Under the Australian Bureau of Statistics Act 1975, the Australian Statistician is responsible for formulating standards for the undertaking of operations for statistical purposes. The purpose of this paper is to provide information on the Australian Bureau of Statistics Data Quality Framework (ABS DQF). This framework provides the standards for assessing and reporting on the quality of statistical information. 

Census data form the basis of many of the ABS' most widely used products and services. One is the official population estimates which are updated each quarter. Census data are also used in compiling the monthly employment and unemployment statistics and the national accounts. The Census counts of the number of people in each geographic area are also used in the framework for selecting the samples used in ABS household surveys.

The aime of this study was to estimate the feasibility, utility and resource implications of electronically captured routine data for health technology assessment by randomised controlled trials (RCTs), and to recommend how routinely collected data could be made more effective for this purpose.

Sometimes, researchers wish to conduct a research project that uses an existing set of data which was previously collected for some other purpose. The data may come from a previous research project, or may be routine data from an educational institution, healthcare provider, government department or authority, or private sector organisation. This existing dataset may have been collected by someone else or by the researcher themselves in another role.

Australia has one of the world’s most comprehensive collections of population-based health data — but its potential is yet to be realised. SURE, the Secure Unified Research Environment, is a high-powered computing environment developed to help make best use of our national knowledge base. It is helping to bring researchers together from across Australia and the world to collaborate on large-scale projects tackling major health and social issues such as population ageing, diabetes and mental health.

This chapter describes data that may be useful for observational CER studies and the sources of these data, including data collected for both research and nonresearch purposes. The chapter also explains how the research question should dictate the type of data required and how to best match data to the issue at hand. Considerations for evaluating data quality (e.g., demonstrating data integrity) and privacy protection provisions are discussed. The chapter concludes by describing new sources of data that may expand the options available to CER researchers to address questions. Recommendations for “best practices” regarding data selection are included, along with a checklist that researchers may use when developing and writing a CER protocol. To start, however, it is important to consider primary data collection for observational research, since the use of secondary data may be impossible or unwise in some situations.

Data linkage is a technique for connecting pieces of information that are thought to relate to the same person, family, place or event.

This resource is a handy check list to perform before analysing your linked data.

One way to earn extra money and help other people is to volunteer for a medical research study. Before a new drug or treatment comes out it must first go through a process of “clinical testing.” These clinical trials make sure that drugs and/or treatments are both safe and effective. Every drug must be tested before it is released to the general public—making clinical trials necessary and important for everyone’s health and well-being.

Some people make thousands of dollars each year by gambling inside an MRI machine, getting tested for allergies, or trying out new vaccines. In many cases, they do this kind of “work” on weekends or around their work schedules — and it doesn’t require any special skills or education.