A least squares approach to latent variables extraction in formative-reflective models
Marco Fattore (),
Matteo Pelagatti () and
Giorgio Vittadini ()
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Marco Fattore: Dipartimento di Metodi Quantitativi per l'Economia e le Scienze Aziendali, Università degli Studi di Milano-Bicocca
No 20120302, Working Papers from Università degli Studi di Milano-Bicocca, Dipartimento di Statistica
In this paper, we propose a new least-squares based procedure to extract exogenous and endogenous latent variables in formative-reflective structural equation models. The procedure is a valuable alternative to PLS-PM and Lisrel; it is fully consistent with the causal structure of formative-reflective schemes and extracts both the structural parameters and the factor scores, without identification or indeterminacy problems. The algorithm can be applied to virtually any kind of formative-reflective scheme, with unidimensional and even multidimensional formative blocks. To show the effectiveness of the proposal, some simulated examples are discussed. A real data application, pertaining to customer equity management, is also provided, comparing the outputs of our approach with those of PLS-PM, which may produce inconsistent results when applied to formative-reflective schemes.
Keywords: path model; formative-reflective model; least squares; reduced rank regression; PLS-PM (search for similar items in EconPapers)
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Journal Article: A least squares approach to latent variables extraction in formative–reflective models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:mis:wpaper:20120302
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