MIS Quarterly· 2015Q1
Consistent Partial Least Squares Path Modeling1
- 2,735citations
- Q1SCImago
- 2015year
Short summary
Consistent Partial Least Squares (PLSc) path modeling corrects for bias in standard PLS estimates for reflective constructs, yielding consistent path coefficients, inter-construct correlations, and indicator loadings.
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Key points
- Introduces Consistent Partial Least Squares (PLSc) to correct for bias in standard PLS path modeling for reflective constructs.
- PLSc yields consistent estimates for path coefficients, inter-construct correlations, and indicator loadings.
- Monte Carlo simulations demonstrate PLSc's parameter estimate bias is comparable to covariance-based structural equation modeling.
- PLSc shows advantages over standard PLS when dealing with non-normally distributed data.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper resumes the discussion in information systems research on the use of partial least squares (PLS) path modeling and shows that the inconsistency of PLS path coefficient estimates in the case of reflective measurement can have adverse consequences for hypothesis testing. To remedy this, the study introduces a vital extension of PLS: consistent PLS (PLSc). PLSc provides a correction for estimates when PLS is applied to reflective constructs: The path coefficients, inter-construct correlations, and indicator loadings become consistent. The outcome of a Monte Carlo simulation reveals that the bias of PLSc parameter estimates is comparable to that of covariance-based structural equation modeling. Moreover, the outcome shows that PLSc has advantages when using non-normally distributed data. We discuss the implications for IS research and provide guidelines for choosing among structural equation modeling techniques.
The authors' abstract, as published at the source. MIS Quarterly, 2015 · DOI ↗
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Field: Computer Networks and Communications
Computer Networks and CommunicationsComputer Science