Page 190 - Štremfel, Urška, and Maša Vidmar (eds.). 2018. Early School Leaving: Contemporary European Perspectives. Ljubljana: Pedagoški inštitut.
P. 190
ear ly school leaving: contempor ary european perspectives
then interviewed every 2 years until the age of 21. The studies based on
YITS provide an overview of the nature of the associations between stu-
dents’ characteristics at age 15 and their educational outcomes at ages 17, 19
and 21, including for those who left school early. In subsequent cycles, some
youth dropped out of school. Bushnik, Barr-Telford and Bussiere (2004)
examined characteristics measured at age 15 associated with dropping out
of school by the age of 17. Similar investigations of data from the third cy-
cle in which the YITS youth were aged 19 were conducted by Knighton
and Bussiere (2006) and data analyses from the fourth YITS cycle were
published in the OECD (2010). In Australia, the PISA student cohorts were
incorporated in longitudinally designed surveys named the Longitudinal
Survey of Australian Youth (LSAY). In the context of ESL, Marks (2007)
studied the PISA 2003/LSAY cohort and Mahuteau and Mavromaras (2014)
the PISA 2006/LSAY cohort. Further, in Switzerland a panel study of the
PISA 2000 cohort of students was followed up by the Transitions from
Education to Employment survey (TREE) through seven annual survey
panels between 2001 and 2007, and an eighth one in 2010. ESL-related find-
ings based on TREE data were derived by Bertschy, Cattaneo and Wolter
(2008) along with Mueller and Wolter (2011).
The key finding in all these studies is that the strongest predictor of ESL
is the PISA achievement score, which is even stronger than the socio-eco-
nomic background of students. For Australia, using multilevel models
Marks (2007) showed that the greatest influence on ESL was student per-
formance in the PISA test and that this effect was almost four times strong-
er than that of socio-economic background. Mahuteau and Mavromaras
(2014) confirmed that, in addition to student (disadvantaged) background,
scores in the PISA test also predict early dropout well, but they pointed
out that low PISA achievers are likely to accumulate other types of dis-
advantage that together then emphasise the probability of dropping out.
Overall, the main message of these findings is that a significant share of
the effect of student achievement on leaving school can be separated from
the socio-economic and other background student characteristics, indicat-
ing that strong competencies seem able to help in overcoming the effects of
disadvantages.
Such findings have also emerged in other countries. Bertschy,
Cattaneo and Wolter (2008), although their study was limited to Swiss stu-
dents in vocational programmes, found that a higher score in PISA, after
controlling for SES, language spoken at home, migration status and region
190
then interviewed every 2 years until the age of 21. The studies based on
YITS provide an overview of the nature of the associations between stu-
dents’ characteristics at age 15 and their educational outcomes at ages 17, 19
and 21, including for those who left school early. In subsequent cycles, some
youth dropped out of school. Bushnik, Barr-Telford and Bussiere (2004)
examined characteristics measured at age 15 associated with dropping out
of school by the age of 17. Similar investigations of data from the third cy-
cle in which the YITS youth were aged 19 were conducted by Knighton
and Bussiere (2006) and data analyses from the fourth YITS cycle were
published in the OECD (2010). In Australia, the PISA student cohorts were
incorporated in longitudinally designed surveys named the Longitudinal
Survey of Australian Youth (LSAY). In the context of ESL, Marks (2007)
studied the PISA 2003/LSAY cohort and Mahuteau and Mavromaras (2014)
the PISA 2006/LSAY cohort. Further, in Switzerland a panel study of the
PISA 2000 cohort of students was followed up by the Transitions from
Education to Employment survey (TREE) through seven annual survey
panels between 2001 and 2007, and an eighth one in 2010. ESL-related find-
ings based on TREE data were derived by Bertschy, Cattaneo and Wolter
(2008) along with Mueller and Wolter (2011).
The key finding in all these studies is that the strongest predictor of ESL
is the PISA achievement score, which is even stronger than the socio-eco-
nomic background of students. For Australia, using multilevel models
Marks (2007) showed that the greatest influence on ESL was student per-
formance in the PISA test and that this effect was almost four times strong-
er than that of socio-economic background. Mahuteau and Mavromaras
(2014) confirmed that, in addition to student (disadvantaged) background,
scores in the PISA test also predict early dropout well, but they pointed
out that low PISA achievers are likely to accumulate other types of dis-
advantage that together then emphasise the probability of dropping out.
Overall, the main message of these findings is that a significant share of
the effect of student achievement on leaving school can be separated from
the socio-economic and other background student characteristics, indicat-
ing that strong competencies seem able to help in overcoming the effects of
disadvantages.
Such findings have also emerged in other countries. Bertschy,
Cattaneo and Wolter (2008), although their study was limited to Swiss stu-
dents in vocational programmes, found that a higher score in PISA, after
controlling for SES, language spoken at home, migration status and region
190
