Schedule

Fall 2026

The schedule is the main course hub. Each session lists the required preparation, class focus, and principal product or lab. Because this course includes a live Univision polling project, production milestones may shift. Changes will be announced in class and posted here.

Access: Open-access readings are linked directly. For items marked UT Libraries/Canvas, sign in through UT Libraries or use the course copy posted on Canvas. Page ranges refer to the editions listed in the complete reading reference.

Foundations: Evidence, error, and measurement

Session 1 · August 24

Why polls fail and what good survey design requires

Required preparation

• AAPOR. 2025. “2024 Pre-Election Polling: An Evaluation of the 2024 General Election Polls,” Executive Summary. Read the complete executive summary. Open access.
• AAPOR. “Polling Accuracy.” Read the complete webpage. Open access.
• Groves et al. 2009. Survey Methodology, Chapter 2, “Inference and Error in Surveys,” pp. 39–64. UT Libraries/Canvas.

In class: Complete the Initial Survey Autopsy diagnostic, establish a shared language for evaluating survey evidence, and introduce the Univision partnership and R/Quarto workflow.

Session 2 · August 31

Research objectives, stakeholders, ethics, and total survey error

Required preparation

• AAPOR. “Code of Professional Ethics and Practices.” Read sections I–III. Open access.
• AAPOR. “Best Practices for Survey Research.” Read the complete webpage. Open access.

The Groves total survey error review is recommended rather than required on this lab day.

In class: Translate stakeholder questions into researchable objectives and identify threats to the intended claims.

Lab 1: RStudio projects, Quarto, importing survey data, reading a codebook, missing values, recoding, summarizing, and reproducible workflow. Lab guide · Student template

September 7

Labor Day: no class

Session 3 · September 14

Concepts, measurement, and questionnaire architecture

Required preparation

• Groves et al. 2009. Chapter 7, “Questions and Answers in Surveys,” pp. 217–253. Focus on constructs, measurements, responses, and response error. UT Libraries/Canvas.
• Krosnick, Jon A., and Stanley Presser. 2010. “Question and Questionnaire Design.” In Handbook of Survey Research, 2nd ed. Read pp. 263–286, through the end of the section on open versus closed questions. Course PDF on Canvas.
• Pew Research Center. “Writing Survey Questions.” Read “Question development,” “Measuring change over time,” and “Open- and closed-ended questions.” Open access.

In class: Move from abstract concepts to indicators, distinguish measurement from operational convenience, and design the overall flow of a questionnaire.

Principal product: Construct map.

Session 4 · September 21

Question wording and response design

Required preparation

• Schwarz, Norbert. 1999. “Self-Reports: How the Questions Shape the Answers.” American Psychologist 54(2): 93–105. Read the complete article. UT Libraries.
• Tourangeau, Roger, and Ting Yan. 2007. “Sensitive Questions in Surveys.” Psychological Bulletin 133(5): 859–883. Read pp. 859–870 and 878–879. UT Libraries.
• Pew Research Center. 2018. “Methods 101: Survey Question Wording.” Watch the video and review the examples. Open access.

In class: Examine comprehension, retrieval, judgment, response mapping, acquiescence, social desirability, question order, response scales, and treatment of uncertainty.

Principal product: Draft question module.

Session 5 · September 28

Testing and multilingual equivalence

Required preparation

• Willis, Gordon B. 2005. Cognitive Interviewing: A Tool for Improving Questionnaire Design, Chapter 1, pp. 1–15. UT Libraries/Canvas.
• Survey Research Center. 2016. “Translation: Overview.” Read Guidelines 1–4 and their rationales. Open access.

In class: Conduct and evaluate cognitive interviews, identify systematic problems, and revise English and Spanish modules for conceptual equivalence.

Lab 2: Distributions, proportions, cross-tabulations, grouped summaries, measurement, scales, and accurate visualization with ggplot2. Lab guide · Student template

Representation and survey production

Session 6 · October 5

Sampling and representation

Required preparation

• Groves et al. 2009. Chapter 3, “Target Populations, Sampling Frames, and Coverage Error,” pp. 69–94. UT Libraries/Canvas.
• Groves et al. 2009. Chapter 4, “Sample Design and Sampling Error,” pp. 97–118, stopping before “Systematic Sampling.” UT Libraries/Canvas.
• Pew Research Center. 2023. “Comparing Two Types of Online Survey Samples.” Read the overview and “How the probability and opt-in samples compare.” Open access.

In class: Define the inferential population, compare sampling frames and recruitment sources, and assess probability and nonprobability approaches.

Principal product: Sampling assessment.

Session 7 · October 12

Modes, recruitment, and nonresponse

Required preparation

• Groves et al. 2009. Chapter 5, “Methods of Data Collection,” pp. 149–162, focusing on the comparison of survey modes. UT Libraries/Canvas.
• Pew Research Center. 2015. “From Telephone to the Web: The Challenge of Mode-of-Interview Effects in Public Opinion Polls.” Read the overview and “Mode differences by question content.” Open access.

In class: Analyze mode effects, coverage, contact strategies, incentives, accessibility, response rates, and nonresponse bias.

Lab 3: Simulation, sampling distributions, standard errors, margins of error, confidence intervals, hypothesis tests, and subgroup comparisons.

Assignment: Individual Poll Autopsy assigned. Due Sunday, October 18, at 11:59 p.m.

Session 8 · October 19

Programming and quality assurance

Required preparation

• U.S. Census Bureau. 2026. Design Guidelines for U.S. Census Bureau Web Surveys and Censuses. Read “Overall Look and Feel,” “Screen Elements,” and the entries for single-select, multi-select, grids, and open-text questions. Open access.
• Dillman, Don A., Jolene D. Smyth, and Leah Melani Christian. 2014. Internet, Phone, Mail, and Mixed-Mode Surveys, Chapter 9, “Web Questionnaires and Implementation.” UT Libraries.
• Course instrument QA protocol. Read the complete checklist posted on Canvas and bring it to class.

In class: Integrate modules, review logic and randomization, test mobile and bilingual displays, and maintain a formal issue log.

Principal product: Programmed instrument and QA log.

Session 9 · October 26

Fieldwork and data quality

Required preparation

• Dillman, Smyth, and Christian. 2014. Chapter 2, “Reducing People’s Reluctance to Respond to Surveys,” pp. 19–55. UT Libraries.
• AAPOR. 2022. Data Quality Metrics for Online Samples. Read Section 1, pp. 1–2, and Section 6, pp. 49–51. Open access.
• Kennedy, Courtney, et al. 2020. Assessing the Risks to Online Polls From Bogus Respondents. Read the overview, pp. 2–8, and the concluding recommendations. Open access.

In class: Establish fieldwork monitoring rules, examine paradata and fraud indicators, and distinguish defensible quality controls from post hoc deletion.

Principal product: Fieldwork dashboard plan.

Analysis and communication

Session 10 · November 2

Cleaning, coding, and weighting

Required preparation

• Pew Research Center. 2018. “For Weighting Online Opt-In Samples, What Matters Most?” Read the overview and conclusion. Open access.
• Pew Research Center. 2018. “How Different Weighting Methods Work.” Read the complete explainer. Open access.

In class: Develop a reproducible workflow for labels, missingness, derived variables, open-ended coding, weighting, effective sample size, and documentation.

Lab 4: Use the R survey package to compare weighted and unweighted estimates, design effects, effective sample size, and defensible toplines.

Session 11 · November 9

Analysis and uncertainty

Required preparation

• Pew Research Center. 2021. “Why Pew Research Center Will Display Margins of Error in Some Graphics.” Read the complete explainer. Open access.
• Pew Research Center. 2018. “Variability of Survey Estimates.” Read the complete explainer. Open access.
• Wasserstein, Ronald L., Allen L. Schirm, and Nicole A. Lazar. 2019. “Moving to a World Beyond ‘p < 0.05.’” The American Statistician 73(sup1): 1–19. Read pp. 1–10. Open access.

In class: Estimate toplines, compare subgroups, quantify uncertainty, account for weights and design effects, and avoid common errors in interpreting close differences.

Principal product: Analysis plan and production sprint.

Session 12 · November 16

Visualization and public interpretation

Required preparation

• Healy, Kieran. 2018. Data Visualization: A Practical Introduction. Read Chapter 1, “Look at Data.” Open access.

In class: Turn results into accurate, accessible graphics and write claims that match the strength of the design and evidence.

Lab 5: Introductory linear and logistic regression, categorical predictors, interactions, predicted probabilities, model-based graphics, and reproducible Quarto reporting.

November 23

Fall break: no class

Session 13 · November 30

Report production and briefing rehearsal

Required preparation: No new reading. Bring the complete team draft and apply the course report style guide and reproducibility checklist before class.

In class: Integrate the substantive narrative, methods statement, tables, graphics, limitations, and partner feedback into a coherent report.

Principal product: Full report draft and partner-briefing rehearsal.

Session 14 · December 7

Final presentation and project retrospective

There is no new reading. Review the final report, peer feedback, decision log, and your individual contribution record before class.

In class: Present the final work, evaluate the production process, document remaining decisions, and complete the project handoff.

Principal product: Final report and presentation.