Page 86 - Šolsko polje, XXX, 2019, št. 5-6: Civic, citizenship and rhetorical education in a rapidly changing world, eds. Janja Žmavc and Plamen Mirazchiyski
P. 86
šolsko polje, letnik xxx, številka 5–6
ic knowledge is also connected to the number of books that student has
at home. Furthermore there is a connection between civic knowledge and
higher education (short-cycle tertiary education (ISCED 5) and bachelor
or equivalent (ISCED 6)) (Klemenčič, Mirazchiyski and Novak, 2018, p.
59). Taking into account the findings in the literature and previous re-
search, we expect to find an association between student SES (as defined
above) and exposure to peer violence of eighth-graders in Slovenia.
The IEA IDB Analyzer used creates SPSS or SAS syntax that can
be used to combine selected files and perform analysis with databases. “It
generates SPSS or SAS syntax that takes into account information from
the sampling design in the computation of sampling variance, and han-
dles the plausible values” (IDB Analyzer, 2019). We used the IDB Ana-
lyzer with SPSS. First, we used merge module in IDB Analyzer to get the
data we wanted for Slovenian students in the eighth grade of elementary
schools. After that we used the Analysis module of IDB Analyzer to test
the association between SES and bullying. We used the data from inter-
national Student Questionnaire file.
Linear regression was performed to test the association between bul-
lying (“Students experience of physical and verbal abuse”) and with varia-
ble “National index of socioeconomic status”. The first variable consisted
of the variables that define different forms of bullying (see Table 2) and,
as we said before, the second variable (SES) was measured with three var-
iables. We also tested the variables on facing different bullying situations
separately in association with SES and used a different reference category
in several combinations with and without plausible values. Furthermore,
we tested the connection between the level of student’s civic knowledge
and bullying in Slovenia. We divided the variable “National index of so-
cioeconomic status” into three categories to see if there would be any dif-
ferent results.
Further analyses were divided into three parts. The first part test-
ed the relationship between bullying and different individual and fam-
ily background (e.g. student gender, migration background, family so-
cio-economic status, etc.) and contextual (e.g. school location, school
safety, school climate, etc.) variables with being bullied at school. There
are two types of background and contextual variables in the data:
1. Categorical, these are Likert-type questions with a fixed number of
categories the respondent can choose from as ordinal variables in the
data, e.g. “Never”, “Sometimes”, “Often”, and “Very often”; and
84
ic knowledge is also connected to the number of books that student has
at home. Furthermore there is a connection between civic knowledge and
higher education (short-cycle tertiary education (ISCED 5) and bachelor
or equivalent (ISCED 6)) (Klemenčič, Mirazchiyski and Novak, 2018, p.
59). Taking into account the findings in the literature and previous re-
search, we expect to find an association between student SES (as defined
above) and exposure to peer violence of eighth-graders in Slovenia.
The IEA IDB Analyzer used creates SPSS or SAS syntax that can
be used to combine selected files and perform analysis with databases. “It
generates SPSS or SAS syntax that takes into account information from
the sampling design in the computation of sampling variance, and han-
dles the plausible values” (IDB Analyzer, 2019). We used the IDB Ana-
lyzer with SPSS. First, we used merge module in IDB Analyzer to get the
data we wanted for Slovenian students in the eighth grade of elementary
schools. After that we used the Analysis module of IDB Analyzer to test
the association between SES and bullying. We used the data from inter-
national Student Questionnaire file.
Linear regression was performed to test the association between bul-
lying (“Students experience of physical and verbal abuse”) and with varia-
ble “National index of socioeconomic status”. The first variable consisted
of the variables that define different forms of bullying (see Table 2) and,
as we said before, the second variable (SES) was measured with three var-
iables. We also tested the variables on facing different bullying situations
separately in association with SES and used a different reference category
in several combinations with and without plausible values. Furthermore,
we tested the connection between the level of student’s civic knowledge
and bullying in Slovenia. We divided the variable “National index of so-
cioeconomic status” into three categories to see if there would be any dif-
ferent results.
Further analyses were divided into three parts. The first part test-
ed the relationship between bullying and different individual and fam-
ily background (e.g. student gender, migration background, family so-
cio-economic status, etc.) and contextual (e.g. school location, school
safety, school climate, etc.) variables with being bullied at school. There
are two types of background and contextual variables in the data:
1. Categorical, these are Likert-type questions with a fixed number of
categories the respondent can choose from as ordinal variables in the
data, e.g. “Never”, “Sometimes”, “Often”, and “Very often”; and
84
