# Summer school 'Advanced Survey Design'

With
Bella Struminskaya
I teach a summerschool lasting five days titled:
Advanced survey design
.

It is taught bi-annually, in the week bridging August and September, on location in Utrecht. The last course took place in 2023, and so the next iteration is foreseen for 2025.

This 5-day course in survey design takes student beyond the introductory courses offered in BA and MA programmes, and discusses the state-of-the-art of one of the most important data collection techniques: surveys. The course focuses on the methodology of how to do surveys, and the use statistical techniques to analyse and correct for some specific survey errors. It combines short 1-hour lectures with exercises on most of the topics discussed. We assume course participants are proficient in working with R. Most of the exercises can also be done with STATA or SPSS, but answers will be provided in R The course assumes basic knowledge of:

- Basic knowledge of social science research methodology
- Multivariate statistics up to the General Linear Model
- The basics of survey methodology (the basic of sampling questionnaire design, collecting and processing data)

**Day-to-day schedule**:

Monday, Day 1:

- 09:00-10:00 Lecture: Introduction to the Total Survey Error Paradigm
- 10:00-11:00 Lecture: Mixed mode surveys
- 11:30-12:00 Exercise: study design and minimizing Total Survey error (in groups)
- 12:00-13:00 Lunch (included)
- 13:00-14:00 Lecture: Push-to-web surveys
- 14:00-15:00 Lecture: Sampling frames, time, costs, and recruitment strategies
- 15:00-16:00 Exercise: Designing a recruitment strategy

Tuesday, Day 2:

- 09:00-10:00 Lecture: Advanced questionnaire design
- 10:00-11:00 Lecture: Designing for mixed mode surveys
- 11:00-12:00 Exercise: Designing for mixed-device surveys (ESS module A)
- 12:00-13:00 Lunch
- 13:00-14:00 Lecture: Adaptive survey designs
- 14:00-15:00 Lecture: Ecological Momentary assessment (experience sampling)
- 15:00-16:00 Exercise: Questionnaire design for a modern survey (ESS module A-> text message mode switch, central question)

Wednesday, Day 3:

- 09:00-10:30 Lecture: Big data and TSE

- Digital trace data
- Organic data
- Administrative data

- 10:30-11:00 Lecture: Designed big data
- 11:00-12:00 Exercise: selection bias in (designed) big data
- 12:00-13:00 Lunch
- 13:00-14:00 Lecture: Data donation
- 14:00-15:00 Exercise: Data donation using your own data
- 14:00-16:00 Exercise: Analyzing digital trace data (computer exercise)

Thursday, Day 4:

- 09:00-09:30 Lecture: Conducting surveys using apps
- 09:30-10:30 Lecture: Passive data collection using mobiles (sensors)
- 10:30-11:00 Lecture: Ethics, consent, willingness
- 11:00-12:00 Lecture: Applications using geo- or accelerometer data
- 12:00-13:00 Lunch
- 13:00-14:00 Exercise: Exercise: geo-data or accelerometer data (choose 1) (introductory exercise)
- 14:00-15:00 Exercise: Applications using text or image data
- 15:30-16:00 Exercise: Object recognition, text recognition, text exercises (introductory exercise)

Friday, Day 5:

- 09:00-10:00 Lecture: Data integration at level of individual respondents
- 10:00-11:00 Exercise: Data integration at level of sample
- 11:00-16:00 Exercise: Your own project

Consultations with teachers of the course to discuss your survey questions in more depth. You may bring your own dataset, questionnaire or study design to discuss. Alternatively, there is time to finish some of the exercises earlier or read specific literature

For information about the course, including how to register, please have a look at the Utrecht Summer School website

**Background readings for the course are**:
Aggarwal, C.C. (2018) Machine learning for text. Springer. ISBN: 978-3-319-73530-6, doi: 10.1007/978-3-319-73531-3 (day 4)

Antoun, C., Katz, J., Argueta, J., & Wang, L. (2018). Design heuristics for effective smartphone questionnaires. Social Science Computer Review, 36(5), 557-574 (day 2)

Biemer, P.P., de Leeuw E., Eckman, S., Edwards, B., Kreuter, F., Lyberg, L., Tucker, N.C., West, B., eds. (2017) Total Survey Error in Practice, Wiley, especially chapters 2 and 7 (days 1, 3)

Boeschoten, L., Ausloos, J., M?ller, J. E., Araujo, T., & Oberski, D. L. (2022). A framework for privacy preserving digital trace data collection through data donation. Computational Communication Research, 4(2), 388-423. (day 3)

Brunsdon, C. & Comb, L. (2019) An introduction to R for spatial analysis and mapping (Spatial analysis and GIS). (2nd edition). Sage, London. ISBN-13: 978-1526428509 (day 5)

Dillman, D.A., J.D. Smyth, and L.M. Christian (2009) Internet, Mail and Mixed-Mode: The Tailored Design Method, 3rd Edition. Wiley and Sons, chapters 4 and 5 especially (day 1,2)

Dillman, D. A. (2017). The promise and challenge of pushing respondents to the web in mixed-mode surveys. Survey Methodology, 43(1), 3-31.

Foster, Ian, et al., eds. Big data and social science: A practical guide to methods and tools. CRC Press, 2016 (day 3,4)

Fowler, F.J. (1996) Improving survey questions - design and evaluation. London, Sage, Chapters 1-6 (day 2)

Groves, R.M. et al. (2009), Survey Methodology, 2nd edition. New York: Wiley (days 1-3)

Hox, J.J. (1997) From theoretical concept to survey question. In: Survey Measurement and Process Quality Ed. By L. Lyberg, P. Biemer, M. Collins, E. D. De Leeuw, C. Dippo, N. Schwarz, D. Trewin. Wiley, p. 47-69. (day 2)

Japec, L., Kreuter, F., Berg, M., Biemer, P., Decker, P., Lampe, C., … & Usher, A. (2015). Big data in survey research: AAPOR task force report. Public Opinion Quarterly, 79(4), 839-880. (day 4)

Keusch, F., Wenz, A., & Conrad, F. (2022). Do you have your smartphone with you? Behavioral barriers for measuring everyday activities with smartphone sensors. Computers in Human Behavior, 127, 107054. (day 4)

De Leeuw, E. D., J. J. Hox, and D. Dillman (2008). International Handbook of Survey Methodology. New York, chapters 17 & 19. (days 1-3)

De Leeuw, E. D. (2005). To mix or not to mix data collection modes in surveys. Journal of official statistics, 21(5), 233-255. (day 1)

Lohr, S. (2021). Sampling: design and analysis, third edition. CRC press.

Lynn, P. (2020, April). Evaluating push-to-web methodology for mixed-mode surveys using address-based samples. In Survey Research Methods (Vol. 14, No. 1, pp. 19-30). (day 1)

Meng, X. L. (2018). Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. The Annals of Applied Statistics, 12(2), 685-726 (day 2)

McCool, D., Lugtig, P., Mussmann, O., & Schouten, B. (2021). An app-assisted travel survey in official statistics: Possibilities and challenges. Journal of Official Statistics, 37(1), 149-170. (day 4)

Presser, S. , M.P. Couper, J.T. Lessler, E. Martin, J. Martin, J.M. Rothgeb, and E. Singer (2004) “Methods for Testing and Evaluating Survey Questions”, Public Opinion Quarterly, 68 (1): 109-130. (day 2)

Schouten, B., Calinescu, M., & Luiten, A. (2013). Optimizing quality of response through adaptive survey designs. Survey methodology, 39(1), 29-5 (day 2)

Schouten, B., Peytchev, A., & Wagner, J. (2017). Adaptive survey design. Chapman and Hall/CRC. (day 2)

Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annu. Rev. Clin. Psychol., 4, 1-32. (day 2)

van Driel, I. I., Giachanou, A., Pouwels, J. L., Boeschoten, L., Beyens, I., & Valkenburg, P. M. (2022). Promises and pitfalls of social media data donations. Communication Methods and Measures, 16(4), 266-282. (day 3)

Waal, T. D. (2016). Obtaining numerically consistent estimates from a mix of administrative data and surveys. Statistical Journal of the IAOS, 32(2), 231-243. (day 5)

De Waal, T., van Delden, A., & Scholtus, S. (2020). Multi-source statistics: basic situations and methods. International Statistical Review, 88(1), 203-228. (day 5)

Wrzus, C., & Neubauer, A. B. (2022). Ecological momentary assessment: A meta-analysis on designs, samples, and compliance across research fields. Assessment, 10731911211067538. (day 2)

More specific reading materials will be references in the course slides, which can be found in the day to day schedule.