Review for robust ANZSRC research classification

A review of the Australian and New Zealand Standard Research Classification (ANZSRC)—which is used in the measurement and analysis of research and development (R&D) undertaken in Australia and New Zealand—will ensure our research classifications reflect current practice and remain responsive to change in the sector. 

Chief Executive Officer of the Australian Research Council (ARC), Professor Sue Thomas, today announced the signing of a Memorandum of Understanding between the ARC and the Australian Bureau of Statistics (ABS) for the commencement of the review.

The ANZSRC review will be jointly carried out by the ARC, ABS, Statistics New Zealand, and the New Zealand Ministry of Business, Innovation and Employment. Planning will start in 2018, with a full review to occur during 2019, and resulting revisions to ANZSRC expected to be released by mid-2020.

In place since 2008, ANZSRC refers to a set of three related classifications for the measurement and analysis of R&D undertaken across research institutions in Australia and New Zealand. The three classifications included are:

  • Type of Activity (TOA)
  • Fields of Research (FOR)
  • Socio-economic Objective (SEO).

In order to achieve a balance between the two competing objectives of the ANZSRC—to reflect current practice and be sufficiently robust to allow for long-term data analysis—the ABS and Statistics New Zealand undertake a revision every ten years.

Professor Thomas said that there is a need for the classification to remain contemporary—to capture changes happening in the R&D sector, and to provide data relevant to users' needs.

“It is vital that ANZSRC reflects current research practices. The ANZSRC FORs are particularly important for the higher education research sector and the ARC. They are used to identify the fields of research for grant applications, and also in our Excellence in Research for Australia (ERA) and Engagement and Impact (EI) assessments,” said Professor Thomas.

“Another important consideration when developing a statistical classification is the need to build in sufficient robustness to allow for long-term usage. This facilitates meaningful ‘time series’ analysis of data assigned to that classification.

“Hence, the necessary undertaking of this review.

“We look forward to working with our stakeholders throughout the review consultation process.”

The ARC and ABS will provide further information regarding the review process, including avenues for providing feedback, in the coming months.


Media contact:

ARC Stakeholder Relations 
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