Abstract
Structural equation modeling (SEM) is a family of statistical techniques and methods for testing hypotheses about causal effects among observed or proxies for latent variables. There are increasing numbers of SEM studies published in the research literatures of various disciplines, including psychology, education, medicine, management, and ecology, among others. Core types of structural equation models are described, and examples of causal hypotheses that can be tested in SEM are considered. Requirements for reporting the results of SEM analyses and common pitfalls to avoid are reviewed. Finally, an example of evaluating model fit is presented along with computer syntax so that readers can reproduce the results.
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