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The Chicago Guide to Writing about Multivariate Analysis by
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.
With many examples (including "Poor", "Better", and "Best" versions), this book shows how to understand your data as well as take the perspective of the reader. Learn to explain everything from a single number to the results of multiple logistic regressions in plain words (though not every type of relationship or test is covered). See also her Supplementary Materials including Podcasts (Video) presentations of slides.