Partial least squares path modeling using ordinal categorical indicators

Florian Schuberth (), Jörg Henseler () and Theo K. Dijkstra ()
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Florian Schuberth: University of Würzburg
Jörg Henseler: University of Twente
Theo K. Dijkstra: University of Groningen

Quality & Quantity: International Journal of Methodology, 2018, vol. 52, issue 1, No 2, 9-35

Abstract: Abstract This article introduces a new consistent variance-based estimator called ordinal consistent partial least squares (OrdPLSc). OrdPLSc completes the family of variance-based estimators consisting of PLS, PLSc, and OrdPLS and permits to estimate structural equation models of composites and common factors if some or all indicators are measured on an ordinal categorical scale. A Monte Carlo simulation (N $$=500$$ = 500 ) with different population models shows that OrdPLSc provides almost unbiased estimates. If all constructs are modeled as common factors, OrdPLSc yields estimates close to those of its covariance-based counterpart, WLSMV, but is less efficient. If some constructs are modeled as composites, OrdPLSc is virtually without competition.

Keywords: Structural equation models; Consistent partial least squares; Ordinal categorical indicators; Common factors; Composites; Polychoric correlation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (85)

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DOI: 10.1007/s11135-016-0401-7

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