- The Oxford Handbook of Polling and Survey Methods
- Introduction to Polling and Survey Methods
- Total Survey Error
- Longitudinal Surveys: Issues and Opportunities
- Mixing Survey Modes and Its Implications
- Taking the Study of Political Behavior Online
- Sampling for Studying Context: Traditional Surveys and New Directions
- Questionnaire Science
- Exit Polling Today and What the Future May Hold
- Sampling Hard-to-Locate Populations: Lessons from Sampling Internally Displaced Persons (IDPs)
- Reaching Beyond Low-Hanging Fruit: Surveying Low-Incidence Populations
- Improving the Quality of Survey Data Using CAPI Systems in Developing Countries
- Survey Research in the Arab World
- The Language-Opinion Connection
- Issues in Polling Methodologies: Inference and Uncertainty
- Causal Inference with Complex Survey Designs: Generating Population Estimates Using Survey Weights
- Aggregating Survey Data to Estimate Subnational Public Opinion
- Latent Constructs in Public Opinion
- Measuring Group Consciousness: Actions Speak Louder Than Words
- Cross-National Surveys and the Comparative Study of Electoral Systems: When Country/Elections Become Cases
- Graphical Visualization of Polling Results
- Graphical Displays for Public Opinion Research
- Survey Experiments: Managing the Methodological Costs and Benefits
- Using Qualitative Methods in a Quantitative Survey Research Agenda
- Integration of Contextual Data: Opportunities and Challenges
- Measuring Public Opinion with Social Media Data
- Expert Surveys as a Measurement Tool: Challenges and New Frontiers
- The Rise of Poll Aggregation and Election Forecasting
Abstract and Keywords
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.
Susanna 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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