
Contents
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Introduction Introduction
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Exploring Raw Data Exploring Raw Data
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Model Building Model Building
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Understanding the Results Understanding the Results
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Model Checking Model Checking
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Presenting Results Presenting Results
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Discussion Discussion
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Acknowledgments Acknowledgments
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References References
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19 Graphical Visualization of Polling Results
Get accessSusanna Makela is a PhD student in the Statistics Department at Columbia University. Her areas of interest include the application of statistical and quantitative methods to global health issues.
Yajuan Si is a Research Assistant Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor campus. Her research lies in cutting-edge methodology development in streams of Bayesian statistics, complex survey inference, missing data imputation, causal inference, and data confidentiality protection.
Andrew Gelman is the Higgins Professor of Statistics, Professor of Political Science, and Director of the Applied Statistics Center at Columbia University. His research spans a wide range of topics in statistics and social sciences, survey methodology, experimental design, statistical inference, computation, and graphics.
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Published:06 February 2017
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Abstract
This chapter argues that it is wasteful to do a large, expensive poll and then just report a few percentages. Statistical modeling allows researchers to make the most effective use of available data, and graphs make it possible to convey more information more directly, both to general audiences and to specialists. Graphs are an invaluable tool at each step of the modeling process: exploring raw data, building and refining the model, and understanding and communicating the results are all made easier with graphs. In addition, graphical methods can be useful to survey researchers to understand weighting and other aspects of survey construction and analysis. The chapter includes several examples.
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