Authors: Jane E. Miller
ISBN-13: 9780226527833, ISBN-10: 0226527832
Format: Paperback
Publisher: University of Chicago Press
Date Published: August 2005
Edition: 1st Edition
Jane E. Miller is on the faculty at the Institute for Health, Health Care Policy, and Aging Research and the Edward J. Bloustein School of Planning and Public Policy at Rutgers University. She is the author of The Chicago Guide to Writing about Numbers, published by the University of Chicago Press.
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.
1 | Introduction | 1 |
Pt. I | Principles | |
2 | Seven basic principles | 13 |
3 | Causality, statistical significance, and substantive significance | 34 |
4 | Five more technical principles | 50 |
Pt. II | Tools | |
5 | Creating effective tables | 81 |
6 | Creating effective charts | 120 |
7 | Choosing effective examples and analogies | 167 |
8 | Basic types of quantitative comparisons | 184 |
9 | Quantitative comparisons for multivariate models | 207 |
10 | Choosing how to present statistical test results | 231 |
Pt. III | Pulling it all together | |
11 | Writing introductions, conclusions, and abstracts | 257 |
12 | Writing about data and methods | 272 |
13 | Writing about distributions and associations | 301 |
14 | Writing about multivariate models | 317 |
15 | Speaking about multivariate analyses | 349 |
16 | Writing for applied audiences | 380 |
App. A | Implementing "generalization, example, exceptions" (GEE) | 407 |
App. B | Translating statistical output into table and text | 417 |
App. C | Terminology for ordinary least squares (OLS) at logistic models | 423 |
App. D | Using a spreadsheet for calculations | 433 |