mind the gap_explaining the variation between and within schools in norrköping

1

Mind the gap: explaining the variation between and

within schools in Norrköping

Kenisha S. Russell Jonsson

PhD. Candidate in Sociological Research

Department of Sociology

Essex University

https://sites.google.com/site/russelljonsson/

Tel: +46760648592 email:[email protected]

https://sites.google.com/site/russelljonsson/

2

Contents Huvudsakliga fynd ................................................................................................................... 3

Key findings .............................................................................................................................. 4

Study limitations and suggestions for future analysis ........................................................... 5

1 Introduction ............................................................................................................................. 8

1.2 The case of Norrköping .................................................................................................... 10

1.3 Data .................................................................................................................................... 12

1.4 Dependent Variables ........................................................................................................ 13

1.5 Analytical strategy .............................................................................................................. 16

1.6 The advantages of using a multilevel model. ........................................................... 17

1.7 Model description: Overview of models on school and class performance ................ 18

2 Performance in Mathematics ................................................................................................. 20

2.1 Results for students taking Swedish............................................................................ 20

2.2 Results for students taking Swedish as a Second Language .................................... 25

3 Performance in English ......................................................................................................... 28

3.1 Results for students taking Swedish............................................................................ 28

3.2 Results for students taking Swedish as a second language ....................................... 32

4. Performance in Swedish as a native language ..................................................................... 35

5. Additional models ................................................................................................................ 39

6. Overall achievement ............................................................................................................. 40

7. Conclusion ............................................................................................................................ 52

8. Future analyses ..................................................................................................................... 53

9. References ............................................................................................................................ 54

10. Appendix ............................................................................................................................ 56

3

Huvudsakliga fynd

De genomförda analyserna indikerar att det i stort fanns små, ej signifikanta,

skillnader i studieprestationer mellan kommunala skolor i kärnämnena (matematik,

engelska och svenska) eller vad gäller genomsnittliga meritvärden.

Det var större, i högre utsträckning signifikanta, skillnader mellan klasser i

kärnämnena och genomsnittligt meritvärde. När det gäller individnivån, stod denna för den största variationen mellan elever i

studieprestationer i såväl kärnämnena som i genomsnittligt meritvärde.

Skillnader i prestation förklaras till stor del av:

Variabler på individnivå

- Kön, där pojkar presterar signifikant sämre än flickor i samtliga ämnen samt i

genomsnittligt meritvärde

- Elever med utländsk bakgrund som läser svenska som andraspråk presterar signifikant

sämre än elever med svensk bakgrund liksom de med utländsk bakgrund som läser

svenska som modersmål. Det är dock ingen skillnad i prestation mellan de med

europeiskt respektive icke-europeiskt ursprung som läser svenska som andraspråk.

Variabler på klassnivå

- Elevens ekonomiska situation, där elever vars familjer erhållit försörjningsstöd

åtminstone en gång under året före det som analyserats, har sämre betyg

- Elever med utländsk bakgrund som läser svenska som modersmål presterar signifikant

bättre än elever med svensk bakgrund i kärnämnena engelska, svenska och matematik.

Motsvarande skillnader återfinns dock inte när det gäller genomsnittliga meritvärden

mellan grupperna.

- Klassrummets sociala sammansättning – resultaten indikerar att både de med utländsk

och de med svensk bakgrund presterar sämre ju högre andel med utländsk bakgrund i

en klass. Även i klasser med hög andel social utsatthet presterar dock elever med

utländsk bakgrund bättre än elever med svensk bakgrund.

Variabler på skolnivå

- En stor andel elever med lågutbildade föräldrar och en hög andel elever med utländsk

bakgrund har bidragit till lägre prestation. Dessa variabler tycks dock förstärka

varandra.

Det tycks vara så att när väl elever placeras i svenska som andraspråk så fastnar de i denna

undervisningsform. Trots de negativa effekterna på prestation i andra ämnen går de inte vidare

till att läsa svenska som modersmål.

Ett viktigt resultat av analyserna är att det blir tydligt att elever inte presterar signifikant sämre

på grund av någon enskild faktor. Det är snarare en kombination av faktorer på individuell,

skol- och klassnivå som avgör en elevs resultat. Ett exempel på det är att elever med utländsk

bakgrund, som läser svenska som andraspråk presterar signifikant sämre jämfört med de som

läser svenska som modersmål. Skolor med hög andel elever som läser svenska som andraspråk

har också en totalt sett högre andel elever med utländsk bakgrund i klasserna. Detta verkar i sin

tur predicera sämre resultat hos såväl elever som läser svenska som andraspråk som de som

läser svenska som modersmål.

4

Key findings

Considering school variation, there are small, non-significant differences across the

core subjects (mathematics, English and Swedish) and grade point average

(meritvärde).

Considering class variation, there are larger more significant differences in the

achievement of students across the core subjects and grade point average.

Regarding the individual, this accounted for the largest variation in the achievement

of students across the core subjects and grade point average.

Differences in achievement has largely been explained by:

Variables at individual level

- Gender, whereby boys perform significantly worse than girls in all core subjects and in

overall student performance

- Students taking Swedish as a second language, whereby students with a foreign

background studying Swedish as a second language performed significantly worse

when compared to students taking Swedish as a native language. However, among

students taking Swedish as a second language, there is no difference in achievement

among European and Non-European students with a foreign background (i.e. the share

of Swedish born with foreign parents and the share of foreign born with foreign parents)

Variables at class level

- The student’s economic situation, whereby students whose families received social

benefits (försörjningsstöd) at least once in the year prior to the analysis had lower grades

- Foreign background, whereby students with a foreign background studying Swedish as

a native language outperformed native Swedish students across the core subjects, but

there is no significant difference in grade point average between the groups.

- Classroom social composition, whereby both non-native and native Swedish speakers

perform worse as the share of foreign students in a class increases. Non-native speakers

tend to perform better than native speakers in low socio-economic environments.

Variables at School level

- A high share of students with parents having low/no education and a high share of

students with a foreign background contributed to lower achievement. Although, it must

be said that these variables appear to inflate each other.

It seems that once students are placed in Swedish as a second language, despite the detrimental

relationship between this and their performance in other subjects, they do not progress to

Swedish as a native language.

Students do not perform significantly worse by virtue of any single factor. Instead, it seems to

be the interaction between individual, school and class level factors which determines a student

outcome. For e.g. students with a foreign background who take Swedish as a second Language

have significantly lower grades in comparison to Students with a foreign background who take

Swedish as a native Language. Moreover, schools that offered Swedish as a second language

had a higher proportion of foreigners per class and based on the results this leads to lower

performance among both native and non-native Swedish speakers.

5

Study limitations and suggestions for future analysis

The type, quality and quantity of data also needs to be reconsidered for any future analysis.

In this section both the limitations and some suggestions for future analysis is made.

Cross-sectional data

One of the main drawbacks of this analysis was the cross-sectional nature of the data. As

such the analysis was based on a single ‘cohort’ of students. The fact that they started their

education several years prior to the analysis means that the usefulness for predicting the

outcome of future students is somewhat diminished (Goldstein, 1997). Future studies would

therefore benefit from longitudinal data, whereby school performance may be judged over time.

An additional drawback related to the cross-sectional nature of the data, is the difficulty in

determining the direction of causality. For example, among students who have not fulfilled the

requirement to move on to high school, the question which remains is, whether this is due to

the fact that they have a high proportion of absences? Or is it that students who realize they

have not fulfilled the requirements to move on to high school, lost motivation and therefore

decided not to attend classes and as a consequence have a greater proportion of absences? With

the current data, it was not possible to determine causality. It would be easier to provide the

right type of assistance for students and provide practical courses of action for schools, parents

and policy makers alike if we had longitudinal data, and was able to determine causality and

pinpoint changes in student behaviour and performance.

Aggregated data

With regards to both the quality and the quantity of the data, a disadvantage of the current

analyses is the fact that the majority of the data was aggregated. Data at the individual level for

students and parents is required to improve the models. The current study had five individual

level explanatory variables: gender; an indicator for student’s mother-tongue; student’s year 6

result from the national exam in year 6; indicator for if student’s attended the same pre-school

and primary school and the proportion of absences. Therefore, the current analyses were largely

based on data aggregated at the school and class level. Although, there are still some missing

variables (for e.g. the quality of the teaching, teacher experience and qualification) which may

provide us with some insight into the achievement variation between and within schools. If the

aim is to focus on factors within the school system that can be changed from a policy

perspective, then this study has fulfilled that criterion to some extent. However, there are a

6

wide range of individual level factors that may have a significant impact on student performance

for which we have not accounted. For example, if students have family problems, which could

explain the high proportion of absences, which in turn could explain their performance1.

Individual data is therefore required to improve future studies.

Bigger data sets

Models may be improved by a bigger data set with more observations at the individual, class

and school levels. This could be achieved by using several cohort of students from the same

school, for example the results of year 9 students for the last 5 years. An advantage of this

method is that we would be able to examine changes in results over time. The optimal size of

the number of upper level entities (in this case, schools), is still in question given that there is

currently no consensus on the number of entities required for best model fit. The suggestion in

the literature is between 20 and 30 upper level entities is required to have enough variation.

(Kreft, Kreft, & de Leeuw, 1998).

Independent schools

The current analyses detailed the variation in student’s achievements only for municipal

controlled schools. Given that there are several privately run schools (friskolor) in Norrköping,

it would benefit society to also undertake analyses of the student performances within these

schools, as there are still many unanswered questions. Do communal schools perform better or

worse than independent schools? Whether they perform better or worse, the question that then

arises is, how can we account for the differences? The suggestion therefore for future analyses

is that we undertake a comparative analysis of the student achievement for communal versus

privately run independent schools.

Test-Retest

According to Goldstein, the only way one can really assert that school has had an impact

on learning is by examining the intake results and then assess how much school has contributed

to later achievements (Goldstein, 1997). Thus a suggestion for the improvement for future

studies could be the addition of background variables of students at intake or at an earlier point

in their studies. In that way one could explore the factors which may have affected their

1 This statement is to some extent a simplification. To say anything definitive about the chain of events described

here, both individual and longitudinal data is also required.

7

performance at that particular time point and this may assist in explaining what individual

factors may have contributed to better or worse performance throughout the period of schooling.

Other relevant variables

Based on discussions and a review of the data received for this analysis, it was unclear if

financial resources invested in schools produce improvements in student achievement.

Consequently there are two important variables that are missing from these analysis, the first is

the overall spending of the schools and secondly any additional subsidies received. Thus a

suggestion for future analysis is a clear accounting of how funds related within the school is

allocated.

Moreover, to have a more significant impact and in order for us to gain a more

comprehensive understanding of the factors affecting student performance, a wider more

relevant set of explanatory variables is required. Information which includes health assessments

of students, information on students family background, teacher education and experience,

school and class room evaluations, school spending etcetera.

8

1 Introduction

There is a general and widespread agreement that schools have an influence on student

achievement. Despite this, there is much debate on the specific factors within schools which

have an impact on student achievement, and of the factors identified the extent of their impact

(Rumberger & Palardy, 2004). It is this particular debate which has sparked the start of school

effectiveness research. School effectiveness research has been defined as a “… line of research

that investigates the performance differences between and within schools, as well as the

malleable factors that enhance school performance (usually using student achievement scores

to measure the latter)”(Visscher & Witziers, 2005).

The starting point of this research dates back to the publication of the ”Equality of

Educational opportunity” by James S Coleman et al. (1966). The report simply referred to as

the Coleman Report”, was commissioned by the United States Department of Health,

Education, and Welfare in response to provisions of the Civil Rights Act of 1964. The goal was

to assess the availability of equal educational opportunities for children of different race, color,

religion, and national origin. In this well-publicized and debated report, they concluded that the

socio-economic background of students had a more significant impact on achievement relative

to the impact of the schools which they attend. The publication of this report, which marked the

start of both a substantive and methodological debate and research in school effectiveness has

led to an extensive literature which has sought to identify and explain the main characteristics

of an effective school. These studies which has both an academic and policy focus has yet to

reach consensus. For instance, in a more recent study, Coleman has argued that the social

composition of the student body is more highly related to achievement, independent of the

student’s own social background, than is any [single] school factor” (James Samuel Coleman,

1990, p. 119). One might argue that the overarching goal of these studies is to find the right

combination of factors which will contribute to a more ‘equitable’ school system, with similar

achievement levels for all students. This is however not a simple task, given the aforementioned

disagreement for the heterogeneity between and within schools.

Thus far, the between school heterogeneity has been shown to be related to a wide range of

factors, some of these are based on:

(1) Individual characteristics (motivation; gender; students personal ability; relationship

with parents, family situation mental health and general wellbeing);

(2) School (class room size; school and classroom composition; teacher experience and

educational background; internal and external source of funding; opportunity for extra

educational support; geographic location; type of school; decision-making; homework);

9

(3) Family characteristics (educational background; family income; family size; order of

birth in the family, immigrant status; length of residence);

The list of factors which may impact a student achievement is lengthy (for a more extensive

list of individual, school and classroom factors which may impact student achievement (See for

e.g. Bosker & Scheerens, 1994; Rumberger & Palardy, 2004). Moreover, these factors

sometimes interact, whereby the combination of two or more characteristics may vary across

individuals. Moreover, the same combination may have different impact on different students

because of inherent individual qualities.

It is this inconsistency in the findings and the varying explanations which has been offered

that has contributed to the ongoing debate on how to improve achievement levels in schools

and have also spurred the development of school effectiveness research in Sweden.

In the Swedish context, this line of research has gained national attention for two reasons.

The first, is that Sweden has lost its international place as having one of the best school systems

in the world (measured by PISA, TMSS etc). Secondly, school which has long been viewed as

“the great equalizer”, that is, a place which in spite of gender, socioeconomic background,

ethnicity, home conditions and/or any of these combinations should provide a similar, if not the

same opportunities for all students. Yet, the variation in achievement between and across

schools points to a decline in equity (Böhlmark & Holmlund, 2012; J.-E. Gustafsson & Yang-

Hansen, 2009; J. Gustafsson & Yang Hansen, 2009; von Greiff, 2009). This is despite, the

many school reforms that have been undertaken to correct this by policy maker and through the

work of the Swedish National Agency for Education (Skolverket), which has sought to promote

equity in education. They have characterized an equitable school system as one in which

(Böhlmark & Holmlund, 2012; von Greiff, 2009) [Own translation]:

(1) The variation in results between students is small.

(2) The results between schools is small.

(3) The importance of a student’s socioeconomic background for their school results is

small.

(4) The importance of a student’s immigration background for school results is small.

(5) School segregation with regards to socio-economic and immigration background is

small.

(6) The importance of the individual student results with regards to the socio-economic

composition of the school and with regards to the immigrant composition at the school

is small.

10

The results from various reports has however indicated a wide variation in student

achievement within and between schools across Sweden, and has offered a wide range of factors

to explain these differences. von Greiff (2009) points to the decentralization of the school

system, with the shift from central government control to a system where the municipalities and

school leadership decides on the day-to-day running of the school, as an explanation for the

heterogeneity in achievement. Asserting further that after the 1991 municipal reforms resource

distribution to schools have lost priority when compared to other concerns, and this has

contributed to the quality of education (von Greiff, 2009). The view that the reform of the school

system which took place during the 1990s has had an impact on student achievement is shared

by many ((Björklund, Edin, Freriksson, & Krueger, 2004; Böhlmark & Holmlund, 2012; Östh,

Andersson, & Malmberg, 2013). The effect has however been interpreted in different ways.

Böhlmark and colleague, for example, argues that it is the interaction between family

characteristics, school reforms and wider societal changes that has led to the variation in

achievement among students (Böhlmark & Holmlund, 2012). On the other hand, for Östh et al.

(2013), school reform is a distal factor which provided students with school choice which in

turn seems to have contributed to segregation of student by ethnicity and socioeconomic status

and as a consequence to the increase in the between school variation in achievement.

With no consensus on the reason for the differences in achievement studies have argued for

the importance of smaller class size (Fredriksson, Oosterbeek, & Öckert, 2012); the subjectivity

of grade setting (Lindahl, 2007; Sandqvist, 2007); discrimination- with studies arguing that girls

and non-natives are given more generous grades ((Hinnerich, Höglin, & Johannesson, 2011;

Lindahl, 2007); family’s support and engagement with their children studies (Erikson, 2008;

Högdin, 2006), the effect of absences, the well-being and motivation of students; the role of the

school rector (Böhlmark, Grönqvist, & Vlachos, 2012); teachers experience and qualification

(Andersson, 2007; Jönsson & Rubinstein Reich, 2004) among many other factors.

1.2 The case of Norrköping

Norrköping is one of the 290 municipalities in Sweden that face the challenge of providing

students with equal educational opportunities. According to a national comparison of school

achievement and resource indicators across all the municipalities for the year 2013/2014 (SKL

2015), the municipality of Norrköping ranks 208 when both municipal and privately run schools

are considered. When only municipal schools are considered, the municipality ranks 237. In

2007/2008, an inspection by Swedish National Agency for Education concluded that the share

11

of students in primary schools who have passed all the courses necessary to attend high school

varies considerably between the schools2 in the municipality. This issue has not been resolved

and overall achievement among students in Norrköping has declined. The result from figure 1

below demonstrates the variation in achievement among school in the municipality.

The figure presents the grade point average for the spring term (2010-2014) across the 11

municipal schools. Overall, grade point average fluctuated across the schools during the period

in question. Between 2010 and 2014, there is a 1% decrease when the changes across all schools

are considered. There were 6 schools which had small increases ranging from 1-13%. Råssla

and Hultdal, had the highest percentage increase at 13% and 10% respectively. In contrast,

Borgsmo and Söderport had the largest percentage decline at 10% and 15% respectively. During

the four year period, the difference in grade point average between the school with the worst

performance and the best performance has also increased. In 2010, the difference in

performance was 12%, by 2014 this had doubled to 24%.

Figure 1. Grade point average (2010-2014) among Norrköping municipal controlled primary schools

2 Own translation. http://www.skolverket.se/om-skolverket/press/pressmeddelanden/2008/norrkoping-behover-

vidta-atgarder-for-att-forbattra-skolornas-resultat-1.79395

0

50

100

150

200

250

Grade point average (2010-2014)

2010 2011 2012 2013 2014

12

A recent study providing an in-depth qualitative analysis of the school results in

Norrköping (Andersson, 2015), has identified and described several groups, which are at risk

for long-term unemployment, psychological ill-health and later life difficulties, arising from

low educational achievements. The results from that study, has further underscored the

importance of gaining a thorough understanding of the determinants of school achievements.

This is the means through which school achievements may be improved and the necessary

changes undertaken to the current school system so as to provide ‘equitable’ outcomes for all

students, despite gender, socio-economic background, immigration history, geography or other

factors. Building on earlier qualitative studies in the municipality which has identified several

explanatory factors related to student performance, the aim of this study is to:

(1) Assess how much the factors identified have contributed to the variation in the results

between communal schools in the municipality

(2) Assess how much the factors identified have contributed to the variation in the results within

communal schools in the municipality

1.3 Data

The data for this analysis was taken from several sources. From the municipal database

(hypernet) anonymized individual level demographic and school achievement data, of all 9

grade students who were registered in one of the 11 municipal primary schools in the autumn

of 2014. To this data, school level information which was taken from The Swedish National

Agency for Education (Skolverket) was also added. Moreover, aggregated information at the

school and class level about the parents (parents’ educational level, an indicator for immigrant

status and the length of time they have resided in Sweden and their economic situation i.e.

whether or not they are in receipt of any social benefits, their employment status) was also

added by the National Statistics Office (SCB). After initial data preparation, the final sample

was 960 students, within 38 classes across 11 schools. Of these, 781 students took Swedish as

a native language, while 179 took Swedish as a Swedish second language. A more fine grained

breakdown of the various languages spoken by the students may be found in the appendix (Table

A11). The average grade 9 had approximately 87 students. The school with the smallest grade

9 had 46 students, while the largest had 151. The number of classes per school ranged from 2

to 6, with an average class size of approximately 26 students. The smallest class had 15 students

(Navesstadskolan), while the largest class had 40 students (Borgsmoskolan). Of the 11 schools

in the data only 8 had classes with students taking Swedish as a second language. The 3 schools

13

in which there were no students taking Swedish as a second language were Råsslaskolan,

Vikbolandsskolan and Mosstorpskolan.

1.4 Dependent Variables

The dependent variable used here is students’ achievement in several core subjects

(Mathematics, English, Swedish and Swedish as a second language) and grade point average,

as measured during the autumn semester (2014) for students in year 9. It should be noted that

these are not the students’ final grades. For the core subjects, students are scored on a scale

ranging from 0 to 203 for all outcomes. Meanwhile the grade point average 4 ranges from 0-

320. The scores have been treated as a continuous variable in the multilevel models with higher

scores indicating a higher knowledge and an overall better result. Table 1 below, provides a

description of the variables included in this analysis. The variables that have been included in

this analysis have largely been based on data availability, the finding from the earlier mentioned

qualitative study by Andersson (2015) and the general literature which has examined various

aspects of school effectiveness.

3 In the new grading system this is equivalent to a grade of F to A. With F indicating that a student has failed the course. 4 The grade point average may be calculated by summing the 16 best grades for each student. According to the new system these are: A=20, B=17.5, C=15, D=12, 5 E=10 and F= 0. The grades according to the old grading system is MVG=20, VG=15 and G=10.

14

Table 1. Variable Description

Variable Variable Description

Outcome Measures

Grade point average(meritvärde) The grade point average for primary schools (grundskolans eller specialskolans) is created by summing the

students 16 best final grades. The grade according to the old grading system is MVG=20, VG=15 and G=10.

The grade point average may be calculated according to the new by summing the value of each given grade:

A=20, B=17, 5, C=15, D=12,5 E=10 and F= 0.( For more information see www.skolverket.se)

Mathematics Course grade as at Autumn 2014. They have been designated as A=20, B=17, 5, C=15, D=12, 5 E=10 and F= 0

according to the new school laws (www.skolverket.se). In all models they are entered as a continuous measure.

English Course grade as at Autumn 2014. They have been designated as A=20, B=17, 5, C=15, D=12, 5 E=10 and F= 0


Swedish Course grade as at Autumn 2014. They have been designated as A=20, B=17, 5, C=15, D=12, 5 E=10 and F= 0


Individual level

Boy Dichotomous variable coded 0=girl and 1=boy.

Non-native speaker Dichotomous variable 0=Native Swedish speaker, 1=non-native Swedish speaker. This is created from

information based on each student’s mother-tongue. In some analyses this category has been further divided to

indicate whether this is European or non-Europeans (see Appendix Table A11).

Absences This is a measure of the proportion of absences by individual level students. The variable has been coded as less

than 5% (ref.), 5-10%,10-20%,20-50%, 50% and more (Non-linear effect).

Year 6 school

Results from National Exams

Indicator for the school students attended in Year 6.

Indicator for the students results in national exams (Math, English and Swedish) taken in Year 6

Class level

Low/unknown parental education Combined share of parents with an unknown education level and the share of parents a primary education level

within a class (linear effect),

Share of foreign language speakers Indication of the share of non-native speakers in a class. This is a non-linear variable coded as 0/.425=1

.426/.60=2 .601/max=3.

Share foreign background This indicator is created by combining the share of Swedish born with foreign born parents and the share of

foreign born with foreign parents.

http://www.skolverket.se/

15

Human development Index(hdilg)

The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of

human development: a long and healthy life, being knowledgeable and have a decent standard of living. The

HDI is the geometric mean of normalized indices for each of the three dimensions.

http://hdr.undp.org/en/content/human-development-index-hdi

Share of students with 2 parents having job

Social Benefits

Indication of the share of students in a class with both parents working.

Indication of the share of students with families that has received social benefits at least once in the year prior to

the analysis (linear effect).

School level

Low/unknown parental education Combined share of parents with an unknown education level and the share of parents a primary education level

within a school (linear effect).

Share of students with two parents from abroad Share of Swedish born students' with foreign born parents and foreign born students with foreign parents within

a school (linear effect).

Table 2. : List of school codes used in analysis

Borgsmoskolan = 1 Mosstorpskolan=7

Djäkneparksskolan=2 Navesstadskolan=8

Ektorpsskolan=3 Råsslaskolan= 9

Enebyskolan=4 Söderporten 4-9=10

Hagaskolan=5 Vikbolandsskolan=11

Hultdalsskolan=6

16

1.5 Analytical strategy

Analysis for this report was undertaken through multilevel modeling. This is a statistical

method which allow for the examination of the contextual (school characteristic) and individual

contributions (students’ characteristics) ton student achievement simultaneously. By taking into

account the multilevel structure of the data, it is possible to reduce both conceptual5 and

statistical issues. If the contextual level of the data is ignored, i.e. the effect of the school

environment on student’s achievements, we will be excluding the effect of school on learning.

Thus, by aggregating the data to focus on the contextual analysis and ignoring the individual

level, this could potentially lead us to draw the wrong conclusions e.g. the ecological fallacy.

On the other hand, if the focus of the analysis is based only on the individual student

characteristics, the analysis would also be flawed. By ignoring the contextual level, and

conducting the analysis only at the individual level, this could lead to an underestimation of the

standard errors and result in invalid statistical tests. Thus, one can say that a multilevel model

has two parts. These may be described as the fixed part of the model which specifies the overall

mean relationship between the response and the predictor variables; that is, the relationship that

applies in the average school. The second part of the model, is the random part of the model,

this specifies how the school and school specific relationships differ from this overall mean

relationship. Below is a diagram6 of the proposed model (students within classes within

schools). We can formalize the model as follows:

Yijk=β0 + vk + ujk+eijk

Vk ~ N(0,σ2v)

ujk ~ N(0,σ2u)

eijk ~ N(0,σ2e)

Where Yijk is the observed grades for student i from the autumn term 2014 in class j in

school k, β0 is the mean response across all schools, vk is the effect of school k, ujk is the

effect of class j within school k, and eijk is the residual error term. The random effects and

5 Within a multilevel framework, where children are nested in schools, the argument is simply that the residual

variance is partitioned into a between-school component (the variance of the school-level residuals) and a

within-school component (the variance of the child-level residuals). The school residuals, often called ‘school

effects’, represent unobserved school characteristics that affect child outcomes. It is these unobserved variables

which lead to correlation between outcomes for children from the same school.

http://www.bristol.ac.uk/cmm/learning/multilevel-models/what-why.html 6 http://www.bristol.ac.uk/cmm/learning/multilevel-models/data-structures.html

http://www.bristol.ac.uk/cmm/learning/multilevel-models/what-why.html

17

residual errors are assumed independent of one another and normally distributed with

zero means and constant variances.

Figure 2. Classification of students within classes within schools

1.6 The advantages of using a multilevel model.

There are several advantages in using a multilevel model for analysing student achievement

within schools (Aitkin & Longford, 1986; Bosker & Scheerens, 1994; Goldstein, 1997;

Goldstein et al., 1993; Sandoval-Hernandez, 2008). A few of these are listed below:

(1) Correct inferences: Traditional multiple regression techniques treat the units of analysis as

independent observations. One consequence of failing to recognise hierarchical structure of the

data is that the standard errors of regression coefficients will be underestimated, leading to an

overstatement of statistical significance. The standard errors for the coefficients of higher-level

predictor variables will be the most affected by ignoring grouping.

(2) Substantive interest in group effects: In many situations a key research question concerns

the extent of grouping in individual outcomes, and the identification of ‘outlying’ groups. In

evaluations of school performance, for example, interest centers on obtaining ‘value-added’

school effects on pupil attainment. Such effects correspond to school-level residuals in a

multilevel model which adjusts for prior attainment.

(3) Estimating group effects simultaneously with the effects of group-level predictors: An

alternative way to allow for group effects is to include dummy variables for groups in a

traditional (ordinary least squares) regression model. Such a model is called an analysis of

variance or fixed effects model. In many cases there will be predictors defined at the group

18

level, for example, type of school (independent vs. communal schools). In a fixed effects model,

the effects of group-level predictors are confounded with the effects of the group dummies, that

is, it is not possible to separate out effects due to observed and unobserved group characteristics.

In a multilevel (random effects) model, the effects of both types of variable can be estimated.

1.7 Model description: Overview of models on school and class performance

The results of the analyses have been divided into four main sections, with each section

describing the results as it pertains to each of the core subjects (Mathematics, English, and

Swedish) and the grade point average in the 11 communal run schools. The results are

organized as follows:

The first section presents the predicted results for students in each of the core subjects. These

results are presented for students who study Swedish and those studying Swedish as a second

language. Within this section of the report, there are five models for each of the core subjects,

with the models being assessed in stepwise procedure. Model 1, is a null model (that is, a model

without explanatory variables) and this is presented graphically. From this model, one is able

to assess the mean performance of each school and class without consideration for any

individual, school or class level variables. This is followed by model 2, individual level

characteristics (gender and non-native speaker) are tested. In Model 3, school level

characteristics (the share of parents with low/unknown education7 and the share of students

with a foreign background) are added to the model. In the fourth model class indicators are

added (the share of parents with low/unknown education8 and the share of students with a

foreign background, share of foreign language speakers). In the fifth and final models, the

results from students who completed national exams in Swedish, English and mathematics in

year 6 was examined as a predictor of year 9 performance.

The national exams in Swedish, English and Mathematics has several individually graded

sections (delprov), this led to some difficulty in creating a variable that works well in a model.

A difficulty or disadvantage in creating and using a variable from the various sections of the

exams is the fact that the results were coded as ‘did not participate’, ‘did not pass’ and ‘pass’9.

One could use this variable as is, i.e. as a categorical variable, or create a dichotomous variable

7 It was not possible to include the share of parents with unknown educational background and those with the share of low education in the same model, one is dropped from the analysis due to collinearity, therefore a variable is made where both measures are combined 8 It was not possible to include the share of parents with unknown educational background and those with the share of low education are in the same model one is dropped from the analysis due to collinearity. 9 For the English Language exam, for the listening, verbal, reading and understanding portion of the exam results was coded as points.

19

to look at the performance of year 9 students’ who had passed/not passed the national exams in

year 6. When the variable was categorized and the results of students for the various sections

of the exam was included in the model, it was not possible to run some of the models. The main

reason for this, is that there is collinearity between the variables. In addition, where the models

did work and it was possible to test some models, the results of these models were inconsistent,

making it difficult to interpret.

To resolve both of the above issues factor analysis was used to create a useable variable.

Factor analysis may be defined as a data reduction method which allows one to: (1)

define/create a small(er) set of variables from a larger set of variables; (2) create indexes which

measure similar things conceptually. Using factor analysis the results of students from the

various sections of the exam (delprov) has been grouped.

In the models where student’s grade point average was used as the outcome variable,

multilevel analyses included some variables that were not included in the earlier models but

have been discussed in the literature as explanatory variables, which have an impact on the

performance of students were tested further and/or similar variable used to check if the

relationship holds. For example, in the earlier models children were assessed simply by whether

they were native or non-native Swedish speakers. In these models, a more fine-grained

assessment on the impact of language was carried out through a comparison of student

performance among students with a European versus non-European background. Additional

model also examine the percentage of absences (frånvaro), the employment situation of parents,

competence for moving to higher education (behörighet), the effect on student achievement

when students remained in the same primary school as the preschool they attended and among

migrants we assess impact of coming from a poor country (Human development index). These

are important additions to the earlier models given that students with an immigrant background,

especially those studying Swedish as a second language; students that have a high proportion

of absences and those who have not fulfilled the requirements to move on to higher education

has been identified as being at risk for long term-unemployment, and internal and external

psychological problems (Andersson, 2015)

One issue that is worth noting among several of the models is the missing value, where the

level of school and class variation should be provided. The problems with estimating the

between school and between class variation after taking into account, the individual, school

and class level explanatory variables, could be interpreted as indication that most of the

performance differences have been explained by the variables in the model. This is doubtful

20

given the wide range of factors proposed to have an impact on student achievement. On the

other hand, the lack of variation between school and class could simply be an indication that

there is not enough schools in the analysis to fully assess the variation (Kreft et al., 1998).

2 Performance in Mathematics

2.1 Results for students taking Swedish

Figure 3a and 3b below, presents the result of a model which examines the variation between

school and classes without any explanatory variables for student achievement in mathematics

among students taking Swedish as a native language. In figure 3a are shown approximate 95%

confidence intervals for estimate of the intercept residual, that is the school 'effect' estimated at

the mean math score for each of the 11 municipal schools in the data. The result clearly

demonstrate that there is a slight variation in math achievement among schools. Figure 3b, on

the other hand, presents the approximate 95% confidence intervals for the mean math score for

each class within a school. These results depict larger more significant differences between

classes with schools. For example, when math achievement for all the schools are compared,

school 7 (Mosstorp) is among the schools with less than average math performance. Yet, if we

look specifically at the performance of classes, a class from Mosstorp is the middle of the

ranking table, while at the same time two of the classes from Mosstorp are at the lower end of

the performance ranking.

7 8 9 5 110

6 2 4 113

-1-.

50

.51

Sch

oo

l re

sid

ua

l

0 5 10

School rank

sub/slb (mean) reff1

Math scores - schools

21

Figure 3a. Predicted math scores by schools.

Figure 3b. Predicted math scores by classes within schools.

In table 3 below, the predicted math scores are shown for students in the year 9 students at the

student, class and school level. In the first model (model 1) only individual level characteristics

are estimated. The results of the model, indicates marginally worse math performance for boys

and marginally better performance for foreign language speakers.

Similarly, in model 2, although the results are non-significant there seems to be an indication

that boys are driving the low scores in math. On the other hand, there is some indication that

students with a foreign background taking Swedish as a native language achieve better results

in math when compared to native Swedish students.

When class level variables are considered, the results indicate that the share of students with

a foreign background is driving the lower math scores. In classes where there is a high share of

foreign language students in a class (non-linear effect), students perform significantly worse.

The non-linear effect suggest that there may be a threshold at which the proportion of students

with a foreign background does not matter. In models not shown, when there was a more even

division of the proportion of students with foreign background the results were insignificant.

In models where the effect of having received social benefits are considered the results are

somewhat inconsistent. In comparison to the reference group, in classes with students where

between 10 and 20% of the household has received social benefits at least once in the year prior

to the analysis, students have a lower predicted math score. However, in classes where 20-30

8 5 78 2 9 5 7 3 1 9

2 8 1 610 3 2 410 6 7 3 4 511 2 6 2 5 4 211

8 33

-4-2

02

4

Cla

ss r

esid

ua

l

0 10 20 30 40

Class rank

cub/clb (mean) reff2

Math scores - classes

22

% of the students are from households with social benefits, the results are positive. There is

again a negative effect of being in classes with students from social benefit receiving

households, when more than 30% of the students are from these types of households10.

Table 3. Students' achievement in maths at the school, class and student levels among students

taking Swedish as a native language.

Model 1 Model 2 Model 3 Model 4

Student level

boy -.72(.32) -.73(.37) -.60(.37) -.17 (.32)

non-native speakers .30(.45) .33(.47) .52(.52) .42(.52)

Year 6 Math NP

del A/del B 3.28***(.45)

del C/del D .76(.49)

School level

low/unknwown parental education -.42(.24) -.18(.26) -.18(.26)

share of foreign background .15(.09) .12(.10) .00(.10)

Class level

share of low/unknown parental education -.02(.05) -.03(.04)

share of foreign language speakers

0/.425 (ref.)

.426/.60 -2.15(.84)* -2.45(.79)*

.601/max -3.77(1.12)*** -4.23(1.05)***

share from social benefit receiving households

0/10 (ref.)

10.1/20 -1.26(.63)* -.92(.58)*

20.1/30 .04(.95) .53(.92)

30.1/max -1.77(.84) -.78(1.05)

intercept 11.35(.36)*** 11.36(.34)*** 12.79(.53)*** 12.56(.53)***

Random

school variance .11(.41) - .44(.40)

class variance 1.21(.69) 1.10(.57) -

between-class language variance - -

student variance 24.71(1.31) 24.72(1.31) 16.91(1.99)

Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale ranging from 0

to 20, in the new grading system this is equivalent to a grade of F to A, with F indicating that a student has failed the

course.

10 It is unlikely that these unstable results are due to the number of classes in each group, the categories were

divided so that 16, 8, 6 and 8 classes were in each respective group.

23

In the fourth model, alongside the variables discussed earlier, year 6 achievement in the

national exams is included as a predictor of math performance in year 9. As discussed above,

the national exam in math was divided into several parts, therefore to include the results in the

models factor analysis was used to derive a factor for each student which was then entered in

the model. The results of the factor analysis indicated that a two factor model is the best fit for

the data11.

The first factor (which includes section C and D of the national exams) ranges from -3.40 to

0.35. Factor one, may be described as the scores related to the written part of the exam.

Meanwhile, factor two, is related to the verbal and mental arithmetic (section A and B of the

national exams) respectively. Factor two has scores ranging from -3.60 to 0.25. Higher scores

indicate that students have taken and passed all parts of the exam, while lower scores indicated

that students did not complete the exam and or failed all parts of the exam12. This simply means

that a positive regression outcome in the analysis indicates that students with higher factor

scores are expected to have higher predicted math scores in year 9.Therefore, a negative

regression outcome is related to lower expected predicted math scores in year 9.

Overall, the indicator for students who successfully completed the national exams in math

in year 6, indicated that students perform better in year 9. Though the completion of verbal and

mental arithmetic indicates that students perform better in year 9, the results are non-significant.

The results indicated however, that successfully completing the written part of the exam, is

related to significantly better grades. It is also interesting to see that the share of foreigners in a

class had a higher significantly negative relationship with math achievement when student

results from year 6 is included in the model.

Given the above result, an attempt was made to gain a better understanding of classroom

composition on student performance. As such an interaction between the language spoken by

the students (i.e. among native and non-native Swedish speakers) and the share of foreign

language speakers in a class was tested in an additional model. The effect of this interaction is

highly significant at p>0.001, with results indicating that non-native speakers have a higher

performance in math compared to native Swedish speakers. The results further indicate that

11 Section C/Section D is the written part of the exam where students are required to explicitly demonstrate how

they arrived at their solution. Section A/Section B is the verbal and mental arithmetic sections of the exam. 12 8 students did not participate in the national exams in math while 479 passed all four sections of the exam and

21 students failed all four section of the exam

24

both non-native and native Swedish speakers perform marginally worse as the share of foreign

students in a class increases13.

Figure 4. Class composition- interaction between the share of foreign language speakers in a class, non-native

Swedish speakers and native speakers

The effect of classroom social composition was further examined through an interaction

between native Swedish speakers, non-native Swedish Speakers and the share of students in a

class whose families were in receipt of social benefits. The results indicate that non-native

speaker’s perform better than native Swedish speakers in low socio-economic environments.

This effect is statistically significant.

13 Share of foreign language speakers , number of classes in each category <42%=20, 42-60% =10, >60%=8

510

15

Pre

dic

ted

ma

th s

co

re

< 42% 42-60% > 60%Share of foreign language speakers

Native Swedish speaker Non-native speaker

Math scores

25

Figure 5. Class composition- Interaction between the share of students from social benefit receiving households,

non-native Swedish speakers and native speakers

.

2.2 Results for students taking Swedish as a Second Language

With models similar to those described above, school and class level variation in math scores

was assessed across the 8 municipal schools with students taking Swedish as a second language.

In figure 6a are shown approximate 95% confidence intervals for estimate of the intercept

residual, that is the school 'effect' estimated at the mean math score for each of the 8 municipal

schools with classes in Swedish as a second language. In comparison to students taking native

Swedish language courses there is more variation in results at the school level when the results

are compared for students who are taking Swedish as a second language (compare figure 3a

above with figure 6a below).

Figure 6b, on the other hand, presents the approximate 95% confidence intervals for the

mean math score for each class within a school. Math results at the class level are far more

heterogeneous among students taking Swedish as a second language when compared to students

taking Swedish as a native language. In fact, the class level variation among students taking

Swedish as a second language is more than twice that of students Swedish as a native language.

810

12

14

Pre

dic

ted

ma

th s

co

re

<9.9% 10-20% 20-30% >30%% families receiving social benefits

Native Swedish speaker Non-native speaker

Math scores

26

Figure 6a. Predicted math scores by schools.

Figure 6b. Predicted math scores by classes within schools.

Let us now turn our attention to the models in Table 4 (below) for students taking Swedish

as a second language. These models are comparable to those presented above for students taking

Swedish as a native language with the exception that there is no indicator the difference in

performance among native and non-native speakers. It is of course fair to assume that all the

5 1 4 8 6 10 2 3

-4.0

0e-0

8-2.0

0e-0

8

0

2.0

0e-0

84.0

0e-0

8

School r

esid

ual

0 2 4 6 8

School rank


Math scores - schools

1

5 2 2 58 8 4 3

2 8 10 10 6 3 48 2 3 3 10

1 83

2 2

-4-2

02

4

Cla

ss r

esid

ual

0 5 10 15 20 25

Class rank


Math scores - classes

27

students taking Swedish as a second language are non-native speakers. Therefore, in the first

model we control only for the effect of gender. The results indicate that boys with a foreign

background have lower predicted math scores when compared to girls with a foreign

background. This finding is of course similar to the models tested for students taking Swedish

as a native language, where we saw that boys had a lower predicted math achievement.

In model 2, controls were added for school characteristics (low/unknown parental

education and the share of students with a foreign background), the results clearly indicate that

schools with a high proportion of students with parents with low/unknown education has lower

predicted scores in math. On the other hand, the effect of the share of foreign students in the

class predicts low but positive effects on math performance.

Table 4. Students' achievement in maths at the school, class and student levels for students

taking Swedish as a second language.


Student level

boy -.19(.90) -.15(.90) -.30(.90) .04 (.99)

Year 6 Math NP 2.40***(.59)

School level

low/unknwown parental education -.44(.37) -.12(.39) .54(.50)

share of foreign background .16(.14) .12(.13) -.13(.16)

Class level



0/.425 (ref.)

.426/.60 -2.60(1.67) -1.91(2.40)

.601/max -3.15(2.09) -2.68(2.68)


0/10 (ref.)

10.1/20 -1.60(2.44) 1.69(3.78)

20.1/30 -1.21(2.36) 6.56(3.73)

30.1/max -3.24(2.49) 1.94(4.59)

intercept 8.44(.74)*** 8.41(.69)*** 12.52(2.45)*** 7.64(3.9)*

Random

school variance - - - -

class variance - - - -

between-class language

variance

- - -

student variance 33.86(3.89) 34.43(4.11) 33.94(3.95) 23.06(3.18)

Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale

ranging from 0 to 20, in the new grading system this is equivalent to a grade of F to A, with F indicating

that a student has failed the course.

28

The results of model 3 shows the effect of class characteristics on student outcomes and

this is tested with the inclusion of the share of low/unknown education of students’ parents,

the share of foreign language speakers in a class and the share of students from social benefit

receiving households. All three variables have a negative effect on math scores.

In model 4 the results of the national exams in mathematics taken in year 6 are added as an

additional predictor to the models. The factor scores ranged from -3.07 to 0.74. Higher scores

indicate that students have taken and passed all parts of the exam, while lower scores indicated

that students did not complete the exam14. This simply means that a positive regression outcome

in the analysis indicates that students with higher factor scores are expected to have higher

predicted math scores in year 9.Therefore a negative regression outcome is related to lower

expected predicted math scores in year 9. With the inclusion of the results from the national

exams in year 6, the strength of the effect of several variables became weaker (gender, share of

parents with low/unknown education and share of students from social benefit receiving

households) but the sign did not change. Among the students that had completed the math exam

in year 6, they were predicted to perform significantly better in math in year 9.

3 Performance in English

3.1 Results for students taking Swedish

Figure 7a and 7b below, presents the results of a model which examines the variation

between school and classes without any explanatory variables for student achievement in

English among students taking Swedish as a native language. In figure 7a are shown

approximate 95% confidence intervals for estimates of the intercept residual, that is the school

'effect' estimated at the mean English score for each of the 11 municipal schools. The results

clearly indicate that there is no variation in English language achievement at the school level.

Figure 7b, on the other hand, presents the approximate 95% confidence intervals for the mean

English score for each class within a school. This shows larger more significant differences

between classes with schools. For example, when English achievement for all the schools are

compared, school 1 (Borgsmo) has performed the worst in English. Yet, if we look specifically

at the performance of classes, a class from Borgsmo is at the lower end of the ranking, while at

the same time one of the classes from Borgsmo is at the higher end of the performance ranking.

14 Among the students taking Swedish as a second language 105 students took the exam, but of these only 42

students completed and passed all sections of the exam. It is also clear from the descriptive statistics that 8 student

failed all four section of the exam. Meanwhile 74 students had no information with regards to the exam, it is clear

that they did not sit the national exams in mathematics in year 6. In addition, there were 4 student who were

registered but did not sit the exam.

29

Figure 7a. Predicted English scores by schools.

Figure 7b. Predicted English scores by classes within schools.

In table 5 below, the predicted English language scores are shown for year 9 students at the

student, class and school level. In model 1, only individual level characteristics explanatory

1 7 10 3 9 6 8 11 2 4 5

-.0

00

02

-.0

00

01

0

.00

00

1.0

00

02

Sch

oo

l re

sid

ua

l

0 5 10

School rank


English scores - schools

7

1

2 310810 6 3 8 9 1 3 8 6 2 5 2 8 511 4 9 7 3 7 4 3 2 411 6 2

2

58

5

-4-2

02

4

Cla

ss r

esid

ual

0 10 20 30 40

Class rank


English scores - classes

30

variables are included in the model. The results of indicate that boys perform significantly

worse than girls, while non-native speakers perform significantly better as compared to native

Swedish students. Even with the addition of school level explanatory (model 2) factors the

effect for non-native speakers and boys remain. The results indicate that the share of students

with a foreign background predict marginally better scores in English.

Table 5. Students' achievement in english at the school, class and student levels for students

taking Swedish.


Student level

boy -1.01(.38)** -1.05(.38)** -.92(.37)* -.88 (.27)

non-native speakers 1.19(.46)* 1.19(.54)* 1.39(.47)** .60(.36)

Year 6 English NP

del A/ C 1.48(.17)***

del B1/del B2 3.44(.16)***

School level

low/unknwown parental education -.37(.26) .11(.29) -.44(.34)

share of foreign background .10 (.10) -.02(.11) -.16(.13)

Class level



0/.425 (ref.)

.426/.60 -.75(.89) -.91(.68)*

.601/max -2.73(1.24)* -2.65(.95)**


0/10 (ref.)

10.1/20 -.19(.75) 1.12(.50)*

20.1/30 1.88(.99) 3.22(.81)***

30.1/max .40(1.09) 3.24(.94)***

intercept 13.03(.37)*** 13.04(.36)*** 13.18(.58)*** 12.46(.52)***

Random

school variance - - -.34(.45) -

class variance 1.69(.75) 1.46(1.56) .24 (.42) -

between-class language variance - - -

student variance 25.21(1.43) 25.11 (1.71) 24.93(1.32)*** - Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale ranging

from 0 to 20, in the new grading system this is equivalent to a grade of F to A, with F indicating that the

student has failed the course.

On the other hand, a high share of students with parents that have low/unknown educational

background predicts marginally lower English language performance as compared to students

with parents that have higher levels of education.

31

In model 3, controls are added for class composition, share of low/unknown education

students in class, share of foreign language speakers in class. A high share of foreign language

speakers in a class had a strong negative effect on English scores. Surprisingly, the share of

students from social benefit receiving households in a class predicts higher achievement among

students.

In the fourth model, alongside the variables discussed earlier, year 6 achievement in the

national exams is included as a predictor of English performance in year 9. Similar to Math, the

English national exams is made up of several sections. Consequently, factor analysis was used

to reduce the number of variables. The results from this indicated that two continuous factors

were created, and these were added to the model in an attempt to explain how much prior

performance explains later achievements.

The first factor (which includes section B1 and B2 of the national exams) ranges from -2.70

to 1.48. Factor one, may be described as the scores related to the reading/listening and

understanding sections of the exam. Meanwhile, factor two, is related to the verbal and written

sections (section A and c) of the exam respectively. The factor scores ranged from -4.37 to .97.

For both factors, higher scores indicate that students have taken and passed all parts of the exam,

while lower scores indicated that students did not complete the exam and or failed all parts of

the exam. This simply means that a positive regression outcome in the analysis indicates that

students with higher factor scores are expected to have higher predicted English language scores

in year 9. Therefore, a negative regression outcome is related to lower expected predicted

English scores in year 9.

The inclusion of year 6 achievement in the model indicated that students who successfully

completed English in year 6 is predicted to perform significantly better in grade 9, with higher

average grades in English. Completing the verbal and listening sections of the exam is predicted

to have a particularly strong impact on later achievements.

With the inclusion of year 6 English scores in the model, the share of foreign language

speakers at the class level is shown to have a strong negative non-linear effect on English scores.

One explanation for weak performance in English among students with a foreign background

is that students may choose to communicate in their mother-tongue. Appendix Table A13,

indicates that many of the students with foreign backgrounds are from Arabic speaking

countries. A high proportion in one environment would contribute to this. The estimates from

the model also indicated that higher shares of students from social benefit receiving households

32

in a class predicts better results in English. This result is somewhat counterintuitive and I really

have no explanation for this finding.

3.2 Results for students taking Swedish as a second language

Figure 8a and 8b, presents the result of a model which examines the variation between school

and classes without any explanatory variables for student achievement in English language for

each of the 8 municipal schools with classes in Swedish as a second language. In figure 8a are

shown approximate 95% confidence intervals for the estimate of the intercept residual, that is

the school 'effect' estimated at the mean English score. Figure 8b, on the other hand, presents

the approximate 95% confidence intervals for the mean English score for each class within a

school.

In comparison to students taking native Swedish language courses there is far greater

variation in results at the school level when the results are compared to students who are taking

Swedish as a second language (compare figure 7a above with figure 8a below). Similarly,

English results at the class level are far more heterogeneous among students taking Swedish as

a second language when compared to students taking Swedish as a native language. In fact, the

class level variation among students taking Swedish as a second language is more than twice

that of students taking Swedish as a native language.

Figure 8a. Predicted English scores by schools.

8

1

5

6 34

10 2

-4-2

02

4

School re

sid

ual

0 2 4 6 8

School rank


English scores - schools

33

Figure 8b. Predicted English scores by classes within schools.

The results of models 1-4, for students taking Swedish as a second language (table 6, below) is

similar to that of students taking Swedish as a native language. At the individual level boys are

predicted to perform worse than girls. While at the school and class level a high share of

students with parents that have low/unknown education and a high share of foreign language

speakers predict lower scores. Across all models the effect of high share of students with a

foreign background and a high share of students from social benefit receiving households in a

school is positively associated with student outcomes. These relationships however seem to be

a spurious relationship and may be due to collinearity. To test these effects further more

individual level variables are needed.

In model 4, the results of the national exams in English language taken in year 6 is added as

an additional predictor to the model. The results from the factor analyses indicated that two

factors was the most appropriate fit for the data. The factor scores for the speaking, reading and

written part of the exam ranged from -2.25 to 1.16 while the listening section of the exam had

factor scores ranging from -1.77 to 1.86. As with the earlier models discussed, higher factor

scores indicate that students have taken and passed all parts of the exam, while lower scores

indicated that students did not complete the exam. This simply means that a positive regression

outcome in the analysis indicates that students with higher factor scores are expected to have

8

1 25 2 8 3 3

103 8 4 6 5 1 4 8

3 2 3 10 2 10 8 2 2

-6-4

-20

24

Cla

ss r

esid

ua

l

0 5 10 15 20 25

Class rank


English scores - classes

34

higher predicted English scores in year 9.Therefore a negative regression outcome is related to

lower expected predicted English scores in year 9.

The inclusion of these factors indicated that students who successfully completed English in

year 6 is predicted to perform significantly better in grade 9, with higher average grades in

English.

Table 6. Students' achievement in English at the school, class and student levels for

students taking Swedish as a second language.


Student level

boy -1.14(.93) -1.00(.93)** -1.40(.91) -1.03 (.91)

Year 6 English NP

del A/B1 & C 3.80(.5)***

del B2 2.54(.58)***

School level

low/unknwown parental education -1.3(.46)** -1.04(.40)** -1.34(.45)**

share of foreign background .49 (.13)** .44(.14)*** .49(.15)***

Class level

share of low/unknown parental education -.03(.04) .10(.08)


0/.425 (ref.)

.426/.60 -2.91(1.70) -2.77(2.21)

.601/max -4.79(2.11)* -3.37(2.46)


0/10 (ref.)

10.1/20 .26(2.46) 2.79(3.53)

20.1/30 -.36(2.38) 3.36(3.53)

30.1/max 2.21(2.51) 4.77(4.37)

intercept 7.93(1.06)*** 8.09(.80)*** 9.38(2.47)*** 6.88(3.68)

Random

school variance 3.33(3.62) - - -

class variance - 3.41(2.53) - -

between-class language variance - -

student variance 35.17(4.00) 35.07 (4.91) - 19.7 (2.67) Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale

ranging from 0 to 20, in the new grading system this is equivalent to a grade of F to A, with F indicating

that the student has failed the course.

35

4. Performance in Swedish as a native language

Figure 9a and 9b below, presents the results of a model which examines the variation between

school and classes without any explanatory variables for student achievement in Swedish as a

native language. In figure 9a, are shown approximate 95% confidence intervals for the estimate

of the intercept residual, that is the school 'effect' estimated at the mean Swedish score. These

results clearly demonstrates that there is no variation in Swedish achievement among schools.

Figure 9b, on the other hand, presents the approximate 95% confidence intervals for the mean

English score for each class within a school. This figure indicates that there are some small but

significant differences in performance between classes with schools.

Figure 9a. Predicted Swedish scores by schools.

1 10 9 4 6 3 5 2 11 7 8

-2.0

0e

-08

-1.0

0e

-08

0

1.0

0e

-08

2.0

0e

-08

Sch

oo

l re

sid

ua

l

0 5 10

School rank


Swedish scores - schools

36

Figure 9b. Predicted Swedish scores by classes within schools.

Table 7 below, presents the result for the predicted achievement in Swedish. The results

from these models indicate that boys have lower grades in Swedish than girls and that students

with a foreign background taking Swedish as a native language get better grades when

compared to native Swedish students. These results hold for all models.

In model 2, where controls are added for school characteristics, the effect of language

increases marginally, with estimates indicating that non-native Swedish speakers perform better

in Swedish when compared to native speakers. Share of students from lower/unknown

education backgrounds predicts negative Swedish performance but schools with higher share

of students with a foreign background have a higher performance.

When both school and class characteristics are added to the model (model 3), the difference

in performance in Swedish among students with a foreign background and native Swedish

speakers becomes even larger. Non-native Swedish speakers get better grade in Swedish. The

results indicate, however, that in classes with a higher share of foreign speakers the performance

of students become worse. It seems that there may be some high performing non-native

speakers in predominantly native speaking classes that are driving the effect but then there are

also native speakers in non-native classes performing badly. To examine this further, an

11 4 5 3 2 5 910 3 6 8 3 610 8 2 911 7 4 7 4 2 8 2 2 2 3 5

611 5 73

8

-4-2

02

4

Cla

ss r

esid

ual

0 10 20 30 40

Class rank


Swedish scores - classes

37

interaction model between the share of foreign language speakers in a class, non-native Swedish

speakers and native speakers was tested.

.

Figure 10. Class composition- interaction between the share of foreign language speakers in a class, non-native

Swedish speakers and native speakers

The estimates (figure 10) indicate that as the share of foreign language speakers in a class

increases, performance in Swedish decline. It is interesting to note that non-native speakers

score higher in Swedish when compared to native Swedish speakers. The reason for this may

be that non-native speakers are more motivated to learn the language, to show for example

employers and to be accepted in higher education courses. It is however a given that native

Swedes “know the language”.

Although the Swedish national exam had several individually graded sections only one factor

was created ranging from -5.20 to .38. Higher scores indicated that students have taken and

passed all parts of the exam, while lower scores indicated that students did not complete the

exam15. This simply means that a positive regression outcome in the analysis indicates that

students with higher factor scores are expected to have higher predicted Swedish scores in year

9.Therefore a negative regression outcome is related to lower expected predicted Swedish

scores in year 9.

15 7 students did not participate in the national exams in Swedish in year 6 while 515 passed all four sections of

the exam.

38

Table 7. Students' achievement in Swedish at the school, class and student levels.


Student level

Boy -2.70(.31)*** -2.72(.31)*** -2.61(.30)*** -2.09(.30)***

Non-native speakers .79(.38)* .94(.40)* 1.11(.39)** .83(.39)*

Year 6 Swedish NP 1.76(.17)***

School level

low/unknwown parental education -.18 (.21) .05(.21) .05(.19)**

share of foreign background .04(.08) .01(.08) -.16 (.07)*

Class level



0/.425 (ref.)

.426/.60 -1.08 (.77) -1.57 (.69)*

.601/max -2.30(1.00)* -3.31(.89)***

share of students from social benefit receiving households

0/10 (ref.)

10.1/20 -.62(.61) -.16(.53)

20.1/30 .33(.88) 1.00(.79)

30.1/max -.92(.95) -.40(.88)

intercept 13.62(.29)*** 13.47(.30)*** 14.30(.66)*** 14.09(.45)***

Random

school variance - - - -

class variance - .42(.36) .33 (.33) .11 (.21)

between-class language variance - - - -

student variance 16.90 16.87(.90) 16.83 (1.63) 14.31 (.81)

Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a

scale ranging from 0 to 20, in the new grading system this is equivalent to a grade of F to A, with

F indicating that the student has failed the course.

The results from the regression analyses demonstrates that controlling for student

performance in Swedish in year 6 national exams, marginally reduces the effect of gender and

non-native speakers. The estimates indicate that when controls for prior achievement is

included in the model, the share of students from lower/unknown education backgrounds at the

school level, predicts negative Swedish performance. On the other hand, the effect of having a

higher share of students with a foreign background at the school level indicates a lower

performance.

39

5. Additional models

Some additional models were explored to examine the effect of absences, the length of time

students with a foreign background has been living in Sweden, and predicted estimates for the

performance of students with foreign background, comparing student with European and non-

European backgrounds.

When math (table A1 & table A2) is examined as the outcome with only individual level

predictors, there are a few results that should be noted. The first is that, student with a foreign

background who study Swedish as a second language perform significantly worse than native

Swedish students (model 1). When this result is compared with model 2, one sees that it is non-

native, non-Europeans who take Swedish as a second language performance that is driving these

results. When absences and competence to move on to high school is considered, the effect of

gender disappears, while the effect of absences decreases. In table 4, when class and school

level predictors are entered in the model, the gender, non-native students taking Swedish as a

second language, were predicted to have low math scores. The percent of foreign language

speakers in a class seemed to have an effect on math outcomes but this disappeared when the

economic conditions of the family is added to the model.

In general when English language (table A3 & A4) is considered as the outcome, the results

are similar to that of previous models with respect to the effect of gender and non-native

students taking Swedish as a second language. However, the effect of absences had a far weaker

effect than expected, especially in models where competence to move on to high school is

considered. When class and school predictors are entered in the model there is a strong negative

effect in classes where the share of foreign speakers in a class is high. Where economy is

considered, there is a marginally positive effect on student performance when the share of both

parents working is high.

In table A5 and A6, the performance of student taking Swedish as a native language is

considered. It is very interesting to see that the performance of non-native students and non-

native non-Europeans is significantly better than native Swedish speaking students. One

explanation for this is that non-natives require good grades to show employers their competence

in the language, while for native Swedish students it is a “given” that they are able to speak the

language.

Where the results of students taking Swedish as a second language (table A7 & A8) is

considered, there is no significant difference in the performance of students who have a

European versus those with a non-European background. The results were also strongly tied to

student absences and the proportion of students in a class who spoke a foreign language. The

40

share of students with 2 parents from abroad seemed to have a significant positive effect on

grades. This result may however be due to the fact that all the students taking that course has a

foreign background, and that is the effect we are actually seeing.

As in earlier analysis, in all models and across all outcomes tested, there is a greater variation

in results among class than between schools. This may be the true picture of things but some

might argue that there are two few schools in the analysis to truly determine if class has a bigger

effect than schools.

6. Overall achievement

In this section of the analyses the effect of schooling on grade point average is examined.

The grade point average for primary schools is created by summing the student’s 16 best final

grades giving maximum score of 320 points16. The grade according to the old grading system

is MVG=20, VG=15 and G=10. The grade point average may be calculated according to the

new system by summing the value of each given grade: A=20, B=17, 5, C=15, D=12, 5 E=10

and F= 0. As with the core subjects, when grade point average is examined the results of model

1 (table A9) indicated that boys performed significantly worse than girls. The results of this

model also indicated that non-native students studying Swedish had a significantly higher

predicted grade point average when compared to native Swedish students’. On the other hand,

non-native students studying Swedish as a second language were predicted to have lower scores

as compared to students with a foreign background and native Swedes taking Swedish as a

native language.

In model 2, we investigated further the impact of having a foreign background on student

achievements through an examination of the effects of schooling among students originating

from another European country in comparison to students with a foreign background originating

from a Non-European country. Interestingly the results, demonstrate that European or non-

European language background does not have a significant impact on student outcomes. This

is demonstrated by the marginal differences in predicted outcomes between the two groups.

These result retained their significance even after an addition control was included in the next

model (model 3, table A9) to test the effect of the number of years that the students had moved

to Sweden.

16 There are a few students who take more than 16 courses which gives them a grade point average over this

amount.

41

One variable that was not included in the earlier discussed models, is an indicator for

students who are attending the primary school they attended in pre-school (model 8 & 9, table

A9). The performance of these students did not appear to be significantly different from

students who had changed school.

Figure 11. Predicted grade point average among students taking Swedish as a second language versus those

taking native Swedish language courses

Figure 11 (based on results from model 3, Table A9) displays the predicted grade point

average for students taking Swedish as a second language versus those taking native Swedish

language courses. The number of students in each category is also displayed. The effect is

shown separately for students with a foreign background by region of origin (i.e. Europeans

versus non-Europeans). The green line represents students with a foreign background, while

the pink line represents native-Swedish students who study Swedish as a native language. When

one compares the results among these two categories of students, the results clearly indicate

that there is no difference in achievements among Swedish students and those with a foreign

background in terms of achievement. If we then turn our attention to students who take Swedish

as a second language (the blue lines), students region of origin seems to have no impact on

performance, the results demonstrate similarly low achievement among these groups. In

contrast, when students taking Swedish as a native language (both those with a foreign

42

background and native Swedish students) is compared with students taking Swedish as a second

language we see that students taking Swedish as a second language and are of non-European

origin have significantly lower grade point average. As previously stated, students with

European origins also have similarly low grades, this is however not significant due to the small

number of students in this category.

Given the seemingly detrimental association between Swedish as a second language and

student achievement, it seems that very few students’ progress to Swedish as a native language.

In figure 12 and figure 13, we see the proportion of students taking Swedish as a second

language after having moved to Sweden in the last 4 years and 6 years respectively17.

Figure 12. Proportion of students taking Swedish as a second language after having moved to Sweden in the last

4 years

17 The numbers on the graph represents the school and class identifiers, this information is not necessary for

interpretation of the figures

The intercept presented in the figures may be defined as the

mean grade point average for native Swedish girls with less

than 5% absences.

43

The figures show that there is a high proportion of students taking Swedish as a second language

although they have been residing in Sweden for an extended period of time. Evidence of this is

depicted in the shift in the school positions between year 4 and year 6. From the figures we note

also that approximately 75% of students in Söderport (coded 102 and 101) taking Swedish as a

second language has been residing in Sweden for between 4 and 6 years. If there is progression

in language learning, it is hard to understand how these students could still be taking Swedish

as a second language.

Figure 13. Proportion of students taking Swedish as a second language after having moved to Sweden in the last

6 years

One of the most striking individual level predictors is the proportion of recorded absences.

As the proportion of absences increase the models predict significant declines in grade point

average. The results from two separate models which includes the effects of absences are

shown in figure 14 and figure 15 below. In both figures statistically non-significant effects are

faded.

44

The results from figure 14 (table A9) indicates that as the proportion of absences among

students increase grade point average decreases. When a range of school and class level factors

are considered (figure 15, table A10), the negative effect of absences on student performance

becomes larger.

The results of the other predictors in the models shown above in figure 14 and figure 15

largely predict a negative relationship with grade point average. Indicating that boys have

significantly lower grade point average in comparison to girls. Similarly, having moved to

Sweden within the last four years ; and not having fulfilled the criteria to attend high school; a

high proportion of parents with low/unknown education; high share of students who are foreign

language speakers and high share students coming from countries with a low human

development index, all predict lower grade point average.

Figure 14. Predicted grade point average among with controlling for the effect of students who migrated to

Sweden in the last four years

45

Figure 15. Predicted grade point average among with controlling for a wide range of school and class level

effects

Below several of the variables that seem to have the strongest impact on the models are

explored further. Figure 16 and figure 17 (table A10), provides a graphical representation of

the predicted grade point average for the share of foreign language speakers in a class. The

results indicate that a high share of foreign language speakers is associated with lower

performance among students. Both Söderport and Navestad has classes with a high proportion

of students that are foreign language speakers, there is however a significant difference in

performance in these classes, with the students in Navestad performing significantly worse.

More than any other school in the data, Söderport has several classes where more than 75% of

the students are foreign language speakers. The students’ in these classes have achievement

levels similar to that of many schools with lower proportions of foreign speakers. This is an

indication of two things. The first is that having an immigrant background does not necessarily

mean that students will have lower achievements. Secondly, it seems that despite the students

background the school has been able to produce students with reasonable achievements and as

such maybe other schools should examine their approach and try to replicate the aspects which

seem to be working.

46

Figure 16. Predicted grade point average for the share of foreign language speakers by class

On closer examination of figure 17, which shows the predicted grade point average for the

share of foreign language speakers in a class by school there are several noteworthy things.

There is generally more variation between classes in schools where there are classes with a high

proportion of foreigners in a class. In Hagaskolan for example, there are four classes. The two

with a low share of foreigners had very similar grade point average, approximately 260.

However, there was more variation between the classes with a higher proportion of foreigners

and they had significantly lower grade point averages (approximately 225). It is also interesting

to see that Råsslaskolan has no classes with students with a foreign background, while Hultdal

and Mosstorp have very low proportion of students with a foreign background. Within these

schools there is little or no variation in grade point average between the classes and students

generally have a grade point average of approximately 250 points. What these results suggest

is that a more even distribution of students with foreign background within classes and across

schools may reduce the variation between classes, which in turn may reduce the individual level

achievement gaps.

47

These results are also seem to explain the findings in the descriptive statistics. The overview

of the grade point average from 2010-2014, indicates a large gap between the lowest and highest

performing schools. What we can see from these models is that schools and classes with a high

proportion of students with a foreign background account for the lower achievements.

Figure 17. Predicted grade point average for the share of foreign language speakers by class by school

In the next set of figures (figure 18 and figure 19, below) we examine the relationship between

achievement and the share of students coming from poor countries (defined as countries

identified having a low human development index). The results indicate simply that a higher

share of students in a class from poor countries predict lower achievement. This is clearly

exemplified in figure 19, which shows the predicted grade point average for the share of

students from poor countries by class and school.

48

Figure 18. Predicted grade point average for the share of foreign language speakers by class

49

Figure 19. Predicted grade point average for the share of students from poor countries by class and school

Figure 20 and figure 21 below depicts the relationship between achievement and the share

of students with two parents employed. The results from figure 20 indicate that grade point

achievement is higher among students with both parents in employment. The graph further

visualizes the variation in performance among classes within schools, and similar to the earlier

figures discussed, a class in Navestad stands out. There is one with where there are no students

with both parents employed. Figure 20, allows for a closer examination of these differences at

the class level. One can see that Borgsmo and Navestad has both the lowest level of

achievements among students and the lowest share of students with both parents in

employment. On the other hand, Hagaskolan and Djäknepark have the highest achievements

and the highest proportion of parents in employment. These results confirm and strengthen the

earlier findings indicating the importance of the economic situation of the family, and points to

the importance of family background for school achievement. This is of course plausible

because parents that have a better economic situation is able to supplement their children’s

50

education through human and social capital. For example, the purchase of study help and

tutorial, to provide extra support to their children if it is required.

Figure 20. Predicted grade point average for the share of students with two parents that are employed by school

51

Figure 21. Predicted grade point average for the share of students with two parents that are employed by class

and school

Figure 16, 18 and 20, tells an interesting story. In figure 20, in the far left hand side we see that

majority of students with a low grade point average are also in classes where there is a low

proportion of students with two parents in employment. The results from figure 18, seems to

explain that children with a low share of two working parents have a foreign background (high

share of foreign language speakers in a class) and that they are from Low HDI countries (figure

16). The school with the most students from low HDI countries, has lowest share of students

with two employed parents and have a highest proportion of foreign language students,

Navestadskolan. As discussed previously the students with these characteristics have

significantly lower achievement levels when compared to other students, it is therefore not

surprising that Navestad is one of the lowest performing school among the municipal schools

in Norrköping. Further background characteristics of the students in this school, as it relates to

this analysis, may be found in table A12.

52

7. Conclusion

This report had two overarching aims. These were to assess the between and within school

variation in achievement among communal run primary schools in the municipality of

Norrköping. Based on the analysis the findings indicate that among schools, there are small,

non-significant differences in achievement across the core subjects and grade point average. On

the other hand, when class variation is considered there are larger more significant differences

in the achievement of students across the core subjects and grade point average. Beyond this,

individual level differences was the largest explanatory determinant of student outcomes.

However, due to data limitations, it was not possible to thoroughly assess the effect of these

factors.

At the individual level, gender had one of the strongest relationships with achievement,

whereby boys perform significantly worse than girls in all core subjects and in overall student

performance. In addition, there was significantly lower achievement among students with a

foreign background studying Swedish as a second language when compared to students taking

Swedish as a native language. However, among students taking Swedish as a second language,

there is no difference in achievement among European and Non-European students with a

foreign background.

Differences in achievement at the classroom level has been explained by the student’s

economic situation, whereby students whose families received social benefits at least once in

the year prior to the analysis and/or they are from households where both parents were not in

employment, had lower grades. There were also significant differences in student achievement

among students with a foreign background studying Swedish as a native language and native

Swedish students. Students with a foreign background studying Swedish as a native language

outperformed native Swedish students across the core subjects, but there is no significant

difference in grade point average between the groups. Classroom social composition, seemed

to play an integral role in the achievement of students. The results indicated that both non-native

and native Swedish speakers perform worse as the share of foreign students in a class increases.

Nevertheless, non-native Swedish speakers tend to perform better than native Swedish speakers

in low socio-economic environments.

School level variation has been explained by, a high share of students with parents having

low/no education and a high share of students with a foreign background. These factors have

contributed to lower achievement among students. Although, it must be said that these variables

appear to inflate each other.

53

Overall, it seems that once students are placed in Swedish as a second language, despite the

detrimental association with their performance in other subjects, they do not seem to progress

to Swedish as a native language.

In conclusion, it is important to note that students do not perform significantly worse by

virtue of any single factor. Instead, it seems to be the interaction between individual, school and

class level factors which determines a student outcome. For e.g. students with a foreign

background who take Swedish as a second Language have significantly lower grades in

comparison to students with a foreign background who take Swedish as a native Language.

Moreover, schools that offered Swedish as a second language had a higher proportion of

foreigners per class and based on the results this leads to lower performance among both native

and non-native Swedish speaker.

8. Future analyses

Although the results of this study has provided some insights into the differences in school

achievements and has provided starting point for making improvements, many unanswered

questions still remain that would add significantly to our knowledge. For instance, would

students with the same individual level characteristics have the same level of achievement in

different schools? We do know that classes with high share of students with a foreign

background have lower achievements. Is it only non-native speakers that drive the average

grades down? How much of this poor performance can be attributed to native Swedish students?

Among native Swedish students with low performance, how much of that could be attributed

to economic factors? Does recently arriving immigrants adapt to the schooling environment at

a faster rate and begin to perform better in classes with higher proportions of native-Swedish

students? Is the level of achievement among newly arriving immigrants’ worse in less deprived

environment? What is, if any, the time of adaptation for newly arriving immigrants? How does

it relate to economic factors? How does it differ if students are placed in Swedish as a native

language instead of Swedish as a second language? How does the performance of students in

communal schools differ from students in independent schools? What accounts for these

differences?

It was not possible to answer the above questions due to the type, quality and quantity of

data. This limitation was largely due to the fact that the data was cross-sectional and aggregated.

This has meant that it was not possible to investigate changes over time or to make any

conclusions with regards to causality. In addition, the aggregated nature of the data has meant

54

that there were few individual level variables, which is necessary to investigate the majority of

the questions mentioned here. This has also contributed to what can be seen as a missing

variable problem, whereby many of the explanatory variables identified in the literature were

not included in the current analyses. With longitudinal and individual level data, it would be

possible to answer many of the current unanswered questions and thereby allow for the correct

policy changes to reduce the gap in student achievement.

9. References

Aitkin, M., & Longford, N. (1986). Statistical modelling issues in school effectiveness studies.

Journal of the Royal Statistical Society. Series A (General), 1-43.

Andersson, C. (2007). Lärartäthet, lärarkvalitet och arbetsmarknaden för lärare: Institutet för

arbetsmarknadspolitisk utvärdering (IFAU).

Andersson, J.(2015). Alla barn genom skolan. Norrköping Kommun.

Björklund, A., Edin, P.-A., Freriksson, P., & Krueger, A. B. (2004). Education, equality and

efficiency: An analysis of Swedish school reforms during the 1990s. IFAU report, 1,

72.

Bosker, R. J., & Scheerens, J. (1994). Alternative models of school effectiveness put to the test.

International Journal of Educational Research, 21(2), 159-180.

Böhlmark, A., Grönqvist, E., & Vlachos, J. (2012). Rektors betydelse för skola, elever och

lärare. Stockholm: IFAU Rapport, 15.

Böhlmark, A., & Holmlund, H. (2012). Lika möjligheter. Familjebakgrund och

skolprestationer 1988–2010, 40.

Coleman, J. S. (1990). Equality and achievement in education: Westview press.

Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D.,

& York, R. (1966). Equality of educational opportunity. Washington, dc, 1066-5684.

Erikson, L. (2008). Föräldrar och skola–olika innebörder. Ur Vi lämnar till skolan det käraste

vi har… Om samarbete med föräldrar–en relation som utmanar. Myndigheten för

skolutveckling.

Finnman, J, Andersson,B, Bjelvenius, H, Båvner, B, Fridell, M, Melchert,R & Mohammad,

J. (2015). Öppna jämförelser. Grundskola 2015: Tema systematiskt kvalitetsarbete

med skl:s analyshandbok. SKL Rapport. http://webbutik.skl.se/sv/artiklar/oppna-

jamforelser-grundskola-2015.html

Fredriksson, P., Oosterbeek, H., & Öckert, B. (2012). Långsiktiga effekter av mindre klasser:

IFAU Rapport.

Goldstein, H. (1997). Methods in School Effectiveness Research∗. School effectiveness and

school improvement, 8(4), 369-395.

Goldstein, H., Rasbash, J., Yang, M., Woodhouse, G., Pan, H., Nuttall, D., & Thomas, S.

(1993). A multilevel analysis of school examination results [1]. Oxford review of

education, 19(4), 425-433.

Gustafsson, J.-E., & Yang-Hansen, K. (2009). Resultatförändringar i svensk grundskola. Vad

påverkar resultaten i svensk grundskola, 40-82.

Gustafsson, J., & Yang Hansen, K. (2009). Vad påverkar resultaten i svensk grundskola?

Kunskapsöversikt om betydelsen av olika faktorer: Skolverket Stockholm.

Hinnerich, B. T., Höglin, E., & Johannesson, M. (2011). Are boys discriminated in Swedish

high schools? Economics of Education Review, 30(4), 682-690.

55

Högdin, S. (2006). Hemmets resurser. Om ungdomars upplevelse av föräldrars stöd och

engagemang i deras skolgång. Pedagogisk forskning i Sverige, 11(1), 1-19.

Jönsson, A., & Rubinstein Reich, L. (2004). Invandrade akademiker som lärare i den svenska

skolan,(Rapport 2004: 18). Uppsala: Institutet för arbetsmarknadspolitisk utvärdering.

Kreft, I. G., Kreft, I., & de Leeuw, J. (1998). Introducing multilevel modeling: Sage.

Lindahl, E. (2007). Comparing teachers' assessments and national test results: evidence from

sweden: Working Paper, IFAU-Institute for Labour Market Policy Evaluation.

Rumberger, R. W., & Palardy, G. J. (2004). Multilevel models for school effectiveness research.

Handbook of quantitative methodology for the social sciences, 235-258.

Sandoval-Hernandez, A. (2008). School effectiveness research: a review of criticisms and some

proposals to address them. Educate~, 1(1), 31-44.

Sandqvist, J. (2007). Provbetyg-slutbetyg-likvärdig bedömning?: en statistisk analys av

sambandet mellan nationella prov och slutbetyg i grundskolans årskurs 9, 1998-2006:

Skolverket.

Visscher, H. L., * Adrie, & Witziers, B. (2005). School effectiveness research: From a review

of the criticism to recommendations for further development. School effectiveness and

school improvement, 16(3), 249-279.

von Greiff, C. (2009). Lika skola med olika resurser. En ESO-rapport om likvärdighet och

resursfördelning, ESO rapport, 5.

Östh, J., Andersson, E., & Malmberg, B. (2013). School choice and increasing performance

difference: A counterfactual approach. Urban Studies, 50(2), 407-425.

56

10. Appendix

Table A1. Models for math. Individual level variables

Math

(1) (2) (3) (4)

boy -0.64* (0.35) -0.63* (0.35) -1.01*** (0.33) -0.34 (0.24)

non-native, taking Swedish 0.27 (0.47) -0.22 (0.45) -0.31 (0.33)

non-native, taking Swedish as 2nd lang -2.65*** (0.53) -2.73*** (0.51) -0.04 (0.40)

other European, taking Swedish -0.37 (0.62)

other European, taking Swedish as 2nd lang -1.50 (1.18)

other non-European, taking Swedish 0.90 (0.62)

other non-European taking Swedish as 2nd lang -2.67*** (0.54)

absence: less than 5% (ref.)

5-10% -0.33 (0.45) -0.49 (0.33)

10-20% -1.39*** (0.45) -0.73** (0.33)

20-50% -4.11*** (0.52) -1.76*** (0.39)

50% and more -8.01*** (1.03) -2.11*** (0.79)

not qualified for high school -8.53*** (0.31)

Constant 11.29*** (0.38) 11.27*** (0.38) 12.91*** (0.52) 13.61*** (0.41)

class variance 0.95 0.94 0.82 0.83

school variance 0 0 0 0.14

student variance 3.9 3.9 3.9 3.9

Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale ranging from 0 to 20, in the new grading

system this is equivalent to a grade of F to A, with F indicating that the student has failed the course

57

Table A2. Models for math. Class and school level variables

Math

(1) (2) (3) (4) (5)

boy -1.01*** (0.33) -1.00*** (0.33) -1.00*** (0.33) -0.99*** (0.33) -0.96*** (0.33)

non-native, taking Swedish -0.22 (0.45) -0.26 (0.46) -0.14 (0.46) -0.17 (0.46) -0.19 (0.46)

non-native taking Swedish as 2nd lang -2.73*** (0.51) -2.75*** (0.55) -2.49*** (0.56) -2.42*** (0.56) -2.38*** (0.57)

Absence: less than 5% (ref.)

5-10% -0.33 (0.45) -0.35 (0.45) -0.37 (0.45) -0.36 (0.45) -0.35 (0.45)

10-20% -1.39*** (0.45) -1.41*** (0.45) -1.41*** (0.45) -1.40*** (0.45) -1.39*** (0.45)

20-50% -4.11*** (0.52) -4.13*** (0.52) -4.12*** (0.52) -4.14*** (0.52) -4.13*** (0.52)

50% and more -8.01*** (1.03) -8.07*** (1.03) -7.96*** (1.03) -7.96*** (1.03) -7.97*** (1.03)

School level

share of students with 2 parents from abroad 0.20 (0.15) 0.25* (0.15) 0.21 (0.17) 0.18 (0.16)

share of low/unknown parental education -0.24 (0.19) -0.18 (0.19) -0.16 (0.21) -0.13 (0.20)

Class level

share of foreign language speakers) -4.02** (1.90) -0.55 (2.81) -0.56 (2.77)

share of students with 2 parents having job 0.04 (0.03) 0.02 (0.03)

hdilg -0.06 (0.04)

Constant 12.91*** (0.52) 12.76*** (0.69) 12.80*** (0.68) 9.30*** (2.25) 10.90*** (2.44)

class variance 0.95 0.94 0.82 0.83 0.84

school variance 0 0 0 0.14 0

student variance 3.9 3.9 3.9 3.9 3.9

Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale ranging from 0 to 20, in the new grading system this is equivalent to a

grade of F to A, with F indicating that the student has failed the course; hdilg=Human Development Index (low).

58

Table A3.Models for English. Individual level variables

English

(1) (2) (3) (4)

boy -1.10*** (0.36) -1.08*** (0.36) -1.41*** (0.34) -0.75*** (0.26)

non-native, taking Swedish 1.07** (0.49) 0.73 (0.47) 0.60* (0.36)

non-native, taking Swedish as 2nd lang -4.49*** (0.55) -4.72*** (0.53) -2.27*** (0.41)

other European, taking Swedish 1.39** (0.63)

other European, taking Swedish as 2nd lang -2.88** (1.21)

other non-European, taking Swedish 0.72 (0.64)

other non-European taking Swedish as 2nd lang -4.76*** (0.57)

absence: less than 5% (ref.) 0.42 (0.47) 0.31 (0.35)

5-10% -0.46 (0.47) 0.23 (0.36)

10-20% -2.68*** (0.55) -0.33 (0.42)

20-50% -7.22*** (1.09) -1.29 (0.85)

50% and more -8.77*** (0.33)

not qualified for high school 13.03*** (0.41) 13.02*** (0.40) 13.94*** (0.52) 14.70*** (0.39)

Constant -1.10*** (0.36) -1.08*** (0.36) -1.41*** (0.34) -0.75*** (0.26)

class variance 1.62 1.62 1.53 0.7

school variance 0.3 0.13 0.48 0.62



grade of F to A, with F indicating that the student has failed the course

59

Table A4. Models for English. Class and school level variables

English

(1) (2) (3) (4) (5)

boy -1.41*** (0.34) -1.41*** (0.34) -1.40*** (0.34) -1.38*** (0.34) -1.34*** (0.34)

non-native, taking Swedish 0.73 (0.47) 0.69 (0.48) 0.95** (0.48) 0.90* (0.48) 0.89* (0.48)

non-native taking Swedish as 2nd lang -4.72*** (0.53) -4.68*** (0.57) -4.32*** (0.58) -4.23*** (0.58) -4.20*** (0.58)


5-10% 0.42 (0.47) 0.41 (0.47) 0.36 (0.47) 0.35 (0.47) 0.37 (0.47)

10-20% -0.46 (0.47) -0.48 (0.47) -0.49 (0.47) -0.50 (0.47) -0.48 (0.47)

20-50% -2.68*** (0.55) -2.72*** (0.55) -2.71*** (0.55) -2.73*** (0.55) -2.71*** (0.55)

50% and more -7.22*** (1.09) -7.28*** (1.10) -7.15*** (1.09) -7.06*** (1.09) -7.05*** (1.09)

School level

share of students with 2 parents from abroad 0.18 (0.13) 0.29** (0.12) 0.24** (0.11) 0.20* (0.11)

share of low/unknown parental education -0.23 (0.17) -0.09 (0.15) -0.06 (0.14) -0.03 (0.13)

Class level

share of foreign language speakers -9.37*** (1.96) -5.00* (2.83) -4.51* (2.74)

share of students with 2 parents having job 0.05** (0.02) 0.03 (0.03)

hdilg -0.08* (0.04)

Constant 13.94*** (0.52) 13.89*** (0.68) 14.00*** (0.62) 9.63*** (2.19) 11.33*** (2.28)

class variance 0.93 0.91 0.82 0.82 0.84




grade of F to A, with F indicating that the student has failed the course; hdilg=Human Development Index (low).

60

Table A5. Swedish as the native language. Individual level variables

Swedish

(1) (2) (3) (4)

Boy -2.70*** (0.31) -2.70*** (0.31) -3.08*** (0.30) -2.70*** (0.24)

non-native, taking Swedish 0.79** (0.38) 0.37 (0.36) 0.19 (0.29)

other European, taking Swedish 0.49 (0.50)

other non-European, taking Swedish 1.10** (0.50)


5-10% -0.03 (0.41) -0.16 (0.32)

10-20% -1.08*** (0.40) -0.60* (0.32)

20-50% -3.35*** (0.49) -1.85*** (0.39)

50% and more -5.79*** (0.93) -1.18 (0.77)

not qualified for high school -6.58*** (0.32)

Constant 13.62*** (0.30) 13.61*** (0.30) 14.95*** (0.39) 15.52*** (0.35)

class variance 0.93 0.91 0.82 0.82

school variance 0 0 0 0.2


Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale ranging from 0 to 20, in the new

grading system this is equivalent to a grade of F to A, with F indicating that the student has failed the course

61

Table A6. Swedish as the native language. Class and school level variables

Swedish

(1) (2) (3) (4) (5)

boy -3.08*** (0.30) -3.09*** (0.30) -3.05*** (0.30) -3.04*** (0.30) -3.03*** (0.30)

non-native, taking Swedish 0.37 (0.36) 0.41 (0.38) 0.57 (0.38) 0.56 (0.38) 0.56 (0.38)


5-10% -0.03 (0.41) -0.04 (0.41) -0.06 (0.41) -0.06 (0.41) -0.05 (0.41)

10-20% -1.08*** (0.40) -1.07*** (0.40) -1.06*** (0.40) -1.06*** (0.40) -1.05*** (0.40)

20-50% -3.35*** (0.49) -3.34*** (0.49) -3.29*** (0.49) -3.29*** (0.49) -3.29*** (0.49)

50% and more -5.79*** (0.93) -5.73*** (0.93) -5.68*** (0.93) -5.61*** (0.93) -5.63*** (0.93)

School level

share of students with 2 parents from abroad 0.12 (0.08) 0.17** (0.08) 0.16* (0.09) 0.15* (0.09)

share of low/unknown parental education -0.19* (0.11) -0.10 (0.11) -0.10 (0.11) -0.08 (0.11)

Class level

share of foreign language speakers -4.88** (1.93) -3.51 (2.53) -3.55 (2.54)


hdilg -0.03 (0.04)

Constant 14.95*** (0.39) 15.20*** (0.49) 15.19*** (0.47) 13.60*** (1.94) 14.17*** (2.08)

class variance 0.93 0.91 0.82 0.82 0.84



Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale ranging from 0 to 20, in the new grading system this is

equivalent to a grade of F to A, with F indicating that the student has failed the course; hdilg=Human Development Index (low).

62

Table A7. Swedish as a second language. Individual level variables

Swedish as a second language

(1) (2)

boy -1.07 (0.81) -1.21 (0.77)

European, taking Swedish as 2nd lang (ref.)

non-European taking Swedish as 2nd lang -0.44 (1.23) -0.19 (1.15)


5-10% -1.57 (1.03)

10-20% -0.74 (1.19)

20-50% -5.05*** (1.12)

50% and more -5.72** (2.38)

Constant 8.53*** (1.68) 10.20*** (1.85)

class variance 0.85 1.33

school variance 2.91 3.23

student variance 5.29 4.9

Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale

ranging from 0 to 20, in the new grading system this is equivalent to a grade of F to A, with F

indicating that the student has failed the course

63

Table A8. Swedish as a second language. class and school level variables

Swedish as a second language

(1) (2) (3) (4) (5)

boy -3.08*** (0.30) -3.09*** (0.30) -3.06*** (0.30) -3.04*** (0.30) -3.04*** (0.30)


5-10% -0.04 (0.41) -0.06 (0.41) -0.08 (0.41) -0.08 (0.41) -0.07 (0.41)

10-20% -1.12*** (0.40) -1.12*** (0.40) -1.13*** (0.40) -1.13*** (0.40) -1.12*** (0.40)

20-50% -3.40*** (0.48) -3.39*** (0.48) -3.38*** (0.48) -3.37*** (0.48) -3.37*** (0.48)

50% and more -5.88*** (0.92) -5.86*** (0.93) -5.85*** (0.92) -5.78*** (0.93) -5.80*** (0.93)

School level

share of students with 2 parents from abroad 0.13 (0.08) 0.18** (0.08) 0.16* (0.09) 0.15* (0.09)

share of low/unknown parental education -0.19* (0.11) -0.11 (0.11) -0.10 (0.12) -0.08 (0.11)

Class level

share of foreign language speakers -4.37** (1.90) -2.88 (2.50) -2.97 (2.50)


hdilg -0.03 (0.04)

Constant 15.06*** (0.38) 15.24*** (0.48) 15.25*** (0.47) 13.52*** (1.94) 14.13*** (2.08)

class variance 0.93 0.91 0.82 0.82 0.84



Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. Students are scored on a scale ranging from 0 to 20, in the new grading system

this is equivalent to a grade of F to A, with F indicating that the student has failed the course; hdilg=Human Development Index (low).

64

Table A9. Grade point average (meritvärde). Individual and class level variables

Grade point average

1 2 3 4 5 6 7 8 9

boy -23.0*** (4.2) -23.4*** (4.2) -23.4*** (4.2) -29.8*** (3.6) -29.8*** (3.6) -22.7*** (2.7) -22.8*** (2.7) -32.4*** (3.8) -32.4*** (3.8)

non-native, taking Swedish 10.2* (5.7) 2.5 (4.9) 3.5 (4.9) 1.7 (3.6) 2.1 (3.6) 2.5 (5.2) 3.0 (5.2)

non-native, taking Swedish as 2nd lang -32.1*** (6.5) -34.3*** (5.7) -29.2*** (5.7) -8.0* (4.3) -6.0 (4.4) -26.0*** (7.3) -23.9*** (7.6)

other European, taking Swedish 9.0 (7.5) 9.8 (7.5)

other European, taking Swedish as 2nd

lang -31.4** (14.3) -27.9** (14.2)

other non-European, taking Swedish 12.1 (7.4) 12.6* (7.4)

other non-European taking Swedish as 2nd

lang -30.5*** (6.8) -24.8*** (6.8)

absence: less than 5 % (ref.)

5-10% -7.1 (4.9) -6.7 (4.9) -7.9** (3.6) -7.8** (3.6) -7.5 (5.2) -7.6 (5.2)

10-20% -23.5*** (4.9) -23.1*** (4.9) -15.7*** (3.7) -15.6*** (3.7) -26.4*** (5.3) -26.3*** (5.3)

20-50% -74.5*** (5.7) -73.8*** (5.6) -47.1*** (4.3) -47.3*** (4.3) -74.3*** (6.2) -74.5*** (6.2)

50 % and more -151.0***

(10.7)

-151.0***

(10.6) -84.2*** (8.3) -84.8*** (8.3)

-154.9***

(11.3)

-154.8***

(11.3)

not qualified for high school -93.1*** (3.4) -92.2*** (3.4)

went to the same school at grade 6 1.2 (5.7) 0.6 (5.7)

Class level

migrated to Sweden past 4 years -0.9*** (0.2) -0.9*** (0.2) -0.3** (0.2) -0.3 (0.4)

Constant 212.3*** (4.8) 212.1*** (4.9) 217.0*** (4.4) 243.2*** (5.6) 247.6*** (5.1) 250.9*** (4.2) 252.4*** (4.2) 247.9*** (5.6) 249.1*** (5.8)

class variance 20.52 20.75 15.88 19.73 14.75 12.67 11.95 14.02 14.22

school variance 0 0 0 3.01 0 4.99 4.85 0 0

student variance 62.2 62.21 62.13 52.67 52.6 39.03 39.01 50.54 50.53

Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001.The grade point average for primary schools is created by summing the 16 best final grades. The grade according to the old

grading system is MVG=20, VG=15 and G=10. The grade point average may be calculated according to the new by summing the value of each given grade: A=20, B=17, 5, C=15, D=12, 5 E=10 and

F= 0; hdilg=Human Development Index (low)

65

Table A10. Grade point average (meritvärde) .Individual, class and school level variables

Grade point average

1 2 3 4 5 6 7

boy -29.8*** (3.6) -29.9*** (3.6) -29.8*** (3.6) -29.9*** (3.6) -29.7*** (3.6) -29.1*** (3.5) -28.8*** (3.5)

non-native, taking Swedish 2.5 (4.9) 2.7 (5.0) 3.7 (5.0) 2.7 (5.0) 5.6 (5.0) 5.1 (5.0) 5.0 (5.0)

non-native taking Swedish as 2nd lang -34.3*** (5.7) -31.9*** (6.0) -33.7*** (6.0) -31.8*** (6.1) -29.8*** (6.1) -28.7*** (6.1) -28.3*** (6.1)


5-10% -7.1 (4.9) -7.3 (4.9) -6.9 (4.9) -7.3 (4.9) -7.1 (4.9) -7.0 (4.9) -6.7 (4.9)

10-20% -23.5*** (4.9) -23.9*** (4.9) -23.0*** (4.9) -23.9*** (5.0) -22.9*** (4.9) -22.6*** (4.9) -22.4*** (4.9)

20-50% -74.5*** (5.7) -74.7*** (5.7) -74.2*** (5.7) -74.7*** (5.7) -73.7*** (5.6) -73.1*** (5.6) -72.9*** (5.6)

50% and more -151.0***

(10.7)

-151.2***

(10.7)

-150.9***

(10.7)

-151.1***

(10.7)

-149.9***

(10.7)

-148.0***

(10.6)

-147.9***

(10.6)

School level

share of students with 2 parents from

abroad 2.6* (1.4) 1.8* (1.1) 1.8 (1.3) 3.3*** (0.9) 2.9*** (0.8) 2.3*** (0.9)

share of low/unknown parental education -3.7** (1.8)

share of unknown parental education -16.8** (8.1) -9.6 (7.0) -11.0* (6.2) -7.9 (6.1)

share of low parental education -3.3* (2.0)

Class level

share of foreign language speakers -1.0*** (0.2) -0.2 (0.3) -0.1 (0.3)

share of students with 2 parents having job 1.0*** (0.3) 0.7*** (0.3)

hdilg -0.8* (0.4)

Constant 243.2*** (5.6) 247.4*** (7.3) 253.4*** (8.0) 246.0*** (7.5) 251.7*** (7.0) 168.4***

(23.8)

187.6***

(24.7)

class variance 19.73 19.12 19.03 19.63 14.66 11.24 10.64

school variance 3.01 0 0 0 0 2.53 0

student variance 52.67 52.66 52.66 52.66 52.63 52.61 52.62

Notes: Standard error in brackets; * p<0.05, ** p<0.01, *** p<0.001. The grade point average for primary schools is created by summing the 16 best final grades. The

grade according to the old grading system is MVG=20, VG=15 and G=10. The grade point average may be calculated according to the new by summing the value of each

given grade: A=20, B=17, 5, C=15, D=12, 5 E=10 and F= 0; hdilg=Human Development Index (low).

66

Table A11: Background characteristics of children by language origin

Education Human Development Index

Group

Single

parent

Financial

Assistance

Both

parents

employed

Unknown Basic High

School

After High

School

Low Middle High Very

High

Swedish 10.5 10.0 69.5 0.2 7.0 44.7 47.3 3.0 4.1 2.4 1.0

other European, taking Swedish 13.4 16.3 59.4 0.7 12.5 35.1 50.8 5.6 8.9 4.1 0.9

other European, taking Swedish

as 2nd lang

15.9 29.9 35.4 1.9 19.9 48.0 30.4 9.7 15.0 2.1 4.3

other non-European, taking

Swedish

14.3 22.4 52.5 0.9 15.2 44.4 39.1 6.8 11.6 5.2 0.6

other non-European taking

Swedish as 2nd lang

16.9 29.2 35.8 3.4 21.7 44.2 31.5 11.8 17.9 5.0 2.2

Note: All the indicators in this table refer to the proportion of children in each class.

67

Figure A1. World map indicating the categories of Human Development Index by country (based on 2013 data, published on July 24, 2014).

68

Table A12: Background information on Navestadsskolan

class 1 2 3 4 6

number of students 28 25 26 22 15

non-native, taking Swedish 46.4 62.5 28 36.4 6.7

non-native, taking Swedish as a

second language 17.9 8.3 8 9.1 93.3

mother-tongue Somaliska,

Arabiska,

Dari/Parsi/Persian,

Syriska,

Serbokroatiska,

Bosniska

Syriska,

Arabiska,

Engelska,

Mandarin,

Somaliska,

Serbokroatiska,

Kurdiska, Finska,

Romani,

Turkiska

Arabiska,

Spanska, Polska

Arabiska,

Spanska,

Kinyarwanda,

Syriska,

Somaliska

Arabiska, Syriska,

Dari/Parsi/Persian,

Somaliska,

Serbokroatiska,

Polska

unknown education 0 0 0 0 17.6

low education 22.2 20 0 13.6 52.9

high school education 51.9 40 24 45.5 17.6

after high school education 22.2 40 72 40.9 17.6

single parent 11.1 16 16 13.6 17.6

benefit receiving households 22.2 32 0 18.2 17.6

both parents employed 40.7 40 72 59.1 0

migrated to Sweden 2 years ago 11.1 0 0 0 52.9

migrated to Sweden 4 years ago 14.8 0 0 13.6 100

migrated to Sweden 6 years ago 22.2 12 0 13.6 100

Note: The socio-economic variables in this table refer to the share of children in each class.

69

Table A13. Classification of languages

European Albanska, Bosniska, Engelska, Finska, Franska, Grekiska, Italienska, Kroatiska, Lettiska, Makedonska, Polska, Ryska, Serbiska, Serbokroatiska, Spanska, Tyska

non-European Arabiska, Bengaliska, Dari/Parsi/Persian, Kinesiska: Mandarin, Kinyarwanda, Kurdiska, centr. (Irak), Kurdiska, norra (Turkiet), Kurdiska, södra (Iran), Lingala, (Kongo-Kinshasa, Kongo-Brazzaville), Luganda/Ganda, (Uganda), OLDKurdiska, OLDRomani, Oromo (Etiopien, Kenya), Persiska, Punjabi (Indien), Romani: Arli, Dzambasi, Gurbeti, Somaliska, Syriska, Syriska, Assyriska, Aturaya, Sooreth, Sureth, Suryaya, Swada, Tagalog (Filippinerna), Thai, (Thailand), Tigre, (Eritrea), Tigrinja ((Etiopien, Eritrea), Tjeckiska, Turkiska

mind the gap_explaining the variation between and within schools in norrköping

Documents