Multiple Regression and Beyond

Multiple Regression and Beyond : An Introduction to Multiple Regression and Structural Equation Modeling

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Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. * Covers both MR and SEM, while explaining their relevance to one another * Also includes path analysis, confirmatory factor analysis, and latent growth modeling * Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: more

Product details

  • Paperback | 600 pages
  • 177.8 x 251.46 x 33.02mm | 997.9g
  • Taylor & Francis Ltd
  • London, United Kingdom
  • English
  • Revised
  • 2nd Revised edition
  • 26 black & white tables
  • 1138811955
  • 9781138811959
  • 764,063

Review quote

I have had the opportunity to read quite a few books on quantitative methods in education during both my graduate work and more recently as an early career researcher, and this book occupies a singular and positive place among these. Todd M. Milford, University of Victoriashow more

About Timothy Z. Keith

Timothy Keith is Professor of Educational Psychology and Program Director of School Psychology at the University of Texas, more

Table of contents

Part I: Multiple Regression. Chapter 1: Simple (bivariate) regression. Chapter 2: Multiple regression: Introduction. Chapter 3: Multiple regression: More detail. Chapter 4: Three and more independent variables. Chapter 5: Three Types of MR. Chapter 6: Analysis of categorical variables. Chapter 7: Categorical & continuous variables. Chapter 8: Continuous variables: Interactions & curves. Chapter 9: Multiple regression: Summary, further study, and problems. Chapter 10: Related methods: Logistic regression and multilevel modeling. Part II: Beyond Multiple Regression: Structural Equation Modeling. Chapter 11: Path modeling: Structural equation modeling with measured variables. Chapter 12: Path analysis: Dangers and assumptions. Chapter 13: Analyzing path models using SEM programs. Chapter 14: Error: The scourge of research. Chapter 15: Confirmatory factor analysis I. Chapter 16: Putting it all together: Introduction to latent variable SEM. Chapter 17: Latent variable models: More advanced topics. Chapter 18: Latent means in SEM. Chapter 19: Confirmatory factor analysis II: Invariance and latent means. Chapter 20: Latent growth models. Chapter 21: Summary: Path analysis, CFA, SEM, and latent growth models. Appendices. Appendix A: Data files. Appendices B: Review of basic statistics concepts. Appendix C: Partial and semipartial more

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