1. Optimal Adaptive Survey Design Lars Lyberg, Frauke Kreuter, and James Wagner ITSEW 2010 Stowe, VT, USA, June 16

3. What Should Be Designed? Requirements+specifications+operations Ideal goal+ Defined goal+Actual results Good survey design means control of accuracy through the specs (QA) and control of operations (QC)

4. Some Early Thinking Hansen-Hurwitz-Pritzker 1967 Take all error sources into account Minimize all biases and select a minimum-variance scheme so that Var becomes an approximation of (a decent) MSE The zero defects movement that later became Six Sigma Dalenius 1969 Total survey design

5. Some More Thinking Textbook on total survey design Hansen-Hurwitz-Cochran-Dalenius Survey models and specific error sources Cochran’s comment from 1968

6. Alternative Criteria of Effectiveness Minimizing MSE for a given budget while meeting other requirements Maximizing fitness for use for a given budget Maximizing comparability for a given budget All these reversed Something else?

7. The Elements of Design Assessing the survey situation (requirements) Choosing methods, procedures, “intensities”, and controls (specifications) Allocating resources Assessing alternative designs Carry out one of them or a modification of it Have a Plan B

8. So, What’s the Problem? No established survey planning theory Multi-purpose, many users The information paradox Uninformed clients/users/designers Much design work is partial, not total Limited knowledge of effects of measures on MSE and cost

9. More Problems Decision theory and economics theory not used to their potential New surveys conducted without sufficient consideration of what is already known No one knows the proper allocation of resources put in before, during and after The literature is small

10. Various Skills Needed Which Calls for a Design Team Survey methodology Subject-matter Statistics (decision theory, risk analysis, loss functions, optimization, process control) Economics (cost functions, utility) IT

11. The Adaptive Element The entire survey process should be responsive to anticipated uncertainties that exist before the process begins and to real time information obtained throughout the execution of the process or Use process data (paradata) to check, and if necessary, adjust the process

12. We Should Assemble What We Know Assessment methods Design principles Trade-offs and their effects The potential offered by other disciplines We shouldn’t accept partial designs

13. Apply Design Principles If pop is skewed then…. If pop is nested then…. If questions are sensitive then…. If a high NR rate is expected then…

14. Apply SOPs, CBMs or Best Practices Part of the design is to use known, dependable methods

15. Examples of Trade-offs Accuracy vs timeliness Response burden vs wealth of detail Conduct survey vs other information collection Large n vs smaller n Mixed vs single mode NR bias vs measurement error NR vs interpretation by family members

16. Process view Upstream thinking (prevention) Understanding variation Measure cost of poor quality and waste Intervention or improvement actions should be based on good data and statistical analysis Continuous monitoring

17. Tentative Course Syllabus The elements of design Real world examples (e.g., CPS Technical Paper 63, PIAAC, the Monthly Retail Trade Survey, the Annual Survey of Hale Mountain Fish & Game Club, VT) The literature on optimal decisions Theory for adaptive treatment design and risk management

18. Course syllabus continued Data for monitoring and decision making Analysis of such data Design lessons learned Examples of bad designs and not so great trade-offs Student project with TSE perspective Student presentations