Construct Validation for a Nonlinear Measurement Model in Marketing and Consumer Behavior Research

Abstract

This study proposes a method to evaluate the construct validity of a nonlinear measurement model. Construct validation is required when applying measurement and structural equation models to measurement data from consumer and related social science research. However, previous studies have not sufficiently discussed the nonlinear measurement model and its construct validation. This study focuses on convergent and discriminant validation as important processes to check whether estimated latent variables represent defined constructs. To assess the convergent and discriminant validity in the nonlinear measurement model, previous methods are extended and new indexes are investigated by simulation studies. The empirical analysis shows that a nonlinear measurement model is better than a linear model in both fitting and validity. Moreover, a new concept of construct validation is discussed for future research: it considers the interpretability of machine learning (such as neural networks) because construct validation plays an important role in interpreting latent variables.