1 Introduction to structural equation modeling

1.1 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). Integrating the measurement (factor analysis) and structural (path analysis) approaches produces a more generalized analytical framework, called a structural equation model (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 1989).

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, conservatism, and so on 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 that can be directly measured, measurement errors are always a concern in statistical ...

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