PEER-REVIEWED FINAL EDITED VERSION OF ARTICLE PRIOR TO PUBLICATION



covered. Therefore the different locations are unlikely to have affected the data
findings. However, as care leavers and other young people in difficulty were
voluntary participants, it is acknowledged by the research team that the findings
may capture the views of more research-willing participants.

Demographic characteristics of the samples

Young women were over-represented among care leavers in this study which
possibly reflects their greater willingness to participate in research (Wigfall and
Cameron 2006). Over one-half of care leavers and young people in difficulty in
the sample were female (Table 1: 69% and 58% respectively). This is slightly
higher than for all care leavers (46%; DfES 2005). Over a quarter of care leavers
and young people in difficulty were from minority ethnic backgrounds (Table 1) -
higher than that found by Broad (13%; 2005) but similar to Wade
et al. (25%;

2006). A quarter of care leavers and young people in difficulty had self-reported
physical or learning difficulties (Table 1) - slightly higher than other research
studies (Broad 2005; Wade
et al. 2006). The study aimed to recruit care leavers
aged 17 - 24, and all those interviewed were within this age band. The average
age for the care leavers was 18 years (Table 1). This is also the most common
age for leaving care (DfES, 2005). The average age for the other young people in
difficulty in this study was 20 (Table 1). As time had elapsed between the point of
leaving care and being interviewed, some participants were older than 24 at the
point of interview (the oldest participant was 29 years). Young people who were
older or younger than 17-24 at the point of interview were excluded from the



More intriguing information

1. A Theoretical Growth Model for Ireland
2. The name is absent
3. Correlates of Alcoholic Blackout Experience
4. Effort and Performance in Public-Policy Contests
5. An Intertemporal Benchmark Model for Turkey’s Current Account
6. The name is absent
7. Testing Panel Data Regression Models with Spatial Error Correlation
8. Three Strikes and You.re Out: Reply to Cooper and Willis
9. Family, social security and social insurance: General remarks and the present discussion in Germany as a case study
10. The name is absent
11. Naïve Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages
12. Are combination forecasts of S&P 500 volatility statistically superior?
13. Natural Resources: Curse or Blessing?
14. The name is absent
15. The name is absent
16. Evolving robust and specialized car racing skills
17. The Prohibition of the Proposed Springer-ProSiebenSat.1-Merger: How much Economics in German Merger Control?
18. The name is absent
19. CONSIDERATIONS CONCERNING THE ROLE OF ACCOUNTING AS INFORMATIONAL SYSTEM AND ASSISTANCE OF DECISION
20. Climate change, mitigation and adaptation: the case of the Murray–Darling Basin in Australia