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

This study proposes a method to evaluate the construct validity for a nonlinear measurement model. Construct validation is required when applying measurement and structural equation models to questionnaire 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 estim ated 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. Empirical analysis is also provided, which shows that a nonlinear measurement model is better than 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 variable s.