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| 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 |
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...
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