Australia's national VET data custodian

Publications

1 – 20 of 34 results
Evaluating machine learning for projecting completion rates for VET programs

By Michelle Hall, Melinda Lees, Cameron Serich, Richard Hunt

This technical paper summarises exploratory analysis undertaken to evaluate the effectiveness of using machine learning approaches to calculate projected completion rates for VET programs and compares with the current Markov chains methodology used by NCVER.

Internet job postings: preliminary skills analysis

By Patrick Korbel

This technical analysis looks at data sourced from online job postings to see whether it can provide new and valuable insights on the skills employers ask for when advertising jobs.

Total VET program completion rates

By Brad McDonald

This paper outlines how the methodology used to determine government-funded VET program completion rates can now also be used for total VET activity program completion rates.

Student Outcomes Survey: self-reported graduate model review

By Ben Sanders

This technical paper reports on the 2018 self-reported graduate model review. This statistical model is used to predict whether Student Outcomes Survey participants who self-report as graduates are ‘actual’ graduates according to official definitions.

Linking NAPLAN scores to LSAY

By Davinia Blomberg, Marilyn Lumsden, Patrick Lim, Ronnie Semo

This report looks at whether it is possible to link data from the Longitudinal Surveys of Australian Youth (LSAY) with external data sources to improve the breadth of information available from the survey.

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.

An analysis of self-reported graduates

By Ben Braysher

The Student Outcomes Survey collects information on the outcomes of two groups of students: those who completed a qualification (graduates) and those who completed only part of a course and then left the VET system (module completers). For many years several students surveyed as module completers have stated that they had gained a qualification and graduated (self-reporting graduates), and these students have been reclassified as graduates for reporting purposes. This biases the survey results as it was found that about two-thirds of them may not have completed the qualification after all. This paper outlines a predictive model which will be used for 2012 Student Outcomes Survey reporting and will ensure that this problem is resolved. As this methodology will change estimates from previous surveys substantially, they will be back cast to 2005 using the new model.

An investigation of wellbeing questions in the Longitudinal Surveys of Australian Youth

By John Stanwick, Shu-Hui Liu

This report investigates the Longitudinal Surveys of Australian Youth (LSAY) for its coverage of wellbeing questions. It is an area of considerable interest to policy-makers, and having a valid set of wellbeing questions in LSAY will enhance the capacity to investigate the links between wellbeing and other domains of interest for young people. The analysis finds three robust factors, which can be termed social wellbeing, material wellbeing and career. The three factors do not cover all the dimensions of wellbeing discussed in the literature, particularly the psychological aspects. Suggestions are made for enhancing the current list of wellbeing questions in LSAY.

Measuring student satisfaction from the Student Outcomes Survey

By Peter Fieger

The Student Outcomes Survey is an annual national survey of vocational education and training (VET) students. Since 1995, participants have been asked to rate their satisfaction with different aspects of their training, grouped under three main themes: teaching, assessment, and general skills and learning experiences. In this paper we review and compare three different methods of creating summary measures — Rasch analysis, weighted means and simple means — that encapsulate the three main themes of student satisfaction. We find that all three methods yield similar results and so recommend using the simple means method to create the summary measures.