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Estimation issues with PLS and CBSEM: Where the bias lies!

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Sarstedt, Marko, Hair, Joseph F., Ringle, Christian M., Thiele, Kai O., Gudergan, Siegfried P.·2016·Journal of Business Research
DOI 10.1016/j.jbusres.2016.06.007
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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.

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Metadata

Authors
Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P.
Venue
Journal of Business Research
Publisher
Elsevier BV
Published
2016-10
Volume
69
Issue
10
Pages
3998-4010
ISSN
0148-2963 (ISSN-L)
DOI
10.1016/j.jbusres.2016.06.007
OpenAlex
W2460403273
Cited by
1,795
References
16
FWCI
104.74
Type
journal article
Language
en
Open access
hybrid
Licence

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CoChat. (2026). Citation check: Estimation issues with PLS and CBSEM: Where the bias lies! https://app.cochat.ai/papers/10.1016/j.jbusres.2016.06.007
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Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (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

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