European Conference on Quality in Survey Statistics

1. European Conference on Quality in Survey Statistics

2. Quality in Official Statistics:Some Recent and Not so Recent Developments Lars Lyberg Statistics SwedenQ2006

3. Why We Have a Q Conference • One of the LEG recommendations • The ESS mission, where it is stated that ESS shall provide the EU and indeed the world, with high quality information, available to everyone, on various areas and levels for decision-making, research and debate. • The ESS vision with keywords such as world leader, scientific principles, continuous improvement, harmonization, and basis for democracy and progress. • Westat, Inc.

4. Evaluation of data quality (92) Sampling and estimation (65) Nonresponse (44) Questionnaire development and testing (22) Confidentiality (17) Burden (5) Knowledge economy (5) Quality management of systems and organizations (63) Frameworks (11) Reporting (52) Process control (36) Auditing and self-assessment (11) Customers (36) Standards (11) Harmonization (12) Contents of Q so Far (# ms)

5. Somewhat Neglected Topics • Respondents • Costs • Trade-offs • Standardization • Fitness for use • User perception of quality • Trust • Audits and self-assessment • The nitty-gritty of QM

6. Issue No.10The Concept of Quality • Statistical Process Control (30’s and 40’s) • Small errors indicate usefulness (Kendall, Jessen, Palmer, Deming, Stephan, Hansen, Hurwitz, Tepping, Mahalanobis) • Decomposition of MSE around 1960 • Data quality (Kish, Zarkovich 1965) • Quality frameworks 70’s • CASM movement 80’s • Quality and users • The UN Fundamental Principles

7. Components of Quality

8. Defining Quality • Fitness for use or fitness for purpose • Framework components • Getting the job done, on time, within budget, so that it meets the specified requirements

9. Quality Assurance and Quality Control • QA makes sure that the processes are capable of delivering a good product • QC makes sure that the product is actually good

10. Controlling Quality Quality Level Main stake-holders Control instrument Measures Indicators Product User Product specs Framework dimensions, error est., MSE Process Survey designer Process variables, SPC, CBM, SOP , checklists Variation via control charts, other paradata analysis Organization NSI, owner, society Excellence models, CoP, Reviews, Audits, Self-assessments Scores, Strong and weak points, Are we measuring up?

11. Issue No. 9Quality Measurement and Quality Reporting • Objective: To ensure that users have access to measures or indicators of quality, presented in ways that meet their particular needs • The typical framework: relevance, accuracy, timeliness and punctuality, accessibility and clarity, comparability, and coherence

12. Examples of Reports • Dataset-specific quality assessments for different kinds of economic statistics (IMF) • Process data handbook (LEG/UK) • Quality guidelines (Stats Canada, Stats Finland) • Questions and Answers (OMB) • National or organizational frameworks • Quality profiles • Guidelines for quality reporting (Stats Can, ONS,, Stats Sweden, FCSM)

13. The user has not been consulted How should dimensions be measured? How do we handle information gaps? Some quality indicators are dubious Dimensions are in conflict What happened to total survey error or total quality? Särndal and Platek (2001) Do we need global harmonization? Concerns

14. Issue No. 8Deming’s 13 points • The 13 factors that affect the usefulness of a survey • To point out the need for directing effort toward all of them in the planning process with a view to usefulness and funds available • To point out the futility of concentrating on only one or two of them • To point out the need for theories of bias and variability that correlate accumulated experience

15. The 13 Points • Variability in Response • Differences between Different Kinds and Degrees of Canvass • Bias and Variation Arising from the Interviewer • Bias of the Auspices • Imperfections in the Design of the Questionnaire and Tabulation Plans

16. 13 Points Continued • Changes that Take Place in the Universe before Tabulations Are Available • Bias Arising from Nonresponse • Bias Arising from Late Reports • Bias Arising from an Unrepresentative Selection of Data for the Survey or of the Period Covered • Bias Arising from an Unrepresentative Selection of Respondents

17. 13 Points Continued • Sampling Errors and Biases • Processing Errors • Errors in Interpretation

18. Issue No. 7The Race for the No.1 Spot • Started with The Economist’s ranking • There is an element of positioning in some of the visions presented by statistical organizations

19. But: • There is no justification for competition • There is no framework, jury or reward • Statistical organizations have the same problems and tasks and need to collaborate • Statistical organizations should capitalize on their strengths and develop excellence centre networks and share knowledge

20. Global Coordination • Kotz (2005): The statistical community is witnessing an astonishing lack of coordination between many hundreds of statistical offices and agencies scattered throughout the world…. • ..without an overall planning, some of the efforts of civil servants and researchers are largely wasted…. • …well-planned international measures are urgent…. • …new basic global definitions of basic concepts need to be developed…

21. Issue No. 6Quality Management • TQM, Business reengineering, Balanced scorecard, business excellence models, Six Sigma • Tools and core values • Aversion to QM acronyms • The management principles cannot be used uniformly across countries and companies • Operations vs research culture • Culture eats strategy for breakfast • We are left with a set of very useful tools and work principles

22. Examples • The process view • Key process variables, paradata, control charts • Spirit of continuous improvement • Extensive user involvement • Adoption of the PDCA cycle • The importance of leadership • Organizing work, inspiration, focussing on important issues, going for root cause, benchmarking, developing staff competence, evaluating approaches used, promoting good examples, empowerment, communication

23. From Good to GreatJim Collins • What’s so special with businesses that have • been very successful for at least 15 years? • Level 5 leadership • First who, then what • Confront the brutal facts, yet never lose faith • The hedgehog concept • Culture of discipline

24. Issue No. 5Competence • Staff competence • Excellent programs within the U.S. Federal System (JPSM, USDA) • Stats Canada, INSEE, ONS, ABS • Excellent university programs • User competence

25. Competence Issues • Existing programs heavy on methodology • Sampling and estimation • Software • Specialization • Not much on broader aspects of quality • Many NSIs talk about the need to skill up • Any examples of vigorous attempts vis-a-vis the user?

26. Issue No. 4 Comparative Studies Comparative studies are increasingly • important: • Short term economic indicators • Literacy surveys • Social surveys (EU-SILC, ESS) • Education surveys

27. Examples of challenges • Existing systems for input and output harmonization are not sufficient • Developing a questionnaire that works in all countries and languages • Concepts, questions, translation, interpretation • Extensive quality control and supervision • Varying methodological and financial resources • Increased distance between user and producer

28. Issue No. 3The Process View • Traditional large-scale evaluations are expensive and results come too late • Small-scale evaluations must be conducted to get estimates of error components (gold standard, latent class analysis, responsive designs, multi-level modelling) • Long-term improvements are achieved via improved processes controlled by paradata

29. Process characteristics Upper control limit (UCL) The central limit (CL) Lower control limit (LCL) Time Generic Control Chart

30. Understanding Variation (I) Common cause variation • Common causes are the process inputs and conditions that contribute to the regular, everyday variation in a process • Every process has common cause variation • Example: Percentage of correctly scanned data, affected by people’s handwriting, operation of the scanner…

31. Understanding Variation (II) Special cause variation • Special causes are factors that are not always present in a process but appear because of particular circumstances • The effect can be large • Special cause variation is not present all the time • Example: Using paper with a colour unsuitable for scanning

32. Action • Eliminate special cause variation • Decrease common cause variation if necessary • Do not treat common cause as special cause

33. Standards • Purposes • To control processes, variability and costs • To improve comparability • To define a minimum level of performance • Examples • Classification • CBMs and checklists • Standard Operating Procedures • ISO

34. Problems with Standards • They must be adhered to • They must be maintained and updated • In stovepipe systems it’s easy to find excuses to deviate • Standard, policy, guideline, best practice, recommended practice……?

35. In place: The principle of openness (OMB 1978) Responsibility to inform users (many agencies in the 70’s) Dissemination procedures Customer satisfaction and image surveys Councils and service level agreements Problems: How should quality information be communicated? How do we distinguish between different kinds of users? How do users and producers use quality information and metadata? How do producers and users collaborate on fitness for use? (ABS) Issue No. 2The User

36. Issue No. 1Image Is Everything

37. Eliminate special cause variation • Decrease common cause variation if necessary

38. European Conference on Quality in Survey Statistics