Structural Equation Modeling

doi:10.1017/9781009010054.026

25 - Structural Equation Modeling

from Part IV - Statistical Approaches

Published online by Cambridge University Press: 25 May 2023

Rex B. Kline

Edited by

Austin Lee Nichols and

John Edlund

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Austin Lee Nichols: Affiliation:
Central European University, Vienna
John Edlund: Affiliation:
Rochester Institute of Technology, New York

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Summary

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.

Keywords

Structural Equation Modeling Covariance Structure Analysis Covariance-Based SEM Causal Models

Type: Chapter
Information: The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
Volume 1: Building a Program of Research
, pp. 535 - 558

DOI: https://doi.org/10.1017/9781009010054.026 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2023

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25 - Structural Equation Modeling

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