Among structural equation modeling (SEM) techniques, by far the most well known are covariance-based methods as exemplified by software such as LISREL, EQS, AMOS, SEPATH, and RAMONA. In fact, to many social science researchers, the covariancebased procedure is tautologically synonymous with the term SEM. Yet, an alternative and less widespread technique known as partial least squares (PLS) is also available for researchers interested in doing SEM-based analysis. Depending on the researcher’s objectives and epistemic view of data to theory, properties of the data at hand, or level of theoretical knowledge and measurement development, the PLS approach can be argued to be more suitable. PLS can be a powerful method of analysis because of the minimal demands on measurement scales (ie, do measures need to be at an interval or ratio level?), sample size, and residual distributions (Wold, 1985). Although PLS can be used for theory confirmation, it can also be used to suggest where relationships might or might not exist and to suggest propositions for testing later. Compared to the better known factor-based covariance-fitting approach for latent structural modeling, the component-based PLS avoids two serious problems: inadmissible solutions and factor indeterminacy (Fornell & Bookstein, 1982). Because the iterative algorithm performed in a PLS analysis generally consists of a series of ordinary least squares (OLS) analyses, identification is not a problem for recursive models nor does it presume any distributional form for measured variables. The utility of the PLS method has been documented elsewhere (Falk & Miller, 1992) as possibly more appropriate for a large percentage of the studies and data sets typically used among researchers.

The objective of this chapter, therefore, is to provide a nontechnical introduction to the PLS approach. As a logical base for comparison, the PLS approach for structural path estimation is contrasted to the covariance-based approach. In so doing, a set of considerations are then provided with the goal of helping the reader