Multiple Regression and Beyond

Series
Pearson
Author
Timothy Z. Keith  
Publisher
Pearson
Cover
Softcover
Edition
1
Language
English
Total pages
496
Pub.-date
November 2013
ISBN13
9781292027654
ISBN
1292027657
Related Titles


Product detail

Product Price CHF Available  
9781292027654
Multiple Regression and Beyond
90.30 approx. 7-9 days

eBook

You'll find the eBook here.:

Free evaluation copy for lecturers


Description

This book is designed to provide a conceptually-oriented introduction to multiple regression. It is divided into two main parts: the author concentrates on multiple regression analysis in the first part and structural equation modeling in the second part.

Features

  • Provides an introduction to multiple regression analysis, path analysis, confirmatory factor analysis, and structural equation modeling.
  • Students gain a foundation in multiple regression. Multiple regression is used as a jumping-off place for structural equation modeling with measured variables (path analysis).
  • Conceptual, rather than formula oriented, approach
  • Extensive use and explanation of computer output
  • Numerous examples drawn from the research literature in psychology, education, as sociology
  • A large national data set is used throughout the text; this is supplemented by actual and simulated data from the research literature.
  • Demonstrates how to write up the results of various analyses. It’s not enough to know how to conduct analyses, researchers have to be able to communicate findings to others clearly.

Table of Contents

  • I. MULTIPLE REGRESSION.
  • 1. Introduction and Simple (Bivariate) Regression.
  • 2. Multiple Regression: Introduction.
  • 3. Multiple Regression: More Detail.
  • 4. Three and More Independent Variables and Related Issues.
  • 5. Three Types of Multiple Regression.
  • 6. Analysis of Categorical Variables.
  • 7. Categorical and Continuous Variables.
  • 8. Continuous Variables: Interactions and Curves.
  • 9. Multiple Regression: Summary, Further Study, and Problems.
  • II. BEYOND MULTIPLE REGRESSION.
  • 10. Path modeling: Structural equation modeling with measured variables.
  • 11. Path Analysis: Dangers and Assumptions.
  • 12. Analyzing Path Models Using SEM Programs.
  • 13. Error: The Scourge of Research.
  • 14. Confirmatory Factor Analysis.
  • 15. Putting It All Together: Introduction to Latent Variable SEM.
  • 16. Latent Variable Models: More Advanced Topics.
  • 17. Summary: Path Analysis, CFA, and SEM.
  • 18. Appendices.
  • 19. References.