The Centre for Sample Survey Methodology (CSSM) undertakes fundamental and contract research, major consulting projects and training in statistical methodology for the design and analysis of sample surveys. Its mission is to provide international leadership in statistical theory and practice for sample surveys and censuses, through research, teaching, postgraduate supervision and partnerships with governments and industry.
Centre for Sample Survey Methodology
Director: Senior Professor David Steel
Phone: +61 2 42213823
Location: Building 39C Room 262
The CSSM is a centre of excellence in the design of data collection strategies for complex populations and their subsequent analysis. The Centre has international expertise in survey design and analysis; complex data analysis and estimation methods; small area statistics; privacy and confidentiality analysis and methodology for combining and analysing data from different sources, including administrative and linked data.
The research focus of CSSM builds on the fact that modern data requirements require moving away from the traditional concept of a sample survey as a free-standing information collection and analysis entity. Target populations are more dynamic, much less clearly defined (e.g. networks) and much harder to measure. Furthermore, conventional methods of sampling are rapidly becoming more expensive. As a consequence, modern sampling design is evolving from its traditional emphasis on how selection is implemented to how samples from quite different sources, and of varying methodologies, can be readily integrated. Sampling inference is adapting to this new data collection paradigm, with the traditional focus on sampling error accompanied by increasing emphasis on how basic ideas like uncertainty should be characterised in the resulting mix of non-response errors, linking errors, measurement errors and model specification errors.
The aim of CSSM is to ensure that sampling statisticians continue to make substantial contributions to how data are collected, analysed and interpreted in this emerging environment, thus ensuring that the value from the information in these data is maximised. In this context, CSSM has research projects in:
- statistical survey design for populations with complex structure;
- data quality and survey methods - particularly telephone, household, internet and environmental surveys;
- analysis of complex data, including data obtained via complex sample survey design; data with longitudinal, temporal and spatial characteristics and data with aggregation structure; and
- statistical modelling and analysis for continuous, categorical and network data with complex variability and dependence structure and drawn from heterogeneous and complex sources, including robust and semi-parametric methods, generalised latent variable methods and Monte Carlo methods.
Major research projects
- Statistical Inference for Probability-Linked Longitudinal Data
- The Dynamic Analyses to Optimise Ageing (DYNOPTA)
- The role of households, neighbourhoods and networks in social statistics
- Handling Missing Data in Complex Household Surveys
- New methods for small group analysis from sample surveys
- Methodology Development Partnership with Australian Bureau of Statistics
- Sampling for Sub-populations in Household Surveys with application to Maori and Pacific Sampling
- Collaboration with New Zealand Ministry of Health (with A/Prof Robert Clark)
- Health Track
- Telephone Sampling Methods - (IHMRI)
- Spatio-temporal Statistics NSF-Census Research Network (STSN) at the University of Missouri (with Distinguished Professor Noel Cressie)