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  4. Estimation issues with PLS and CBSEM: where the bias lies!
 
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Estimation issues with PLS and CBSEM: where the bias lies!

Citation Link: https://doi.org/10.15480/882.1814
Publikationstyp
Journal Article
Date Issued
2016-06-25
Sprache
English
Author(s)
Sarstedt, Marko  
Hair, Joseph F.  
Ringle, Christian M.  orcid-logo
Thiele, Kai Oliver  
Gudergan, Siegfried  
Institut
Personalwirtschaft und Arbeitsorganisation W-9  
TORE-DOI
10.15480/882.1814
TORE-URI
http://hdl.handle.net/11420/1817
Journal
Journal of business research  
Volume
69
Issue
10
Start Page
3998
End Page
4010
Citation
Journal of Business Research 10 (69): 3998-4010 (2016)
Publisher DOI
10.1016/j.jbusres.2016.06.007
Scopus ID
2-s2.0-84989844061
Publisher
Elsevier
Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about themeaning 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.
Subjects
common factor models
composite models
reflective measurement
formative measurement
structural equation modeling
partial least squares
DDC Class
330: Wirtschaft
Lizenz
https://creativecommons.org/licenses/by-nc-nd/4.0/
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