Australia's national VET data custodian

Publications

1 – 5 of 5 results
Data linkage and statistical matching: options for the Longitudinal Surveys of Australian Youth

By Nhi Nguyen, Sinan Gemici

Combining the Longitudinal Surveys of Australian Youth (LSAY) with other data sources would enhance the breadth of information without adding respondent burden. This discussion paper explores two issues: the potential for linking data from existing administrative collections, such as Medicare, to LSAY; and the feasibility of combining data from the Longitudinal Study of Australian Children and LSAY via statistical matching.

The impact of schools on young people's transition to university

By Sinan Gemici, Patrick Lim, Tom Karmel

This report uses Longitudinal Surveys of Australian Youth (LSAY) data to look at the impact of schools on a student’s tertiary entrance rank (TER) and the probability of them going to university (controlling for TER).

Getting tough on missing data: a boot camp for social science researchers

By Alice Bednarz, Patrick Lim, Sinan Gemici

Research in the social sciences is routinely affected by missing data. Not addressing missing data appropriately may yield research findings that are either 'slightly off' or 'plain wrong'. This study demonstrates why and how frequently used simple remedies for missing data can impact on research results. The authors provide the target audience (i.e. producers and consumers of social science research) with a step-by-step guide on how to implement multiple imputation, which is the standard method for dealing with missing data. They encourage researchers to carefully consider the potential impact of incomplete information and to use modern missing data methods whenever possible in their own work.

Measuring the socioeconomic status of Australian youth

By Patrick Lim, Sinan Gemici

When measuring the socioeconomic status of young people, the authors find that the SocioEconomic Indexes for Areas (SEIFA) works well when reporting participation in higher education at aggregate levels, but performs very poorly when classifying individuals. This has policy implications because it means that programs directing resources to increase educational participation for those with low socioeconomic status may be doing so based on incorrect information. The data used come from the 2003 cohort of the Longitudinal Surveys of Australian Youth (LSAY), looking at young people aged 15 to 25 years.