Model with demographic and socio-economic

Model with

background

Total variance Total variance

empty (or fully

demographic and

Model with school

and with school

Total variance

within schools between schools

unconditional) 1 socio-economic 2 policies and 3 policies and 4 in student

as a percentage as a percentage

practices performance

of total variance of total variance

% % australia

Within- between- Within- between- Within- between- Within- between-

OECD austria

Czech republic

New Zealand

slovak republic

united Kingdom

united states

oeCD average

P artners azerbaijan

Dubai (uae)

Hong Kong-China

russian federation

Chinese Taipei

Trinidad and Tobago

1. Multilevel regression model consists of the student- and school-levels.

2. Multilevel regression model: Reading performance is regressed on the variables of demographic and socio-economic background.

3. Multilevel regression model: Reading performance is regressed on the variables of school policies and practices.

4. Multilevel regression model: Reading performance is regressed on the variables of demographic and socio-economic background and on the variables of school

1 policies and practices. 2 http://dx.doi.org/10.1787/888932343285

166 © OECD 2010 PISA 2009 ReSultS: WhAt MAkeS A School SucceSSful? – VoluMe IV

Results foR countRies And economies: Annex B1

[ Part 2/2 ]

Within- and between-school variation in reading performance and variation explained Table IV.2.4a by school governance

between-school variance expressed as a percentage of the average of within-school variance in student performance

Within-school variance expressed as a percentage

of the average of between-school variance in student performance

in reading across oeCD countries

in reading across oeCD countries n -

w ce by students’ and

ee accounted for

solely

b et ia n schools’ socio-

accounted for

g in n economic and ar for by school socio-economic and economic and for by school socio-economic and lv demographic

solely

Jointly accounted for

ariance by students’ and

solely

Jointly accounted for

accounted

by students’ and schools’

schools’ socio-

accounted

by students’ and schools’

demographic background ai o o background

and school governance em r sc h %

school

demographic background

hool v

and school governance

remaining within- sc

OECD belgium

Czech republic

New Zealand

slovak republic

united Kingdom

5.1 0.0 -0.5

united states

5.3 0.0 -0.5

oeCD average

artners P azerbaijan

Dubai (uae)

Hong Kong-China

russian federation

Chinese Taipei

Trinidad and Tobago

1. Multilevel regression model consists of the student and school levels.

2. Multilevel regression model: Reading performance is regressed on the variables of demographic and socio-economic background.

3. Multilevel regression model: Reading performance is regressed on the variables of school policies and practices.

4. Multilevel regression model: Reading performance is regressed on the variables of demographic and socio-economic background and on the variables of school

1 policies and practices. 2 http://dx.doi.org/10.1787/888932343285

PISA 2009 ReSultS: WhAt MAkeS A School SucceSSful? – VoluMe IV

Annex B1 : Results foR countRies And economies

[ Part 1/1 ]

Table IV.2.4b Relationships between school governance and reading performance

school governance 1

Index of school responsibility

for resource allocation

for curriculum and assessment

school competes

(higher values indicate

with other schools

more autonomy)

for students in the same area Private school

Change in score s.e. australia

Change in score

s.e.

Change in score

s.e.

Change in score

OECD austria

Czech republic

New Zealand

slovak republic

united Kingdom

united states

oeCD average

P artners azerbaijan

Dubai (uae)

Hong Kong-China

russian federation

Chinese Taipei

Trinidad and Tobago

(15.3) Note: Values that are statistically significant are indicated in bold (see Annex A3).

1 1. Multilevel regression model (student and school levels): Reading performance is regressed on the variables of school policies and practices presented in this table. 2 http://dx.doi.org/10.1787/888932343285

168 © OECD 2010 PISA 2009 ReSultS: WhAt MAkeS A School SucceSSful? – VoluMe IV

Results foR countRies And economies: Annex B1

[ Part 1/2 ]

Relationships between school governance and reading performance, accounting for students’ and Table IV.2.4c schools’ socio-economic and demographic background

school governance 1 student socio-economic and demographic background 1

Index of school

PIsa index responsibility

school

of economic, PIsa index for resource

Index of school

competes

student’s

social and of economic, allocation

responsibility for

with other

language at

cultural status social and (higher values

curriculum and

schools for

student

home is the

of student cultural status indicate more

assessment (higher

students in

without an

same as the

(1 unit of student autonomy)

values indicate

the same

Private

student is

immigrant

language of

increase) (squared) Change

more autonomy)

area

school

a female

background

assessment

Change Change in score s.e.

s.e. in score s.e. in score s.e. australia

in score

s.e.

in score s.e. in score s.e. in score s.e. in score s.e. in score

OECD austria

Czech republic

c c 45.9 (3.3)

New Zealand

c c 48.8 (3.5)

slovak republic

switzerland -13.3

c c 31.4 (2.8)

united Kingdom

united states

oeCD average

P artners azerbaijan

c c 22.7 (2.4)

c c 47.5 (3.8)

Colombia -11.9

c c 35.1 (3.4)

Dubai (uae)

Hong Kong-China

Kyrgyzstan -24.8

c c 47.4 (3.0)

c c 54.8 (2.7)

c c 42.0 (3.6)

c c 16.2 (3.9)

11.0 (2.2) -0.1 (1.2)

russian federation

c c 41.6 (2.6)

c c 27.3 (2.8)

c c 26.5 (2.3)

Chinese Taipei

Thailand -6.5

Trinidad and Tobago

15.4 (2.0) -0.2 (0.9) Note: Values that are statistically significant are indicated in bold (see Annex A3).

1. Multilevel regression model (student and school levels): Reading performance is regressed on the variables of school policies and practices as well as on socio-

1 economic and demographic background variables presented in this table. 2 http://dx.doi.org/10.1787/888932343285

PISA 2009 ReSultS: WhAt MAkeS A School SucceSSful? – VoluMe IV

Annex B1 : Results foR countRies And economies

[ Part 2/2 ]

Relationships between school governance and reading performance, accounting for students’ and Table IV.2.4c schools’ socio-economic and demographic background

school socio-economic and demographic background 1 school average PIsa index

of economic, social

school size

school in a small town

and cultural status

school size

(per 100 students)

or village school in city

(1 unit increase)

(per 100 students)

(squared)

(15 000 or less people) (100 000 or more people)

s.e. in score s.e. australia

in score

s.e.

in score

s.e.

in score

s.e.

in score

OECD austria

Czech republic

New Zealand

slovak republic

united Kingdom

united states

oeCD average

artners P azerbaijan

Dubai (uae)

Hong Kong-China

russian federation

Chinese Taipei

Trinidad and Tobago

12.8 (7.4) Note: Values that are statistically significant are indicated in bold (see Annex A3).

1. Multilevel regression model (student and school levels): Reading performance is regressed on the variables of school policies and practices as well as on socio- economic and demographic background variables presented in this table. 1 2 http://dx.doi.org/10.1787/888932343285

170 © OECD 2010 PISA 2009 ReSultS: WhAt MAkeS A School SucceSSful? – VoluMe IV

Results foR countRies And economies: Annex B1

[ Part 1/1 ]

Ratio of schools’ posting achievement data publicly and the relationship between school Table IV.2.5 autonomy in allocating resources and reading performance

Model for prevalence of schools’ posting achievement data publicly

(ols regression estimates) Gross model

Net model

Coef. s.e. school autonomy for resource allocation

Coef.

s.e.

(1.45) × Percentage of students in schools that post achievement data publicly (additional 10%)

school autonomy for curriculum and assessment

Private school

PIsa index of economic, social and cultural status of student (esCs)

PIsa index of economic, social and cultural status of student (esCs squared)

student is a female

student’s language at home is the same as the language of assessment

student without an immigrant background

school average PIsa index of economic, social and cultural status

school in a city (100 000 or more people) -2.36

school in a small town or village (15 000 or less people)

school size (100 students)

school size (100 students, squared) -0.01

267 425 Note: estimates significant at the 5% level (p < 0.05) are in bold. Both net and gross models include country fixed effects, estimate no intercept, are run for oecD countries

only and use BRR weights to account for the sampling design. All countries are weighted equally. 1 2 http://dx.doi.org/10.1787/888932343285

[ Part 1/1 ]

Table IV.2.6 likelihood of attending schools competing for students in the same area

logistic regression estimates student model

student and school Model

Coef. s.e. PIsa index of economic, social and cultural status of student (esCs)

PIsa index of economic, social and cultural status of student (esCs squared)

student is a female

student’s language at home is the same as the language of assessment

student without an immigrant background

school average PIsa index of economic, social and cultural status

school in a city (100 000 or more people)

school in a small town or village (15 000 or less people) -1.28

school size (100 students)

school size (squared)

Private school

267 553 Note: estimates indicate log-odds of attending a school that competes with other schools for enrolment. estimates significant at the 5% level (p < 0.05) level are in bold.

Both net and gross models include country fixed effects, do not estimate an intercept, are run for oecD countries only and use BRR weights to account for the sampling

design. All countries are weighted equally. 1 2 http://dx.doi.org/10.1787/888932343285

PISA 2009 ReSultS: WhAt MAkeS A School SucceSSful? – VoluMe IV

Annex B1 : Results foR countRies And economies

[ Part 1/1 ]

Parents’ reports on reasons for choosing schools for their children as “very important”, by quarters of the PisA index of economic, social and cultural status of students (escs)

Table IV.2.7 Results based on reports from students’ parents Percentage of parents reporting the following reasons as “very important” in choosing a school for their child Distance (% of parents who reported

low expenses (% of parents who reported

financial aid (% of parents who reported

“very important” when choosing schools)

bottom second Third

Top esCs

esCs esCs esCs Top esCs

quarter quarter quarter quarter

% s.e. % s.e. % s.e. % s.e. Chile

s.e. %

s.e.

% s.e.

OECD Denmark

New Zealand

8 oeCD countries average 21.1 (0.5) 19.3 (0.5) 17.9 (0.5) 16.1 (0.5) 20.3 (0.5) 15.8 (0.4) 12.7 (0.4)

Hong Kong-China

artners P lithuania

Percentage of parents reporting the following reasons as “very important” in choosing a school for their child High academic achievement (% of parents who reported

safe environment (% of parents who reported

“very important” when choosing schools)

bottom esCs

second esCs

Third esCs

Top esCs

bottom esCs

second esCs Third esCs Top esCs

quarter quarter

s.e. % s.e. % s.e. Chile

OECD Denmark

New Zealand

8 oeCD countries average

Hong Kong-China

artners P lithuania

64.4 (1.3) Note: estimates in bold indicate statistically significant differences between top and bottom eScS quartile at the 5% level (p < 0.05). Average missing rates on these parent

questionnaire items are 2% in Macao-china, 4% in korea, lithuania and hong kong-china, 5% in hungary, 11% in croatia and chile, 13% in Italy, 21% in Panama,

25% in Portugal, 26% in New Zealand, 35% in Qatar, 38% in Germany, 41% in Denmark. 1 2 http://dx.doi.org/10.1787/888932343285

[ Part 1/1 ]

systems’ school competition rates and the relationship between reading performance Table IV.2.8 and socio-economic background of students and schools

Model for school competition (ols regression estimates)

Coef. s.e. PIsa index of economic, social and cultural status of student (esCs)

24.60 (1.37) x Percentage of students in schools that compete with other schools for students in the same area (additional 10%)

school average PIsa index of economic, social and cultural status

36.14 (5.19) x Percentage of students in schools that compete with other schools for students in the same area (additional 10%)

student is a female

student’s language at home is the same as the language of assessment

student without an immigrant background

school in a city (100 000 or more people)

school in a small town or village (15 000 or less people)

school size (100 students)

school size (100 students) (squared) -0.01

267 553 Note: estimates significant at the 5% level (p < 0.05) are in bold. Models include country fixed effects, estimate no intercept, are run for oecD countries only and use BRR

weights to account for the sampling design. All countries are weighted equally. 1 2 http://dx.doi.org/10.1787/888932343285

Results foR countRies And economies: Annex B1

PISA 2009 ReSultS: WhAt MAkeS A School SucceSSful? – VoluMe IV

[ Part 1/1 ] Table IV.2.9a

Within- and between-school variation in reading performance and variation explained by schools’ assessment and accountability policies

Variance

remaining variance

Model with demographic and socio-economic