- 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 presents issues and complications in statistical inference and uncertainty assessment using public opinion and polling data. It emphasizes the methodologically appropriate treatment of polling results as binomial and multinomial outcomes, and highlights methodological issues with correctly specifying and explaining the margin of error. The chapter also examines the log-ratio transformation of compositional data such as proportions of candidate support as one possible approach for the difficult analysis of such information. The deeply flawed Null Hypothesis Significance Testing (NHST) is discussed, along with common inferential misinterpretations. The relevance of this discussion is illustrated using specific examples of errors from journalistic sources as well as from academic journals focused on measures of public opinion.
Jeff Gill is a Distinguished Professor, Department of Government, Professor, Department of Mathematics and Statistics, and member of the Center for Behavioral Neuroscience at American University. His research applies Bayesian modeling and data analysis (decision theory, testing, model selection, and elicited priors) to questions in general social science quantitative methodology, political behavior and institutions, and medical/health data.
Jonathan Homola is an Assistant Professor at Rice University. He is a political methodologist and a comparativist. His substantive research interests include party competition, representation, political behavior, gender and politics, and immigration.
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