FINDING THROUGH-TIME PATTERNS - AN EMERGING METHODOLOGICAL PROBLEM
In several of our projects, we are confronting an analytical problem that is unfamiliar to us - that of characterising through-time patterns in some key datasets. The problem is best explained using our lifelong learning project as an example.
We are interested in understanding the learning pathways that Australians follow throughout their lifetimes. Starting with data collected in the 2001 Survey of Education and Training (SETIT), we have compiled "education event profiles" for about 24,000 people. Each profile records the dates at which key education events occurred, such as completing secondary school, trade certificate, or university degree. Recently, we have done some analysis of the probabilities of certain education transitions, such as the probability that someone who completed year 12 will obtain a first post-school qualification within one year, two years, three years and so on.
The next phase of our lifelong learning project attempts to address three questions:
We would like to develop a taxonomy or classification of pathways - to group our 24,000 records into broad families that display similar patterns of educational experience. Having such a taxonomy would be helpful in several ways:
- What are the typical "education pathways" that Australians follow? A pathway might encompass, say, the education events experienced, their sequence, their duration, and the intervals between completion of one experience and commencement of another. Each of these aspects of "pathways" can provide us with useful insights into the learning experiences of Australians.
- How are education pathways evolving? Are some forms of education becoming more or less common, or are the sequences, durations or intervals of education events changing over time?
- How do education pathways differ between one subpopulation and another? Are there differences according to sex, age, Indigenous status, ethnic background, and so on? Are changes in pathways more pronounced or more rapid for some subpopulations?
This style of analytical problem is also emerging in other parts of our work program - such as describing the evolution of prices and quantities in supermarket scanner datasets and describing the life-histories of businesses in longitudinal databases.
For more information, please contact Shiji Zhao on (02) 6252 6053.
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- It would allow us to write soundly-based narratives about the dominant forms of lifelong learning in Australia.
- It would provide a basis for analysing the evolution of education pathways, using either our first database (constructed from SETIT2001) or future, richer databases (constructed from past and future SETITs and auxiliary datasets).