- 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
Some textbooks on questionnaire design claim it is an art. That would make the criterion for a “good” question entirely subjective—a worrying conclusion given that surveys are often used to discover important facts about people. Are our discoveries about people also entirely subjective? This chapter shows that it is possible to study what a “good” or a “bad” question is by experimentation. There is already a body of scientific evidence on questionnaire design that can be taken into account when designing a questionnaire. The chapter reviews some of this evidence and shows how it can be used to the advantage of the survey researcher. Questionnaire science is far from complete. On the one hand, this means that some of our conclusions may still be more art than science. On the other, it means that we can agree on one aspect of questionnaire science: more of it is needed.
Daniel L. Oberski is an Associate Professor of Data Science Methodology in the Methodology & Statistics Department at Utrecht University. His research focuses on the problem of measurement in the social sciences.
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