| Abstract: |
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. |