QUALIFYING QUALITY PROGRAM
The Qualifying Quality program began as a research and development project within the Statistical Consultancy and Training Section within Methodology Division (MD), with the broad task of enhancing strategies for presenting information about the quality of data to users. This work has since culminated in the May 2002 Management Meeting Paper 'Making Data Quality Visible - A Focus for 2002-03', which provides much of the underlying framework for this program plan, and the discussion paper 'Qualifying Quality - A Framework for Supporting Quality-Informed Decisions', which overviews much of the conceptual framework for the program.
The program has since progressed to a broader ABS program under the direction of a Qualifying Quality Program Board, with representation from Economic Statistics Group (ESG), Population Statistics Group (PSG) and Information Management Division. The program will draw on the activities of multiple teams within the Methodology Division and the broader ABS, which may be specific to or overlap with the Qualifying Quality Program. This work will be coordinated by a small team, whose role it will be to monitor the status of the various activities, ensuring that the tasks both meet the objectives of the Qualifying Quality Program and link into each other as required.
The aim of the Qualifying Quality Program is to progress the outputs incrementally, drawing on existing infrastructure and opportunities arising from related projects. As such, a key role of the Program Board will be to consider the range of potential projects within the Qualifying Quality Program and provide guidance on the priorities, feasibilities and sequencing of the work program in the context of both the desired outcomes from the Qualifying Quality Program and the overall demands and priorities of the broader ABS work program.
The program proposed four objectives:
- increase the information available externally about the quality of (ABS) data;
- educate our users (and ourselves) about quality, and how knowledge of it should inform the uses of statistics;
- publish and promote guidelines and frameworks about quality, and to
One key output from the program to date has been the development of a training course 'Making Quality-Informed Decisions' with the assistance of staff from the Learning and Development Section. The course introduces the concept of a data quality framework, using the framework originally developed by Statistics Canada, and provide participants with a set of techniques they can use to apply the framework in their work. In particular, the course considers the use of the framework in the context of:
- use information about data quality to manage and improve our statistical processes.
- refining the understanding of the data needs of end users;
- describing the quality of existing data;
- assessing the degree to which potential data sources meet these needs (both individually and in combination); and
By the end of 2002, the course will have been run six times with over one hundred ABS staff having received the training, including courses being delivered in the Melbourne and Adelaide offices. More courses are also planned for 2003. In addition, a Statistical Impact Seminar has been presented overviewing the principles covered in the courses and their applications within the ABS. The intention is to repeat this seminar in both the ESG and PSG seminar series.
Other initiatives within the Qualifying Quality program include: the extension of the framework provided in the course to the development of information development plans and the coordination of overall data strategies; and the use of quality measures in both quality assuring survey data and developing quality improvement strategies for surveys. Details of progress on these and other initiatives will be included in future issues of Methodological News.
For more information, please contact Bill Allen on (02) 6252 6302.
Email : firstname.lastname@example.org
- managing the risks identified when considering the selected data sources through risk mitigation and contingency planning.