1. Paul P. Biemer RTI International Lars E. Lyberg Statistics Sweden I ntroduction to S urvey Q uality

2. Course Content Concepts of data quality Survey measurement process Coverage error and nonresponse Survey instrument Interviewers and interviewing

3. Mode and Setting Data processing Evaluation methods Practical implications

4. The Evolution of Survey Process Quality Chapter 1

5. Concepts Survey Survey methodology Quality Survey quality dimensions Survey process quality

6. Quality assurance Quality control Error sources Mean squared error

7. The Concept of a Survey concerns a set of objects comprising a population population under study has one or more measurable properties goal is to describe the population by one or more parameters defined in terms of the measurable properties

8. The Concept of a Survey (con’d) access to the population requires a frame is needed sample is selected in accordance with a sampling design specifying a probability mechanism and a sample size

9. The Concept of a Survey (con’d) observations are made in accordance with a measurement process based on the measurements an estimation process is applied to compute estimates purpose is to infer to the population

10. Typical Shortcomings target population is changed during the study selection probabilities are not known for all selected units correct estimation formulas are not used

11. Types of Surveys One-time Repeated or continuing The survey environment The survey infrastructure

12. A Brief History Biblical censuses Political arithmetic 1650-1800 The 1895 ISI proposal regarding representative investigations Bowley argues for random sampling 1913

13. The 1934 Neyman paper on the representative method Neyman develops theories for sampling and confidence intervals Nonsampling error theory in the 1940s

14. Interpenetration The US Census Bureau survey model Developments in other disciplines Questions and interviewers The response process

15. The Quality Revolution Deming’s 14 points Juran’s spiral of progress Ishikawa’s 7 quality control tools Joiner’s triangle (quality, scientific approach, teamwork)

16. Shewhart’s control chart for process control Dodge and Romig’s acceptance sampling A theory for statistical process control

17. Definitions of Quality Fitness for use Quality of design Quality of conformance

18. Quality dimensions in official statistics (one of them is accuracy) Quality according to some business excellence model Performance indicators

19. Eurostat’s Quality Dimensions Relevance of statistical concepts Accuracy of estimates Timeliness and punctuality in disseminating results Accessibility and clarity of the information

20. Comparability Coherence Completeness

21. The Process View Product characteristics are established together with the user The quality of the product is decided by the processes generating the product The processes are controlled via key process variables

22. Measuring and Documenting Quality Accuracy can be measured Other quality dimensions are qualitative and can be seen as constraints Quality profiles Quality reports Performance measures

23. Examples of Tools Self-assessment via excellence model Checklists Quality management External auditing Customer satisfaction surveys

24. Improving Quality Changing processes Project teams Standardization via current best methods documents Development of quality guidelines

25. We Concentrate on Accuracy Data must be of sufficient quality for decision-making Other dimensions are constraints Accuracy is much more difficult to understand It is important to convey information on error sources and their contributions to total survey error