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  4. Treating unobserved heterogeneity in PLS-SEM: a multi-method approach
 
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Treating unobserved heterogeneity in PLS-SEM: a multi-method approach

Publikationstyp
Book Part
Date Issued
2017-08-14
Sprache
English
Author(s)
Sarstedt, Marko  
Ringle, Christian M.  orcid-logo
Hair, Joseph F.  
Institut
Personalwirtschaft und Arbeitsorganisation W-9  
TORE-URI
http://hdl.handle.net/11420/3818
Start Page
197
End Page
216
Citation
Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications: 197-216 (2017)
Publisher DOI
10.1007/978-3-319-64069-3_9
Scopus ID
2-s2.0-85041699927
Publisher
Springer
Accounting for unobserved heterogeneity has become a key concern to ensure the validity of results when applying partial least squares structural equation modeling (PLS-SEM). Recent methodological research in the field has brought forward a variety of latent class techniques that allow for identifying and treating unobserved heterogeneity. This chapter raises and discusses key aspects that are fundamental to a full and adequate understanding of how to apply these techniques in PLS-SEM. More precisely, in this chapter, we introduce a systematic procedure for identifying and treating unobserved heterogeneity in PLS path models using a combination of latent class techniques. The procedure builds on the FIMIXPLS method to decide if unobserved heterogeneity has a critical impact on the results. Based on these outcomes, researchers should use more recently developed latent class methods, which have been shown to perform superior in recovering the segment-specific model estimates. After introducing these techniques, the chapter continues by discussing themeans to identify explanatory variables that characterize the latent segments. Our discussion also broaches the issue of measurement invariance testing, which is a fundamental requirement for a subsequent comparison of parameters across groups by means of a multigroup analysis.
DDC Class
330: Wirtschaft
TUHH
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