317
Views
1
CrossRef citations to date
0
Altmetric
Research Article

VET market prognostications two decades later: using ‘big data’ to compare omens with outcomes

ORCID Icon
Pages 494-512 | Received 25 Oct 2019, Accepted 18 Jun 2020, Published online: 25 Jun 2020
 

ABSTRACT

Publishing the results of thoughtfully designed research projects allows contemporary investigators to revisit the findings years later. The willingness of academics to make specific predictions about the impact of introducing user-choice principles into a state vocational education and training market has enabled comparisons to be drawn using extensive statistical collections. These data sets do not cover each of the forecasts; for example, employer reactions to increased marketisation can only be inferred. However, extensive information has been recorded on the training provider and student responses. The jurisdiction under study has a long history of centralised government decision-making and bureaucratic regulatory control; which allows for a substantial level of confidence in the causal relationship between public policy implementation and outcomes observed in the vocational education and training market. By making data-driven comparisons between what was hypothesised and the results over a long period of time, it is re-affirmed that good research is relevant and can assist governments in achieving their intended public policy priorities.

Disclosure statement

No potential conflict of interest was reported by the author.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 337.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.