Having spent much of the past two years talking about how to approach quality assuring administrative data, my thinking about the Quality pillar of the refreshed Code was firmly grounded on our Quality Assurance of Administrative Data (QAAD) framework (check it out here for pointers and case examples).

But the Quality pillar is more than that, and respondents to our consultation rightly pointed out that our draft Code had not gone far enough to address the practices to other data types. So we have revisited these principles, to make them more widely applicable and also to be simpler.

The Code and Quality following the Consultation

The structure of the Quality pillar is essentially the same as in the draft Code, based around a basic statistics process model: Suitable Data Sources; Sound Methods, and Assured Data Quality. But we are thinking carefully about how the principle of coherence fits into that model. We absolutely agree with the importance of the practices covering coherence, consistency and comparability – we tend to think, though, that they will be clearer when integrated into the relevant principles.

So, for example, the practice about internally coherent and consistent data fits with the principle on Suitable Data Sources.  And a practice around using harmonised standards, classifications and definitions fits with the Sound Methods principle.

In fact, we are considering different aspects of coherence across the three pillars:

  • We are adding an emphasis on promoting coherence and harmonisation into the Heads’ of Profession for Statistics role in Trustworthiness
  • In Value, the Insightful principle promotes explaining consistency and comparability with other related statistics
  • We are emphasising the use of consistent and harmonised standards when collecting data in the Efficient Data Collection and Use principle in Value, as these support data integration and the more efficient use of data

The European Statistical System Five Dimensions of Quality

Another area that received a lot of comment in the consultation regarded our definition of quality when compared with the Quality Assurance Framework of the European Statistical System (QAF).

QAF presents five quality dimensions: relevance, accuracy and reliability, timeliness and punctuality, coherence and comparability, and, accessibility and clarity.

We completely agree with the importance of these dimensions but our structure of Trustworthiness, Quality and Value frames them in a way that helps relate the practice to the outcome we are seeking:

  • We see ‘relevance’ and ‘accessibility and clarity’ as central to our Value pillar. They are critical aspects of providing information to support decision making
  • We see ‘timeliness and punctuality’ and ‘coherence and comparability’ as cross-cutting each of the pillars – they speak to organisational processes and policies, meeting the needs of users for timely and comparable information, as well as relating directly to the nature of the quality of the statistics
  • We see accuracy and reliability as central to our Quality pillar; they inform each of the principles. We have revised the principle ‘Assured Data Quality’ to reflect the need for quality indicators to cover the areas of timeliness and coherence, as well as accuracy.
  • Producers should also monitor user satisfaction with each of the five quality dimensions under Value principle, Reflecting the Range of Users and Uses.

By regularly monitoring the quality indicators for the five quality dimensions and reporting them transparently, statistics producers can reassure users of the suitability of the statistics to meet their intended uses.

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