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Structural Equation Modeling: Applications Using Mplus by Xiaoqian Wang, Jichuan Wang

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Chapter 1

Introduction

The origins of structural equation modeling (SEM) stem from factor analysis (Spearman, 1904; Tucker, 1955) and path analysis (or simultaneous equations) (Wright, 1918, 1921, 1934). By integrating the measurement (factor analysis) and structural (path analysis) approaches, a more generalized analytical framework is produced, called SEM (Jöreskog, 1967, 1969, 1973; Keesling, 1972; Wiley, 1973). In SEM, unobservable latent variables (constructs or factors) are estimated from observed indicator variables, and the focus is on estimation of the relations among the latent variables free of the influence of measurement errors (Jöreskog, 1973; Jöreskog and Sörbom, 1979; Bentler, 1980, 1983; Bollen, 1989a).

SEM provides a mechanism for taking into account measurement error in the observed variables involved in a model. In social sciences, some constructs, such as intelligence, ability, trust, self-esteem, motivation, success, ambition, prejudice, alienation, and conservatism, cannot be directly observed. They are essentially hypothetical constructs or concepts, for which there exists no operational method for direct measurement. Researchers can only find some observed measures that are indicators of a latent variable. The observed indicators of a latent variable usually contain sizable measurement errors. Even for variables, which can be directly measured, measurement errors are always a concern in statistical analysis. Traditional statistical methods [e.g., multiple ...

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