The Chicago Guide to Writing about Multivariate Analysis, Second Edition
576 pages
|
2 halftones, 87 figures, 52 tables
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6 x 9
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© 2005, 2013
Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions. Researchers use these advanced statistical techniques to analyze relationships among multiple variables, such as how exercise and weight relate to the risk of heart disease, or how unemployment and interest rates affect economic growth. Yet, despite the widespread need to plainly and effectively explain the results of multivariate analyses to varied audiences, few are properly taught this critical skill.
The Chicago Guide to Writing about Multivariate Analysis is the book researchers turn to when looking for guidance on how to clearly present statistical results and break through the jargon that often clouds writing about applications of statistical analysis. This new edition features even more topics and real-world examples, making it the must-have resource for anyone who needs to communicate complex research results.
For this second edition, Jane E. Miller includes four new chapters that cover writing about interactions, writing about event history analysis, writing about multilevel models, and the “Goldilocks principle” for choosing the right size contrast for interpreting results for different variables. In addition, she has updated or added numerous examples, while retaining her clear voice and focus on writers thinking critically about their intended audience and objective. Online podcasts, templates, and an updated study guide will help readers apply skills from the book to their own projects and courses.
This continues to be the only book that brings together all of the steps involved in communicating findings based on multivariate analysis—finding data, creating variables, estimating statistical models, calculating overall effects, organizing ideas, designing tables and charts, and writing prose—in a single volume. When aligned with Miller’s twelve fundamental principles for quantitative writing, this approach will empower readers—whether students or experienced researchers—to communicate their findings clearly and effectively.
The Chicago Guide to Writing about Multivariate Analysis is the book researchers turn to when looking for guidance on how to clearly present statistical results and break through the jargon that often clouds writing about applications of statistical analysis. This new edition features even more topics and real-world examples, making it the must-have resource for anyone who needs to communicate complex research results.
For this second edition, Jane E. Miller includes four new chapters that cover writing about interactions, writing about event history analysis, writing about multilevel models, and the “Goldilocks principle” for choosing the right size contrast for interpreting results for different variables. In addition, she has updated or added numerous examples, while retaining her clear voice and focus on writers thinking critically about their intended audience and objective. Online podcasts, templates, and an updated study guide will help readers apply skills from the book to their own projects and courses.
This continues to be the only book that brings together all of the steps involved in communicating findings based on multivariate analysis—finding data, creating variables, estimating statistical models, calculating overall effects, organizing ideas, designing tables and charts, and writing prose—in a single volume. When aligned with Miller’s twelve fundamental principles for quantitative writing, this approach will empower readers—whether students or experienced researchers—to communicate their findings clearly and effectively.
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. The “Goldilocks Problem” in Multivariate Regression
11. Choosing How to Present Statistical Test Results
Part III. Pulling It All Together
12. Writing Introductions, Conclusions, and Abstracts
13. Writing about Data and Methods
14. Writing about Distributions and Associations
15. Writing about Multivariate Models
16. Writing about Interactions
17. Writing about Event History Analysis
18. Writing about Hierarchical Linear Models (with Julie Phillips)
19. Speaking about Multivariate Analyses
20. Writing for Applied Audiences
Appendix A. Implementing “Generalization, Example, Exceptions” (GEE)
Appendix B. Translating Statistical Output into Table and Text
Appendix C. Terminology for Common Types of Multivariate Models
Appendix D. Using a Spreadsheet for Calculations
Appendix E. Comparison of Research Papers, Speeches, and Posters
Notes
Reference List
Index
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. The “Goldilocks Problem” in Multivariate Regression
11. Choosing How to Present Statistical Test Results
Part III. Pulling It All Together
12. Writing Introductions, Conclusions, and Abstracts
13. Writing about Data and Methods
14. Writing about Distributions and Associations
15. Writing about Multivariate Models
16. Writing about Interactions
17. Writing about Event History Analysis
18. Writing about Hierarchical Linear Models (with Julie Phillips)
19. Speaking about Multivariate Analyses
20. Writing for Applied Audiences
Appendix A. Implementing “Generalization, Example, Exceptions” (GEE)
Appendix B. Translating Statistical Output into Table and Text
Appendix C. Terminology for Common Types of Multivariate Models
Appendix D. Using a Spreadsheet for Calculations
Appendix E. Comparison of Research Papers, Speeches, and Posters
Notes
Reference List
Index
For more information, or to order this book, please visit http://www.press.uchicago.edu
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