This work presents a study of statistical models used for estimation of the customer satisfaction. The main contributions are: a) a general description about the estimation methods of the customer satisfaction, mainly about the methods based on Structural Equation Models, grouped by: CSM (Covariance Structure Model) and PLS (Partial Least Square); b) a comparison between the estimation methods of the LISREL (Linear Structural Relations) and PLS with base on information function between the estimated scores and the true ones. The used model was the Brazilian Customer Satisfaction Index for Electric Energy (IASC) of the ANEEL (Brazilian Electricity Regulatory Agency); c) an alternative unidimentional model for the IASC based on the Samejima´s Model for Graded Response of the Item Response Theory (IRT). About the comparative analysis of the scores estimation methods, the PLS was better than LISREL if the latent variables relations were weak, in module. In opposite, LISREL was the best when the latent variables relations were stronger. However, this analysis was based on simulated data and it was necessary to consider some conditions to guarantee validity, estimation convergence and good fit of the models. About the proposed model based on IRT, many benefits were observed if compared with Structural Equation Models, mainly with regard to survey item analysis. Finally, it was observed that both methodologies are important to improve IASC methodology, so they have to be used together to guide the manager actions and the investments to improve utilities services for the customers.