estimation issues with PLS and CBSEM: Where the bias lies!},
author = {Sarstedt, Marko and Hair, Joseph F. and Ringle, Christian M. and Thiele, Kai Oliver and Gudergan, Siegfried Paul},
year = {2016},
journal = {Journal of Business Research},
doi = {10.1016/j.jbusres.2016.06.007}
}Citations in this library
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