This is a one-day course run by the School of Population and Global Health at The University of Melbourne that provides an introduction to administrative data and data linkage. The course is designed for policy makers, researchers, managers, analysts, and others working in the public sector with an interest in generating evidence. No knowledge of data linkage is required; however, basic familiarity with research methods is an asset. On completion of this course, participants will have a basic understanding of the practical considerations involved in using administrative data, and an awareness of the lifecycle of linked data research.

Specifically, this course will discuss the application of linked administrative data to real world problems, and outline how data linkage can be used to provide valuable, policy-relevant evidence.

  • Date/Time: This course will next be held in 2022 on a date to be set. The course duration is from 9.30am to 4.30pm AEST/AEDT (Melbourne, Sydney, Canberra). For other time zones, use World Clock Meeting Planner available here.
  • Location: Online, and potentially in person. (This course was moved to online in 2021 due to COVID-related restrictions. Future courses can be held online, or in person subject to  COVID-related restrictions.)
  • To book: Click the Apply now button on this page to email the course administrator to be placed on a wait list for the next course. Once the 2022 date has been set, a Book now button will be accessible on this page for you to register and make payment.

EOI for course participation:  If you wish to express interest to undertake this course, please contact the course administrator at linkage-shortcourse@unimelb.edu.au and include your preference for online and/or in person delivery.  You will be then be placed on a wait list for the next course date.

For groups of 10 people or more, this course can be tailored to your organisation's needs and delivered online, or on site at your workplace depending on your location and subject to COVID-related restrictions.

Course outline

Module 1: Administrative Data and Data Linkage In this module you will gain an understanding of the diverse types of administrative data that can potentially be used for research. You will learn what data linkage is, and gain a basic understanding of how data can be linked for research purposes. We will explore some of the strengths and limitations of using linked data for research, including some of the key practical and methodological considerations associated with data linkage. The session will conclude with some examples of research using linked data, and discussion of how these studies have informed policy or practice.

Module 2: Acquiring Data In this module we will introduce the key concepts involved in scoping the data required to answer a question of interest. The module will introduce design concepts such as bias, censoring, sampling and missingness, and you will learn how to critically appraise administrative data resources. The module will cover practical considerations such as ethics and privacy principles, and will provide an overview of a typical data acquisition process.

Module 3: Data Management In this module we will provide an overview of the data management process as it relates to linked data. This will include the key steps prior to, and immediately after, gaining access to linked data. We will discuss the basic manipulation of data, including identifying the layout of your data, methods of transferring between long and wide formats, and considerations when combining datasets. We will detail the basics of data security followed by advice on setting up data analysis sessions to maximise the transferability and repeatability of research studies. Finally, we will discuss some common pitfalls of linked data, focusing on free-text, coded and date-time variables.

Module 4: Generating and Communicating Results This module will provide an overview of the unique considerations involved in generating and communicating results from linked administrative data. The module will introduce common approaches and techniques for the analysis of linked data, and the reasoning behind them. We will provide an overview of data analysis techniques commonly applied to big data, and common strengths and limitations associated with analysis of linked data. The module will introduce practical ways of communicating results that maximise impact beyond the research sector, and equip you with the basic knowledge to engage with and contribute to data linkage research.

Course leaders

Prof Stuart Kinner

Dr Jesse Young

How much is it?

Course Fees:

Early Bird – $500 (GST exclusive)
Normal Rates (non-early bird) –  $575 (GST exclusive)
Student Rate: $250 (GST exclusive) for a limited number of places for registrants who are current students

Includes all course materials, lunch, morning and afternoon tea for in-person attendees. For 'virtual' attendees, access will be given to course e-materials and a Zoom Meeting link will be emailed to you prior to the course date.

IMPORTANT: This course may be cancelled one month prior to the course date if it has not reached the minimum number of participants. Registration fees will be reimbursed.

Application procedure

  • Payment using credit card is via the University’s eCart. A link to eCart will be provided on this page once the date/s for the 2022 course/s have been set.
    If your organisation needs an invoice for payment, please request via email to 


  • Registration is via eCart when payment is made.
  • Further enquiries, or to express interest in this course: linkage-shortcourse@unimelb.edu.au

Justice Health Unit
Melbourne School of Population and Global Health
The University of Melbourne
VIC  3010  Australia

Course Information

Key dates

2022 - Date/s TBA


Early bird $500 ex-GST

Normal rate (non-early bird) $575 ex-GST

Student rate $250 ex-GST. Limited number of registrations available at this price for current students.


via Zoom Meeting if online

Graduate House, Carlton VIC 3053, if in person

Entry requirements

No knowledge of data linkage is required; however, basic familiarity with research methods is an asset.