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{ "item_title" : "Elements of Structural Equation Models (Sems)", "item_author" : [" Kenneth A. Bollen "], "item_description" : "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.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/00/931/284/1009312847_b.jpg", "price_data" : { "retail_price" : "65.00", "online_price" : "65.00", "our_price" : "65.00", "club_price" : "65.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Elements of Structural Equation Models (Sems)|Kenneth A. Bollen

Elements of Structural Equation Models (Sems)

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Overview

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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Details

  • ISBN-13: 9781009312844
  • ISBN-10: 1009312847
  • Publisher: Cambridge University Press
  • Publish Date: February 2026
  • Dimensions: 10 x 7 x 1.3 inches
  • Shipping Weight: 2.43 pounds
  • Page Count: 646

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