The use and convergence of quality assurance frameworks for international and supranational

organisations compiling statistics

The European Conference on Quality in Official Statistics July 8-11 2008, Rome, Italy

Antonio Baigorri and Håkan LindenStatistical Governance, Quality and Evaluation

European Commission, Eurostat

2

The context of Total Quality Management

To have an encompassing approach with respect to quality work.

To implement the principles of institutional quality frameworks and in particular the principles related to statistical processes and outputs.

To improve the measurement, monitoring and management of data quality.

To coordinate ongoing quality initiatives (process descriptions, quality reports, evaluation activities etc.).

To build on existing quality work (standards, best practices etc.).

To promote a culture of systematic quality improvement work.

3

Institutional Quality Frameworks

User needs

Statistical

products

Production

processes

Institutionalenvironment

Management systems and

leadership

Support processes

Principles: 1.1, 1.3, 1.4, 1,5, 2.1, 2.2, 2.3, 3.1, 3.2,4.1, 5.3, 5.6, 6.1, 6.2, 7.1, 8.2, 9.1, 10.3, 10.4

Principles: 4.2, 5.1, 5.3, 5.4, 5.5, 8.3, 8.4, 9.2, 9.3, 9.4, 9.5, 10.1

Principles: 1.1, 1.2, 4.3, 4.4, 4.5, 4.6, 5.2, 6.2, 7.1, 7.2, 8.1, 10.2, 10.5

TQM

CCSA Principles

Institutional frameworks, like the Principles Governing Statistical Activities, the European Statistics Code of Practice and the IMF Data Quality Assessment Framework, can be seen as general superstructures forming the necessary basis for all other measures an International organisation needs for improving quality at statistical output and product level.

User needs

Statistical products

Productionprocesses

Institutionalenvironment

Management systems and leadership

Support processes

1 Professional independence 2 Mandate for data collection3 Adequacy of resources 4 Quality commitment 5 Statisticalconfidentiality 6 Impartiality and objectivity

7 Sound methodology 8 Appropriate statistical procedures9 Non-excessive burden on respondents 10 Cost effectiveness

11 Relevance 12 Accuracy and reliability 13 Timeliness andPunctuality 14 Coherence and comparability 15 AccessibilityAnd clarity

TQM

Code of Practice

Source: Eurostat (2007)

User needs

Statistical products

Productionprocesses

Institutionalenvironment

Management systems and

leadership

Support processes

0. Prerequistes of Quality (Legal and institutionalenvironment, Resources, Relevance, Other quality management 1. Assurance of Integrity (Professionalism, Transparency and Ethical Standards

2. Methodological Soundness 3.Accuracy and Reliability

4. Serviceability5. Accessibility

TQM

DQAF

4

Quality Assurance Frameworks Quality assurance frameworks (or frameworks for statistics production) have

the objective to establish, in a specific statistical organisation, a system of coordinated methods and tools guaranteeing the adherence to minimum requirements concerning the statistical processes and products. Similarly to institutional frameworks, this includes some kind of assessment.

Product/ output quality requirements are being explicitely documented.

Processes are defined and made known to all staff.

The correct implementation of the processes is monitored on a regular basis.

Users are being informed on the quality of the products and possible deficits.

A procedure is implemented that guarantees that the necessary improvement measures are being planned, implemented and evaluated.

5

Data quality aspects

1) The perception of the statistical product by the user.

2) The characteristics of the statistical product (or key statistical outputs)

3) The characteristics of the statistical production process.

6

Co

nce

ptu

al f

ram

ew

ork

(2

)

IT conditions (11) – Management, planning and legislation (12) – Staff, work conditions and competence (13)

User needs(3)

Data collection

(4)

Validation

Country level (5)International level (6)

Confidentiality (7)

Dissemination (9)

Documentation (8)

Follow-up (10)

RELEVANCE ACCURACY ACCESSIBILITY/CLARITY

TIMELINESS/ PUNCTUALITY

COMPARABILITY COHERENCE

Relationship between process and output quality

Source: Eurostat Process Quality Assessment Checklist [Eurostat, 2007]

7

Product/ Output Quality Components

OECD: relevance, accuracy, credibility, timeliness (and punctuality), accessibility, interpretability, coherence (within dataset, across datasets, over time, across countries)

Eurostat: relevance, accuracy, timeliness and punctuality, accessibility and clarity, coherence (within dataset, across dataset), comparability (over time, across countries)

ECB: accuracy/reliability, methodological soundness, timeliness, consistency

IMF: prerequisites of quality, accuracy and reliability, assurances of integrity, methodological soundness, serviceability (timeliness and periodicity), accessibility, serviceability (within dataset, across dataset, over time, across countries)

FAO: relevance (completeness), accuracy, timeliness, punctuality, accessibility, clarity (sound metadata), coherence, comparability

UNESCO: relevance, accuracy, interpretability, coherence

UNECE: relevance, accuracy (credibility), timeliness, punctuality, accessibility, clarity, comparability (across datasets, over time, across countries)

8

Product/ Output Quality Components

Relevance

Accuracy (and reliability)

Timeliness

Punctuality

Accessibility

Clarity/ interpretability

Coherence/ consistency

Comparability

9

Quality and metadata

Collection and sharing of metadata

- SDMX technical standards

- SDMX Content oriented guidelines (incl. Cross domains concepts)

Dissemination of metadata on quality

- Special Data Dissemination Standards (SDDS)

- Euro-SDMX Metadata Structure (ESMS)

Assessment and monitoring of metadata

Integrated information on quality assessment

10

Methods and tools for the assessment of statistics production

Production processes Statistical products

User perception

Process variables

Quality indicators

Quality reports

User satisfaction survey

Self assessments Quality reviews

Labelling

Institutional/ legal environment

User requirements Standards

III. Conformity

II. Evaluation

I. Documentation Measurement

Improvementactions

N.B. Figure derived from draft Handbook on Data Quality Assessment Methods and Tools (DatQAM), version 31.01.2007.

11

How to apply process oriented quality assessment tools

The office-wide management approach

Institutional preconditions (procedures and legislations)

Assessment methods already in use

Relevance – size and periodicity

Relevance – importance and specific legal frameworks

12

Quality Assessment Packages

Fundamentalpackage

Intermediatepackage

Advancedpackage

Process descriptions, product documentation, quality guidelines

Quality reports

Self assessments

Quality reviews

User satisfaction surveys

Quality indicators

Key process variables

Labelling

N.B. Figure derived from draft Handbook on Data Quality Assessment Methods and Tools (DatQAM), version 31.01.2007.

13

The assessment methods and tools

Documentation and measurement

- process descriptions

- quality reports (“Full Quality Report”, “Summary Quality Report”, and “Basic Quality Information”).

- user satisfaction surveys

Evaluation

- self assessments of all production processes (Quality Assessment Checklist)

- quality reviews for key statistical outputs

Conformity

- a process for labelling of key international statistics

14

Principles for implementation

Minimise burden for production domains

- test the approach in advance

- provide support

- flexibility

Build on existing information

- process analysis

- metadata on quality (quality reports etc.)

Profit from synergies with other horizontal activities

- evaluation function requirements

- cost/ benefit analysis

- input for management programming

15

Data quality assessment recommendations

Top management commitment

The role of middle managers

Data quality assessment is a long term project

Most methods should be implemented and fine-tuned in pilot projects

Standardise the use of the methods

Establish clear responsibilities and authorities

Sufficient resources allocated for supporting the assessments

16

Conclusions

Each international organisation should have a quality assurance framework in place.

The framework and the applied quality principles should be made explicit.

A quality assurance framework needs to be compatible with the general quality management model and office-wide procedures and rules.

It should be built into the organisational structure.

It contributes to increased awareness of quality concepts and promotes best practices.

It provides a mechanism for reengineering and quality improvements

It should always acknowledge performance/ cost.

Convergence of quality assurance frameworks by applying common concepts, standards, methods and tools (both content oriented and technical).

Development and sharing of “best practices” for statistics production between all stakeholders is maybe the most important for continuous quality improvement of a global statistical system.