Page 64 - Štremfel, Urška, ed., 2016. Student (Under)achievement: Perspectives, Approaches, Challenges. Ljubljana: Pedagoški inštitut. Digital Library, Documenta 11.
P. 64
Statistical Indices
In PISA 2009, background data was collected in accordance with assessment
frameworks (OECD, 2009). The following indices were significant for the anal-
ysis in this paper: index of economic, social and cultural status (ESCS), students’
reports on the usefulness of strategies for writing a summary - the index of
summarising (METASUM), students’ reports on the usefulness of strategies for
understanding and memorizing a text - the index of understanding and remem-
bering (UNDREM), frequency of use of control strategies when studying - index
of use of control strategies (CSTRAT), frequency of use of elaboration as a learn-
ing strategy - index of use of elaboration strategies (ELAB), frequency of use of
memorization as a learning strategy when studying - index of memorization
strategies (MEMOR), index of diversity of reading materials (DIVREAD), index of en-
joyment of reading (JOYREAD), frequency of the use of libraries - index of the use
of libraries (LIBUSE), frequency of online reading activities - index of online read-
ing activities (ONLNREAD), frequency of computer use at home for schoolwork
- index of computer use at home for schoolwork (HOMSCH), frequency of com-
64 puter use at home for leisure/entertainment - index of computer use at home
for leisure (ENTUSE). Items related to these indices and internationally compa-
rable values are available in international reports of PISA 2009 (OECD, 2010b,
2010c, 2010d).1
For the analysis in this paper, the above indices were standardised for the
target population, which means each index has an average value of 0 and a
standard deviation of 1 for the population of students of Year 1 of upper sec-
ondary schools in Slovenia. This also enables direct comparisons between val-
ues of different indices and their associations with achievements within the
target population.
Statistical Analyses2
Two approaches were used for the analysis of associations between read-
ing-related factors and achievement in low, basic, and higher-achievement
groups. The first approach is a calculation of mean index values by gender and
1 Based on the data collected, indices were constructed in the database on interval scales, with an
OECD mean of 0 and standard deviation of 1 (in computing the mean and standard deviation, an
equal weight was given to each of the participating countries) (OECD, 2012b). Negative values of the
index in the international database do not imply that students responded negatively to the under-
lying question, but rather that they responded less positively (or more negatively) than the average
response across OECD countries. Likewise, positive values imply more positive (or less negative) re-
sponses than the average response in OECD countries.
2 SPSS 21.0 predictive analytics software package was used for the analyses, with the addition of the
Replicates Module, which enabled calculations of statistical parameters and their population esti-
mates with standard errors with the use of suitable sample weights and all five plausible values of
achievement in PISA 2009 database.
student (under)achievement: perspectives, approaches, challenges
In PISA 2009, background data was collected in accordance with assessment
frameworks (OECD, 2009). The following indices were significant for the anal-
ysis in this paper: index of economic, social and cultural status (ESCS), students’
reports on the usefulness of strategies for writing a summary - the index of
summarising (METASUM), students’ reports on the usefulness of strategies for
understanding and memorizing a text - the index of understanding and remem-
bering (UNDREM), frequency of use of control strategies when studying - index
of use of control strategies (CSTRAT), frequency of use of elaboration as a learn-
ing strategy - index of use of elaboration strategies (ELAB), frequency of use of
memorization as a learning strategy when studying - index of memorization
strategies (MEMOR), index of diversity of reading materials (DIVREAD), index of en-
joyment of reading (JOYREAD), frequency of the use of libraries - index of the use
of libraries (LIBUSE), frequency of online reading activities - index of online read-
ing activities (ONLNREAD), frequency of computer use at home for schoolwork
- index of computer use at home for schoolwork (HOMSCH), frequency of com-
64 puter use at home for leisure/entertainment - index of computer use at home
for leisure (ENTUSE). Items related to these indices and internationally compa-
rable values are available in international reports of PISA 2009 (OECD, 2010b,
2010c, 2010d).1
For the analysis in this paper, the above indices were standardised for the
target population, which means each index has an average value of 0 and a
standard deviation of 1 for the population of students of Year 1 of upper sec-
ondary schools in Slovenia. This also enables direct comparisons between val-
ues of different indices and their associations with achievements within the
target population.
Statistical Analyses2
Two approaches were used for the analysis of associations between read-
ing-related factors and achievement in low, basic, and higher-achievement
groups. The first approach is a calculation of mean index values by gender and
1 Based on the data collected, indices were constructed in the database on interval scales, with an
OECD mean of 0 and standard deviation of 1 (in computing the mean and standard deviation, an
equal weight was given to each of the participating countries) (OECD, 2012b). Negative values of the
index in the international database do not imply that students responded negatively to the under-
lying question, but rather that they responded less positively (or more negatively) than the average
response across OECD countries. Likewise, positive values imply more positive (or less negative) re-
sponses than the average response in OECD countries.
2 SPSS 21.0 predictive analytics software package was used for the analyses, with the addition of the
Replicates Module, which enabled calculations of statistical parameters and their population esti-
mates with standard errors with the use of suitable sample weights and all five plausible values of
achievement in PISA 2009 database.
student (under)achievement: perspectives, approaches, challenges
