Measurement of composite reliability in research using partial least squares: some issues and an alternative approach

Authors:
Miguel I. Aguirre-Urreta;George M Marakas;Michael E. Ellis
Affiliations:
School of Accountancy and MIS, Richard H. Driehaus College of Business, DePaul University;Department of Decision Sciences & Information Systems, College of Business, Florida International University;School of Business, University of Kansas
Venue:
ACM SIGMIS Database
Year:
2013

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Abstract

The accurate estimation of reliability is of great importance to the conduct and interpretation of empirical research as it is used to judge the quality of reported research, often plays a role in publication decisions, and is a key element of meta-analytic reviews. When employing partial least squares (PLS) as the method of analysis, the reliability of the composites involved in the model is typically the parameter examined. In this research, we describe the existence of three important issues concerning the accuracy of composite reliability estimation in PLS analysis: the assumption of equal indicator weights, the bias in loading estimates, and the lack of independence between indicator loadings and weights. We subsequently present an alternative approach to correct these issues. Using a Monte Carlo simulation we provide a demonstration of both the effects of these issues on research decisions and the improved accuracy of the alternative method.