Do individual background characteristics influence tertiary completion rates?
Our congratulations to Patrick Lim, NCVER, who is one of 12 successful NCSEHE 2014 research grants applicants.
Patrick’s project, titled “Do individual background characteristics influence tertiary completion rates?” will compare the university completion rates of young people with different background characteristics. Of particular interest are young people’s different socioeconomic background, Indigenous status, language background and regionality. Further factors that will be included are completion rates by school type (government, catholic and independent) and the contribution that schools have to university completion.
Over the past seven years, I have been undertaking research using the Longitudinal Surveys of Australian Youth (LSAY). The focus of a majority of these projects has been investigating pathways into education and employment. Further, there have been several policies introduced to increase university enrolment (for example, recommendations from the Bradley review), particularly for those individuals who come from low SES backgrounds. Other initiatives, such as demand driven funding and uncapped university places has also seen an increase in the total number of young people pursuing a university education. Post-school education participation in education is important for young people, however, it is equally important that they then go on to complete their course(s). There is no doubt that the idea of education is to increase the productivity of Australia as a whole, and it is not enough for people just to enrol.
The question that arises is whether increasing access to university is resulting in equality in course completions. That is, do young people complete their courses at differing rates, and are these rates influenced by their background characteristics, in particular, their socio-economic status? Further, are people with different SES backgrounds changing courses more often than others? It is these types of questions that inspired me to apply for this research grant. The Longitudinal Surveys of Australian Youth data is a valuable and rich data source that enable these issues to be thoroughly investigated. This data allows for a reliable measure of SES to be calculated, that is, one that doesn’t rely on where a person lives, but rather on their own personal circumstances when they were growing up (such as their parent’s occupation and education and resources available in the home). The LSAY data-set also allows us to control for individual academic achievement, which allows us to delve deeper into completion rates and compare like with like. There is evidence that shows that higher achieving students are more likely to complete university, and so the LSAY data gives us the opportunity to remove this to compare individuals of similar academic ability. The longitudinal nature of LSAY further means that young people are tracked over time, and so we have a good record of when they changed courses, the reasons why and ultimately what they are doing at age 25.
The research grant provided by the National Centre for Student Equity in Higher Education and the power of the Longitudinal Surveys of Australian Youth gives me a real opportunity to explore the issue of equity in university and course completions. If the study finds that there are differences in completion rates for different SES groups, then this study may provide some good background to help universities develop strategies to assist their student cohorts (regardless of their background) in completing their university studies.
I am excited to be working with the NCSEHE on this important topic.
Patrick’s project will conclude in November 2014, after which time his full report will be made available here on the NCSEHE website.
ABOUT PATRICK LIM
Patrick is a senior research fellow within the research and consulting branch of NCVER. He has 15 years experience in quantitative research and mainly specialises in research that requires advanced statistical techniques such as logistic regression, multiple linear regression, mixed and multi-level models. He is experienced in analysing data sets that have particular relevance to youth transitions and attainment, and in demand by fellow researchers seeking his statistical and modelling expertise to guide and assist their research.