Structural equation modeling (SEM) is a comprehensive statistical modeling tool for analyzing multivariate data involving complex relationships between and among variables (Hoyle, 1995). SEM surpasses traditional regression models by including multiple independent and dependent variables to test associated hypothesizes about relationships among observed and latent variables. SEM explain why res...

This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary T and N by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time series analysis (T large and N D 1) and conventional...

background: there can be little doubt that the construction is the most hazardous industry in the worldwide. this study was designed to modeling the factors affecting unsafe behavior from the perspective of safety supervisors. methods: the qualitative research was conducted to extract a conceptual model. a structural model was then developed based on a questionnaire survey (n=266) by two stage ...

The purpose of this study was to investigate the structural pattern of the effect of corporate governance mechanisms (internal organization) on audit quality considering the role of the intermediary variable of behavioural components of auditors. The statistical population of this research is independent auditors and auditors working in audit firms. A sample of 395 people was selected by simple...

Structural equation models constrain mean vectors and covariance matrices are frequently applied in the social sciences. Frequently, structural model is misspecified to some extent. In many cases, researchers nevertheless intend work with a target of interest. this article, simultaneous statistical inference for sampling errors misspecification discussed. A modified formula variance matrix para...

This paper considers the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors. We list a number of different generalizations of the linear structural equation model, and discuss how three common estimation approaches for linear equations — the “instrumental variables,” “fitted value,” and “control function” approaches — may or may not b...