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Structural Equation Modeling for Social and Personality Psychology
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Structural Equation Modeling for Social and Personality Psychology

February 2011 | 120 pages | SAGE Publications Ltd
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Structural Equation Modeling offers a nontechnical presentation of SEM with an emphasis on applications in social and personality psychology. The presentation begins with a discussion of the relation between SEM and statistical strategies widely used in social and personality psychology such as analysis of variance, multiple regression analysis, and factor analysis. This introduction is followed by a nontechnical presentation of the terminology, notation, and steps followed in a typical application of SEM. The reminder of the volume offers a practically-oriented presentation of specific applications using examples typical of social and personality psychology and offering advice for dealing with relevant issues such as missing data, choice of software, and best practices for interpreting and reporting results.

The SAGE Library in Social and Personality Psychology Methods provides students and researchers with an understanding of the methods and techniques essential to conducting cutting-edge research.

Each volume within the Library explains a specific topic and has been written by an active scholar (or scholars) with expertise in that particular methodological domain. Assuming no prior knowledge of the topic, the volumes are clear and accessible for all readers. In each volume, a topic is introduced, applications are discussed, and readers are led step by step through worked examples. In addition, advice about how to interpret and prepare results for publication are presented.

 
Background
 
Model Specification
 
Estimation and Fit
 
Modification, Presentation and Interpretation
 
Modeling Possibilities and Practicalities

This is too short to use as key reading, but is a useful resource for students.

Dr Claire Hart
Psychology , Southampton University
September 22, 2011
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This is a nice and competent introduction to structural equation modeling and I can recommend it to everybody who wants to get a thorough overview. However, it is not suited as a main course book for an introduction to SEM course for psychologists because it is to dense. I think the audience for this book are psychologists with some background in statistical modeling who would like to get a a short introduction into the topic in order to understand better papers using SEM or who for someone who is considering learning SEM. I also can recommend it as a refresher.

Dr Daniel Stahl
Biostatistics and Computing, King's College London
May 23, 2011
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