"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.
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2017 senior seminar. Reference book for quantitative methods. Data visualization. Writing and summarizing.
评分2017 senior seminar. Reference book for quantitative methods. Data visualization. Writing and summarizing.
评分2017 senior seminar. Reference book for quantitative methods. Data visualization. Writing and summarizing.
评分2017 senior seminar. Reference book for quantitative methods. Data visualization. Writing and summarizing.
评分2017 senior seminar. Reference book for quantitative methods. Data visualization. Writing and summarizing.
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