Annex A. Technical notes on sampling procedures, response ratesand adjudication for TALIS 2018

A note regarding Israel

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

Sampling procedures and response rates

The objective of the Teaching and Learning International Survey (TALIS) in 2018 was to obtain, in each participating country and economy, a representative sample of teachers for each ISCED level in which the country and economy participated. Moreover, a representative sample of teachers teaching students of the appropriate age in schools selected for the Programme for International Student Assessment (PISA) in 2018 was required for each country and economy that opted to participate in the TALIS-PISA link. TALIS 2018 identified policy issues that encompass the classroom, teachers, schools and school management, so the coverage of TALIS 2018 extends to all teachers of each concerned ISCED level and to the principals of the schools where they teach. The international sampling plan prepared for TALIS 2018 used a stratified two-stage probability sampling design. This means that teachers (second stage units, or secondary sampling units) were to be randomly selected from the list of in-scope teachers in each of the randomly selected schools (first stage units, or primary sampling units). A more detailed description of the survey design and its implementation can be found in the TALIS 2018 Technical Report (OECD, 2019[1]).

A teacher of ISCED level 1, 2 or 3 is one who, as part of his or her regular duties in their school, provides instruction in programmes at that ISCED level. Teachers who teach a mixture of programmes at different ISCED levels in the target school are included in the TALIS universe. There is no minimum cut-off for how much teaching these teachers need to be engaged in at any of the three ISCED levels.

The international target population of TALIS 2018 restricts the survey to those teachers who teach regular classes in ordinary schools and to the principals of those schools. Teachers teaching to adults and teachers working in schools exclusively devoted to children with special needs are not part of the international target population and are deemed out of scope. Unlike in TALIS 2008, however, teachers working with special needs students in a regular school setting were considered in scope in TALIS 2013 and 2018. When a school is made up exclusively of these teachers, the school itself is said to be out of scope. Teacher aides, pedagogical support staff (e.g. guidance counsellors and librarians) and health and social support staff (e.g. doctors, nurses, psychiatrists, psychologists, occupational therapists and social workers) were not considered to be teachers and, thus, not part of the TALIS international target population.

For national reasons, participating countries could choose to restrict the coverage of their national implementation of TALIS 2018 to parts of the country. For example, a province or state experiencing civil unrest or in an area struck by a natural disaster could be removed from the international target population to create a national target population that does not include these provinces or states. Participating countries were invited to keep these exclusions to a minimum by keeping the national survey population to at least 95% of the teachers.

TALIS 2018 recognised that attempting to survey teachers in very small schools can be inefficient and difficult. For each ISCED level, surveying teachers in schools with no more than three teachers at a specific ISCED level and those teaching in schools located in geographically remote areas could be a costly, time-consuming and statistically inefficient exercise. Therefore, participating countries were allowed to exclude those teachers for TALIS 2018 data collection, thus creating a national survey population different from the national target population. The national project manager (NPM) for each country was required to document the reasons for exclusion, the size, the location, the clientele, etc., of each excluded school. This documentation was required for each ISCED level in which a country participated. The school exclusions for the TALIS-PISA link were the same as those used in PISA 2018.

Within a selected in-scope school, the following categories of teachers were excluded from the sample:

  • teachers teaching in schools exclusively serving special needs students

  • teachers who also act as school principals: no teacher data collected, but school principal data collected

  • substitute, emergency or occasional teachers

  • teachers on long-term leave

  • teachers teaching exclusively to adults

  • teachers who had taken part in the TALIS 2018 field trial.

Sample size requirements

For each ISCED level, the same requirements for sample size and precision of estimates were established. To allow for reliable estimation and modelling, while allowing for some amount of non-response, the minimum sample size was set at 20 teachers within each participating school. A minimum sample of 200 schools was to be drawn from the population of in-scope schools. Thus, the nominal international sample size was a minimum of 4 000 teachers for each ISCED level in which a country or economy participated. Participating countries and economies could choose to augment their national sample by selecting more schools, by selecting more teachers within each selected school or by increasing both. Some countries and economies were asked to increase the within-school sample to counterbalance the effect of selecting too many schools with fewer than 20 teachers. The sample size requirement was reduced for some participating countries and economies because of the smaller number of schools available for sampling. In a few cases, because the average number of teachers in the schools was fewer than expected in the international plan, the number of schools sampled was increased to maintain a minimum total number of participating teachers.

In many countries, the separation of grades in ISCED levels does not correspond to a physical separation of school buildings or administrations: schools that offer grades 8 to 12 straddle ISCED levels 2 and 3, but all of ISCED level 2 would not be covered by those schools. In countries and economies that participated in more than one ISCED level, arrangements were made with the NPM and their team to optimise the selection of the school sample by either minimising the overlap of the respective samples (one school is selected for participation in only one ISCED level) or maximising the sample overlap (a selected school contributes to all concerned ISCED levels). However, in the case of maximised overlap, teachers who taught at more than one level would be asked to participate in only one.

Definition of teachers

As in previous cycles, TALIS 2018 followed the INES (Indicators of Educational Systems) data collection definition of a teacher for sampling and analysis:

“A classroom teacher (ISCED 0-4) is defined as a person who plans, organises and conducts a group of activities with the aim of developing students’ knowledge, skills and competencies as stipulated by educational programmes.” (OECD, 2018, p. 43[2]).

Adjudication process

The basic principle that guides the adjudication is to determine, for each participating country/economy and for each of the TALIS options, whether the data released to the countries and economies are fit to provide policy relevant, robust international indicators and analysis on teachers and teaching in a timely and cost effective manner.

To establish fitness for use, a number of quality assurance processes were designed and activated throughout the survey process. Some processes relied on expert advice and opinion; some relied on qualitative information and learned judgement; some relied on quantitative information. For more detailed information, please refer to the TALIS 2018 Technical Report (OECD, 2019[1]).

During the adjudication session, each individual dataset – that is, the combination of participating countries/economies, survey options and questionnaire types – was submitted to the same examination. For the first time in a TALIS cycle, principal data were evaluated on their own. In other words, principal and teacher data received separate adjudication evaluation per TALIS option and per country/economy.

The issues evaluated concerned the questionnaire adaptation to national context, translation and verification, quality of the sampling frame, handling of out-of-scope and refusal units (i.e. teachers and/or schools), within-school sampling, data collection, data cleaning, the reports of quality observers, participation rates and overall compliance with the technical standards. Once each survey process had been assessed, a recommended rating was formulated, accounting for the participation rates, and for any unresolved issue.

The adjudication rules, based on participation rates for principals and teachers, are displayed in Table AI.A.1 and Table AI.A.2.

Table AI.A.1. Adjudication rules for school or principal data in TALIS 2018

School participation (returned principal questionnaires)

Risk of school non-response bias

Rating

Before replacement

After replacement

≥75%

≥75%

Good

50% - 75%

≥75%

Fair (A)

50% - 75%

Low

Fair (C)

High

Poor (D)

<50%

Insufficient

Table AI.A.2. Adjudication rules for teacher data in TALIS 2018

Rating

Good

Fair (A)

Fair (B)

Fair (C)

Poor (D)

Poor (E)

Poor (F)

Insufficient

Risk of teacher non-response bias

Low

High

Teacher participation after school replacement

≥75%

50% - 75%

≥75%

50% - 75%

School participation (minimum teacher participation)

After replacement

≥75%

≥75%

50% - 75%

≥75%

< 75%

Before replacement

≥75%

50% - 75%

50% - 75%

< 50%

< 50%

The following bulleted list is a simple guide aimed at helping data users appreciate the limitations on use or quality:

  • Good: the participating country’s/economy’s data can be used for all reporting and analytical purposes and can be included in international comparisons

  • Fair (A): national and sub-national estimates can be produced; some teacher characteristics may suffer from a larger standard error (s.e.), hence the warning “Fair” and no additional warnings to users appear necessary

  • Fair (B, only for teacher data adjudication): national and sub-national estimates can be produced; some sub-national estimates may be of lower precision (larger s.e.) if sample size is locally low, hence the warning “Fair” and no additional warnings to users appear necessary

  • Fair (C):

    • national and sub-national estimates can be produced

    • some sub-national estimates may be of lower precision (larger s.e.) if sample size is locally low, hence the warning “Fair”, but a note on data quality could appear pointing to the outcome of the non-response bias analysis (NRBA)

    • since school participation is somewhat lower than under (B), comparing sub-national estimates should be done with care, as some of those results are based on few schools

    • comparing small sub-national estimates with similar groups from other countries is likely to uncover any statistically meaningful differences as s.e. are likely too large

  • Poor (D):

    • in addition to the warnings issued for the previous category, a note should warn users of indications of non-response biases in some estimates

    • comparisons of sub-national estimates should be limited to the groups with the larger sample sizes

    • at this point, the sample represents between 37% and 56% of the teaching workforce, from a rather small sample of schools

    • comparisons with similar groups in foreign countries would not be encouraged

  • Poor (E, only for teacher data adjudication): sub-national estimates would not be recommended; there should be a note pointing out the difficulty of obtaining a representative sample of schools

  • Poor (F, only for teacher data adjudication): limitations similar to those of line E, but there should be a note pointing out the difficulty of obtaining at least 50% participation of the selected sample of schools; risks of having a non-representative sample of schools

  • Insufficient: weights should not be calculated for any official tabulations; hence, data should not be incorporated into international tables, models, averages, etc.

The participation rates and the adjudication rating per participating country/economy and by ISCED level are presented in Table AI.A.3 to Table AI.A.8.1

Notes regarding the use and interpretation of the data

This section lists issues to be noted regarding the sampling or field operations that should be considered when interpreting the data reported for these countries.

  • Alberta (Canada):

    • TALIS data collection conducted during a labour dispute.

    • Non-response bias analysis shows no evidence of high risk of school non-response bias on the investigated variables for teachers or principals in ISCED 2 or ISCED 3 and, as such, their rating was upgraded from “poor” to “fair”.

  • Australia:

    • For both ISCED 1 and 2, the data collection window for both teachers and principals was extended from the end of the academic year in 2017 to the beginning of the following academic year in 2018.

    • For ISCED 1 principals and teachers and for ISCED 2 principals, data from Australia are located below the line in selected tables in this report and not included in the calculations for the international average. This is because Australia did not meet the international standards for participation rates, as shown in Table AI.A.3 to Table AI.A.5.

  • Colombia: Non-response bias analysis shows no evidence of high risk of school non-response bias on the investigated variables for teachers or principals and, as such, their rating was upgraded from “poor” to “fair”.

  • Czech Republic: Trend items should be interpreted carefully due to complications arising from the translation process.

  • Denmark: Non-response bias analysis shows no evidence of high risk of school non-response bias on the investigated variables for teachers or principals in ISCED1, ISCED 2 or ISCED 3 and, as such, their rating was upgraded from “poor” to “fair”.

  • Flemish Community of Belgium: For both ISCED 1 and 2, entries on the sampling frame are administrative units and not “schools” as they are usually defined; a “school” may be comprised of one or several administrative units and the principal would be reporting for the school and not only the selected administrative unit; therefore, users should exercise care when analysing and comparing school-level statistics.

  • Georgia:

    • Items repeated from the TALIS 2013 questionnaire (hereafter “trends items”) should be interpreted carefully due to complications arising from the translation process.

    • Some translation issues could still exist in the Georgian and Azerbaijani version of the questionnaires.

  • Israel: Coverage falls below 95%, after post facto exclusion of ultraorthodox schools for low response rates, making coverage identical to that of TALIS 2013. Because translation issues could still exist in the trends items, users need to exercise caution when comparing results across TALIS cycles.

  • Italy: Trends items should be interpreted carefully due to complications arising from the translation process.

  • Latvia:

    • Trends items should be interpreted carefully due to complications arising from the translation process.

    • Some translation issues could still exist in the national instruments that could affect the data.

  • Korea: For ISCED 2, in four schools, teacher listings were found to be incorrect; those schools were set to “non-participant”.

  • Netherlands:

    • For ISCED 1 and 2, the Netherlands had a six-week early start and extended collection window.

    • For ISCED 1 and 2, the Netherlands had an unapproved collection protocol that resulted in the inclusion of some 50 “national” schools that were not included in the international dataset but left on the national dataset; participation rates were computed on the international dataset.

    • For ISCED 1 principals and teachers, data from the Netherlands are located below the line in in the result tables of this report. This is because the Netherlands did not meet the international standards for participation rates, as shown in Table AI.A.3 and Table AI.A.4.

  • New Zealand: Coverage was extended to small schools (four or fewer teachers). While the impact of this action on the target population of teachers was negligible, the impact on the target population of principals is important because, compared to TALIS 2013, the target population for principals nearly doubled in size. The TALIS 2018 results reported in the result tables on changes since 2013 for New Zealand were estimated after excluding from New Zealand’s TALIS 2018 sample those schools with four or fewer eligible teachers. This was done in order to ensure comparability with their TALIS 2013 results (as such, small schools were excluded from the TALIS 2013 sampling frame). Therefore, these results can differ from those reported for the full TALIS 2018 sample, especially those derived from the school and/or principal data.

  • Russian Federation: Coverage falls below 95% after the exclusion of Moscow.

  • Saudi Arabia: Coverage falls below 95% after the sampling excluded two provinces bordering Yemen.

  • Spain: Trends items should be interpreted carefully due to complications arising from the translation process.

  • United Arab Emirates:

    • Comparisons with TALIS 2013 must be limited to Abu Dhabi.

    • Because of the selection of multi-level schools, the principal data were copied from the original ISCED level 2 principal questionnaire to the ISCED level 1 and ISCED level 3 corresponding forms, except for question 17 in the principal questionnaire.

Table AI.A.3. ISCED 1 principals’ participation and recommended ratings

Number of participating principals

Principals’ participation before replacement (%)

Principals’ participation after replacement (%)

Recommended rating

Australia

223

48.8

77.9

Insufficient

Flemish Community (Belgium)

185

70.1

92.0

Fair

CABA (Argentina)*

175

85.0

87.5

Good

Denmark

145

56.6

73.2

Fair

England (UK)

161

76.4

89.5

Good

France

178

89.3

91.5

Good

Japan

197

97.2

99.5

Good

Korea

161

78.0

80.5

Good

Netherlands

135

40.7

69.6

Insufficient

Spain

436

98.2

98.2

Good

Sweden

166

84.7

87.4

Good

Chinese Taipei

200

99.8

100.0

Good

Turkey

171

99.3

99.3

Good

United Arab Emirates

502

90.6

90.6

Good

Viet Nam

194

100.0

100.0

Good

* CABA (Argentina) refers to the Ciudad Autónoma de Buenos Aires, Argentina.

Table AI.A.4. ISCED 1 teachers’ participation and recommended ratings

Number of participating schools

Number of participating teachers

Estimated size of teacher population

School participation before replacement (%)

School participation after replacement (%)

Teacher participation in participating schools (%)

Overall teacher participation (%)

Recommended rating

Australia

213

3 030

133 915

48.8

74.0

76.4

56.5

Insufficient

Flemish Community (Belgium)

178

2 672

30 204

67.2

88.6

92.0

81.5

Fair

CABA (Argentina)*

167

2 514

16 236

81.0

83.5

86.9

72.5

Good

Denmark

154

2 592

34 185

58.6

77.8

87.5

68.1

Fair

England (UK)

152

2 009

225 2

66.3

80.0

85.7

68.6

Fair

France

178

1 429

209 981

88.6

91.2

92.1

84.0

Good

Japan

197

3 308

355 655

97.0

99.5

98.8

98.3

Good

Korea

182

3 207

128 94

86.0

91.0

91.9

83.6

Good

Netherlands

130

2 019

68 672

39.3

67.3

87.2

58.7

Insufficient

Spain

442

7 246

210 627

99.3

99.5

95.4

95.0

Good

Sweden

178

2 404

57 237

90.0

93.7

78.8

73.8

Good

Chinese Taipei

200

3 494

89 694

99.5

100.0

97.6

97.6

Good

Turkey

172

3 204

213 362

99.4

99.4

98.5

97.9

Good

United Arab Emirates

552

9 188

16 417

99.6

99.6

96.6

96.2

Good

Viet Nam

194

3 991

386 062

100.0

100.0

98.3

98.3

Good

* CABA (Argentina) refers to the Ciudad Autónoma de Buenos Aires, Argentina.

Table AI.A.5. ISCED 2 principals’ participation and recommended ratings

Number of participating principals

Principals’ participation before replacement (%)

Principals’ participation after replacement

(%)

Recommended rating

Alberta (Canada)

129

54.4

66.2

Fair

Australia

230

49.0

75.7

Insufficient

Austria

277

96.0

100.0

Good

Belgium

311

86.5

95.7

Good

Flemish Community (Belgium)

192

82.4

93.7

Good

Brazil

184

88.0

95.4

Good

Bulgaria

200

97.5

100.0

Good

Chile

170

78.9

87.6

Good

CABA (Argentina)*

121

77.5

86.2

Good

Colombia

141

68.8

70.9

Fair

Croatia

188

95.0

95.6

Good

Cyprus1, 2

88

88.9

88.9

Good

Czech Republic

216

99.0

99.0

Good

Denmark

140

51.5

71.4

Fair

England (UK)

157

71.9

81.8

Fair

Estonia

201

88.6

100.0

Good

Finland

148

100.0

100.0

Good

France

195

97.6

98.0

Good

Georgia

182

91.7

91.7

Good

Hungary

182

91.2

94.3

Good

Iceland

101

74.3

74.3

Fair

Israel

184

90.9

93.7

Good

Italy

190

92.4

98.6

Good

Japan

195

93.9

99.4

Good

Kazakhstan

331

100.0

100.0

Good

Korea

150

68.1

77.8

Fair

Latvia

136

80.4

91.9

Good

Lithuania

195

100.0

100.0

Good

Malta

54

93.1

93.1

Good

Mexico

193

90.6

97.0

Good

Netherlands

125

58.1

86.3

Fair

New Zealand

189

71.7

92.0

Fair

Norway

162

67.5

81.0

Fair

Portugal

200

97.7

100.0

Good

Romania

199

100.0

100.0

Good

Russian Federation

230

99.1

100.0

Good

Saudi Arabia

192

96.5

96.5

Good

Shanghai (China)

198

100.0

100.0

Good

Singapore

167

97.0

98.8

Good

Slovak Republic

180

84.4

90.5

Good

Slovenia

119

74.8

79.3

Good

South Africa

169

92.3

92.3

Good

Spain

396

98.7

99.2

Good

Sweden

171

85.9

89.1

Good

Chinese Taipei

202

100.0

100.0

Good

Turkey

196

99.0

99.0

Good

United Arab Emirates

476

91.4

91.4

Good

United States

164

63.1

77.6

Fair

Viet Nam

196

100.0

100.0

Good

* CABA (Argentina) refers to the Ciudad Autónoma de Buenos Aires, Argentina.

1. Note by Turkey: The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the “Cyprus issue”.

2. Note by all the European Union Member States of the OECD and the European Union: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus.

Table AI.A.6. ISCED 2 teachers’ participation and recommended ratings

Number of participating schools

Number of participating teachers

Estimated size of teacher population

School participation before replacement (%)

School participation after replacement (%)

Teacher participation in participating schools (%)

Overall teacher participation (%)

Recommended rating

Alberta (Canada)

122

1 077

9 991

51.8

62.6

83.0

52.0

Fair

Australia

233

3 573

116 679

50.3

76.6

77.7

59.6

Fair

Austria

246

4 255

45 882

85.9

88.8

84.4

75.0

Good

Belgium

306

5 333

34 494

86.0

95.1

86.8

82.6

Good

Flemish Community (Belgium)

186

3 198

18 746

80.0

90.7

84.3

76.5

Good

Brazil

185

2 447

568 719

89.9

96.6

94.9

91.6

Good

Bulgaria

200

2 862

21 221

97.1

100.0

98.3

98.3

Good

Chile

180

1 971

55 979

82.6

91.5

94.3

86.2

Good

CABA (Argentina)*

130

2 099

10 219

81.3

86.7

88.6

76.8

Good

Colombia

154

2 398

164 143

73.9

77.4

93.4

72.3

Fair

Croatia

188

3 358

15 762

95.4

96.2

87.0

83.7

Good

Cyprus1

88

1 611

3 861

89.8

89.8

90.3

81.0

Good

Czech Republic

219

3 447

42 354

100.0

100.0

93.8

93.8

Good

Denmark

141

2 001

22 492

51.1

72.0

86.8

62.5

Fair

England (UK)

149

2 376

193 195

72.7

81.5

83.6

68.1

Fair

Estonia

201

3 083

7 248

88.6

100.0

95.4

95.4

Good

Finland

148

2 851

18 938

100.0

100.0

96.2

96.2

Good

France

176

3 006

197 013

87.3

87.8

88.1

77.3

Good

Georgia

198

3 214

38 150

99.5

99.5

95.9

95.4

Good

Hungary

189

3 245

44 013

94.9

97.7

95.0

92.8

Good

Iceland

122

1 277

1 864

89.7

89.7

75.5

67.8

Good

Israel

172

2 627

32 603

85.3

87.3

84.9

84.9

Good

Italy

190

3 612

190 447

91.7

99.0

93.5

92.5

Good

Japan

196

3 555

231 118

92.4

99.5

99.0

98.5

Good

Kazakhstan

331

6 566

195 659

100.0

100.0

99.8

99.8

Good

Korea

163

2 931

75 848

70.5

81.5

92.2

75.1

Fair

Latvia

135

2 315

12 006

77.1

91.2

87.9

80.2

Good

Lithuania

195

3 759

19 861

100.0

100.0

97.4

97.4

Good

Malta

55

1 656

1 941

94.8

94.8

86.5

82.0

Good

Mexico

193

2 926

255 079

90.4

96.3

94.3

90.8

Good

Netherlands

116

2 584

66 491

58.3

80.2

80.8

64.7

Fair

New Zealand

184

2 255

23 411

62.6

79.3

79.6

63.2

Fair

Norway

185

4 154

21 828

77.4

92.6

83.2

77.0

Good

Portugal

200

3 676

39 703

97.9

100.0

92.7

92.7

Good

Romania

199

3 658

66 078

100.0

100.0

98.3

98.3

Good

Russian Federation

230

4 011

647 381

98.7

100.0

99.9

99.9

Good

Saudi Arabia

179

2 744

99 693

89.7

89.7

86.0

77.1

Good

Shanghai (China)

198

3 976

38 876

100.0

100.0

99.5

99.5

Good

Singapore

169

3 280

11 546

98.2

100.0

99.2

99.2

Good

Slovak Republic

176

3 015

24 756

82.4

88.9

95.4

84.7

Good

Slovenia

132

2 094

7 422

82.2

88.0

91.5

80.5

Good

South Africa

170

2 046

92 127

92.3

92.9

89.1

82.3

Good

Spain

399

7 407

186 187

99.5

100.0

94.6

94.6

Good

Sweden

180

2 782

31 435

89.1

93.9

81.3

76.3

Good

Chinese Taipei

200

3 835

53 243

99.0

99.0

97.2

96.2

Good

Turkey

196

3 952

277 675

99.0

99.0

98.5

97.5

Good

United Arab Emirates

521

8 648

14 510

100.0

100.0

96.0

96.0

Good

United States

165

2 560

1 145 071

60.1

76.8

89.6

68.8

Fair

Viet Nam

196

3 825

295 532

100.0

100.0

96.3

96.3

Good

* CABA (Argentina) refers to the Ciudad Autónoma de Buenos Aires, Argentina.

1. See notes 1 and 2 of Table A A.5.

Table AI.A.7. ISCED 3 principals’ participation and recommended ratings

Number of participating principals

Principals’ participation before replacement (%)

Principals’ participation after replacement (%)

Recommended rating

Alberta (Canada)

115

51.8

59.6

Fair

Brazil

187

91.4

97.5

Good

Croatia

145

96.7

96.7

Good

Denmark

96

58.3

70.8

Fair

Portugal

195

98.0

99.5

Good

Slovenia

103

69.6

69.6

Fair

Sweden

174

91.6

93.8

Good

Chinese Taipei

151

100.0

100.0

Good

Turkey

448

98.0

98.0

Good

United Arab Emirates

366

89.7

89.7

Good

Viet Nam

199

100.0

100.0

Good

Table AI.A.8. ISCED 3 teachers’ participation and recommended ratings

Number of participating schools

Number of participating teachers

Estimated size of teacher population

School participation before replacement (%)

School participation after replacement (%)

Teacher participation in participating schools (%)

Overall teacher participation (%)

Recommended rating

Alberta (Canada)

112

1 094

7 819

51.6

56.6

80.2

45.4

Fair

Brazil

186

2 828

421 208

92.2

97.4

94.5

92.0

Good

Croatia

147

2 661

14 818

97.9

97.9

89.7

87.9

Good

Denmark

111

1 670

16 739

65.5

79.9

85.7

68.5

Fair

Portugal

195

3 551

36 194

99.0

99.7

91.3

91.0

Good

Slovenia

119

2 200

5 401

80.4

80.4

87.8

70.6

Good

Sweden

181

2 933

26 916

95.3

97.8

81.7

79.9

Good

Chinese Taipei

148

2 800

41 246

98.1

98.1

95.8

94.1

Good

Turkey

457

8 342

252 770

100.0

100.0

98.0

98.0

Good

United Arab Emirates

405

6 118

10 163

99.3

99.3

95.7

95.0

Good

Viet Nam

199

3 884

175 317

100.0

100.0

97.7

97.7

Good

References

[1] OECD (2019), TALIS 2018 Technical Report, OECD, Paris.

[2] OECD (2018), OECD Handbook for Internationally Comparative Education Statistics 2018: Concepts, Standards, Definitions and Classifications, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264304444-en.

Note

← 1. Tables AI.A.3 to AI.A.8 display the participation rate estimates that were the most favourable for the adjudication rating. The most favourable estimates could have been weighted or unweighted depending on the characteristics of the country/economy, the teacher and principals population and the educational level.

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