Partial possibilistic regression path modeling for subjective measurement

This work presents the use of the Partial Possibilistic Path Model in the context of subjective measurement, where ordinal data are collected from rating surveys to measure latent concepts. The method combines the principles of PLS path modeling to model the net of relations among the latent concepts, and the principles of possibilistic regression to model the vagueness of the human perception. Possibilistic regression defines the relation between variables through possibilistic linear functions and considers the error due to the vagueness of the human perception as reflected in the model via interval-valued parameters. A case study on the the motivational and emotional aspects of teaching is used to illustrate the method.