PISA 2022 Results (Volume II)

Annex A1. Construction of indices

Explanation of the indices

This section explains the indices derived from the PISA 2022 student, school, well-being and Information and Communication Technology (ICT) familiarity questionnaires used in this volume. Several PISA measures reflect indices that summarise responses from students or school representatives (typically principals) to a series of related questions. The questions were selected from a larger pool on the basis of theoretical considerations and previous research. The PISA 2022 Assessment and Analytical Framework (OECD, 2023[1]) provides an in-depth description of this conceptual framework. Item response theory (IRT) modelling and classical test theory were used to test the theoretically expected behaviour of the indices and to validate their comparability across countries. For a detailed description of the methods, see the section “Statistical criteria for reporting on scaled indices” in this chapter, and the PISA 2022 Technical Report (OECD, forthcoming[2]).

This volume uses four types of indices: simple indices, complex composite indices, new scale indices and trend scale indices. In addition to these indices, several single items of the questionnaires are used in this volume. The volume also uses data collected on students’ performance in mathematics, reading and science. These assessments are described in the PISA 2022 Assessment and Analytical Framework (OECD, 2023[1]), the PISA 2022 Technical Report (OECD, forthcoming[2]) and in Volume I of PISA 2022 Results (OECD, forthcoming[3]).

Simple indices are constructed through the arithmetic transformation or recoding of one or more items in the same way across assessments. Here, item responses are used to calculate meaningful indices, such as the recoding of the four-digit ISCO-08 codes into “Highest parents’ socio-economic index (HISEI)” or teacher-student ratio based on information from the school questionnaire.

Complex composite indices are based on a combination of two or more indices. The PISA index of economic, social and cultural status (ESCS) is a composite score derived from three indicators related to family background.

Scale indices are constructed by scaling multiple items. Unless otherwise indicated, the two-parameter logistic model (2PLM) (Birnbaum, 1968[4]) was used to scale items with only two response categories (i.e. dichotomous items), while the generalised partial credit model (GPCM) (Muraki, 1992[5]) was used to scale items with more than two response categories (i.e. polytomous items).1 Values of the index correspond to standardised Warm likelihood estimates (WLE) (Warm, 1989[6]).

For details on how each scale index was constructed, see the PISA 2022 Technical Report (OECD, forthcoming[2]). In general, the scaling was done in two stages:

1. The item parameters were estimated based on all students from approximately equally weighted countries and economies;2 only cases with a minimum number of three valid responses to items that are part of the index were included. For the trend scales, the scaling process began by fixing the item parameters of the trend items to the parameters that had been estimated for each group in the previous assessment, a procedure called fixed parameter linking. To compute trends, a scale needed to have at least three trend items, but some trend scales consisted of both trend items and new items. In this case, the item parameters for the trend items were fixed at the beginning of the scaling process, but the item parameters for the new items were estimated using the PISA 2022 data.

2. For new scale indices, the Warm likelihood estimates were then standardised so that the mean of the index value for the OECD student population was zero and the standard deviation was one (countries were given approximately equal weight in the standardisation process2). For the trend scales, to ensure the comparability of the scale scores from the current assessment to the scale scores from the previous assessment, the original WLEs of PISA 2022 were transformed using the same transformation constants of the original WLEs from the assessment to which the current assessment was linked.

Sequential codes were assigned to the different response categories of the questions in the sequence in which the latter appeared in the student, school, ICT or well-being questionnaire. For reversed items, these codes were inverted for the purpose of constructing indices or scales.

Negative values for an index do not necessarily imply that respondents answered negatively to the underlying questions (e.g. reporting no support from teachers or no school safety risks). A negative value merely indicates that a respondent answered more negatively than other respondents did on average across OECD countries. Likewise, a positive value on an index indicates that a respondent answered more favourably, or more positively, on average, than other respondents in OECD countries did (e.g. reporting more support from teachers or more school safety risks).

Some terms in the questionnaires were replaced in the national versions of the student, school, ICT or well-being questionnaire by the appropriate national equivalent (marked through brackets < > in the international versions of the questionnaires). For example, the term < qualification at ISCED level 5A > was adapted in the United States* to “Bachelor’s degree, post-graduate certificate program, Master’s degree program or first professional degree program”. All the context questionnaires, including information on nationally adapted terms, and the PISA international database, including all variables, are available through www.oecd.org/pisa.

Statistical criteria for reporting on scaled indices

The internal consistency of scaled indices and the invariance of item parameters are the two approaches that were used to decide on the reporting of indices. All indices reported in this volume met the criteria of both approaches. Indices were omitted for countries and economies where one or more of the criteria were not met. For countries/economies with more than one language version (e.g. Finland offered versions of the student questionnaire in Finnish and Swedish), the criteria were judged independently for each language version.3 Details about the scaling procedures and the construct validation of all context questionnaire data are provided in the PISA 2022 Technical Report (OECD, 2023[1]).

Internal consistency of scaled indices

The internal consistency was used in PISA 2022 to examine the reliability of scaled indices and as a criterion for reporting. Internal consistency refers to the extent to which the items that make up an index are inter-related. Cronbach’s Alpha was used to check the internal consistency of each scale within countries/economies and to compare it across countries/economies. The coefficient of Cronbach’s Alpha ranges from 0 to 1, with higher values indicating higher internal consistency. Similar and high values across countries/economies indicate reliable measures across countries/economies. Commonly accepted cut-off values are 0.9 for excellent, 0.8 for good, and 0.7 for acceptable internal consistency. Indices are not reported for countries and economies with values below 0.6.

Cross-country comparability of scaled indices

The invariance of item parameters was used in PISA 2022 to examine the cross-country comparability of scaled indices and as a criterion for reporting. It determined whether the item parameters of an index could be assumed to be the same or invariant across countries/economies and across language versions (international item parameter).

In a first step, item parameters were estimated using data from all individuals with available data from all countries/economies. In a second step, the fit of the international parameters for each item was evaluated for each country/economy and language version using the root mean square deviance (RMSD). Values close to zero signal a good item fit, indicating that the international model accurately describes student responses within countries/economies and across language versions. In 2022 PISA used an even more conservative approach than in previous assessments: any country/economy and language version that received a value above 0.25 was flagged. In 2018 and 2015, a cut-off of 0.3 was used. For any flagged item specific parameters were calculated. Steps were repeated until all items exhibited RMSD values below 0.25.

For each index, a country/economy needed to have at least three items with international parameters to be considered comparable to the results of other countries/economies and language versions. Indices are not reported for countries/economies in which one or more language version had fewer than three items with international parameters. For the reporting on trends for indices, a country/economy needed to have at least three trend items with international parameters in order to be considered comparable to the results of the previous assessment to which the current assessment was linked. Results for the trends of indices were not reported for countries/economies in which one or more language groups had fewer than three trend items with international parameters for the index.

The different indices used in this volume are described in the following sections. Those countries/economies and language versions that received specific item parameters are highlighted. The PISA 2022 Technical Report (OECD, forthcoming[2]) provides more details on the cross-country comparability of indices, including the items concerned and the specific item parameters for each country/economy and language version listed.

Complex composite indices

The PISA index of economic, social and cultural status (ESCS)

The PISA index of economic, social and cultural status (ESCS) is a composite score derived, as in previous assessments, from three indicators related to family background: parents’ highest education, in years (PAREDINT), parents’ highest occupational status (HISEI) and home possessions (HOMEPOS).

Parents’ highest level of education, in years (PAREDINT): The index of the highest education of parents, in years, was based on the median cumulative years of education associated with completion of the highest level of education attained by parents (HISCED). Parents’ highest level of education was derived from students’ responses to questions about their parents’ education (ST005 and ST006 for mother’s level of education, and ST007 and ST008 for father’s level of education). Responses were classified according to ISCED-11 (UNESCO, 2012[7]) using the following categories: (1) Less than ISCED Level 1, (2) ISCED level 1 (primary education), (3) ISCED level 2 (lower secondary), (4) ISCED level 3.3 (upper secondary education with no direct access to tertiary education), (5) ISCED level 3.4 (upper secondary education with direct access to tertiary education), (6) ISCED level 4 (post-secondary non-tertiary), (7) ISCED level 5 (short-cycle tertiary education [at least two years]), (8) ISCED level 6 (Bachelor’s or equivalent first or long first-degree programme [three to more than four years]), (9) ISCED level 7 (Master’s or equivalent long first-degree programme [at least five years]) and (10) ISCED level 8 (Doctoral or equivalent level). In the event that students’ responses to the two questions about their mothers’ and fathers’ level of education conflicted (e.g. if a student indicated in ST006 that their mother has a postsecondary qualification but indicated in ST005 that their mother had not completed lower secondary education), the higher education level provided by the student was used. This differs from the PISA 2018 procedure where the lower level was used. Indices with these categories were provided for a student’s mother (MISCED) and father (FISCED). In addition, the index of parents’ highest level of education (HISCED) corresponded to the higher ISCED level of either parent.

The index of parents’ highest level of education was recoded into the estimated number of years of education (PAREDINT). This international conversion was determined by using the PISA 2018 measure of cumulative years of education associated with parents’ completion of the highest level of education across countries/economies for each ISCED level. The correspondence is available in the PISA 2022 Technical Report (OECD, forthcoming[2]).

Parents’ highest occupational status (HISEI): Occupational data for both the student’s father and the student’s mother were obtained from responses to open-ended questions (ST014 and ST015). The responses were coded to four-digit ISCO codes (ILO, 2007) and then mapped to the international socio-economic index of occupational status (ISEI) using the 2008 version of both (Ganzeboom and Treiman, 2003[8]). Three indices were calculated based on this information: father’s occupational status (BFMJ2); mother’s occupational status (BMMJ1); and the highest occupational status of parents (HISEI), which corresponds to the higher ISEI score of either parent or to the only available parent’s ISEI score. For all three indices, higher ISEI scores indicate higher levels of occupational status.

Home possessions (HOMEPOS): Home possessions were used as a proxy measure for family wealth. In PISA 2022, students reported the availability of household items at home, including books at home and country-specific household items that were seen as appropriate measures of family wealth in the country’s context. HOMEPOS is a summary index of all household and possession items (ST250, ST251, ST253, ST254, ST255, ST256). Some HOMEPOS items used in PISA 2018 were removed in PISA 2022 while new ones were added (e.g. new items developed specifically with low-income countries in mind). Furthermore, some HOMEPOS that were previously dichotomous (yes/no) items were revised to polytomous items (1, 2, 3, etc.) making it possible to capture a greater variation in responses. Note that all countries/economies and language versions received unique item parameters for the country/economy-specific items (i.e. no international parameters were estimated for these items) and that for some items, the response categories were collapsed to align with the response categories used in previous assessments (see Tables 19.15 and 19.16 of the PISA 2022 Technical Report (OECD, forthcoming[2]) for details).

For the purpose of computing the PISA index of economic, social and cultural status (ESCS), values for students with missing data on one of the three components (PAREDIND, HISEI or HOMEPOS) were imputed (see (OECD, 2020[9]; Avvisati, 2020[10]; OECD, forthcoming[2]) for details). If students had missing data for more than one component, the ESCS was not computed; a missing value was assigned instead. In PISA 2022, ESCS was computed by attributing equal weight to the three components. The final ESCS variable is standardised, so that 0 is the score of an average OECD student and 1 is the standard deviation across approximately equally weighted OECD countries.²

ESCS scores for PISA 2012, PISA 2015 and PISA 2018 were recomputed to be comparable to the respective scores for PISA 2022. More details are provided in the PISA 2022 Technical Report (OECD, forthcoming[2]).

Time in regular lessons

Time in regular lessons per week was calculated by combining answers from the student (ST059) and school principal (SC175) questionnaires. Students reported the number of class periods they are required to attend in all subjects per week, and school principals reported the average number of minutes per class period attended by students in the national modal grade for 15-year-olds. Time in regular lessons per week was obtained by multiplying the number of class periods by the average number of minutes per class period. This combination may create some noise induced by the potential misreporting or misunderstanding of the definition of a class period, either by students or school principals.

Simple indices

Availability of computers and tablets

School principals were asked to report the number of computers and tablet devices available at school (SC004). The index of availability of computers (RATCMP1) is the ratio of computers available to 15-year-olds for educational purposes to the total number of students in the modal grade for 15-year-olds (SC004Q01TA). The index of availability of tablet devices (RATTAB) is the ratio of tablet devices available to 15-year-olds for educational purposes to the total number of students in the modal grade for 15-year-olds. School principals answered similar questions about the number of computers available to 15-year-olds at school for educational purposes in 2012, 2015 and 2018.

Class size

Principals were asked about the average size of test language (SC003) and mathematics classes (SC176) in their school. The nine response categories were “15 students or fewer”, “16-20 students”, “21-25 students”, “26-30 students”, “31-35 students”, “36-40 students”, “41-45 students”, “46-50 students”, and “More than 50 students”. The average class size (CLSIZE in test language and MCLSIZE in mathematics) was derived from the midpoint of each response category, resulting in a value of 13 for the lowest category, and a value of 53 for the highest.

Concentration of immigrant students in schools

Schools were divided into having a high or low concentration of immigrant students according to the percentage of students with an immigrant background (IMMIG). A school with a low (high) concentration of immigrant students is a school where less than (at least) 10% of 15-year-old students have an immigrant background.

Duration of time spent in early childhood education and care

Questions ST125 and ST126 measure the starting age in ISCED 1 and ISCED 0. The indicator DURECEC is built as the difference of ST126 and ST125 plus the value of “2” to indicate the number of years a student spent in early childhood education and care.

Education level

PISA collects data on study programmes available to 15-year-old students in each country/economy. This information is obtained through the student tracking form and the Student Questionnaire (ST002). All study programmes were classified using the International Standard Classification of Education (ISCED 1997). From this information, a study programme level and orientation index (ISCEDP) was derived: a three-digit index that describes whether students were at the lower or upper secondary level (ISCED 2 or ISCED 3) and the type of programme in which they were enrolled. This index was used to classify students into those attending upper vs. lower secondary education programmes.

Expectation of a career in health and ICT

Students were asked to report on the kind of job that they expected to have at age 30 and to provide a job title or a description of this job (ST329). The responses were coded to four-digit ISCO-08 codes (OCOD3).

Based on these codes, students’ expectations were classified into health- and ICT-related careers:

• Health professionals: All health professionals in sub-major group 22 (e.g. doctors, nurses, veterinarians), with the exception of traditional and complementary medicine professionals (minor group 223).

• ICT professionals: All information and communications technology professionals (sub-major group 25).

Grade compared to modal grade

The relative grade index (GRADE) was computed to capture between-country/economy variation. It indicates whether students are in the country/economy’s modal grade (value of 0), or the number of grades below or above the modal grade in the country. The information about students’ grade level was obtained from school records from the student sampling data and validated by comparing students’ responses in the Student Questionnaire (ST001). For the analysis in this volume, all grades different from the modal grade in the country/economy were coded as 1.

Grade repetition

Students’ answers to question ST127 of whether and, if yes, how often they have ever repeated a grade at ISCED levels 1, 2, and 3 were combined into the index REPEAT. Each item included three response options (“No, never”, “Yes, once”, “Yes, twice or more”). REPEAT took the value of “0” if the student never repeated a grade (student did not select options 2 or 3 for any of the three items) and the value of “1” if the student repeated a grade at least once (student selected options 2 or 3 for at least one of the three items). The index was assigned a missing value if none of the three response options were selected in any levels.

Immigrant background

Information on the country of birth of the students and their parents was collected from students (ST019). Three binary country-specific indices indicate whether the student (COBN_S), mother (COBN_M) and father (COBN_F) were born in the country of assessment or elsewhere. The index on immigrant background (IMMIG) is calculated from these indices, and has the following categories: (1) native students (those students who had at least one parent born in the country of assessment); (2) second-generation students (those born in the country of assessment but whose parent[s] were born in another country); and (3) first-generation students (those students born outside the country of assessment and whose parents were also born in another country). Students with missing responses for either the student or for both parents were given missing values for this variable.

Long-term student absenteeism from primary to upper secondary school

Question ST260 asked students if they had ever missed primary, lower or upper secondary school (ISCED 1, 2 or 3) for more than three consecutive months (“no, never”, “yes, once”, “yes, twice or more”). Students’ answers were combined into the index of long-term student absenteeism at any education level (MISSSC). The index takes the value of 1 if a student answered “yes, once” or “yes, twice or more” at least once for any of the three education levels, and the value of 0 otherwise.

Quantity of teaching staff at school

Principals were asked to report the number of teachers fully certified by the appropriate authority (SC018Q02) as well as the total number of teachers at their school (TOTAT). The proportion of fully certified teachers (PROATCE) was computed by dividing the number of fully certified teachers by the total number of teachers.

School location

Using principals’ answers to the question about the community in which their school is located (SC001), the locations of the schools were classified as either in a rural area or village (fewer than 3 000 inhabitants), in a town (3 000 to 100 000 inhabitants) or city (over 100 000 inhabitants).

School size

The index of school size (SCHSIZE) contains the total enrolment at a school. It is based on the enrolment data provided by the school principal, summing up the number of girls and boys at a school (SC002). This index was calculated in 2022 and in all previous assessments.

School type

For most of the analysis on school type, schools were classified as either public or private, according to principals’ answers to question SC013 (whether the school is public or private).

A more detailed analysis was conducted for Chapter 6, which focuses on school governance, based on a classification that also took into account principals’ answers to question SC016, which focused on the source of resources. The index SCHLTYPE indicates whether a private entity or a public agency has the ultimate power to make decisions concerning its affairs. Public schools are managed directly or indirectly by a public education authority, government agency or governing board appointed by a government or elected by public franchise. Private schools are managed directly or indirectly by a non-governmental organisation, such as a church, trade union, business or other private institution. Schools were classified into the following three categories:

• Private independent: If school principals answered that their school is “a private school” and that less than half of the total funding for a typical school year comes from the government or more than half of it comes from student fees or school charges paid by parents or guardians, benefactors, donations, bequests, sponsorships, parent or guardian fundraising or other sources

• Private government-dependent: If school principals answered that their school is “a private school” and that more than half of the total funding for a typical school year comes from the government

• Public: If school principals answered that their school is “a public school”.

In some countries and economies, such as Ireland,* the information from SC013 was combined with administrative data to determine whether the school is privately or publicly managed. In the United Kingdom* (excluding Scotland), the school type was derived exclusively from the national adaptation of question SC013, which included three categories: “Your school is maintained via the Local Authority (in England and Wales) or grant-aided (in Northern Ireland*) (for example, community school, voluntary controlled school, foundation school)”; “Your school is maintained by central government (for example, city technology college, academy, free school)”; and “Your school is an independent school”.

Since PISA 2018, sampling information (PRIVATESCH) has been used to improve the public/private indicators. If question SC013 is missing, information from PRIVATESCH is used to create SCHLTYPE. As in 2018, Ireland* had special treatment for this designation, based solely on the stratum.

School responsibility for curriculum

Question SC202 asked principals about who had the main responsibility for various decisions or activities at their school. The six response categories for this question were “Principal”, “Teachers or members of school management team”, “School governing board”, “Local or municipal authority”, “Regional or state authority”, and “National or federal authority”. An index of the relative level of responsibility of school staff in deciding issues related to curriculum and assessment (RESPCUR) was computed from the principals’ reports regarding who had the main responsibility for four items in SC202. The index was calculated on the basis of the ratio of responses for “Principal”, “Teachers or members of school management team”, or “School governing board”, on the one hand, to responses for “Local or municipal authority”, “Regional or state authority”, or “National or federal authority”, on the other hand.

In the first step, a measure for school responsibility was calculated by counting the number of “Principal”, “Teachers or members of school management team”, and “School governing board” responses. In the second step, a measure for non-school responsibility was calculated by counting the number of “Local or municipal authority”, “Regional or state authority”, and “National or federal authority”. In the third step, the school responsibility measure was divided by the non-school responsibility measure. To avoid dividing by ”0”, “1” was added to both the numerator and denominator; when the ratio of school responsibility to non-school responsibility was 4:0, an index value of 4 was assigned. Higher values indicated relatively higher levels of school responsibility in deciding issues related to curriculum and assessment.

School responsibility for resources

Question SC202 asked principals about who had the main responsibility for various decisions or activities at their school. The six response categories for this question were “Principal”, “Teachers or members of school management team”, “School governing board”, “Local or municipal authority”, “Regional or state authority”, and “National or federal authority”. An index of the relative level of responsibility of school staff in deciding issues related to allocating resources (RESPRES) was computed from the principals’ reports regarding who had the main responsibility for six items in SC202. The index was calculated on the basis of the ratio of responses for “Principal”, “Teachers or members of school management team”, or “School governing board”, on the one hand, to responses for “Local or municipal authority”, “Regional or state authority”, or “National or federal authority”, on the other hand.

In the first step, a measure for school responsibility was calculated by counting the number of “Principal”, “Teachers or members of school management team”, and “School governing board” responses. In the second step, a measure for non-school responsibility was calculated by counting the number of “Local or municipal authority”, “Regional or state authority”, and “National or federal authority”. In the third step, the school responsibility measure was divided by the non-school responsibility measure. To avoid dividing by ”0”, “1” was added to both the numerator and denominator; when the ratio of school responsibility to non-school responsibility was 6:0, an index value of 6 was assigned. Higher values on the scale indicated relatively higher levels of school responsibility in this area.

School selectivity

Question SC012 asked principals about admissions policies at their school, including student academic performance and recommendation by feeder schools. The three response categories for this question were “Never”, “Sometimes”, and “Always”. An index of academic school selectivity (SCHSEL) was computed by assigning schools to one of three categories based on how often two factors, namely “Student’s record of academic performance” (SC012Q01TA) and “Recommendation of feeder schools” (SC012Q02TA), were considered when admitting students to the school as follows:

1. The two factors (student’s record of academic performance and recommendation of feeder schools) were never considered (if SC012Q01TA=1 and SC012Q02TA=1)

2. At least one of the factors was considered sometimes but neither was always considered (if SC012Q01TA=2 or SC012Q02TA=2, and if SC012Q01TA3 and SC012Q02TA3)

3. At least one of the factors was always considered (if SC012Q01TA=3 or SC012Q02TA=3).

Socio-economic profile of the school

The average PISA index of economic, social and cultural status (ESCS) of a school was used as an indicator of the socio-economic profile of a school. To define advantaged and disadvantaged schools, all schools in each PISA-participating education system are ranked according to their average PISA index of economic, social and cultural status (ESCS) and then divided into four groups with approximately an equal number of students (quarters). Schools in the bottom quarter are referred to as “socio-economically disadvantaged schools”; and schools in the top quarter are referred to as “socio-economically advantaged schools”.

Student-teacher ratio

The student-teacher ratio (STRATIO) was obtained by dividing the number of enrolled students (SC002) by the total number of teachers (TOTAT) provided by the school principals.

Student truancy and lateness

PISA measured student truancy and lateness by asking students to report the number of times (“never”, “one or two times”, “three or four times”, “five or more times”) they had skipped a whole day of school (ST062Q01TA), had skipped some classes (ST062Q02TA) and had arrived late (ST062Q03TA) for school during the two full weeks of school prior to the assessment.

Two additional indicators of student truancy (SKIPPING) and lateness (TARDYSD) were constructed that take a value of 0 if students reported that they had not skipped any class or whole day of school or had never arrived late for school in the two weeks prior to the PISA assessment. The index of student truancy (SKIPPING) takes a value of 1 if students reported that they had skipped classes or days of school at least once in the same period. The index of student lateness (TARDYSD) takes a value of 1 for occasional late arrivals if students reported that they had arrived late for school one or two times, and 2 for frequent late arrivals if students reported they had arrived late for school three or more times in the same period.

Study programme level and orientation

PISA collects data on study programmes available to 15-year-old students in each country/economy. This information is obtained through the student tracking form and the Student Questionnaire (ST002). In the final database, all national programmes (PROGN) are included where the first six digits represent the National Centre code, and the last two digits are the nationally specific programme code. All study programmes were classified using the International Standard Classification of Education (ISCED 1997).

The study programme level and orientation index (ISCEDP) is a three-digit index that describes whether students were at the lower or upper secondary level and (ISCED 2 or ISCED 3) and whether their programmes were general or vocational and sufficient for level completion with direct access to tertiary or post-secondary non-tertiary education.

Time spent on homework

A measure of time spent on homework in all subjects was derived from students’ reports on the time they spend on homework in a typical school week (ST296Q04): “up to 30 minutes a day”, “more than 30 minutes and up to 1 hour a day”, etc., and “more than 4 hours a day”. The average time spent on homework was converted to a continuous variable by taking the midpoint of each time interval and using 4.5 hours if the answer was “more than 4 hours”.

Time spent on digital devices for learning or leisure at school

The measure of time spent on digital devices was based on students’ reports on the number of hours they usually spend on digital devices per day during the current school year for learning (ST326Q01) or leisure (ST326Q04): “none”, “up to 1 hour”, “more than 1 hour and up to 2 hours”, etc., and “more than 7 hours”. The average time spent on digital devices was converted to a continuous variable by taking the midpoint of each time interval and using 7.5 hours if the answer was “more than 7 hours”.

Trend scale indices

Disciplinary climate in mathematics lessons

Students were asked how often (“never or hardly ever”, “some lessons”, “most lessons”, “every lesson”) certain things happen in their mathematics classes (e.g. “Students do not listen to what the teacher says” and “There is noise and disorder”). The seven statements of question ST273 were combined to create the index of disciplinary climate (DISCLIM) with an average of zero and a standard deviation of one across OECD countries. Positive values on the index mean that the student reported a better disciplinary climate in mathematics lessons than did students on average across OECD countries. In 2012 students responded to similar statements about the disciplinary climate in mathematics lessons. One or more items from the scale received specific item parameters for Brunei Darussalam (English), Cambodia (Khmer), Estonia (Russian), Guatemala (Spanish), Japan (Japanese), Jordan (Arabic), Latvia* (Russian), Macao (China) (Chinese, Portuguese), Malta (English), the Palestinian Authority (Arabic, English), Qatar (Arabic), Slovenia (Slovenian-ISCED2), Türkiye (Turkish) and Viet Nam (Vietnamese).

Exposure to bullying

Students answered a question (ST038) on how often (“never or almost never”, “a few times a year”, “a few times a month”, “once a week or more”) during the 12 months prior to the PISA test they had the following experiences in school (the question clarified that “some experiences can also happen in social media”): “Other students left me out of things on purpose” (relational bullying); “Other students made fun of me” (verbal bullying); “I was threatened by other students” (verbal bullying); “Other students took away or destroyed things that belong to me” (extortion bullying); “I got hit or pushed around by other students” (physical bullying); “Other students spread nasty rumours about me” (relational bullying); “I was in a physical fight on school property” (physical bullying); “I stayed home from school because I felt unsafe” (any type of bullying); “I gave money to someone at school because they threatened me” (extortion bullying). The nine statements were combined into a single index of exposure to bulling (BULLIED) with an average value of zero and a standard deviation of one across OECD countries. Positive values in the index indicate that the student is more exposed to bullying at school than are students on average across OECD countries.

The additional indicator, “frequently bullied students”, was constructed. All students across all PISA-participating education systems were ranked according to their value in the index of exposure to bullying (BULLIED). Then, the sample of students was divided into ten subsamples with approximately equal numbers of students (deciles). Students in the top 10% student sample of the index of exposure to bullying across all countries/economies were considered as frequently bullied students.

Since students who participated in PISA 2015 and PISA 2018 provided answers to some of the questions concerning exposure to bullying, PISA 2022 can show changes in school bullying using comparable data across countries/economies. Three items were not distributed, their item parameters could not be estimated or the responses for the items were suppressed in Australia* (English).

Mathematics anxiety

The index of mathematics anxiety (ANXMAT) was constructed using the six student responses to question ST345. This question asked students how much they agree (“strongly agreed”, “agreed”, “disagreed” or “strongly disagreed”) with six statements about their feelings when studying mathematics (e.g. “I often worry that it will be difficult for me in mathematics classes”; “I get very tense when I have to do mathematics homework”). Positive values in this index mean that students reported greater anxiety towards mathematics than did students on average across OECD countries.

One or more items from the scale received specific item parameters for Baku (Azerbaijan) (Azeri, Russian), Brazil (Portuguese), Cambodia (Khmer), the Czech Republic (Czech), Georgia (Georgian, Azerbaijani, Russian), Kazakhstan (Kazakh, Russian), Malaysia (Malay), the Republic of Moldova (Russian), Mongolia (Mongolian, Kazakh), the Slovak Republic (Slovak, Hungarian), Ukraine (Ukrainian, Russian) and Uzbekistan (Uzbek, Karakalpak).

School resources

As in PISA 2015 and 2018, PISA 2022 included a question (SC017) about school resources, measuring school principals’ perceptions of potential factors hindering instruction at school (“Is your school’s capacity to provide instruction hindered by any of the following issues?”). The four response categories were: “not at all”, “very little”, “to some extent”, “a lot”. Two new items on digital resources were added in 2022 but were not included in indices. To be comparable to the data collected in PISA 2015 and 2018, the index of staff shortage (STAFFSHORT) was derived from the first four out of ten items: a lack of teaching staff; inadequate or poorly qualified teaching staff; a lack of assisting staff; inadequate or poorly qualified assisting staff. The index of educational material shortage (EDUSHORT) was derived from the second set of four items: a lack of educational material; inadequate or poor-quality educational material; a lack of physical infrastructure; inadequate or poor-quality physical infrastructure. Positive values in this index mean that principals viewed the amount and/or quality of the human or educational resources in their schools as an obstacle to providing instruction to a greater extent than did principals on average across OECD countries. One or more items from the scale STAFFSHORT received specific item parameters for Australia* (English), Austria (German), Cambodia (Khmer), the Dominican Republic (Spanish), Germany (German), Greece (Greek), Hungary (Hungarian), Indonesia (Indonesian), Ireland* (English, Irish), Kazakhstan (Russian), Latvia* (Latvia*n), the Palestinian Authority (Arabic), Paraguay (Spanish), Poland (Polish), Spain (Spanish, Galician, Basque, Valencian), Switzerland (German, French, Italian) and the United States* (English). One or more items from the scale EDUSHORT received specific item parameters for Baku (Azerbaijan) (Azeri), Canada* (English), El Salvador (Spanish), Guatemala (Spanish), Latvia* (Latvia*n), Macao (China) (English), Montenegro (Montenegrin), Chinese Taipei (Chinese) and Viet Nam (Vietnamese).

Sense of belonging at school

The index of sense of belonging at school (BELONG) was constructed using students’ responses to the trend question ST034. Students were asked whether they agree (“strongly disagree”, “disagree”, “agree”, “strongly agree”) with six school-related statements (e.g. “I make friends easily at school”, “Other students seem to like me”, “I feel lonely at school”). These statements were combined into an overall index of sense of belonging at school whose averages are zero and standard deviations are one across OECD countries. Positive values on this scale mean that a student reported a stronger sense of belonging at school than did students on average across OECD countries.

Students’ sense of belonging at school has been assessed since 2012, but as the scale was revised for PISA 2015, only data collected in 2015 and 2018 are comparable to the data collected in 2022. One or more items from the scale received specific item parameters for Belgium (French), France (French), Georgia (Georgian, Azerbaijani, Russian), Guatemala (Spanish), Paraguay (Spanish), Romania (Romanian, Hungarian), Switzerland (French), Uruguay (Spanish) and Viet Nam (Vietnamese).

Teacher support in mathematics

Students were asked how often (“never or hardly ever”, “some lessons”, “most lessons”, “every lesson”) certain things happen in their mathematics classes (e.g. “The teacher shows an interest in every student’s learning”; “The teacher gives extra help when students need it”). The four statements of question ST270 were combined to create an index of teacher support (TEACHSUP) with an average of zero and a standard deviation of one across OECD countries. Positive values on the indices mean that the student reported more frequent teacher support in mathematics lessons than did students on average across OECD countries.

In 2012 students answered similar statements about teacher support and disciplinary climate in mathematics lessons. One item from the scale received specific item parameters for Hong Kong* (China) (Chinese).

New scale indices

Confidence in the capacity for self-directed learning

Students were asked how confident (“not at all confident”, “not very confident”, “confident”, “very confident”) they are about different aspects related to self-directed learning (e.g. “Finding learning resources on line on my own”; “Planning when to do schoolwork on my own”) if their school building closed again in the future. Students’ responses to the eight statements (ST355) were combined into an index (SDLEFF) whose average is zero and standard deviation is one across OECD countries.4 Positive values in the index indicate that the student felt more confident than did students on average across OECD countries.

One or more items from the scale received specific item parameters for Cambodia (Khmer), Indonesia (Indonesian), Kazakhstan (Kazakh), Mongolia (Mongolian, Kazakh), Montenegro (Montenegrin, Albanian), the Philippines (English) and Thailand (Thai).

Educational leadership

Question SC201 asked principals about how often they or other members of their school management team engaged in activities or behaviours related to educational leadership during the previous 12 months (e.g. “Collaborating with teachers to solve classroom discipline problems”, “Providing parents or guardians with information on the school and student performance”). The five response categories for the seven items in the scale on educational leadership (EDULEAD) were “never or almost never”, “about once or twice a year”, “about once or twice a month”, “about once or twice a week”, and “every day or almost every day”. Positive values indicate more frequent engagement by the principal and school management team in educational leadership activities than on average across OECD countries, while negative scale values indicate less frequent than the OECD average engagement by the principal and school management team in educational leadership activities.

One or more items from the scale received specific item parameters for Australia* (English), Belgium (Dutch, French, German), Brazil (Portuguese), Bulgaria (Bulgarian), Cambodia (Khmer), Colombia (Spanish), Croatia (Croatian), the Czech Republic (Czech), Denmark* (Danish), the Dominican Republic (Spanish), Estonia (Estonian), France (French), Georgia (Georgian, Azerbaijani, Russian), Germany (German), Greece (Greek), Guatemala (Spanish), Hungary (Hungarian), Indonesia (Indonesian), Ireland* (English, Irish), Israel (Hebrew), Italy (Italian, German), Jordan (Arabic), Kazakhstan (Kazakh, Russian), Latvia* (Latvian), Malaysia (Malay, English), Mexico (Spanish), the Republic of Moldova (Romanian, Russian), Mongolia (Mongolian), Morocco (Arabic), New Zealand* (English), Norway (Bokmål), the Palestinian Authority (Arabic), Panama* (Spanish, English), the Philippines (English), Poland (Polish), Portugal (Portuguese), Qatar (Arabic, English), Romania (Romanian), Saudi Arabia (Arabic, English), Singapore (English), the Slovak Republic (Slovak), Spain (Spanish, Catalan, Galician, Basque, Valencian), Sweden (Swedish), Chinese Taipei (Chinese), Thailand (Thai), United Arab Emirates (Arabic, English), the United Kingdom* (English, Welsh), the United States* (English), Uruguay (Spanish), Uzbekistan (Uzbek, Russian) and Viet Nam (Vietnamese).

Experience with learning at home

In question ST354 students rated their agreement (“strongly disagree”, “disagree”, “agree”, “strongly agree”) with positive statements (e.g. “I enjoyed learning by myself”) and negative statements (e.g. “I felt lonely”) related to their experience with learning at home (FEELLAH) while the school building was closed due to COVID-19. The six statements were combined into an index of experience with learning at home (FEELLAH) whose average is zero and standard deviations is one across OECD countries. Positive values on these indices mean that the student reported a more positive experience than did students on average across OECD countries.

Family support

Family support (FAMSUP) was measured by asking students, in question ST300, how often (“never or almost never”, “about once or twice a year”, “about once or twice a month”, “about once or twice a week”, “every day or almost every day”) their parents or someone in their family do different things with them indicative of family support (e.g. “Discuss how well you are doing at school”; “Eat the main meal with you”; or “Spend time just talking with you”). An index of family support with an average of zero and a standard deviation one across OECD countries is formed by combining students’ responses to ten scenarios. Students with positive values on this index perceived their family as more supportive than did students on average across OECD countries.

One or more items from the scale received specific item parameters for Albania (Albanian), Denmark* (Danish), Estonia (Russian), Guatemala (Spanish), Hong Kong* (China) (Chinese), Japan (Japanese), Macao (China) (Chinese, Portuguese), the Netherlands* (Dutch), North Macedonia (Albanian), Poland (Polish), Qatar (Arabic), the Slovak Republic (Slovak, Hungarian) and Thailand (Thai).

Feeling safe at school

Question ST265 asked students if they agree (“strongly disagree”, “disagree”, “agree”, “strongly agree”) that they feel safe on their way to school, on their way home from school, in classrooms and at other places at school (e.g. in hallways and in the cafeteria). Answers to the four statements were used to build the index of feeling safe at school (FEELSAFE) with an average value of zero and a standard deviation of one across OECD countries. Positive values in the index indicate that the student reported feeling safer at and around school than did students on average across OECD countries.

Instructional leadership

Question SC201 asked principals about how often they or other members of their school management team engaged in activities or behaviours related to teaching or instructional leadership during the previous 12 months (e.g. “Providing feedback to teachers based on observations of instruction in the classroom”, “Taking actions to ensure that teachers feel responsible for their students' learning outcomes”). The five response categories for the five items in the scale on instructional leadership (INSTLEAD) were “never or almost never”, “about once or twice a year”, “about once or twice a month”, “about once or twice a week”, and “every day or almost every day”. Positive values on the scale indicate more frequent engagement by the principal and school management team in instructional leadership activities than on average across OECD countries, while negative values indicate less frequent engagement than on average by the principal and school management team in instructional leadership activities.

One or more items from the scale received specific item parameters for Bulgaria (Bulgarian), Cambodia (Khmer), the Dominican Republic (Spanish), Estonia (Estonian), France (French), Georgia (Georgian, Azerbaijani, Russian), Germany (German), Greece (Greek), Indonesia (Indonesian), Ireland* (English, Irish), Israel (Hebrew), Jordan (Arabic), Kazakhstan (Kazakh, Russian), Malaysia (Malay, English), Mexico (Spanish), the Republic of Moldova (Romanian, Russian), Mongolia (Mongolian), Morocco (Arabic), the Palestinian Authority (Arabic), Panama* (Spanish, English), Poland (Polish), Portugal (Portuguese), Qatar (Arabic, English), Singapore (English), Spain (Spanish, Catalan, Galician, Basque, Valencian), Chinese Taipei (Chinese), Thailand (Thai), the United Arab Emirates (Arabic), the United Kingdom* (English, Welsh), the United States* (English), Uruguay (Spanish) and Uzbekistan (Uzbek, Russian).

Problems with self-directed learning

Students were asked to report on how often (“never”, “a few times”, “about once or twice a week”, “every day or almost every day”) they had different problems when completing their schoolwork (e.g. “Problems with Internet access”; “Problems with finding a quiet place to study”; “Problems with motivating myself to do schoolwork”) while their school building was closed due to COVID-19 (ST35). The eight statements were combined into an index of problems with self-directed learning (PROBSELF) whose average is zero and standard deviations is one across OECD countries. Positive values on the index mean that the student reported more problems than did students on average across OECD countries.

One or more items from the scale received specific item parameters for Albania (Albanian), Baku (Azerbaijan) (Azeri), Belgium (French), the Dominican Republic (Spanish), El Salvador (Spanish), Indonesia (Indonesian), Japan (Japanese), Jordan (Arabic), Kosovo (Albanian, Serbian), Macao (China) (Chinese, Portuguese), the Republic of Moldova (Romanian), Mongolia (Mongolian, Kazakh), North Macedonia (Macedonian and Albanian), the Palestinian Authority (Arabic, English), Peru (Spanish), the Philippines (English), Qatar (Arabic), Saudi Arabia (Arabic, English), Thailand (Thai) and Uzbekistan (Uzbek, Karakalpak).

Quality of student-teacher relationships

Students’ ratings of their agreement with the eight statements (e.g. “The teachers at my school are respectful towards me”, “When my teachers ask how I am doing, they are really interested in my answer”) in question ST267 were scaled into the index of quality of student-teacher relationships (RELATST). Note that this scale used a within-construct matrix sampling design. Each of the eight items included in this scale had four response options (“strongly disagree”, “disagree”, “agree”, “strongly agree”). Students with positive values on this index perceived the student-teacher relationships at school as more positive than did students on average across OECD countries.

One or more items from the scale received specific item parameters for Albania (Albanian), Denmark* (Danish), Finland (Finnish, Swedish), Georgia (Georgian, Azerbaijani, Russian), Japan (Japanese), Qatar (Arabic, English), Singapore (English), Sweden (Swedish, English), Thailand (Thai), the United Arab Emirates (English) and Viet Nam (Vietnamese). One item was not distributed, the item parameters could not be estimated or the responses for the item were suppressed for Hong Kong* (China) (Chinese).

School actions to maintain learning and well-being

In 2022, PISA collected information on students’ perception of school actions/activities to maintain learning and well-being (ST348) by asking them how often (“never”, “a few times”, “about once or twice a week”, and “every day or almost every day”) someone from their school did different actions or activities while their school building was closed due to COVID-19 (e.g. “Sent you learning materials to study on your own”; “Asked you to submit completed school assignments”; “Checked in with you to ask how you were feeling”). From these eight statements, an index of school actions/activities to maintain learning and well-being (SCHSUST) was created that has an average of zero and standard deviation of one across OECD countries. A student with positive values in the index reported more actions/activities than did students on average across OECD countries.

One or more items from the scale received specific item parameters for Albania (Albanian), Baku (Azerbaijan) (Azeri), Cambodia (Khmer), the Dominican Republic (Spanish), Indonesia (Indonesian), Israel (Hebrew), Japan (Japanese), Kosovo (Albanian, Serbian), the Netherlands* (Dutch), North Macedonia (Albanian), the Philippines (English), Qatar (Arabic), Thailand (Thai) and Uzbekistan (Uzbek, Karakalpak).

School autonomy

Question SC202 asked principals about who had the main responsibility for various decisions or activities at their school (e.g. “Appointing or hiring teachers”, “Determining teachers’ salary increases”). The six response categories for the 12 items in the scale were “Principal”, “Teachers or members of school management team”, “School governing board”, “Local or municipal authority”, “Regional or state authority”, and “National or federal authority”. Positive values for the index of school autonomy (SCHAUTO) indicate that the principal perceived the level of autonomy in decision-making activities at their school by the principal, teachers or members of the school management team, and the school governing board as higher than was reported on average across OECD countries.

One or more items from the scale received specific item parameters for Albania (Albanian), Argentina (Spanish), Australia* (English), Austria (German), Chile (Spanish), Colombia (Spanish), Costa Rica (Spanish), Denmark* (Danish), El Salvador (Spanish), Georgia (Georgian, Azerbaijani, Russian), Greece (Greek), Hungary (Hungarian), Ireland* (English, Irish), Italy (Italian, German), Japan (Japanese), Jordan (Arabic), Kazakhstan (Kazakh, Russian), Korea (Korean), Lithuania (Lithuanian), Malaysia (Malay, English), the Republic of Moldova (Romanian, Russian), Mongolia (Mongolian), Morocco (Arabic), Norway (Bokmål), the Palestinian Authority (Arabic), Poland (Polish), Portugal (Portuguese), Qatar (Arabic, English), Romania (Romanian), Saudi Arabia (Arabic, English), Singapore (English), the Slovak Republic (Slovak), Slovenia (Slovenian, Slovenian-ISCED2), Spain (Spanish, Catalan, Galician, Basque, Valencian), Sweden (Swedish), Switzerland (German, French, Italian), Chinese Taipei (Chinese), Thailand (Thai), Türkiye (Turkish), the United Arab Emirates (Arabic), the United Kingdom* (English, Welsh) and Viet Nam (Vietnamese). Two items were not distributed, the item parameters could not be estimated or the responses for the items were suppressed for Ireland* (English, Irish).

School safety risks

The measure of school safety risk asked students if (“yes”, “no”) the following events occurred during the previous four weeks: “Our school was vandalised”; “I witnessed a fight on school property in which someone got hurt”; “I saw gangs in school”; “I heard a student threaten to hurt another student”; “I saw a student carrying a gun or knife at school”. Answers to the five statements of question ST266 were combined into a single index (SCHRISK) with an average value of zero and a standard deviation of one across OECD countries. Positive values in the index indicate that the student perceived greater risks at their school than did students on average across OECD countries.

One item from the scale received specific item parameters for New Zealand* (English), Norway (Bokmål, Nynorsk) and Sweden (Swedish, English). Two items were not distributed, the item parameters could not be estimated or the responses for the item were suppressed in Italy (Italian, German).

Teacher participation

Question SC202 asked principals about who had the main responsibility for various decisions or activities at their school (e.g. “Formulating the school budget”, “Choosing which learning materials are used”). The six response categories for the 12 items in the scale were “Principal”, “Teachers or members of school management team”, “School governing board”, “Local or municipal authority”, “Regional or state authority”, and “National or federal authority”. Positive values for the index of teacher participation (TCHPART) indicate that the teachers or members of the school management team participated to a greater extent in decision-making activities at their school than on average across OECD countries.

One or more items from the scale received specific item parameters for Argentina (Spanish), Brazil (Portuguese), Bulgaria (Bulgarian), Colombia (Spanish), the Dominican Republic (Spanish), El Salvador (Spanish), Estonia (Estonian), Guatemala (Spanish), Japan (Japanese), Korea (Korean), Malaysia (Malay, English), Mongolia (Mongolian), New Zealand* (English), Paraguay (Spanish), Spain (Spanish, Catalan, Galician, Basque, Valencian) and the United Arab Emirates (Arabic).

Views of regulated ICT use in school

The measure on views of regulated ICT use in school is derived from the ICT questionnaire that was distributed in 54 out of the 81 countries/economies that participated in PISA 2022. Students were asked to respond to six statements (IC179): “Students should not be allowed to bring mobile phones to class”, “Students should not be allowed to bring their own laptop (or tablet device) to class”, “Students should collaborate with teachers to decide on the rules regarding the use of digital devices during lessons”, “The school should set up filters to prevent students from going on social media”, “The school should set up filters to prevent students from playing games online” and “Teachers should monitor what students do on their laptops”. Each of the six items had four response options (“strongly disagree”, “disagree”, “agree”, “strongly agree”). Answers to these six statements were scaled into a single index (ICTREG). Positive values in the index indicate that the student was more supportive of stricter regulations on the use of ICT at their school than were students on average across OECD countries. One or more items from the scale received specific item parameters for Albania (Albanian), Bulgaria (Bulgarian), Israel (Arabic), Jordan (Arabic), Kazakhstan (Kazakh), Saudi Arabia (Arabic, English) and Thailand (Thai).

Single items

In addition to the indices listed above, the following single items were used in this report:

• Ability grouping between and within classes (SC042)

• Assessment practices at school (SC034)

• Criteria for choosing a school (PA006)

• Duration of school closures because of COVID-19 (ST347Q01JA)

• Learning resources during COVID-19 school closures (ST351)

• Life satisfaction across domains (WB155)

• Monitoring teacher practice (SC032)

• Parental involvement (SC064Q)

• Quality assurance and improvement actions at school (SC037)

• Overall life satisfaction (ST016)

• Reasons for long-term absenteeism (ST261)

• Reasons for transferring students to another school (SC185)

• School competition for students (SC011)

• School preparedness for remote instruction (SC224)

• Schools providing study help (SC212)

• Sources of school funding (SC016)

• Student gender (ST004)

• Students’ enrolment at their school (ST226)

• Student composition of schools (SC211)

• Student behaviour when using digital devices (ST322)

• Using achievement data for accountability purposes (SC198)

References

[10] Avvisati, F. (2020), “The measure of socio-economic status in PISA: A review and some suggested improvements”, Large-Scale Assessments in Education, Vol. 8/1, pp. 1-37, https://doi.org/10.1186/s40536-020-00086-x.

[8] Ganzeboom, H. and D. Treiman (2003), “Three Internationally Standardised Measures for Comparative Research on Occupational Status”, in Advances in Cross-National Comparison, Springer US, Boston, MA, https://doi.org/10.1007/978-1-4419-9186-7_9.

[5] Muraki, E. (1992), “A generalized partial credit model: Application of an EM algorithm”, Applied Psychological Measurement, Vol. 16/2, pp. 159-177, https://doi.org/10.1002/j.2333-8504.1992.tb01436.x.

[4] Novick, F. (ed.) (1968), Some latent trait models and their use in inferring an examinee’s ability, Addison-Wesley, Menlo Park.

[1] OECD (2023), PISA 2022 Assessment and Analytical Framework, PISA, OECD Publishing, Paris, https://doi.org/10.1787/dfe0bf9c-en.

[9] OECD (2020), PISA 2018 Technical Report, OECD publishing, Paris, https://www.oecd.org/pisa/data/pisa2018technicalreport/.

[2] OECD (forthcoming), PISA 2022 Technical Report, PISA, OECD Publishing, Paris.

[3] OECD (forthcoming), The PISA 2022 Results: The State of Learning and Equity in Education, PISA, OECD Publishing, Paris.

[11] Rasch, G. (1960), Probabilistic models for some intelligence and attainment tests, Nielsen and Lydiche.

[7] UNESCO (2012), International Standard Classification of Education ISCED 2011.

[6] Warm, T. (1989), “Weighted likelihood estimation of ability in item response theory”, Psychometrika, Vol. 54/3, pp. 427-450, https://doi.org/10.1007/BF02294627.

Notes

← 1.  To keep the 2022 trend scales linked to PISA 2012 comparable, the Rasch model (Rasch, 1960[11]) was used to scale the dichotomous items, while the partial credit model (PCM) was used to scale the polytomous items, in line with the models used in PISA 2012.

← 2.  Due to missing data from the countries/economies, countries/economies were only approximately equally weighted.

← 3.  Different language versions were only analysed independently, if the version was distributed to a sample of over 150 and the sum of the weights was over 300. The sum of weights for all cases within a country/economy add up to a constant of 5 000 but varied on a scale-by-scale basis because missing responses varied across scales.

← 4.  Denmark*, Norway and Singapore did not collect data for any of the questions related to students’ responses and experiences during COVID-19 school closures.