Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.1814
This item is licensed with a CreativeCommons licence by-nc-nd/4.0
Publisher DOI: 10.1016/j.jbusres.2016.06.007
Title: Estimation issues with PLS and CBSEM: where the bias lies!
Language: English
Authors: Sarstedt, Marko 
Hair, Joseph F. 
Ringle, Christian M. 
Thiele, Kai Oliver 
Gudergan, Siegfried 
Keywords: common factor models;composite models;reflective measurement;formative measurement;structural equation modeling;partial least squares
Issue Date: 25-Jun-2016
Publisher: Elsevier
Source: Journal of Business Research 10 (69): 3998-4010 (2016)
Journal or Series Name: Journal of business research 
Abstract (english): 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.
URI: http://tubdok.tub.tuhh.de/handle/11420/1817
DOI: 10.15480/882.1814
ISSN: 0148-2963
Institute: Personalwirtschaft und Arbeitsorganisation W-9 
Type: (wissenschaftlicher) Artikel
Appears in Collections:Publications (tub.dok)

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