Results from TALIS 2024

Annex B. Technical notes on analyses in this volume

Use of teacher and school weights

The statistics presented in this report were derived from data obtained through samples of schools, principals and teachers. Samples were collected following a stratified two-stage probability sampling design. This means that teachers (secondary sampling units) were randomly selected from the list of in‑scope teachers for each of the randomly selected schools (first-stage or primary sampling units). For these statistics to be meaningful for a country, they needed to reflect the whole population from which they were drawn and not merely the sample used to collect them. Thus, survey weights must be used in order to obtain design-unbiased estimates of population or model parameters.

Final weights allow the production of country-level estimates from the observed sample data. The estimation weight indicates how many population units are represented by a sampled unit. The final weight is the combination of many factors reflecting the probabilities of selection at the various stages of sampling and the response obtained at each stage. Other factors may also come into play as dictated by special conditions to maintain the unbiasedness of the estimates (e.g. adjustment for teachers working in more than one school).

The statistics presented in this report that are based only on responses of principals were estimated using school weights (SCHWGT). Results based only on responses of teachers or on responses of teachers and principals (i.e. responses from school principals merged with teachers’ responses) were weighted by teacher weights (TCHWGT).

Use of scales

In this report, several scales are used, in particular in regression analyses. TALIS 2024 scales were constructed using the same international model parameters to be statistically equivalent across countries/territories and populations, facilitating scale score comparability across education systems. In general, scale scores created for TALIS 2024 can be used in different types of analyses, including comparisons of country/territory means for each individual scale.

TALIS 2024 scale scores are standardised to have a standard deviation of 2 across all education systems participating in TALIS and where value 10 corresponds to the item mid-point value of the response scale. The only exceptions are the composite scales that combine different scales. These are standardised to have a standard deviation of 2 across all education systems participating in TALIS and a mean of 10.

To note, direct mean comparisons across different scales and across TALIS cycles are not meaningful. It is also important to note that analysing different scale scores based on the same items (e.g. a composite scale and its subscales) requires caution, as shared variance can influence the results. A detailed description of the construction and validation of TALIS 2024 scales can be found in Chapter 11 of the TALIS 2024 Technical Report (OECD, forthcoming[1]).

Self‐efficacy in student engagement

The scale of self‐efficacy in student engagement (T4SEENG) was constructed using teacher responses (“not at all”, “to some extent”, “quite a bit”, “a lot”) about the extent to which the following statements can occur (TT4G27): “Get students to believe they can do well in school work”; “Help students value learning”; “Motivate students who show low interest in school work”; “Help students think critically”.

Self‐efficacy in instruction

The scale of self‐efficacy in instruction (T4SEINS) was constructed using teacher responses (“not at all”, “to some extent”, “quite a bit”, “a lot”) about the extent to which the following can occur (TT4G27): “Craft good questions for students”; “Use a variety of assessment strategies”; “Provide an alternative explanation, for example when students are confused”; “Vary instructional strategies in my classroom”.

Self‐efficacy in classroom management

The scale of self‐efficacy in classroom management (T4SECLS) was constructed using teacher responses (“not at all”, “to some extent”, “quite a bit”, “a lot”) about the extent to which the following can occur (TT4G27): “Control disruptive behaviour in the classroom”; “Make my expectations about student behaviour clear”; “Get students to follow classroom rules”; “Calm a student who is disruptive or noisy”.

Self‐efficacy (overall)

The scale of teacher self‐efficacy overall (T4SELF) is a composite scale score that was constructed as an average of the three subscales: self‐efficacy in student engagement (T4SEENG), self‐efficacy in instruction (T4SEINS) and self‐efficacy in classroom management (T4SECLS). Composite scale scores have a mean of 10 and a standard deviation of 2.

Growth mindset

The scale of growth mindset (T4GROMST) was constructed using teacher responses (“strongly agree”, “agree”, “disagree”, “strongly disagree”) about the following statements related to intelligence and learning (TT4G30): “Everyone has a certain amount of intelligence and no one can really do much to change it”; “People’s intelligence is something about them that they can’t change very much”; “Someone can learn new things, but they can’t really change their basic intelligence”.

Use of digital resources and tools for whole class instruction

The scale of use of digital resources and tools for whole class instruction (T4DRTWCI) was constructed using teacher responses (“never or almost never”, “occasionally”, “frequently”, “always”) about the frequency of doing the following in the target class (TC4G52): “Use digital resources and tools to present information through direct instruction”; “Use digital resources and tools to enable collaboration with other classrooms, schools, or experts outside of this school”; “Support collaboration among students using digital resources and tools”; “Give students problems that can only be solved by using digital resources and tools”.

Based on the results of measurement invariance testing, comparisons across education systems of the results of this scale are affected by substantial model fit issues in the most restricted multi-group model and must be interpreted with considerable caution. For more detail see Chapter 11 of the TALIS 2024 Technical Report (OECD, forthcoming[1]).

Use of digital resources and tools for individualised instruction and assessment

The scale of use of digital resources and tools for individualised instruction and assessment (T4DRTIIA) was constructed using teacher responses (“never or almost never”, “occasionally”, “frequently”, “always”) about the frequency of doing the following in the target class (TC4G52): “Provide digital feedback on student work”; “Use digital resources and tools that provide personalised learning paths for students”; “Use digital resources and tools to assess student learning”; “Provide digital resources and tools that allow students to plan and monitor their own learning”.

Perceived classroom disruption

The scale of perceived classroom disruption (T4CLSDIS) was constructed using teacher responses (“not at all”, “to some extent”, “quite a bit”, “a lot”) about the extent to which the following situations can occur (TT4G54): “There is much disruptive noise and disorder”; “I have to wait a long time for students to quiet down”; “Many students don’t start working for a long time after the lesson begins”; “I lose quite a lot of time because students interrupt the lesson”.

Adaptive learning

The scale of adaptive learning (T4ADLE) was constructed using teacher responses (“never or almost never”, “occasionally”, “frequently”, “always”) about the frequency of performing the following actions in the target class (TC4G55): “I consider students’ prior knowledge and needs when planning a lesson”; “I point students to different materials for learning depending on their needs”; “I change my way of explaining when a student has difficulties understanding a topic or task”; “I adapt my teaching methods to students’ needs”; “I ask questions at various difficulty levels to check students’ understanding of the subject matter”.

Instructional autonomy

The scale of instructional autonomy (T4AUTCH) was constructed using teacher responses (“no autonomy”, “limited autonomy”, “substantial autonomy”, “full autonomy”) about how much autonomy they had over the following aspects (TT4G57): “Implementing the curriculum in a flexible way”; “Selecting teaching methods and strategies”; “Choosing assessment activities”; “Designing and preparing lessons”.

Fulfilment of lesson aims

The scale of fulfilment of lesson aims (complexity of teaching) (T4FULFIL) was constructed using teacher responses (“not at all”, “to some extent”, “quite a bit”, “a lot”) about the extent to which the following aims were fulfilled in the past week (TT4G58): “Presenting the content in a comprehensible way”; “Engaging students in work that challenges them”; “Providing students with feedback to support their learning”; “Offering students opportunities to practise what they learnt”; “Adapting teaching to meet the different needs of students”.

Social and emotional skill development

The scale of social and emotional skill development (T4SESDEV) was constructed using teacher responses (“never or almost never”, “occasionally”, “frequently”, “always”) about the frequency of developing the following student skills in the target class (TC4G61): “Managing their own emotions, thoughts, or behaviour”; “Empathising with others”; “Establishing and maintaining healthy relationships with others”; “Making caring and constructive choices about their personal actions”.

Teacher‐student relations

The scale of teacher‐student relations (T4STUD) was constructed using teacher responses (“strongly disagree”, “disagree”, “agree”, “strongly agree”) about the following statements related to what happens at their school (TT4G65): “Teachers and students usually get on well with each other”; “Most teachers believe that the students’ well-being is important”; “Most teachers are interested in what students have to say”; “If a student needs extra assistance, the school provides it”.

Personal utility motivations to teach

The scale of personal utility motivations to teach (T4MOPU) was constructed using teacher responses (“not important at all”, “of low importance”, “of moderate importance”, “of high importance”) about how important the following factors are to them (TT4G73): “Teaching is a secure job”; “Working hours fit with my family responsibilities”; “Teaching has commitment flexibility (travel, part-time, family commitments)”.

Social utility motivations to teach

The scale of social utility motivations to teach (T4MOSU) was constructed using teacher responses (“not important at all”, “of low importance”, “of moderate importance”, “of high importance”) about how important the following factors are to them (TT4G73): “Teaching allows me to influence the next generation”; “Teaching allows me to work against social disadvantage”; “Teaching makes a worthwhile social contribution”.

Workplace well‐being and stress

The scale of workplace well‐being and stress (T4WELS) was constructed using teacher responses (“not at all”, “to some extent”, “quite a bit”, “a lot”) about the extent to which the following situations occur (TT4G76): “I experience stress in my work”; “My job leaves me time for my personal life”; “My job negatively impacts my mental health”; “My job negatively impacts my physical health”.

Higher values on the workplace well-being and stress scale reflect lower levels of well-being.

Job satisfaction with the profession

The scale of job satisfaction with the profession (T4JSPROT) was constructed using teacher responses (“strongly disagree”, “disagree”, “agree”, “strongly agree”) about the following statements related to how they feel about their job (TT4G78): “The advantages of being a teacher clearly outweigh the disadvantages”; “If I could decide again, I would still choose to work as a teacher”; “I regret that I decided to become a teacher”; “All in all, I am satisfied with my job”.

Job satisfaction with the work environment

The scale of job satisfaction with the work environment (T4JSENVT) was constructed using teacher responses (“strongly disagree”, “disagree”, “agree”, “strongly agree”) about the following statements related to how they feel about their job (TT4G78): “I would like to change to another school if that were possible”; “I enjoy working at this school”; “I would recommend this school as a good place to work”.

Job satisfaction (overall)

The scale of job satisfaction overall (T4JOBSAT) is a composite scale score that was constructed as an average of the two subscales: job satisfaction with the profession (T4JSPROT) and job satisfaction with the work environment (T4JSENVT). Composite scale scores have a mean of 10 and a standard deviation of 2.

Joy of teaching

The scale of joy of teaching (T4JOYTCH) was constructed using teacher responses (“strongly disagree”, “disagree”, “agree”, “strongly agree”) about the following statements (TT4G80): “I like the subject(s) that I teach”; “I often feel happy while I teach”; “I generally teach with enthusiasm”; “The interesting challenges of teaching give me satisfaction”.

Instructional leadership (principal)

The scale of instructional leadership (principal) (T4PLEADS) was constructed using principal responses (“never or rarely”, “sometimes”, “often”, “very often”) about the frequency of engaging in the following activities in the school during the last 12 months (TC4G27): “I took actions to support co-operation among teachers to develop new teaching practices”; “I took actions to ensure that teachers take responsibility for improving their teaching skills”; “I took actions to ensure that teachers feel responsible for their students’ learning outcomes”.

International averages

This report includes eight arithmetic and one weighted average (for more detail about the country coverage of the international averages, see Table A B.1):

• OECD average-27: arithmetic average based on International Standard Classification of Education (ISCED) Level 2 teacher or principal data across 27 OECD education systems with a data adjudication rating of “good”, “fair” or “poor”. The report refers to the average teacher as equivalent shorthand for the average teacher “across the 27 OECD education systems participating in TALIS”.

• OECD average-25: arithmetic average based on ISCED Level 2 teacher data across 25 OECD education systems with a data adjudication rating of “good”, “fair” or “poor” that participated in both the TALIS 2018 and TALIS 2024 cycles.

• OECD average-24: arithmetic average based on ISCED Level 2 principal data across 24 OECD education systems with a data adjudication rating of “good”, “fair” or “poor” that participated in both the TALIS 2018 and TALIS 2024 cycles.

• TALIS average-49: arithmetic average based on ISCED Level 2 teacher or principal data across 49 TALIS 2024 education systems with a data adjudication rating of “good”, “fair” or “poor”.

• TALIS ISCED 1 average-12: arithmetic average based on ISCED Level 1 teacher or principal data across 12 TALIS 2024 education systems with a data adjudication rating of “good”, “fair” or “poor”.

• TALIS ISCED 3 average-8: arithmetic average based on ISCED Level 3 teacher data across eight TALIS 2024 education systems with a data adjudication rating of “good”, “fair”, or “poor”.

• TALIS ISCED 3 average-7: arithmetic average based on ISCED Level 3 principal data across seven TALIS 2024 education systems with a data adjudication rating of “good”, “fair”, or “poor”.

• OECD PIAAC average-29: arithmetic average based on data across the 29 OECD countries and territories that participated in the Survey of Adult Skills 2023.

• EU total-22: weighted average based on ISCED Level 2 teacher or principal data across all European Union (EU) member states that participate in TALIS with a data adjudication rating of “good”, “fair” or “poor”. The EU total represents the 22 EU member states that also participated in TALIS 2024 as a single entity and to which each of the 22 EU member states contribute in proportion to the number of teachers or principals, depending on the basis of the analysis. Therefore, the EU total is calculated as a weighted arithmetic mean based on the sum of final teacher (TCHWGT) or principal (SCHWGT) weights by country, depending on the target population.

Education systems that did not meet the TALIS technical standards with respect to participation rates are not included in international averages:

• ISCED level 1 (teacher and principal data): the Flemish Community of Belgium, the Netherlands and New Zealand.

• ISCED level 2 (teacher and principal data): Alberta (Canada), the Netherlands, New Zealand and Norway.

• ISCED level 3 (principal data): the Flemish Community of Belgium.

Table A B.1. Country coverage of international averages in TALIS 2024

Country/territory	OECD average-27	OECD average-25	OECD average-24	TALIS average-49	TALIS ISCED 1 average-12	TALIS ISCED 3 average-8	TALIS ISCED 3 average-7	OECD PIAAC average-29	EU total-22
Albania				X
Alberta (Canada)								X
Australia	X	X		X	X
Austria	X	X	X	X				X	X
Azerbaijan				X
Bahrain				X
Belgium	X	X	X	X	X				X
Flemish Community (Belgium)						X		X
French Community (Belgium)
Brazil				X	X
Bulgaria				X					X
Chile	X	X	X	X				X
Colombia	X	X	X	X
Costa Rica	X			X
Croatia				X		X	X		X
Cyprus*				X					X
Czechia	X	X	X	X				X	X
Denmark	X	X	X	X		X	X	X	X
Estonia	X	X	X	X				X	X
Finland	X	X	X	X				X	X
France	X	X	X	X	X			X	X
Hungary	X	X	X	X				X	X
Iceland	X	X	X	X
Israel	X	X	X	X				X
Italy	X	X	X	X				X	X
Japan	X	X	X	X	X			X
Kazakhstan				X
Korea	X	X	X	X	X			X
Kosovo				X
Latvia	X	X	X	X				X	X
Lithuania	X	X	X	X				X	X
Malta				X					X
Montenegro				X
Morocco				X	X
Netherlands								X
New Zealand								X
North Macedonia				X
Norway								X
Poland	X			X				X	X
Portugal	X	X	X	X		X	X	X	X
Romania				X					X
Saudi Arabia				X	X	X	X
Serbia				X
Shanghai (China)				X
Singapore				X
Slovak Republic	X	X	X	X				X	X
Slovenia	X	X	X	X	X	X	X		X
South Africa				X
Spain	X	X	X	X	X			X	X
Sweden	X	X	X	X				X	X
Türkiye	X	X	X	X	X	X	X
United Arab Emirates				X	X	X	X
United States	X	X	X	X				X
Uzbekistan				X
Viet Nam				X

Note: *Cyprus did not participate directly in TALIS 2024: its data collection and processing were managed exclusively by the international research consortium. Its data are reported in the result tables listed in Annex C.

The countries/territories not covered by TALIS but included in the OECD PIAAC average-29 are: England (United Kingdom), Germany, Ireland and Switzerland.

In this publication, the OECD average is generally used when the focus is on providing a global tendency for an indicator and comparing its values across education systems. In the case of some education systems, data may not be available for specific indicators, or specific categories may not apply. Therefore, readers should keep in mind that the term “OECD average” refers to the OECD education systems included in the respective comparisons. In cases where data are not available or do not apply to all sub-categories of a given population or indicator, the “OECD average” may be consistent within each column of a table but not necessarily across all columns of a table.

Standard errors and significance tests

The statistics in this report represent estimates based on samples of teachers and principals, rather than values that could be calculated if every teacher and principal in every country had answered every question. Consequently, it is important to measure the degree of uncertainty of the estimates. In TALIS, each estimate has an associated degree of uncertainty that is expressed through a standard error. The use of confidence intervals provides a way to make inferences about the population means and proportions in a manner that reflects the uncertainty associated with the sample estimates. From an observed sample statistic and assuming a normal distribution, it can be inferred that the corresponding population result would lie within the confidence interval in 95 out of 100 replications of the measurement on different samples drawn from the same population. The reported standard errors were computed with a balanced repeated replication (BRR) methodology.

Differences between sub-groups

Differences between sub-groups along teacher (e.g. female and male teachers) and school characteristics (e.g. schools with a high concentration of students from socio-economically disadvantaged homes and schools with a low concentration of students from socio-economically disadvantaged homes) were tested for statistical significance. All differences marked in bold in the data tables of this report are statistically significantly different from 0 at the 95% level.

In the case of differences between sub-groups, the standard error is calculated by taking into account that the two sub-samples are not independent. As a result, the expected value of the covariance might differ from 0, leading to smaller estimates of standard error as compared to estimates of standard error calculated for the difference between independent sub-samples.

Differences between cycles

Differences between TALIS cycles (e.g. change between 2018 and 2024) were tested for statistical significance. All differences marked in bold in the data tables of this report are statistically significant at the 95% level. As samples from different TALIS cycles are considered independent, the standard error for any comparison between cycles is calculated with the expected value of the covariance being equal to 0.

Statistics based on regressions

Regression analysis was conducted to explore the relationships between different variables. Multiple linear regression was used in those cases where the dependent (or outcome) variable was considered continuous. Binary logistic regression was employed when the dependent (or outcome) variable was a binary categorical variable. Regression analyses were carried out for each country separately. Similarly to other statistics presented in this report, the OECD and TALIS averages refer to the arithmetic mean of country-level estimates, while the EU total is calculated as a weighted arithmetic mean based on the sum of final teacher (TCHWGT) or principal (SCHWGT) weights by country, depending on the target population.

Control variables included in a regression model are selected based on theoretical reasoning and to the extent possible limited to the most objective measures or those that do not change over time:

• Controls for teacher characteristics: teacher’s gender, age and years of teaching experience.

• Controls for target class characteristics: class size, class intake of students who have difficulties understanding the language(s) of instruction, class intake of low achieving students, and class intake of students with special education needs.

• Controls for school characteristics: school location, school governance type, school intake of students from socio-economically disadvantaged homes, school intake of students who have difficulties understanding the language(s) of instruction, and school intake of students with special education needs.

In the case of regression models based on multiple linear regression, the explanatory power of the regression models is also highlighted by reporting the R-squared (R²), which represents the proportion of the observed variation in the dependent (or outcome) variable that can be explained by the independent (or explanatory) variables.

In order to ensure the robustness of the regression models, control variables were introduced into the models in steps. This approach also required that the models at each step be based on the same sample. The restricted sample used for the different versions of the same model corresponded to the sample of the most extended (i.e. with the maximum number of independent variables) version of the model. Thus, the restricted sample of each regression model excluded those observations where all independent variables had missing values.

Regression analyses presented in this report handle missing data by listwise deletion. In the result tables, regression models that are based on 25% to 50% of the full sample are highlighted with a † next to the country/territory label. Analyses based on less than 25% of the full sample are excluded from the report.

Restrictive models that include many independent variables and are based on small samples can lead to limited variability in some independent variables. This, in turn, may cause the corresponding coefficients to appear either very small or very large. In this report, such independent variables are treated as constants and therefore excluded from the regression model. These suppressed variables are marked with the missing value symbol “m” in the results tables. Please note that this approach – suppressing independent variables with little or no variability – is not applied to control variables.

Multiple linear regression analysis

Multiple linear regression analysis provides insights into how the value of the continuous dependent (or outcome) variable changes when any one of the independent (or explanatory) variables varies while all other independent variables are held constant. Everything else held constant, on average, a one-unit increase in the independent variable ($X_i$) is associated with an increase in the dependent variable ($\text{Y}$) by the units represented by the regression coefficient (${\beta }_i$):

$\text{Y = }{\beta }_0+{\beta }_1{X}_1+ \text{…} +{\beta }_i{X}_i+\epsilon$

When interpreting multiple regression coefficients, it is important to keep in mind that each coefficient is influenced by the other independent variables in a regression model. The influence depends on the extent to which independent variables are correlated. Therefore, each regression coefficient does not capture the total effect of independent variables on dependent variables. Rather, each coefficient represents the additional effect of adding that variable to the model, if the effects of all other variables in the model are already accounted for. It is also important to note that, due to the cross-sectional nature of TALIS data, no causal conclusions can be drawn.

Regression coefficients in bold in the data tables are statistically significantly different from 0 at the 95% confidence level.

Binary logistic regression analysis

Binary logistic regression analysis enables the estimation of the relationship between one or more independent (or explanatory) variables and the dependent (or outcome) variable with two categories. The regression coefficient ($\beta$) of a logistic regression is the estimated increase in the log odds of the outcome per unit increase in the value of the predictor variable.

More formally, let $\text{Y}$ be the binary outcome variable indicating no/yes with 0/1, and $p$ be the probability of $\text{Y}$ to be 1, so that $p=prob\left(Y=1\right)$. Let $X_1 , \dots X_k$ be a set of explanatory variables. Then, the logistic regression of $\text{Y}$ on $X_1 , \dots X_k$estimates parameter values for ${\beta }_0, {\beta }_1, \dots {\beta }_k$ via the maximum likelihood method of the following equation:

$Logit\left(p\right)= \log \left(p/\left(1-p\right)\right)= {\beta }_0+{\beta }_1{X}_1+ \text{…} +{\beta }_k{X}_k$

Additionally, the exponential function of the regression coefficient $\left(exp\left(\beta \right)\right)$ is obtained, which is the odds ratio ($OR$) associated with a one-unit increase in the explanatory variable. Then, in terms of probabilities, the equation above is translated into the following:

$p= exp\left({\beta }_0+{\beta }_1{X}_1+ \text{…} +{\beta }_k{X}_k/1+exp\left({\beta }_0+{\beta }_1{X}_1+ \text{…} +{\beta }_k{X}_k\right)\right)$

The transformation of log odds ($\beta$) into odds ratios$\left(exp\left(\beta \right);OR\right)$ makes the data more interpretable in terms of probability. The odds ratio ($OR$) is a measure of the relative likelihood of a particular outcome when a specific condition is present (the antecedent) compared to when it is not. The odds ratio for observing the outcome when an antecedent is present is:

$OR= \left(p_{11}/p_{12}\right)/\left(p_{21}/p_{22}\right)$

where $p_{11}/p_{12}$ represents the “odds” of observing the outcome when the antecedent is present, and $p_{21}/p_{22}$ represents the “odds” of observing the outcome when the antecedent is not present. Thus, an odds ratio indicates the degree to which an explanatory variable is associated with a categorical outcome variable with two categories (e.g. yes/no) or more than two categories. An odds ratio below one denotes a negative association; an odds ratio above one indicates a positive association; and an odds ratio of one means that there is no association. For instance, if the association between being a female teacher and working part-time is being analysed, the following odds ratios would be interpreted as:

• 0.2: Female teachers are five times less likely to work part-time than male teachers.

• 0.5: Female teachers are half as likely to work part-time than male teachers.

• 0.9: Female teachers are 10% less likely to work part-time than male teachers.

• 1: Female and male teachers are equally likely to work part-time than male teachers.

• 1.1: Female teachers are 10% more likely to work part-time than male teachers.

• 2: Female teachers are twice more likely to work part-time than male teachers.

• 5: Female teachers are five times more likely to work part-time than male teachers.

The odds ratios in bold indicate that the relative risk/odds ratio is statistically significantly different from 1 at the 95% confidence level. To compute statistical significance around the value of 1 (the null hypothesis), the relative-risk/odds-ratio statistic is assumed to follow a log-normal distribution, rather than a normal distribution, under the null hypothesis.

Pearson correlation coefficient

In this report, Pearson correlation coefficients are used to quantify relationships between system-level statistics. Correlation coefficients measure the strength and direction of the statistical association between two variables. They vary between -1 and 1; values around 0 indicate a weak association, while the extreme values indicate the strongest possible negative or positive association. The Pearson correlation coefficient (indicated by the letter r) measures the strength and direction of the linear relationship between two variables.

Rotated questionnaire design

The TALIS 2024 applied a rotated questionnaire design, which implies that some questions are administered on all three teacher forms (i.e. common questions), while others are rotated across the different forms and do not appear on all three teacher forms. Rotated questions are mainly administered on two and, in some limited cases, only one out of the three forms.

To the extent possible, this report presents analyses based on at least two-thirds of the full sample. Analysis based on a one-third sample can be viable as well, including for trend analysis. However, analyses of questions administered to a one-third sample that involve cross-tabulation or regression analysis are more likely to result in otherwise statistically significant differences or regression coefficients go undetected. For more detail about the TALIS 2024 rotated questionnaire design, see Chapter 3 of the TALIS 2024 Technical Report (OECD, forthcoming[1]).

Changes between TALIS cycles and implications for analyses

Change in the definition of the target population between TALIS cycles

The fourth TALIS cycle (i.e. TALIS 2024) allows analysis of changes over a 16-year period. Nevertheless, such analysis poses certain challenges: country coverage and the target population within a given country may differ across cycles; the variables of interest could change due to changes in the questionnaires; moreover, the context of teaching and learning might also change. Therefore, comparisons across cycles need to be interpreted with care.

In TALIS 2008, teachers whose teaching was directed entirely or mainly at students with special education needs were not part of the target population. However, this changed for TALIS 2013 and onwards, as teachers of students with special education needs got included in the target population. Hence, estimates representing the change from 2008 to 2024 need to be interpreted with caution. Nevertheless, it is important to note that teachers who work in schools that teach only special education needs students were excluded from all TALIS cycles.

Change in the ISCED classification

The classification of levels of education is based on the International Standard Classification of Education (ISCED). ISCED is an instrument for compiling statistics on education internationally. In TALIS 2008 and 2013, ISCED-97 was used to report on teachers’ and principals’ educational attainment. The first classification, ISCED-97, was revised and the new one, ISCED-2011, was formally adopted in November 2011. ISCED-2011 is the basis of the education levels presented in the TALIS 2018 and 2024 questionnaires for teachers and principals.

The result tables reporting teachers’ educational attainment in this report are based on ISCED-2011. A correspondence table (Table A B.2) was used to translate ISCED-97 education categories used in TALIS 2008 and 2013 into the categories of ISCED-2011, in order to produce tables reporting changes in teachers’ educational attainment from 2008 to 2024, and from 2013 to 2024. This correspondence table was used to compile Table BMUL.TR1.TQ03 in Chapter 1. Changes over time in teachers’ educational attainment will need to be interpreted with caution because of the change in the classification.

For certain countries, the correspondence between ISCED-97 and ISCED-2011 was revised to reflect country specificities, compared to the general approach presented in Table A B.2. Namely, for Table BMUL.TR1.TQ03, ISCED-97 Level 5B was reclassified as ISCED-2011 Level 6 in the cases of the Flemish Community of Belgium and Italy.

In Portugal, the teachers with a “pre-Bologna master’s degree” are categorised as ISCED Level 6. The question is presented in a way that prevents the disaggregation between “pre-Bologna master’s degree” and “doctoral degree”.

In Slovenia, teachers with a “pre-Bologna bachelor’s degree” are categorised as ISCED Level 5 (which typically corresponds to short-term tertiary education). The question is presented in a way that prevents the disaggregation between “pre-Bologna bachelor’s degree” and “bachelor’s degree”.

Table A B.2. Correspondence between ISCED-2011 and ISCED-97 levels used in TALIS 2018 publications

TALIS 2008	TALIS 2013	ISCED-97 categories	TALIS 2018	ISCED-2011 categories
ISCED-97	ISCED-97		ISCED-2011
–	–	–	Level 0	Early childhood education Refers to early childhood programmes that have an intentional education component and aim to develop cognitive, physical and socio-emotional skills necessary for participation in school and society. Programmes at this level are often differentiated by age.
–	–	–	Level 01	Early childhood educational development
Level 0	Level 0	Pre-primary education Initial stage of organised instruction, designed primarily to introduce very young children to a school-type environment.	Level 02	Pre-primary education
Level 1	Level 1	Primary education or first stage of basic education Normally designed to give students a sound basic education in reading, writing and mathematics.	Level 1	Primary education Designed to provide a sound basic education in reading, writing and mathematics and a basic understanding of some other subjects. The entry age is between 5 and 7. Typical duration is 6 years.
Level 2	Level 2	Lower secondary education or second stage of basic education The lower secondary level of education generally continues the basic programmes of the primary level, although teaching is typically more subject‑focused, often employing more specialised teachers who conduct classes in their field of specialisation.	Level 2	Lower secondary education Completes provision of basic education, usually in a more subject-oriented way with more specialist teachers. Programmes may differ by orientation, general or vocational, though this is less common than at upper secondary level. Entry follows completion of primary education and typical duration is 3 years. In some countries, the end of this level marks the end of compulsory education.
Level 3	Level 3	Upper secondary education The final stage of secondary education in most OECD countries. Instruction is often more organised along subject-matter lines than at ISCED Level 2 and teachers typically need to have a higher level, or more subject-specific, qualification than at ISCED Level 2.1	Level 3	Upper secondary education Stronger specialisation than at lower secondary level. Programmes offered are differentiated by orientation: general or vocational. Typical duration is 3 years.
Level 4	Level 4	Post-secondary non-tertiary education These programmes straddle the boundary between upper secondary and post-secondary education from an international point of view, even though they might clearly be considered as upper secondary or post-secondary programmes in a national context.2	Level 4	Post-secondary non-tertiary education Serves to broaden rather than deepen the knowledge, skills and competencies gained in the upper secondary level. Programmes may be designed to increase options for participants in the labour market, for further studies at tertiary level, or both. Usually, programmes at this level are vocationally oriented.
Level 5	Level 5	First stage of tertiary education ISCED Level 5 programmes have an educational content that is more advanced than those offered at Levels 3 and 4.	–	–
Level 5B	Level 5B	ISCED 5B programmes are generally more practical/technical/occupationally specific than ISCED 5A programmes.	Level 5	Short-cycle tertiary education Serves to deepen the knowledge developed at previous levels by imparting new techniques, concepts and ideas not generally covered in upper secondary education.
Level 5A Bachelor’s degree	Level 5A	ISCED 5A programmes are largely theoretically based and are intended to provide sufficient qualifications for gaining entry into advanced research programmes and professions with high skill requirements.	Level 6	Bachelor’s or equivalent level Designed to provide participants with intermediate academic and/or professional knowledge, skills and competencies, leading to a first degree or equivalent qualification. Typical duration is 3-4 years’ full-time study.
Level 5A Master’s degree			Level 7	Master’s or equivalent level Stronger specialisation and more complex content than bachelor’s level. Designed to provide participants with advanced academic and/or professional knowledge. May have a substantial research component.
Level 6	Level 6	Second stage of tertiary education This level is reserved for tertiary programmes that lead to the award of an advanced research qualification. The programmes are devoted to advanced study and original research.	Level 8	Doctoral or equivalent level Designed to lead to an advanced research qualification. Programmes at this level are devoted to advanced study and original research, and exist in both academic and professional fields.

1. There are substantial differences in the typical duration of ISCED Level 3 programmes, both across and between countries, typically ranging from two to five years of schooling.

2. They are often not significantly more advanced than programmes at ISCED Level 3, but serve to broaden the knowledge of participants who have already completed a programme at Level 3. Students are typically older than those in ISCED 3 programmes.

Source: UNESCO-UIS (2012[2]), International Standard Classification of Education: ISCED 2011, UNESCO Institute for Statistics, Montreal, http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf.

References

[1] OECD (forthcoming), TALIS 2024 Technical Report, TALIS, OECD Publishing, Paris.

[2] UNESCO-UIS (2012), International Standard Classification of Education: ISCED 2011, UNESCO Institute for Statistics, Montreal, http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf.