An Overview of Partial Least Squares

16 Pages Posted: 28 Jun 2010

See all articles by Dante M. Pirouz

Dante M. Pirouz

University of Western Ontario - The Richard Ivey School of Business

Date Written: October 10, 2006


Partial least squares analysis is a multivariate statistical technique that allows comparison between multiple response variables and multiple explanatory variables. Partial least squares is one of a number of covariance-based statistical methods which are often referred to as structural equation modeling or SEM. It was designed to deal with multiple regression when data has small sample, missing values, or multicollinearity. Partial least squares regression has been demonstrated on both real data and in simulations (Garthwaite, 1994, Tennenhaus, 1998). It has been very popular in hard science, especially chemistry and chemometrics, where there is a big problem with a high number of correlated variables and a limited number of observations. Its use in marketing has been more limited although data has similar problems (Ryan, Rayner, & Morrison, 1999). This paper provides a brief overview of partial least squares (PLS) and its use as an analytical method in marketing research.

Keywords: partial least squares, structural equation modeling, lisrel, methods, soft modeling

Suggested Citation

Pirouz, Dante M., An Overview of Partial Least Squares (October 10, 2006). Available at SSRN: or

Dante M. Pirouz (Contact Author)

University of Western Ontario - The Richard Ivey School of Business ( email )

1151 Richmond Street North
London, Ontario N6A 3K7


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