Literacy rate, adult male (% of males ages 15 and above) in United Arab Emirates was reported at 99 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United Arab Emirates - Literacy rate, adult male (% of males ages 15 and above) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Graph and download economic data for Literacy Rate, Adult Total for the United Arab Emirates (SEADTLITRZSARE) from 1975 to 2022 about United Arab Emirates, literacy, adult, and rate.

Historical dataset showing UAE literacy rate by year from 1975 to 2022.

View yearly updates and historical trends for United Arab Emirates Adult Literacy Rate. Source: World Bank. Track economic data with YCharts analytics.

Literacy rate, youth female (% of females ages 15-24) in United Arab Emirates was reported at 100 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United Arab Emirates - Literacy rate, youth female (% of females ages 15-24) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Literacy rate, youth (ages 15-24), gender parity index (GPI) in United Arab Emirates was reported at 1 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United Arab Emirates - Ratio of young literate females to males (% ages 15-24) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Abstract

The PIRLS 2006 aimed to generate a database of student achievement data in addition to information on student, parent, teacher, and school background data for the 47 areas that participated in PIRLS 2006.

Geographic coverage

Nationally representative

Analysis unit

Units of analysis in the study are schools, students, parents and teachers.

Universe

PIRLS is a study of student achievement in reading comprehension in primary school, and is targeted at the grade level in which students are at the transition from learning to read to reading to learn, which is the fourth grade in most countries. The formal definition of the PIRLS target population makes use of UNESCO's International Standard Classification of Education (ISCED) in identifying the appropriate target grade:

"…all students enrolled in the grade that represents four years of schooling, counting from the first year of ISCED Level 1, providing the mean age at the time of testing is at least 9.5 years. For most countries, the target grade should be the fourth grade, or its national equivalent."

ISCED Level 1 corresponds to primary education or the first stage of basic education, and should mark the beginning of "systematic apprenticeship of reading, writing, and mathematics" (UNESCO, 1999). By the fourth year of Level 1, students have had 4 years of formal instruction in reading, and are in the process of becoming independent readers. In IEA studies, the above definition corresponds to what is known as the international desired target population. Each participating country was expected to define its national desired population to correspond as closely as possible to this definition (i.e., its fourth grade of primary school). In order to measure trends, it was critical that countries that participated in PIRLS 2001, the previous cycle of PIRLS, choose the same target grade for PIRLS 2006 that was used in PIRLS 2001. Information about the target grade in each country is provided in Chapter 9 of the PIRLS 2006 Technical Report.

Although countries were expected to include all students in the target grade in their definition of the population, sometimes it was not possible to include all students who fell under the definition of the international desired target population. Consequently, occasionally a country's national desired target population excluded some section of the population, based on geographic or linguistic constraints. For example, Lithuania's national desired target population included only students in Lithuanian-speaking schools, representing approximately 93 percent of the international desired population of students in the country. PIRLS participants were expected to ensure that the national defined population included at least 95 percent of the national desired population of students. Exclusions (which had to be kept to a minimum) could occur at the school level, within the sampled schools, or both. Although countries were expected to do everything possible to maximize coverage of the national desired population, school-level exclusions sometimes were necessary. Keeping within the 95 percent limit, school-level exclusions could include schools that:

• were geographically remote,
• had very few students,
• had a curriculum or structure different from the mainstream education system, or
• were specifically for students with special needs.

The difference between these school-level exclusions and those at the previous level is that these schools were included as part of the sampling frame (i.e., the list of schools to be sampled). Th ey then were eliminated on an individual basis if it was not feasible to include them in the testing.

In many education systems, students with special educational needs are included in ordinary classes. Due to this fact, another level of exclusions is necessary to reach an eff ective target population-the population of students who ultimately will be tested. These are called within-school exclusions and pertain to students who are unable to be tested for a particular reason but are part of a regular classroom. There are three types of within-school exclusions.

• Intellectually disabled students
• Functionally disabled students
• Non-native language speakers

Students eligible for within-school exclusion were identified by staff at the schools and could still be administered the test if the school did not want the student to feel out of place during the assessment (though the data from these students were not included in any analyses). Again, it was important to ensure that this population was as close to the national desired target population as possible. If combined, school-level and within-school exclusions exceeded 5 percent of the national desired target population, results were annotated in the PIRLS 2006 International Report (Mullis, Martin, Kennedy, & Foy, 2007). Target population coverage and exclusion rates are displayed for each country in Chapter 9 of the PIRLS 2006 Technical Report. Descriptions of the countries' school-level and within-school exclusions can be found in Appendix B of the PIRLS 2006 Technical Report.

Kind of data

Sample survey data [ssd]

Sampling procedure

The basic sample design used in PIRLS 2006 is known as a two-stage stratified cluster design, with the first stage consisting of a sample of schools, and the second stage consisting of a sample of intact classrooms from the target grade in the sampled schools. While all participants adopted this basic two-stage design, four countries, with approval from the PIRLS sampling consultants, added an extra sampling stage. The Russian Federation and the United States introduced a preliminary sampling stage, (first sampling regions in the case of the Russian Federation and primary sampling units consisting of metropolitan areas and counties in the case of the United States). Morocco and Singapore also added a third sampling stage; in these cases, sub-sampling students within classrooms rather than selecting intact classes.

For countries participating in PIRLS 2006, school stratification was used to enhance the precision of the survey results. Many participants employed explicit stratification, where the complete school sampling frame was divided into smaller sampling frames according to some criterion, such as region, to ensurea predetermined number of schools sampled for each stratum. For example, Austria divided its sampling frame into nine regions to ensure proportional representation by region (see Appendix B for stratification information for each country). Stratification also could be done implicitly, a procedure by which schools in a sampling frame were sorted according to a set of stratification variables prior to sampling. For example, Austria employed implicit stratification by district and school size within each regional stratum. Regardless of the other stratification variables used, all countries used implicit stratification by a measure of size (MOS) of the school.

All countries used a systematic (random start, fixed interval) probability proportional-to-size (PPS) sampling approach to sample schools. Note that when this method is combined with an implicit stratification procedure, the allocation of schools in the sample is proportional to the size of the implicit strata. Within the sampled schools, classes were sampled using a systematic random method in all countries except Morocco and Singapore, where classes were sampled with probability proportional to size, and students within classes sampled with equal probability. The PIRLS 2006 sample designs were implemented in an acceptable manner by all participants.

Sampling deviation

8 National Research Coordinators (NRCs) encountered organizational constraints in their systems that necessitated deviations from the sample design. In each case, the Statistics Canada sampling expert was consulted to ensure that the altered design remained compatible with the PIRLS standards.

These country specific deviations from sample design are detailed in Appendix B of the PIRLS 2006 Technical Report (page 231) attached as Related Material.

Mode of data collection

Face-to-face [f2f]

Research instrument

• PIRLS Background Questionnaires By gathering information about children’s experiences together with reading achievement on the PIRLS test, it is possible to identify the factors or combinations of factors that relate to high reading literacy. An important part of the PIRLS design is a set of questionnaires targeting factors related to reading literacy. PIRLS administered four questionnaires: to the tested students, to their parents, to their reading teachers, and to their school principals.

• Student Questionnaire Each student taking the PIRLS reading assessment completes the student questionnaire. The questionnaire asks about aspects of students’ home and school experiences - including instructional experiences and reading for homework, self-perceptions and attitudes towards reading, out-of-school reading habits, computer use, home literacy resources, and basic demographic information.

• Learning to Read (Home) Survey The learning to read survey is completed by the parents or primary caregivers of each student taking the PIRLS reading assessment. It addresses child-parent literacy interactions, home literacy resources, parents’ reading habits and attitudes, homeschool connections, and basic demographic and socioeconomic indicators.

• Teacher Questionnaire The reading teacher of each fourth-grade class sampled for PIRLS completes a questionnaire designed to gather information about classroom contexts for developing reading literacy. This questionnaire

Additional file 1: Summary of responses to knowledge-related questions

Ordinal regression model-determination of marginal and adequate health literacy.

Abstract

PIRLS provides internationally comparative data on how well children read by assessing students’ reading achievement at the end of grade four. PIRLS 2016 is the fourth cycle of the study and collects considerable background information on how education systems provide educational opportunities to their students, as well as the factors that influence how students use this opportunity. In 2016 PIRLS was extended to include ePIRLS – an innovative assessment of online reading.

The results of PIRLS 2016 demonstrate a number of positive developments in reading literacy worldwide. For the first time in the history of the study, as many as 96 percent of fourth graders from over 60 education systems achieved above the PIRLS low international benchmark.

Geographic coverage

Nationally representative samples of approximately 4,000 students from 150 to 200 schools participated in PIRLS 2016. About 319,000 students, 310,000 parents, 16,000 teachers, and 12,000 schools participated in total.

Analysis unit

The unit of analysis describes:

• Schools

• Students

• Parents

• Teachers

Universe

All students enrolled in the grade that represents four years of schooling counting from the first year of ISCED Level 1, providing the mean age at the time of testing is at least 9.5 years.

All students enrolled in the target grade, regardless of their age, belong to the international target population and should be eligible to participate in PIRLS. Because students are sampled in two stages, first by randomly selecting a school and then randomly selecting a class from within the school, it is necessary to identify all schools in which eligible students are enrolled. Essentially, eligible schools for PIRLS are those that have any students enrolled in the target grade, regardless of type of school.

Kind of data

Sample survey data [ssd]

Sampling procedure

PIRLS is designed to provide valid and reliable measurement of trends in student achievement in countries around the world, while keeping to a minimum the burden on schools, teachers, and students. The PIRLS program employs rigorous school and classroom sampling techniques so that achievement in the student population as a whole may be estimated accurately by assessing just a sample of students from a sample of schools. PIRLS assesses reading achievement at fourth grade. The PIRLS 2016 cycle also included PIRLS Literacy-a new, less difficult reading literacy assessment, and ePIRLS-an extension of PIRLS with a focus on online informational reading.

PIRLS employs a two-stage random sample design, with a sample of schools drawn as a first stage and one or more intact classes of students selected from each of the sampled schools as a second stage. Intact classes of students are sampled rather than individuals from across the grade level or of a certain age because PIRLS pays particular attention to students’ curricular and instructional experiences, and these typically are organized on a classroom basis. Sampling intact classes also has the operational advantage of less disruption to the school’s day-to-day business than individual student sampling.

SAMPLE SIZE

For most countries, the PIRLS precision requirements are met with a school sample of 150 schools and a student sample of 4,000 students for each target grade. Depending on the average class size in the country, one class from each sampled school may be sufficient to achieve the desired student sample size. For example, if the average class size in a country were 27 students, a single class from each of 150 schools would provide a sample of 4,050 students (assuming full participation by schools and students). Some countries choose to sample more than one class per school, either to increase the size of the student sample or to provide a better estimate of school level effects.

For countries choosing to participate in both PIRLS and PIRLS Literacy, the required student sample size is doubled-i.e., around 8,000 sampled students. Countries could choose to select more schools or more classes within sampled schools to achieve the required sample size. Because ePIRLS is designed to be administered to students also taking PIRLS, the PIRLS sample size requirement remains the same for countries choosing also to participate in ePIRLS.

PIRLS STRATIFIED TWO-STAGE CLUSTER SAMPLE DESIGN

The basic international sample design for PIRLS is a stratified two-stage cluster sample design, as follows:

• First Sampling Stage. For the first sampling stage, schools are sampled with probabilities proportional to their size (PPS) from the list of all schools in the population that contain eligible students. The schools in this list (or sampling frame) may be stratified (sorted) according to important demographic variables. Schools for the field test and data collection are sampled simultaneously using a systematic random sampling approach. Two replacement schools are also pre-assigned to each sampled school during the sample selection process, and these replacement schools are held in reserve in case the originally sampled school refuses to participate. Replacement schools are used solely to compensate for sample size losses in the event that the originally sampled school does not participate. School sampling is conducted for each country by Statistics Canada with assistance from IEA Hamburg, using the sampling frame provided by the country’s National Research Coordinator.

• Second Sampling Stage. The second sampling stage consists of the selection of one (or more) intact class from the target grade of each participating school. Class sampling in each country is conducted by the National Research Coordinator using the Within-School Sampling Software (WinW3S) developed by IEA Hamburg and Statistics Canada. Having secured a sampled school’s agreement to participate in the assessment, the National Research Coordinator requests information about the number of classes and teachers in the school and enters it in the WinW3S database.

Classes smaller than a specified minimum size are grouped into pseudo-classes prior to sampling. The software selects classes with equal probabilities within schools. All students in each sampled class participate in the assessment. Sampled classes that refuse to participate may not be replaced.

For countries participating in both PIRLS and PIRLS Literacy, students within a sampled class are randomly assigned either a PIRLS or PIRLS Literacy booklet through a booklet rotation system. This is done to ensure that PIRLS and PIRLS Literacy are administered to probabilistically equivalent samples. In countries taking part in ePIRLS, all students assessed in PIRLS are expected to participate in ePIRLS.

STRATIFICATION

Stratification consists of arranging the schools in the target population into groups, or strata, that share common characteristics such as geographic region or school type. Examples of stratification variables used in PIRLS include region of the country (e.g., states or provinces); school type or source of funding (e.g., public or private); language of instruction; level of urbanization (e.g., urban or rural area); socioeconomic indicators; and school performance on national examinations.

In PIRLS, stratification is used to:

• Improve the efficiency of the sample design, thereby making survey estimates more reliable

• Apply different sample designs, such as disproportionate sample allocations, to specific groups of schools (e.g., those in certain states or provinces)

• Ensure proportional representation of specific groups of schools in the sample School stratification can take two forms: explicit and implicit. In explicit stratification, a separate school list or sampling frame is constructed for each stratum and a sample of schools is drawn from that stratum. In PIRLS, the major reason for considering explicit stratification is disproportionate allocation of the school sample across strata. For example, in order to produce equally reliable estimates for each geographic region in a country, explicit stratification by region may be used to ensure the same number of schools in the sample for each region, regardless of the relative population size of the regions.

Implicit stratification consists of sorting the schools by one or more stratification variables within each explicit stratum, or within the entire sampling frame if explicit stratification is not used. The combined use of implicit strata and systematic sampling is a very simple and effective way of ensuring a proportional sample allocation of students across all implicit strata. Implicit stratification also can lead to improved reliability of achievement estimates when the implicit stratification variables are correlated with student achievement.

National Research Coordinators consult with Statistics Canada and IEA Hamburg to identify the stratification variables to be included in their sampling plans. The school sampling frame is sorted by the stratification variables prior to sampling schools so that adjacent schools are as similar as possible. Regardless of any other explicit or implicit variables that may be used, the school size is always included as an implicit stratification variable.

SCHOOL SAMPLING FRAME

One of the National Research Coordinator’s most important sampling tasks is the construction of a school sampling frame for the target population. The sampling frame is a list of all schools in the country that have students enrolled in the target grade and is the list from which the school sample is drawn. A well-constructed sampling frame provides complete coverage of the national target population without being contaminated by incorrect or duplicate entries or entries that refer to elements that are not

Snapshot img

Overview of the K-12 education market in GCC

Technavio’s market research study identifies the growing awareness of primary education to be one of the primary growth factors for the K-12 education market in GCC. With the steady awareness among parents, education institutions, and investors about the need for primary education, there is a major demand for high-quality education, which will boost the tuition fees of private schools. Additionally, factors such as the increase in disposable income, the growing demand for quality education, rising standards of the education sector, and the growing use of advanced technology in the education sector, will also boost the number of enrollments in primary education institutions. Focusing on providing high-quality pre-primary, primary, and secondary education, governments in GCC countries are also setting up various schools, driving the growth of the K-12 education market in this region. According to our market research analysts, this market will register a CAGR of around 5% by 2022.

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Rapid technological advancements in the education sector will drive the growth of the K-12 education market in GCC throughout the forecast period. The governments of GCC countries are employing advanced educational technologies through various policies and investments. With the inclusion of modern education technologies in schools, there is a significant increase in education tuition fees. With individuals willing to incur high charges for additional resources for their children’s education, institutions are focusing on employing advanced technologies that impart better knowledge along with providing knowledge on practical use.

Companies covered

The K-12 education market in GCC is fragmented due to the presence of several international and regional vendors. To help clients identify new growth opportunities in this competitive market, this K12 school market research report offers an analysis of the competitive environment among the K-12 education companies and also offers information on the key areas they focus on. Moreover, the report also provides information on the products and services companies offer, which will in turn, aid clients improve their revenue share by differentiating their products from the competitors.

This research report provides an analysis of the various companies in this market including –

Al- Jazeera Academy
British International School of Jeddah
Dhuha International School
Dubai International Academy
GEMS Education
International School of London
Nadeen International School
New English School
Taaleem
The American International School of Muscat

Geographical segmentation and analysis of the K-12 education market in GCC

Saudi Arabia
UAE
Kuwait
Oman
Qatar
Bahrain.

According to this K-12 school market research study, Saudi Arabia will be the major revenue contributor to the K-12 education market till 2022. Factors such as the growing number of expatriates, high focus on education, higher overall literacy rate, and high per capita income, will drive the growth of the market in this region.

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Key questions answered in the report include

What will the market size and the growth rate be in 2022?
What are the key factors driving the K-12 education market in GCC?
What are the key market trends impacting the growth of the K-12 education market in GCC?
What are the challenges to market growth?
Who are the key vendors in the K-12 education market in GCC?
What are the market opportunities and threats faced by the vendors in the K-12 education market in GCC?
Trending factors influencing the market share of GCC.
What are the key outcomes of the five forces analysis of the K-12 education market in GCC?

Technavio also offers customization on reports based on specific client requirement.

According to our industry experts, the government support in GCC region for education has increased tremendously. The region has witnessed a massive investment due to oil-based business; such investments have led to the growth of the market. Technavio’s updated research report on the K-12 education market in GCC for 2018-2022 provides a wide range of factors that will prove key to our clients and provide them with actionable insights. This report is an add-on of our previous research on the K-12 education market in GCC and is focused on helping businesses align their market strategies with the rapidly changing market trends and expand their market share in niche segments. This report assists clients to gain higher market share; this research report offers valuable insights into multiple

Mental health literacy is crucial for nursing students to deliver effective patient care; however, its development throughout their academic journey remains underexplored. This study aimed to assess mental health literacy among nursing students in the UAE and examine factors influencing their literacy levels. A quantitative cross-sectional study was conducted from February 2024 to August 2024 using convenience sampling. A total of 295 undergraduate nursing students participated in the study. Data were collected using the Mental Health Literacy Questionnaire (MHLq) and analyzed using SPSS software (version 24). Descriptive and inferential analyses were conducted to calculate means, standard deviations, percentages, and measures of association using t-tests for students’ sociodemographic variables, dimensions, and global scores, with a significance level of 0.05 for the tests. The findings indicated that participants (all female, with a mean age of 20.7 ± 1.85 years) had a mean MHLq score of 108.19 ± 10.53. Fourth-year students scored higher (110.78 ± 9.79) than lower-year students (106.85 ± 10.68). Students with family or friends who were affected had higher scores (48.00 ± 6.16) than those without (45.97 ± 6.16). The highest-scoring domain was knowledge of mental health problems (46.59 ± 6.40), while self-help strategies scored lowest (16.99 ± 2.61). Students with a personal history of mental illness had lower scores. The study also indicated a statistically significant association between students’ marital status, their level of study, and their first-aid skills and help-seeking behavior. The study highlights the importance of integrating mental health literacy into undergraduate nursing curricula to enhance student’s ability to provide patient-centered care for individuals with mental health disorders. Implementing targeted educational strategies focusing on awareness, recognition, and communication may strengthen students’ competency and preparedness for mental health care practice.