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Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis (Latent Variable Models: An Introduction to (Paperback)) - Softcover

 
9780805849103: Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis (Latent Variable Models: An Introduction to (Paperback))

Synopsis

This book introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. A few sections of the book make use of elementary matrix algebra. An appendix on the topic is provided for those who need a review. The author maintains an informal style so as to increase the book's accessibility. Notes at the end of each chapter provide some of the more technical details. The book is not tied to a particular computer program, but special attention is paid to LISREL, EQS, AMOS, and Mx.

New in the fourth edition of Latent Variable Models:
*a data CD that features the correlation and covariance matrices used in the exercises;
*new sections on missing data, non-normality, mediation, factorial invariance, and automating the construction of path diagrams; and
*reorganization of chapters 3-7 to enhance the flow of the book and its flexibility for teaching.

Intended for advanced students and researchers in the areas of social, educational, clinical, industrial, consumer, personality, and developmental psychology, sociology, political science, and marketing, some prior familiarity with correlation and regression is helpful.

"synopsis" may belong to another edition of this title.

Review

"Overall this is an excellent book and has served as a standard textbook for many years....The section on power is really terrific....Loehlin addresses complex topics very clearly." - Dr. Jodie Ullman California State University, San Bernadino; "The uniqueness of Loehlin's book is to mainly use path diagrams with minimum or no equations in presenting the structural equation model (SEM) and factor analysis (FA) model. 'This is good for students who do not have a lot of statistical or mathematical background." - Dr. Ke-Hai Yuan Notre Dame University; "Loehlin's intuitive approach provides a clear and concise description of latent variable models....I would purchase the book for myself as a reference." - Dr. Shaun McQuitty Department of Marketing, New Mexico State University; "I know this book very well having used it in classes for about 10 years....I know the author and his work, and I think he is the most knowledgeable (and likable) person in this field." - Dr. Jack McArdle University of Virginia"

Synopsis

This book introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. A few sections of the book make use of elementary matrix algebra. An appendix on the topic is provided for those who need a review. The author maintains an informal style so as to increase the book's accessibility. Notes at the end of each chapter provide some of the more technical details. The book is not tied to a particular computer program, but special attention is paid to LISREL, EQS, AMOS, and Mx. New in the fourth edition of Latent Variable Models: A data CD that features the correlation and covariance matrices used in the exercises; New sections on missing data, non-normality, mediation, factorial invariance, and automating the construction of path diagrams; Reorganization of chapters 3-7 to enhance the flow of the book and its flexibility for teaching.

Intended for advanced students and researchers in the areas of social, educational, clinical, industrial, consumer, personality, and developmental psychology, sociology, political science, and marketing. Some prior familiarity with correlation and regression is helpful.

"About this title" may belong to another edition of this title.

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