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Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies

Marko Sarstedt and Christian Ringle

Journal of Applied Statistics, 2010, vol. 37, issue 8, 1299-1318

Abstract: In the social science disciplines, the assumption that the data stem from a single homogeneous population is often unrealistic in respect of empirical research. When applying a causal modeling approach, such as partial least squares path modeling, segmentation is a key issue in coping with the problem of heterogeneity in the estimated cause-effect relationships. This article uses the novel finite-mixture partial least squares (FIMIX-PLS) method to uncover unobserved heterogeneity in a complex path modeling example in the field of marketing. An evaluation of the results includes a comparison with the outcomes of several data analysis strategies based on a priori information or k-means cluster analysis. The results of this article underpin the effectiveness and the advantageous capabilities of FIMIX-PLS in general PLS path model set-ups by means of empirical data and formative as well as reflective measurement models. Consequently, this research substantiates the general applicability of FIMIX-PLS to path modeling as a standard means of evaluating PLS results by addressing the problem of unobserved heterogeneity.

Keywords: partial least square (PLS); path modeling; heterogeneity; latent class; finite mixture; market segmentation; corporate reputation (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (29)

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DOI: 10.1080/02664760903030213

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