Among the states in India, Kerala had the highest literary rate with ** percent in 2011. Chandigarh, Himachal Pradesh and the capital territory of Delhi followed Kerala with above average literacy rates. Notably, all the leading states in the country had more literate males than females at the time of the census.

In the past five decades, the global literacy rate among adults has grown from 67 percent in 1976 to 87.36 percent in 2023. In 1976, males had a literacy rate of 76 percent, compared to a rate of 58 percent among females. This difference of over 17 percent in 1976 has fallen to just seven percent in 2020. Although gaps in literacy rates have fallen across all regions in recent decades, significant disparities remain across much of South Asia and Africa, while the difference is below one percent in Europe and the Americas. Reasons for these differences are rooted in economic and cultural differences across the globe. In poorer societies, families with limited means are often more likely to invest in their sons' education, while their daughters take up a more domestic role. Varieties do exist on national levels, however, and female literacy levels can sometimes exceed the male rate even in impoverished nations, such as Lesotho (where the difference was over 17 percent in 2014); nonetheless, these are exceptions to the norm.

In 2020, China had a youth literacy rate of about **** percent. In contrast, Afghanistan's youth literacy rate stood at **** percent in 2021.Indicators for the literacy rateAn indicator which can be seen to affect the literacy rate is the governmental effort in investing in education. The amount of funds invested into the education sector is a factor which can determine a country’s state of education, as the amount of money being spent on education would have an impact on resources, learning environment, and teaching quality. Singapore’s student-teacher ratio in primary education is significantly lower than that of South Asian countries. For instance, Nepal had 20.3 students for every teacher in 2019. Meanwhile, Singaporean teachers only had around **** students on average as of 2020. Notably, South Asia, together with sub-Saharan Africa, had a much higher illiteracy rate compared to the East Asian and Pacific region in 2022. The importance of literacyThe literacy rate indicates the percentage of people within a population who can read and write. This enables them to identify, understand and interpret materials with various contexts. Ensuring literacy for all pupils is a part of the Sustainable Development Goals (SDG) for quality education. This in turn stimulates economic and societal growth for the future.

The average for 2021 based on 13 countries was 86.52 percent. The highest value was in Uzbekistan: 100 percent and the lowest value was in Afghanistan: 37 percent. The indicator is available from 1970 to 2023. Below is a chart for all countries where data are available.

Editor’s Choice

• The Healthcare IT market size is expected to be worth around USD 1728 Bn by 2032
• According to a report by UNESCO, countries in South and South-West Asia have the highest number of illiterate adults in the world, estimated at 388 million.
• Approximately 36% of adult Americans possess only basic or below basic health literacy skills.
• Only 12% of Americans are considered proficient in their health literacy skills.
• Health literacy levels in China increased from 6.48% of the population in 2008 to 23.15% in 2020.
• A recent study analyzing global health literacy research from 1995 to 2020 identified the United States, Australia, and the United Kingdom as major contributors to the international collaboration network on health literacy.
• Mental health has been the most active research field in recent years in the context of health literacy.

As of March 2020, around 4.46 million Mexicans over 15 years of age were unable to write or read, approximately 4.74 percent. Mexico State, the federal entity with the highest share of the population, also registered the highest number of literate people with over 12 million.

The highest literacy rates in Nigeria were registered in the southern regions of the country. In the South West, 89 percent of males and 80.6 percent of females were literate as of 2018. Also, the south zones showed the lowest percentage differences between male and female literacy. Female literacy rate in Nigeria is among the highest in West Africa. The highest female literacy rates were registered in Cabo Verde and Ghana, while Nigeria ranked third.

The National Center for Education Statistics surveyed 12,330 U.S. adults ages 16 to 74 living in households from 2012 to 2017 for the Program for the International Assessment of Adult Competencies (PIAAC), an international study involving over 35 countries. Using small area estimation models (SAE), indirect estimates of literacy and numeracy proficiency have been produced for all U.S. states and counties. By using PIAAC survey data in conjunction with data from the American Community Survey, the Skills Map data provides reliable estimates of adult literacy and numeracy skills in all 50 states, all 3,141 counties, and the District of Columbia.

SAE is a model-dependent approach that produces indirect estimates for areas where survey data is inadequate for direct estimation. SAE models assume that counties with similar demographics would have similar estimates of skills. An estimate for a county then “borrows strength” across related small areas through auxiliary information to produce reliable indirect estimates for small areas. The models rely on covariates available at the small areas, and PIAAC survey data. In the absence of any other proficiency assessment data for individual states and counties, the estimates provide a general picture of proficiency for all states and counties. In addition to the indirect estimates, this website provides precision estimates and facilitates statistical comparisons among states and counties. For technical details on the SAE approach applied to PIAAC, see section 5 of the State and County Estimation Methodology Report.

The U.S. county indirect estimates reported in this data are not directly comparable with the direct estimates for PIAAC countries that are reported by the Organization for Economic Cooperation and Development (OECD). Specifically, the U.S. county indirect estimates (1) represent modeled estimates for adults ages 16-74 whereas the OECD’s direct estimates for participating countries represent estimates for adults ages 16-65, (2) include data for “literacy-related nonresponse” (i.e., adults whose English language skills were too low to participate in the study) whereas the OECD’s direct estimates for countries exclude these data, and (3) are based on three combined data collections (2012/2014/2017) whereas OECD’s direct estimates are based on a single data collection.

Please visit the Skills Map to learn more about this data.

The National Center for Education Statistics surveyed 12,330 U.S. adults ages 16 to 74 living in households from 2012 to 2017 for the Program for the International Assessment of Adult Competencies (PIAAC), an international study involving over 35 countries. Using small area estimation models (SAE), indirect estimates of literacy and numeracy proficiency have been produced for all U.S. states and counties. By using PIAAC survey data in conjunction with data from the American Community Survey, the Skills Map data provides reliable estimates of adult literacy and numeracy skills in all 50 states, all 3,141 counties, and the District of Columbia.

The indirect estimates provided in this data were created using a sophisticated statistical method generally referred to as small area estimation (SAE). SAE is a model-dependent approach that produces indirect estimates for areas where survey data is inadequate for direct estimation. SAE models assume that counties with similar demographics would have similar estimates of skills. An estimate for a county then “borrows strength” across related small areas through auxiliary information to produce reliable indirect estimates for small areas. The models rely on covariates available at the small areas, and PIAAC survey data. In the absence of any other proficiency assessment data for individual states and counties, the estimates provide a general picture of proficiency for all states and counties. For technical details on the SAE approach applied to PIAAC, see section 5 of the State and County Estimation Methodology Report.

The U.S. state indirect estimates reported in this data are not directly comparable with the direct estimates for PIAAC countries that are reported by the Organization for Economic Cooperation and Development (OECD). Specifically, the U.S. state indirect estimates (1) represent modeled estimates for adults ages 16-74 whereas the OECD’s direct estimates for participating countries represent estimates for adults ages 16-65, (2) include data for “literacy-related nonresponse” (i.e., adults whose English language skills were too low to participate in the study) whereas the OECD’s direct estimates for countries exclude these data, and (3) are based on three combined data collections (2012/2014/2017) whereas OECD’s direct estimates are based on a single data collection.Please visit the Skills Map to learn more about this data.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

National Assessment of Adult Literacy, 2003 (NAAL:2003), is a study that is part of the National Assessment of Adult Literacy program. NAAL:2003 (https://nces.ed.gov/naal/) is a cross-sectional assessment that collected information about English literacy among American adults age 16 and older. The study was conducted using direct assessment from 19,000 adults 16 or older, in their homes and some in prisons from the 50 states and District of Columbia. Households and prison inmates were sampled in 2003. The weighted response rate was 62.1 percent for households and 88.3 percent for prison inmates. Key statistics produced from NAAL:2003 include reading skills, general literacy, relationships, demographics, and background characteristics.

Literacy Rate: Tamil Nadu data was reported at 80.100 % in 12-01-2011. This records an increase from the previous number of 73.450 % for 12-01-2001. Literacy Rate: Tamil Nadu data is updated decadal, averaging 58.525 % from Dec 1961 (Median) to 12-01-2011, with 6 observations. The data reached an all-time high of 80.100 % in 12-01-2011 and a record low of 36.390 % in 12-01-1961. Literacy Rate: Tamil Nadu data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Education Sector – Table IN.EDA001: Literacy Rate.

Literacy in India has been increasing as more and more people receive a better education, but it is still far from all-encompassing. In 2023, the degree of literacy in India was about 77 percent, with the majority of literate Indians being men. It is estimated that the global literacy rate for people aged 15 and above is about 86 percent. How to read a literacy rateIn order to identify potential for intellectual and educational progress, the literacy rate of a country covers the level of education and skills acquired by a country’s inhabitants. Literacy is an important indicator of a country’s economic progress and the standard of living – it shows how many people have access to education. However, the standards to measure literacy cannot be universally applied. Measures to identify and define illiterate and literate inhabitants vary from country to country: In some, illiteracy is equated with no schooling at all, for example. Writings on the wallGlobally speaking, more men are able to read and write than women, and this disparity is also reflected in the literacy rate in India – with scarcity of schools and education in rural areas being one factor, and poverty another. Especially in rural areas, women and girls are often not given proper access to formal education, and even if they are, many drop out. Today, India is already being surpassed in this area by other emerging economies, like Brazil, China, and even by most other countries in the Asia-Pacific region. To catch up, India now has to offer more educational programs to its rural population, not only on how to read and write, but also on traditional gender roles and rights.

Analysis of ‘Govt Of India Literacy Rate’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/doncorleone92/govt-of-india-literacy-rate on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This is the official dataset released by the govt. of India based on the census 2001 and 2011 survey.

Content

The data is of 35 Indian states and union territories. The literacy rate is spread across the major parameters - Overall, Rural and Urban. All the data is percentage of the total population of that state.

Acknowledgements

Derived from the govt. of India's official site.

Inspiration

Understand the literacy rate in India and which states/UT's have the highest growth in terms of increased literacy rates.

--- Original source retains full ownership of the source dataset ---

We ask whether party competition improves economic and social well-being, drawing on evidence from the 50 American states for the period 1880-2010. Today, strident party competition and partisan polarization are blamed for many of the ills of national and state politics. But a much deeper political science tradition points to the virtues of competitive party politics. In this historical analysis, we find that states with competitive party systems spend more than other states—and specifically spend more on education, health, and transportation, areas identified as investments in human capital and infrastructure. We find that this spending leads to longer life expectancy, lower infant mortality, better educational outcomes, and higher incomes. Thus we conclude that party competition is not just healthy for a political system but for the life prospects of a state’s residents.

The statistic depicts the literacy rate in Mexico from 2008 to 2020. The literacy rate measures the percentage of people ages 15 and above who can read and write. In 2020, Mexico's literacy rate was around 95.25 percent. The source does not provide data for 2019.Education in MexicoThe literacy rate is commonly defined as the share of people in a country who are older than 15 years and are able to read and write. In Mexico, a state with more than 115 million inhabitants, the literacy rate is above 90 percent, making it significantly higher than the global average. More than 70 percent of Mexico’s population is older than 15 years, a figure than has been quite consistent over the last ten years. Mexico’s compulsory education comprises grades 1 to 9, with an optional secondary education up to grade 12. Literacy is considered basic education. The lowest literacy rates can be found in African countries, the highest in Europe. Additionally, the literacy rate is one of the factors that determines a country’s ranking on the Human Development Index of the United Nations, which ranks the overall well-being of a country’s population. Apart from literacy, it also includes factors such as per-capita income, health and life expectancy and others. Mexico is currently not among the countries with the highest Human Development Index value.

Context

There are more than 26.8 million people or 2.2% of the population currently who have disabilities in India (Census 2011) which itself is said to be a very conservative estimate. There is a lot of stigma associated with the disabled community and a very high inequality in terms of social as well as monetary status between the disabled community and the entire population.

Content

The data in the csv file gives us the statewise values of the following:

1.State 2.number_disabled : It gives the total number of people in the region that are disabled. 3.total_population: It gives the total number of people in the region. 4.percent_disabled: It gives the total percentage of the people disabled in the given region. 5.literacy_rate_disabled : It represents the literacy rate of the disabled community in the region. 6.literacy_rate_general : It shows the total literacy rate of the population in the state. 7.workforce_rate_disabled : It tells us the total percent of all the disabled people that are part of the workforce in the given region.(inclusive all ages). 8.workforce_rate_general : It shows the total percent of all the people that are part of the workforce in the given region(inclusive of all ages).

Distribution of literate population by Age Group and sex in Edo state as at 2006. Data and Resources Distribution of Literate Population by Age...CSV Distribution of literate population by Age Group and sex in Edo state. Explore More information Go to resource

Abstract

The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.

The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.

The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.

Geographic coverage

National

Analysis unit

• Household
• Data collected for women 13-49, indicators calculated for women 15-49

Universe

The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.

SAMPLE SIZE AND ALLOCATION

The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.

The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).

THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.

Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.

In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.

THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.

All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content

Using the latest available data from the U.S. Census Bureau's American Community Survey (ACS), this map examines the housing own/rent decision of people with a college degree (bachelor's degree or higher). While the general pattern is that college graduates end up buying a home at some point in their careers, the map reveals which neighborhoods actually have more renters than home owners, among college graduates.The map's topic is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. To see the full list of attributes available in this map's layers, go to a layer listed under the "Layers" section below and choose the "Data" tab for that layer, and choose "Fields" at the top right on that page. Current Vintage: 2018-2022ACS Table(s): B25013Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 7, 2023National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2022 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.Web Map originally compiled by Jim Herries

The National Assessment of Educational Progress (NAEP) is the largest nationally representative and continuing assessment of what students in elementary and secondary schools in the United States know and can do in various subject areas. Assessments are conducted periodically in mathematics, reading, science, writing, the arts, civics, economics, geography, United States history, and beginning in 2014, in Technology and Engineering Literacy (TEL). Since NAEP assessments are administered uniformly using the same sets of test booklets across the United States, NAEP results serve as a common metric for all states and selected urban districts. The assessment stays essentially the same from year to year, with only carefully documented changes. This permits NAEP to provide a clear picture of student academic progress over time and for teachers, principals, parents, policymakers, and researchers to use NAEP results to assess progress and develop ways to improve education in the United States. For more information, please read An Introduction to NAEP. There are two types of assessments: main NAEP and long-term trend NAEP. Main NAEP is administered to fourth-, eighth-, and twelfth-graders across the United States in a variety of subjects. The Main NAEP is conducted between the last week of January and the first week in March every year. National results are available for all assessments and subjects. Results for states and select urban districts are available in some subjects for grades 4 and 8. The Trial Urban District Assessment (TUDA) is a special project developed to determine the feasibility of reporting district-level NAEP results for large urban districts. In 2009 a trial state assessment was administered at grade 12. Long-term trend NAEP is administered nationally every four years. During the same academic year, 13-year-olds are assessed in the fall, 9-year-olds in the winter, and 17-year-olds in the spring. Long-term trend assessments measure student performance in mathematics and reading, and allow the performance of students from recent time periods to be compared with students since the early 1970s. For example, the 1997 and 2008 NAEP arts assessments were part of the Main NAEP Assessments. The NAEP 1997 Arts Assessment was conducted nationally at grade 8. For music and visual arts, representative samples of public and nonpublic school students were assessed. A special "targeted" sample of students took the theatre assessment. Schools offering at least 44 classroom hours of a theatre course per semester, and offering courses including more than the history or literature of theatre, were identified. Students attending those schools who had accumulated 30 hours of theatre classes by the end of the 1996-97 school year were selected to take the theatre assessment. The NAEP 2008 Arts Assessment was administered to a nationally representative sample of 7,900 eighth-grade public and private school students. Approximately one-half of these students were assessed in music, and the other half were assessed in visual arts. The music portion of the assessment measured students' ability to respond to music in various ways. Students were asked to analyze and describe aspects of music they heard, critique instrumental and vocal performances, and demonstrate their knowledge of standard musical notation and music's role in society. The visual arts portion of the assessment included questions that measured students' ability to respond to art as well as questions that measured their ability to create art. Responding questions asked students to analyze and describe works of art and design. For example, students were asked to describe specific differences in how certain parts of an artist's self-portrait were drawn. Creating questions required students to create works of art and design of their own. For example, students were asked to create a self-portrait that was scored for identifying detail, compositional elements, and use of materials. Most recently, in 2016, a total of 8,800 eighth-graders in the nation's public and private schools responded to and critiqued existing works of music and visual art and created their own original artwork. NCES collected and analyzed the data and released the 2016 report highlighting key findings. Average music and visual arts responding scores are reported separately on a scale of 0 to 300 points. A