A least squares approach to latent variables extraction in formative–reflective models

Marco Fattore, Matteo Pelagatti and Giorgio Vittadini ()

Computational Statistics & Data Analysis, 2018, vol. 120, issue C, 84-97

Abstract: A new least-squares based procedure for the extraction of latent variables in structural equation models with formative–reflective schemes is developed and illustrated. The procedure is a valuable alternative to PLS-PM and SEM since it is fully consistent with the causal structure of formative–reflective schemes and it extracts the factor scores without substantial identification or indeterminacy problems. Moreover, the new methodology involves the optimization of an explicit and simple to interpret objective function, provides a natural way to check the correct specification of the model and is computationally light. The superiority of the new algorithm over its competitors is proved through examples involving both simulated and real data.

Keywords: Path model; Formative–reflective model; Least squares; Reduced rank regression; PLS-PM; SEM (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:120:y:2018:i:c:p:84-97

DOI: 10.1016/j.csda.2017.11.006

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