Comparison of Likelihood and PLS Estimators for Structural Equation Modeling: A Simulation with Customer Satisfaction Data

A Monte Carlo simulation is used to compare the two estimation methods for structural equation models based on customer satisfaction data, and shows that globally PLS estimates are generally better than covariance-based estimates both in terms of bias and precision.

Abstract

Although PLS is a well established tool to estimate structural equation models, more work is still needed in order to better understand its relative merits when compared to likelihood methods. This paper aims to contribute to a better understanding of PLS and likelihood estimators' properties, through the comparison and evaluation of these estimation methods for structural equation models based on customer satisfaction data. A Monte Carlo simulation is used to compare the two estimation methods. The model used in the simulation is the ECSI (European Customer Satisfaction Index) model, constituted by 6 latent variables (image, expectations, perceived quality, perceived value, customer satisfaction and customer loyalty). The simulation is conducted in the context of symmetric and skewed response data and formative blocks, which constitute the typical framework of customer satisfaction measurement. In the simulation we analyze the ability of each method to adequately estimate the inner model coefficients and the indicator loadings. The estimators are analyzed both in terms of bias and precision. Results have shown that globally PLS estimates are generally better than covariance-based estimates both in terms of bias and precision. This is particularly true when estimating the model with skewed response data or a formative block, since for the model based on symmetric data the two methods have shown a similar performance.