In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the proposed model. Markov chain Monte Carlo methods for ob...

In this paper we give worked examples of adding national data to international datasets in order to add value to the secondary analysis of this data. We also show how more sophisticated techniques, including multilevel modelling and structural equation modelling, can be applied to such enhanced data to go beyond the potentially misleading simple correlational analysis. The paper is illustrated ...

This study was intended to verify the effects of implementing the Balance-Scored Card (BSC) on the organizational performances by using BSC as the moderator. Managers and directors in smalland medium-sized enterprises (SMEs) in Taiwan’s electronics industry were interviewed and ROE data was sourced from Taiwan Economic Journal. This study adopted a simple random sampling method to sample the po...

The article uses confirmatory factor analysis (CFA) as a template to explain didactically multilevel structural equation models (ML-SEM) and to demonstrate the equivalence of general mixed-effects models and ML-SEM. An intuitively appealing graphical representation of complex ML-SEMs is introduced that succinctly describes the underlying model and its assumptions. The use of definition variable...

The purpose of this study is to verify and understand how the accumulation of Intellectual Capital (IC) and corporate governance affect the corporate value of Taiwan-listed information and electronics companies. While simple random sampling was used to yield knowledge from the population, the linear Structural Equation Modeling (SEM) was adopted to verify the goodness-of-fit among the overall m...

Evaluating overall model fit for growth curve models involves 3 challenging issues. (a) Three types of longitudinal data with different implications for model fit may be distinguished: balanced on time with complete data, balanced on time with data missing at random, and unbalanced on time. (b) Traditional work on fit from the structural equation modeling (SEM) perspective has focused only on t...