Elements 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). By presenting complex technical content in a clear, structured way, this authoritative resource deepens readers' understanding of SEMs, making it an indispensable guide for both newcomers and experts seeking a definitive treatment of the field.
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Kenneth A. Bollen is the Henry Rudolph Immerwahr Distinguished Professor in the Departments of Psychology and Neuroscience and Sociology at the University of North Carolina at Chapel Hill. He is a leading expert in structural equation models and author of Structural Equations with Latent Variables (1989). Kenneth's pioneering work has earning him many prestigious awards including the Psychometric Society's Career Award for Lifetime Achievement, the Lazarsfeld Award for Distinguished Contributions to Sociological Methodology, and the Sells Award for Distinguished Multivariate Research.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardcover. Etat : new. Hardcover. Elements 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). By presenting complex technical content in a clear, structured way, this authoritative resource deepens readers' understanding of SEMs, making it an indispensable guide for both newcomers and experts seeking a definitive treatment of the field. This book offers a comprehensive guide to structural equation models, blending theory with practical applications. Balancing technical depth with clarity, it is an essential resource for graduate students, researchers, and practitioners in social, behavioral, health, and data sciences written by an expert who helped shape the field. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781009312820
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Hardback. Etat : New. Elements 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). By presenting complex technical content in a clear, structured way, this authoritative resource deepens readers' understanding of SEMs, making it an indispensable guide for both newcomers and experts seeking a definitive treatment of the field. N° de réf. du vendeur LU-9781009312820
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Hardcover. Etat : new. Hardcover. Elements 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). By presenting complex technical content in a clear, structured way, this authoritative resource deepens readers' understanding of SEMs, making it an indispensable guide for both newcomers and experts seeking a definitive treatment of the field. This book offers a comprehensive guide to structural equation models, blending theory with practical applications. Balancing technical depth with clarity, it is an essential resource for graduate students, researchers, and practitioners in social, behavioral, health, and data sciences written by an expert who helped shape the field. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781009312820
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Hardback. Etat : New. Elements 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). By presenting complex technical content in a clear, structured way, this authoritative resource deepens readers' understanding of SEMs, making it an indispensable guide for both newcomers and experts seeking a definitive treatment of the field. N° de réf. du vendeur LU-9781009312820
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