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Cited by 264

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Structural modeling of heterogeneous data with partial least squares

Rigdon, Edward E., Ringle, Christian M., Sarstedt, Marko·2010·Review of Marketing Research
DOI 10.1108/s1548-6435(2010)0000007011
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Abstract

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

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Metadata

Authors
Rigdon, E. E., Ringle, C. M., & Sarstedt, M.
Venue
Review of Marketing Research
Publisher
Emerald Group Publishing Limited
Published
2010-01
Pages
255-296
ISSN
1548-6435 (ISSN-L)
DOI
10.1108/s1548-6435(2010)0000007011
OpenAlex
W167145094
Cited by
264
References
8
FWCI
56.96
Type
book chapter
Language
en
Open access
closed

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