Structural equation models (SEM) are reference techniques for measuring causeeffect relationships in complex systems. In many real cases observations are a priori grouped into homogeneous segments according to a specific characteristic, so that different models can be assessed for each segment. The present paper proposes to adopt an Euclidean metric based on the model parameters in order to determine differences among models. However, estimated models assess the relation structures in different proportions, i.e. the residual component can vary with respect to the different models. In order to overcome this shortcoming the present work suggests to introduce fuzzy regression (FR) inside PLS path modeling (PLS–PM).
Classification of structural equation models based on fuzzy regression
ROMANO, ROSARIA;
2006-01-01
Abstract
Structural equation models (SEM) are reference techniques for measuring causeeffect relationships in complex systems. In many real cases observations are a priori grouped into homogeneous segments according to a specific characteristic, so that different models can be assessed for each segment. The present paper proposes to adopt an Euclidean metric based on the model parameters in order to determine differences among models. However, estimated models assess the relation structures in different proportions, i.e. the residual component can vary with respect to the different models. In order to overcome this shortcoming the present work suggests to introduce fuzzy regression (FR) inside PLS path modeling (PLS–PM).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
