# The Chicago Guide to Writing about Multivariate Analysis

Jane E. Miller

A study guide of exercises is available online.
500 pages | 72 figures, 39 tables | 5-1/2 x 8-1/2 | © 2005
Cloth \$81.00 ISBN: 9780226527826 Published August 2005
Paper \$32.50 ISBN: 9780226527833 Published August 2005
E-book \$7.00 to \$30.00 About E-books ISBN: 9780226527840 Published April 2008
Writing about multivariate analysis is a surprisingly common task. Researchers use these advanced statistical techniques to examine relationships among multiple variables, such as exercise, diet, and heart disease, or to forecast information such as future interest rates or unemployment. Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions. At the same time, many researchers have trouble communicating the purpose and findings of these models. Too often, explanations become bogged down in statistical jargon and technical details, and audiences are left struggling to make sense of both the numbers and their interpretation.

Here, Jane Miller offers much-needed help to academic researchers as well as to analysts who write for general audiences. The Chicago Guide to Writing about Multivariate Analysis brings together advanced statistical methods with good expository writing. Starting with twelve core principles for writing about numbers, Miller goes on to discuss how to use tables, charts, examples, and analogies to write a clear, compelling argument using multivariate results as evidence.

Writers will repeatedly look to this book for guidance on how to express their ideas in scientific papers, grant proposals, speeches, issue briefs, chartbooks, posters, and other documents. Communicating with multivariate models need never appear so complicated again.
Contents
List of Tables
List of Figures
List of Boxes
Preface
Acknowledgments
1. Introduction
Part I. Principles
2. Seven Basic Principles
3. Causality, Statistical Significance, and Substantive Significance
4. Five More Technical Principles
Part II. Tools
5. Creating Effective Tables
6. Creating Effective Charts
7. Choosing Effective Examples and Analogies
8. Basic Types of Quantitative Comparisons
9. Quantitative Comparisons for Multivariate Models
10. Choosing How to Present Statistical Test Results
Part III. Pulling It All Together
11. Writing Introductions, Conclusions, and Abstracts
12. Writing about Data and Methods
13. Writing about Distributions and Associations
16. Writing for Applied Audiences
Appendix A. Implementing "Generalization, Example, Exceptions" (GEE)
Appendix B. Translating Statistical Output into Table and Text
Appendix C. Terminology for Ordinary Least Squares (OLS) and Logistic Models
Appendix D. Using a Spreadsheet for Calculations
Notes
Reference List
Index