Why Should PLS-SEM Be Used Rather Than Regression? Evidence from the Capital Structure Perspective

Nur Ainna Ramli (), Hengky Latan () and Gilbert Nartea
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Nur Ainna Ramli: Universiti Sains Islam Malaysia
Hengky Latan: STIE Bank BPD Jateng

Chapter Chapter 6 in Partial Least Squares Structural Equation Modeling, 2018, pp 171-209 from Springer

Abstract: Abstract This study examines capital structure determinants using a simultaneous causal model with interaction effects between manifest and latent variables. Partial Least Squares (PLS) is an approach to Structural Equation Models (SEM) that allows researchers to analyse the relationships simultaneously. It is interesting to compare and contrast this approach in analysing mediation relationships with the regression analysis. In addition to statistical data, logical arguments are presented supported by two case studies from PLS-SEM and regression models. We find that the choice between regression and PLS-SEM matters even with the simplest scenarios per item for constructs. This study’s originality is the provision of new comparative analyses of PLS-SEM versus regression analysis in the context of capital structure determinants. The “indirect” and “mediate” macro syntax normal theory of the Sobel test, and the bootstrapping techniques are compared with PLS-SEM. We find that the PLS-SEM analysis provides less contradictory results than regression analysis in terms of detecting mediation effects.

Keywords: Capital Structure; Firm Performance; PLS-SEM (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (13)

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DOI: 10.1007/978-3-319-71691-6_6

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