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About this book
The last two decades have seen structural equation modeling (SEM) emerge as a powerful data analysis tool for research in the social sciences, education, and psychology. With the advent of SEM computer programs such as LISREL and EQS, SEM has become a well-established and respected methodology. This book provides an introduction to the subject suitable for beginning graduate students. Its focus is on the basic concepts and applications of SEM within the social and behavioral sciences. The author develops SEM techniques by presenting linear regression, path analysis, confirmatory factor analysis, and then more general structural equation models. In doing so, he is able both to explain clearly the underlying statistical methodology in SEM whilst at the same time illustrate the use of SEM to analyse real data sets drawn from a number of different settings in the behavioral and social sciences.
Contents
Contents: Preface Frequently Used Symbols and Notation List of Figures and Tables CHAPTER 1: Linear Regression and Classical Path Analysis Overview and Key Points. Linear Ordinary Least Squares Regression. Classical Path Analysis. Summary. Exercises. Recommended Readings. CHAPTER 2: Confirmatory Factor Analysis. Overview and Key Points. Specification and Identification of a CFA Model. Data-Model Fit. Model Modification. Validity and Reliability from a CFA Perspective. Summary. Exercises. Recommended Readings. CHAPTER 3: General Structural Equation Modeling. Overview and Key Points. Specification and Identification of a General Structural Equation Model. The Direct, Indirect, and Total Structural Effect Components. Parameter Estimation. The Structural Equation Modeling Process: An Illustrated Review and Summary. Conclusion. Exercises. Recommended Readings. APPENDIX A: The Simplis Command Language. APPENDIX B: Location, Dispersion and Association. APPENDIX C: Matrix Algebra APPENDIX D: Descriptive Statistics for the SES Analysis. APPENDIX E: Descriptive Statistics for the HBI Analysis. References. Index.
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