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The Essence of Multivariate Thinking: Basic Themes and Methods, 2nd Edition

"Lisa Harlow empowers readers to master the art of multivariate analysis with her latest edition. The book guides readers from assumption testing to interpretation and write-up by providing us with pertinent examples and computer-generated output (SPSS, SAS) for the leading multivariate methods utilized in social science research." – Jennifer Ann Morrow, University of Tennessee, USA

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By focusing on underlying themes, this book helps readers better understand the connections between multivariate methods. For each method the author highlights: the similarities and differences between the methods, when they are used and the questions they address, the key assumptions and equations, and how to interpret the results. The concepts take center stage while formulas are kept to a minimum. Examples using the same data set give readers continuity so they can more easily apply the concepts. Each method is also accompanied by a worked out example, SPSS and SAS input, and an example of how to write up the results. EQS code is used for the book’s SEM applications.

This extensively revised edition features:

  • New SEM chapters including an introduction (ch.10), path analysis (ch.11), confirmatory factor analysis (ch.12), and latent variable modeling (ch.13) the last three with an EQS application.
  • A new chapter on multilevel modeling (ch. 8) that is now used more frequently in the social sciences.
  • More emphasis on significance tests, effect sizes, and confidence intervals to encourage readers to adopt a thorough approach to assessing the magnitude of their findings.
  • A new data set that explores the work environment.
  • More discussion about the basic assumptions and equations for each method for a more accessible approach.
  • New examples that help clarify the distinctions between methods.
  • A new website that features the datasets for all of the examples in the book for use in both SPSS and SAS and in EQS for the SEM chapters

Intended for advanced undergraduate and/or graduate courses in multivariate statistics taught in psychology, education, human development, business, nursing, and other social and life sciences, researchers also appreciate this book‘s applied approach. Knowledge of basic statistics, research methods, basic algebra, and finite mathematics is recommended.

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Table of Contents:

1. Introduction and Multivariate Themes

2. Background Themes

3. Multiple Regression

4. Analysis of Covariance

5. Multivariate Group Methods with Categorical Variables

6. Discriminant Function Analysis

7. Logistic Regression

8. Multi-level Modeling

9. Principal Components and Factor Analysis

10. Structural Equation Modeling

11. Path Analysis

12. Confirmatory Factor Analysis

13. Latent Variable Modeling

14. Integration of Multivariate Methods Appendix A Codebook for Data Used in Example

Appendix B Matrices and Multivariate Methods

 

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