The Measurement of School-to-work Transitions as Processes

ABSTRACT

Measuring individual transitions means capturing a process with a specific time dimension. The established analysis of school-to-work transitions focuses on single status changes, such as those between education and unemployment. As longitudinal datasets became increasingly available, the periodical character of transitions has deserved attention, mostly in terms of studies that used event history models. But even these kinds of studies continued to focus on single status changes, which are not determined by theory but by the respective research question, or by data availability. Hence, the analysis of micro-level transitions remains selective, because there is no common idea how to explore them. As a result, school-to-work transition research is in danger to overlook important aspects of this life-course trajectory. This paper argues that the reason is the missing theoretical definition of a transition. Given the growing complexity of school-to-work transitions, the status change concept becomes inappropriate for their analysis. The predominance of hypothesis-testing methods together with an underrepresentation of explorative methods lead to a disregard of the process character of school-to-work transitions. This considerably limits the gain of new scientific insights. Recent methodological developments regarding the explorative analysis of longitudinal processes, namely sequence analysis, offer the possibility to cope with the complexity of school-to-work transitions. The paper aims at comparing the advantages and drawbacks of two methods in analysing transitions, and advocates a combined research design between explorative and hypothesis-testing methods.

Key words:

• school-to-work transition
• sequence analysis
• event history analysis
• life-course research
• trajectory

Notes

¹ A platform-independent implementation of various tasks for sequence analysis including optimal matching is the Stata ado-package SQ (Brzinsky-Fay et al. 2006). Additionally, Stata plugins for optimal matching and other algorithms for sequence analysis are available from the website http://teaching.sociology.ul.ie/seqanal/ maintained by Brendan Halpin.

² For R, the software package ‘TraMineR’ is available (http://mephisto.unige.ch/traminer/), which offers a couple of functions that allow for comprehensive applications of sequence analysis.

³ The result of the sorted and grouped sequences from the sample used in can be found in Brzinsky-Fay (2007).

Additional information

Notes on contributors

Christian Brzinsky-Fay

Christian Brzinsky-Fay is senior researcher at the WZB Berlin Social Research Center. His main scientific interests are life-course research, education and labour market research and empirical methods.