Two features of the labour market for vocationally qualified workers are explored in this technical paper: the likelihood of self-employment versus wage employment and the determinants of income.
This technical paper outlines the methodology used to adjust the original Program of International Student Assessment (PISA) weights to ensure that each Longitudinal Surveys of Australian Youth (LSAY) wave represents the original population. The author also provides guidance to researchers in applying the weights to their analysis of LSAY data.
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.
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.