Principles and Practice of Structural Equation Modeling: Third Edition (Methodology in the Social Sciences) Hardcover – 7 Sep 2010
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"Kline provides a text that is accessible for graduate students, practitioners, and researchers who are not intimately familiar with SEM techniques. In addition, he effortlessly summarizes current information that researchers who already use SEM should have….A major strength of the book is the individual chapter examples with explanation of the values provided from a variety of statistical analysis packages." - James B. Schreiber, Center for Advancing the Study of Teaching and Learning, Duquesne University, USA
"The coverage is excellent and the writing style is friendly and direct, with a subtle humor that I find refreshing. I especially like the new topic boxes in the third edition, most of which discuss issues that I have had to address separately in lectures." - Jacob Marszalek, Division of Counseling and Educational Psychology, University of Missouri–Kansas City, USA
About the Author
Rex B. Kline, Department of Psychology, Concordia University, Montreal, Quebec, Canada
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The book starts off with a concise overview of the statistical methods which are at the roots of SEM. This overview is just right to give you the broader perspective and the important techniques without going too much into details. It is also used later in the book for reference when details of the SEM methods are discussed.
The book then discusses in depth the various aspects of the SEM methodology. It addresses it from the perspective of the analysis process as well as the analysis techniques. From the process point of view the book takes you from the model specification stage, highlighting the analysis consideration to be taken already at this point, through model identification, analysis and re-evaluation. I especially appreciated the author's emphasis on the important place of theory in the statistical analysis. It's the theory that drives the model and not vice versa.
The book explains well the three different models of SEM: Path Analysis (PA), Confirmative Factor Analysis (CFA) and Structural Regression. It does this in a way that provides you with a good overview for the first reading and then allows for more details once you decide to start using a specific model. The extensive reference list that is provided with the book allows you to further dig into levels of details that you require.
I am sure that this book will accompany me throughout my research work.
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