Mending the Education Divide

Annex A. Technical notes on TALIS 2018

The objective of the Teaching and Learning International Survey (TALIS) in 2018 was to obtain, in each participating country or territory, a representative sample of teachers for each International Standard Classification of Education (ISCED) level in which the country or territory participated. 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 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/territories 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, states or areas. 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/territory 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/territory participated.

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 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 territory participated. Participating countries and territories 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 territories 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 territories 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/territories, 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. Arrangements were made with the NPM and their team to optimise the selection of the school sample either by 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 or territory and for each of the TALIS options, whether the data released to the countries and territories 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/territories, 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 evaluations per TALIS option and per country/territory.

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 issues.

The adjudication rules, based on participation rates for principals and teachers, are displayed in Table A A.1 and Table A A.2. An explanation of the codes used is given below.

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

School participation (minimum teacher participation)		Teacher participation after school replacement	Risk of teacher non‑response bias	Rating
Before replacement	After replacement
≥75%	≥75%	≥75%		Good
		50% - 75%		Fair (A)
50% - 75%	≥75%	≥75%		Fair (B)
		50% - 75%	Low	Fair (C)
			High	Poor (D)
50% - 75%	50% - 75%			Poor (E)
< 50%	≥75%			Poor (F)
< 50%	< 75%			Insufficient

The following bulleted list is a simple guide to help data users appreciate the limitations on use or quality:

• Good: The participating country’s/territory’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/territories is likely to uncover any statistically meaningful differences, as the 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/territory at ISCED level 2 are presented in Table A A.3 and Table A A.4.1

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 ISCED level 2 data reported for these countries.

• Alberta (Canada):

  • TALIS data collection was 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 and, as such, their rating was upgraded from “poor” to “fair”.

• Australia:

  • 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 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 A A.3 and Table A A.4.

• 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”.

• Denmark: 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”.

• Flemish Community of Belgium: 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.

• French Community of Belgium: Items regarding the share of students with special needs should be interpreted carefully due to complications that could arise from the interpretation of the definition of special needs. Students studying for a differentiated first degree, which is organised for students who did not pass their primary certificate and who receive extra support and resources, are formally identified as having learning difficulties but most of them do not suffer from any kind of disability.

• Georgia: 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.

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

• Korea: In four schools, teacher listings were found to be incorrect; those schools were set to “non‑participant”.

• Netherlands:

  • The Netherlands had a six‑week early start and extended collection window.

  • 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 were left on the national dataset; participation rates were computed on the international dataset.

• 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.

• 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.

• Singapore: Coverage included both privately and publicly managed schools. Nevertheless, private schools were excluded from the sample in TALIS 2013 due to confidentiality issues.

• United Arab Emirates: Because of the selection of multi‑level schools, the principal data were copied from the original ISCED level 2 principal questionnaire to the corresponding ISCED level 1 and ISCED level 3 forms, except for Question 17 in the principal questionnaire.

Table A A.3.  ISCED 2 principals’ participation and recommended ratings

	Number of participating principals	Estimated size of principal population	Principals’ participation before replacement (%)	Principals’ participation after replacement (%)	Recommended rating
Alberta (Canada)	129	1 038	54.4	66.2	Fair
Australia	230	2 680	49.0	75.7	Insufficient
Austria	277	1 483	96.0	100.0	Good
Flemish Community (Belgium)	188	721	82.5	94.0	Good
French Community (Belgium)	119	448	93.3	99.2	Fair
Brazil	184	52 187	88.0	95.4	Good
Bulgaria	200	1 730	97.5	100.0	Good
Chile	169	5 214	78.9	87.6	Good
CABA (Argentina)1	121	488	77.5	82.6	Good
Colombia	141	10 392	68.8	70.9	Fair
Croatia	188	896	95.0	95.6	Good
Cyprus	88	99	88.9	88.9	Good
Czech Republic	218	2 606	99.5	99.5	Good
Denmark	140	1 457	51.5	71.4	Fair
England (UK)	157	3 990	71.9	81.8	Fair
Estonia	195	389	88.3	100.0	Good
Finland	148	706	100.0	100.0	Good
France	195	6 770	97.6	98.0	Good
Georgia	177	2 151	91.7	91.7	Good
Hungary	182	2 640	91.2	94.3	Good
Iceland	101	136	74.3	74.3	Fair
Israel	184	1 196	90.9	93.7	Good
Italy	190	5 622	92.4	98.6	Good
Japan	195	10 071	93.9	99.4	Good
Kazakhstan	331	6 302	100.0	100.0	Good
Korea	150	3 134	68.1	77.8	Fair
Latvia	136	653	80.4	91.9	Good
Lithuania	195	833	100.0	100.0	Good
Malta	54	58	93.1	93.1	Good
Mexico	193	16 327	90.6	97.0	Good
Netherlands	125	524	56.2	85.6	Fair
New Zealand	189	1 732	71.7	92.0	Fair
Norway	162	1 091	67.5	81.0	Fair
Portugal	200	1 255	97.7	100.0	Good
Romania	199	4 658	100.0	100.0	Good
Russian Federation	230	31 948	99.1	100.0	Good
Saudi Arabia	192	6 119	96.5	96.5	Good
Shanghai (China)	198	630	100.0	100.0	Good
Singapore	167	193	97.0	98.8	Good
Slovak Republic	180	1 593	84.4	90.5	Good
Slovenia	119	448	74.8	79.3	Good
South Africa	169	8 026	92.3	92.3	Good
Spain	396	6 861	98.7	99.2	Good
Sweden	171	1 739	85.9	89.1	Good
Chinese Taipei	202	935	100.0	100.0	Good
Turkey	196	16 100	99.0	99.0	Good
United Arab Emirates	476	521	91.4	91.4	Good
United States	164	65 095	63.1	77.6	Fair
Viet Nam	196	10 799	100.0	100.0	Good

1. CABA (Argentina): Ciudad Autónoma de Buenos Aires, Argentina.

Table A A.4.  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	45 869	85.9	88.8	84.4	75.0	Good
Belgium	302	5 257	34 442	86.0	95.1	86.9	82.6	Good
Flemish Community (Belgium)	182	3 122	18 615	80.0	91.0	84.4	76.8	Good
French Community (Belgium)	120	2 135	15 827	93	100	89.7	89.7	Fair
Brazil	185	2 447	568 510	89.9	96.6	94.9	91.6	Good
Bulgaria	200	2 862	21 208	97.1	100.0	98.3	98.3	Good
Chile	179	1 963	55 969	82.6	91.5	94.3	86.2	Good
CABA (Argentina)1	130	2 099	10 218	81.3	86.7	88.6	76.8	Good
Colombia	154	2 398	164 225	73.9	77.4	93.4	72.3	Fair
Croatia	188	3 358	15 762	95.4	96.2	87.0	83.7	Good
Cyprus	88	1 611	3 860	89.8	89.8	90.3	81.0	Good
Czech Republic	219	3 447	42 348	100.0	100.0	93.8	93.8	Good
Denmark	141	2 001	22 475	51.1	72.0	86.8	62.5	Fair
England (UK)	149	2 376	193 134	72.7	81.5	83.6	68.1	Fair
Estonia	195	3 004	7 354	86.6	100.0	95.2	95.2	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	192	3 101	38 195	99.5	99.5	95.8	95.3	Good
Hungary	189	3 245	44 018	94.9	97.7	95.0	92.8	Good
Iceland	123	1 292	1 883	90.4	90.4	75.8	68.5	Good
Israel	172	2 627	32 603	85.3	87.3	84.9	84.9	Good
Italy	191	3 612	190 447	92.8	99.1	93.5	93.0	Good
Japan	196	3 555	230 558	92.4	99.5	99.0	98.5	Good
Kazakhstan	331	6 566	195 383	100.0	100.0	99.8	99.8	Good
Korea	163	2 931	75 654	70.5	81.5	92.2	75.1	Fair
Latvia	135	2 315	12 003	77.1	91.2	87.9	80.2	Good
Lithuania	195	3 759	19 848	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	254 794	90.4	96.3	94.3	90.8	Good
Netherlands	116	1 884	66 672	56.7	79.5	80.9	64.3	Fair
New Zealand	185	2 257	23 227	62.8	79.5	79.6	63.3	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	66 039	100.0	100.0	98.3	98.3	Good
Russian Federation	230	4 011	646 405	98.7	100.0	99.9	99.9	Good
Saudi Arabia	179	2 744	99 661	89.7	89.7	86.0	77.1	Good
Shanghai (China)	198	3 976	38 902	100.0	100.0	99.5	99.5	Good
Singapore	169	3 280	11 544	98.2	100.0	99.2	99.2	Good
Slovak Republic	176	3 015	24 746	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	89.7	83.3	Good
Spain	399	7 407	186 171	99.5	100.0	94.6	94.6	Good
Sweden	180	2 782	31 421	89.1	93.9	81.3	76.3	Good
Chinese Taipei	200	3 835	53 208	99.0	99.0	97.2	96.2	Good
Turkey	196	3 952	277 187	99.0	99.0	98.5	97.5	Good
United Arab Emirates	521	8 648	14 489	100.0	100.0	96.0	96.0	Good
United States	165	2 560	1 144 751	60.1	76.8	89.6	68.8	Fair
Viet Nam	196	3 825	295 033	100.0	100.0	96.3	96.3	Good

1. CABA (Argentina): Ciudad Autónoma de Buenos Aires, Argentina.

References

[1] OECD (2019), TALIS 2018 Technical Report, OECD, Paris, http://www.oecd.org/education/talis/TALIS_2018_Technical_Report.pdf.

[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.  Table A A.3 and Table A A.4 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/territory, the teacher and principal populations and the educational level.