Elements of Structural Equation Models (SEMs)
Elements of Structural Equation Models (SEMs)

Elements of Structural Equation Models (SEMs)

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Elements of Structural Equation Models (SEMs)Author(s): Kenneth A. Bollen\nFormat: Hardback\nPublisher: Cambridge University Press, United Kingdom\nImprint: Cambridge University Press\nISBN-13: 9781009312820, 978-1009312820\nSynopsis\nElements of Structural Equation Models (SEMs) blends theoretical foundations with practical applications, serving as both a learning tool and a lasting reference. Synthesizing material from diverse sources, including the author's own contributions, it provides a rigorous yet accessible guide for graduate students, faculty, and researchers across social, behavioral, health, and data sciences. The book covers essential SEM concepts model assumptions, identification, estimation, and diagnostics while also addressing advanced topics often overlooked, such as Bayesian SEMs, model-implied instrumental variables, and categorical variables. Readers will gain insights into missing data, longitudinal models, and comparisons with Directed Acyclic Graphs (DAGs). .

Elements of Structural Equation Models (SEMs)

Elements of Structural Equation Models (SEMs)Author(s): Kenneth A. Bollen\nFormat: Hardback\nPublisher: Cambridge University Press, United Kingdom\nImprint: Cambridge University Press\nISBN-13: 9781009312820, 978-1009312820\nSynopsis\nElements of Structural Equation Models (SEMs) blends theoretical foundations with practical applications, serving as both a learning tool and a lasting reference. Synthesizing material from diverse sources, including the author's own contributions, it provides a rigorous yet accessible guide for graduate students, faculty, and researchers across social, behavioral, health, and data sciences. The book covers essential SEM concepts model assumptions, identification, estimation, and diagnostics while also addressing advanced topics often overlooked, such as Bayesian SEMs, model-implied instrumental variables, and categorical variables. Readers will gain insights into missing data, longitudinal models, and comparisons with Directed Acyclic Graphs (DAGs). .

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