Multilevel Statistical Models 3e / Edition 3

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Overview

It is now generally recognised in many areas of the social, medical and other sciences that statistical data typically have complex hierarchical or multilevel structures in which individuals are grouped together in communities or institutions. This grouping affects their behaviour and multilevel modelling is now the accepted statistical technique for the analysis of this type of data. An understanding of these methods is vital for researchers in fields such as education, epidemiology, geography, child growth and social surveys, among others. This new edition brings the book fully up to date, explaining important new developments such as the use of Markov Chain Monte Carlo methods, bootstrapping and mulitvariate models. The book has been completely restructured for this third edition and extra space has been given to discussion of key issues such as missing data, measurement errors and multivariate models. Real-life examples are used throughout to illustrate clearly the theoretical concepts.
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Product Details

  • ISBN-13: 9780340806555
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 5/1/2003
  • Series: Kendall's Library of Statistics
  • Edition description: REV
  • Edition number: 3
  • Pages: 272
  • Product dimensions: 6.46 (w) x 9.53 (h) x 0.83 (d)

Meet the Author

Harvey Goldstein, FBA is Director of the International Centre for Research on Assessment, Institute of Education, London, UK.
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Table of Contents

1. An introduction to multilevel models.

2. The basic two-level model.

3. Three-level models and more complex hierarchical.

4. Multilevel models for discrete response data.

5. Models for repeated measures data.

6. Multivariate multilevel data.

7. Multilevel factor analysis and structural equation.

8. Nonlinear multilevel models.

9. Multilevel modelling in sample surveys.

10. Multilvel event history models.

11. Cross-classified data structures.

12. Multiple membership models.

13. Measurement errors in multilevel models.

14. Missing data in multilevel models.

15. Software for multilevel modelling.

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