ANALYTICAL SKILLS FOR ABS STAFF
In January 2001, the Statistical Skills for ABS Staff booklet was launched. It encapsulates thinking about the skills needed by those who work on the national statistical system, clustered under such themes as statistical leadership, research and development, analysis, data collection, data processing and dissemination. It has been very influential in shaping ABS training programs and in providing guidance to our staff about their professional development.
Among other things, Statistical Skills includes a high-level view of the analytical competencies needed by our staff. During the past year, there has been considerable thinking about the kinds of analysis that are undertaken at different stages of the statistical cycle, the analytical skills needed by staff in our various organisational units, and ways of enhancing our analytical capacity. Much of this thinking has been inspired by the Business Statistics Innovation Program, which is overhauling the ABS approach to economic statistics, but similar issues arise in social, environmental and other statistics.
Work is afoot to spell out in greater detail the analytical competencies needed for ABS work. This embraces:
A rough draft has been prepared and during the next couple of months progressively refined drafts will be vetted by interested parties. The material will be integrated with the Statistical Skills booklet (probably in e-document form) during the first half of 2003.
For more information, please contact Ken Tallis on (02) 6252 7290.
- Knowledge. The economic, social and environmental conceptual frameworks that inform and underlie analysis; key views of data; themes and variations in the analyses done by policy agencies, researchers and other users.
- Understanding. Strategies for undertaking an analytical project; interactions between the question at hand, the dataset and the analytical technique; pitfalls in analysis; quality of analysis - defining quality, assuring quality and making quality visible.
- Skills. Assembling a suitable dataset; choosing an analytical technique that is appropriate to the question and the datasets at hand; communicating the story told by the analysis to diverse groups of users.