- Instructor: Sebastian Daza
- Email: sebastian.daza@gmail.com
- Location: 2.A.04
- Tuesdays: 18:00 - 20:45
- Office hours: By appointment
This course provides an introduction to survey methodology. We will cover the generation of survey data, their limitations, and techniques for adjusting and analyzing data to obtain accurate estimates. The course explores various aspects such as interview methods, sampling concepts, and instrumentation issues. A significant portion of the course is dedicated to reviewing research that examines the impact of survey design features on survey errors and practical approaches for sampling and analyzing survey data.
This course includes classroom lectures and activities. By successfully completing the course, you will gain an understanding of the potential and limitations of survey methodology and how they relate to data science challenges and issues.
Readings of the course combine book chapters and research papers. All the readings will be available through Zotero. It would be best if you asked to be part of the course group here to access research papers and books. Please, create an account if you don't have one.
The key books of the course are:
- SM = Groves, Robert M., Floyd J. Fowler Jr, Mick P. Couper, James M. Lepkowski, Eleanor Singer, and Roger Tourangeau. 2009. Survey Methodology. 2nd ed. Wiley.
- HSM = Gideon, Lior, ed. 2012. Handbook of Survey Methodology for the Social Sciences. New York, NY: Springer New York.
- HSR = Marsden, Peter V., and James D. Wright, eds. 2010. Handbook of Survey Research. Second edition. Bingley, UK: Emerald.
- TSE = Biemer, Paul P., Edith Desirée de Leeuw, Stephanie Eckman, Brad Edwards, Frauke Kreuter, Lars Lyberg, Clyde Tucker, and Brady T. West, eds. 2017. Total Survey Error in Practice. Hoboken, New Jersey: Wiley.
The course combines lectures, short student presentations, and practical exercises. We will read approximately two chapters/articles per week.
Students' grades will depend on the following:
- Active participation (discussion) + short presentations during the class (25%)
- Weekly short assignments (25%)
- Final assignment (50%)
In each class, each student will present one research paper in about 3 minutes (no slides) as a way to practice your elevator speech. Each presentation should be able to summarize a paper by presenting the question o problem it's trying to solve, why it's important, how authors face the issue, the results conclusion, good features, and limitations of the paper or chapter. The rest of the students will ask questions to the presenter after the presentation.
There will be 2-3 practice exercises, which will also serve as the foundation for class discussions.
The final assignment will be a comprehensive task that covers the majority of the class material and involves coding. It is important to note that you are expected to work on this project independently.
All course assignments must adhere to the following professional format: 1-inch margins, double spacing, 12-point font size (Times New Roman), and page numbers. Please ensure that you submit only PDF files. It is important to keep the assignments concise and focused.
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Syllabus introduction
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Introduction to survey methodology
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Total error concept
- SM Chapter 2: Inference and errors in surveys
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Non-response
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Introduction to statistical inference
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Introduction to sampling
- SM Chapter 6: Nonresponse in sample surveys
- SM Chapter 3: Target populations, sampling frames, and coverage error
- The State of Survey Methodology: Challenges, Dilemmas, and New Frontiers in the Era of the Tailored Design
- Chapter 1: New Directions in Public Opinion (The Practice of Survey Research Changes and Challenges)
- TSE Chapter 3: Total survey error in practice (Big Data A Survey Research Perspective)
- TSE Chapter 2: Total survey error in practice (Total Twitter Error)
- Integrating Survey Data and Digital Trace Data: Key Issues in Developing an Emerging Field
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Sampling design
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Sampling with R
- SM Chapter 4: Sampling design and sampling error
- Survey Nonresponse Trends and Fieldwork Effort in the 21st Century: Results of an International Study across Countries and Surveys
- Evolution of the Initially Recruited SHARE Panel Sample Over the First Six Waves
- Using p-values to test a hypothesis https://lakens.github.io/statistical_inferences/01-pvalue.html
- Robust misinterpretation of confidence intervals
- Beyond Power Calculations Assessing Type S (Sign) and Type M (Magnitude) Errors
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Analyzing complex survey data and making sample adjustments
- SM Chapter 10: Postcollection processing of survey data
- Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
- Struggles with Survey Weighting and Regression Modeling
- Implementing machine learning methods with complex survey data: Lessons learned on the impacts of accounting sampling weights in gradient boosting
- Failure and Success in Political Polling and Election Forecasting
- A Review of Conceptual Approaches and Empirical Evidence on Probability and Nonprobability Sample Survey Research
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Analyzing complex survey data (continuation)
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Conceptualization and measurement
- Chapter 4: Making sense of the social world, methods of investigation (conceptualization and measurement)
- Panel Conditioning in Longitudinal Social Science Surveys
- Page switching in mixed-device web surveys: prevalence and data quality
- Using Facebook and Instagram to Recruit Web Survey Participants: A Step-by-Step Guide and Application
- HSM Chapter 7: The art of question phrasing
- Advances in the Science of Asking Questions
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Conceptualization and measurement (continuation)
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Design of questionnaires
- Chapter 7: Making sense of the social world: methods of investigation (Survey Methodology)
- SM Chapter 7: Questions and answers in surveys
- Measuring Public Opinion with Surveys
- HSR Chapter 12: How good is survey measurement? Assessing the reliability and validity of survey measures
- HSR Chapter 13: Interviewers and interviewing
- The Impact of Survey Mode Design and Questionnaire Length on Measurement Quality
- HSM Chapter 24: What Survey Modes are Most Effective in Eliciting Self-Reports of Criminal or Delinquent Behavior?
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Methods of data collection
- SM Chapter 5: Methods of data collection
- HSR Chapter 17: Mixed-mode surveys
- HSM Chapter 22: Sensitive issues in surveys
- HSR Chapter 16: Internet Surveys
- HSM Chapter 20: Building your own online panel via e-mail and other digital media
- Social Desirability Bias in CATI, IVR, and Web Surveys: The Effects of Mode and Question Sensitivity
- Estimating Web Survey Mode and Panel Effects in a Nationwide Survey of Alcohol Use