On the use of structural equation models in experimental designs: Two extensions

Abstract

Bagozzi and Yi (1989) recently introduced new procedures for using structural equation models in experimental designs with . We extend their research by showing that the structural equation analysis of experimental designs can be accomplished via Wold's partial least squares () approach, which can be used without many of the assumptions necessary for maximum likelihood estimation in . We show that is applicable not only to the basic design, but also to other complex designs. We also identify two restrictive assumptions implicit in Bagozzi and Yi's step-down analysis procedures, and describe a more general approach that can be used even when these assumptions are not met. The proposed procedures are illustrated with Bagozzi and Yi's data, and the conditions suitable for alternative procedures are discussed.