The Chicago Guide to Writing about Multivariate Analysis, Second Edition (Chicago Guides to Writing, Editing, and Publishing) - Softcover

Miller, Jane E.

Softcover

ISBN 10: 0226527875 ISBN 13: 9780226527871

Verlag: University of Chicago Press, 2013

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

Jane E. Miller is research professor at the Institute for Health, Health Care Policy and Aging Research and professor in the Edward J. Bloustein School of Planning and Public Policy at Rutgers, the State University of New Jersey. Miller also serves as the faculty director of the Robert Wood Johnson Foundation–funded Project L/EARN research training program. She is the author of The Chicago Guide to Writing about Numbers.

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THE CHICAGO GUIDE TO WRITING ABOUT MULTIVARIATE ANALYSIS

By Jane E. Miller

THE UNIVERSITY OF CHICAGO PRESS

Copyright © 2013 The University of Chicago
All rights reserved.

ISBN: 978-0-226-52787-1

Contents

List of Tables.............................................................
ix
List of Figures............................................................
xiii
List of Boxes..............................................................
xvii
Preface....................................................................
xix
Acknowledgments............................................................
xxi
1. Introduction............................................................
1
PART I. PRINCIPLES.........................................................
11
2. Seven Basic Principles..................................................
13
3. Causality, Statistical Significance, and Substantive Significance.......
34
4. Five More Technical Principles..........................................
49
PART II. TOOLS.............................................................
75
5. Creating Effective Tables...............................................
77
6. Creating Effective Charts...............................................
113
7. Choosing Effective Examples and Analogies...............................
157
8. Basic Types of Quantitative Comparisons.................................
172
9. Quantitative Comparisons for Multivariate Models........................
193
10. The "Goldilocks Problem" in Multivariate Regression....................
211
11. Choosing How to Present Statistical Test Results.......................
230
PART III. PULLING IT ALL TOGETHER..........................................
251
12. Writing Introductions, Conclusions, and Abstracts......................
253
13. Writing about Data and Methods.........................................
268
14. Writing about Distributions and Associations...........................
297
15. Writing about Multivariate Models......................................
312
16. Writing about Interactions.............................................
339
17. Writing about Event History Analysis...................................
366
18. Writing about Hierarchical Linear Models (with Julie Phillips).........
386
19. Speaking about Multivariate Analyses...................................
408
20. Writing for Applied Audiences..........................................
438
Appendix A. Implementing "Generalization, Example, Exceptions" (GEE).......
463
Appendix B. Translating Statistical Output into Table and Text.............
472
Appendix C. Terminology for Common Types of Multivariate Models............
477
Appendix D. Using a Spreadsheet for Calculations...........................
499
Appendix E. Comparison of Research Papers, Speeches, and Posters...........
503
Notes......................................................................
507
Reference List.............................................................
515
Index......................................................................
529

CHAPTER 1

Introduction

Writing about multivariate analyses is a surprisingly common task. Resultsof ordinary least squares (OLS), logistic, and other kinds of multivariateregression models inform decisions of government agencies, businesses,and individuals. In everyday life, you encounter forecasts aboutinflation, unemployment, and interest rates in the newspaper, predictionsof hurricanes' timing and location in television weather reports, andadvice about behaviors and medications to reduce heart disease risk inmagazines and health pamphlets. In many professional fields, multivariateanalyses are included in research papers, grant proposals, policy briefs,and consultant's reports. Economists and meteorologists, health researchersand college professors, graduate students and policy analysts all needto write about multivariate models for both statistical and nonstatisticalaudiences. In each of these situations, writers must succinctly and clearlyconvey quantitative concepts and facts.

Despite this apparently widespread need, few people are formallytrained to write about numbers, let alone multivariate analyses. Communicationsspecialists learn to write for varied audiences, but rarely aretaught specifically to deal with statistical analyses. Statisticians and researcherslearn to estimate regression models and interpret the findings,but rarely are taught to describe them in ways that are comprehensible toreaders with different levels of quantitative expertise or interest. I haveseen poor communication of statistical findings at all levels of trainingand experience, from papers by students who were stymied about how toput numbers into sentences, to presentations by consultants, policy analysts,and applied scientists, to publications by experienced researchersin elite peer-reviewed journals. This book is intended to bridge the gapbetween correct multivariate analysis and good expository writing, takinginto account your intended audience and objective.

Audiences for Multivariate Analyses

Results of multivariate analyses are of interest to a spectrum of audiences,including:

• legislators, members of nonprofit organizations, the general public,and other "applied audiences" who may have little statistical trainingbut want to understand and apply results of multivariate analysesabout issues that matter to them;

• readers of a professional journal in your field who oft en vary substantiallyin their familiarity with multivariate models;

• reviewers for a grant proposal or article involving a multivariateanalysis, some of whom are experts on your topic but not the methods,others of whom are experts in advanced statistical methods butnot your topic;

• an audience at an academic seminar or workshop where everyoneworks with various regression methods and delights in debating statisticalassumptions and dissecting equations.

Clearly, these audiences require very different approaches to writing aboutmultivariate analyses.

Writing for a Statistical Audience

When writing for statistically trained readers, explain not only the methodsand findings but also the reasons a multivariate model is needed foryour particular study and how the findings add to the body of knowledgeon the topic. I have read many papers and sat through many presentationsabout statistical analyses that focused almost solely on equations andcomputer output full of acronyms and statistical jargon. Even if your audienceis well versed in multivariate techniques, do not assume that theyunderstand why those methods are appropriate for your research questionand data. And it behooves you to...

Details zur Ausgabe

• Verlag: University of Chicago Press
• Erscheinungsdatum: 2013
• Sprache: Englisch
• ISBN 10: 0226527875
• ISBN 13: 9780226527871
• Einband: Taschenbuch
• Auflage: 2
• Anzahl der Seiten: 575
• Kontakt zum Hersteller: The University of Chicago Press; https://press.uchicago.edu/index.html; ; 5801 S. Ellis Avenue; Chicago; IL; 60637; USA
• Verantwortliche Person: Nicht verfügbar