India Number of Schools: Secondary School data was reported at 252,176.000 Unit in 2015. This records an increase from the previous number of 244,653.000 Unit for 2014. India Number of Schools: Secondary School data is updated yearly, averaging 114,629.000 Unit from Sep 1950 (Median) to 2015, with 34 observations. The data reached an all-time high of 252,176.000 Unit in 2015 and a record low of 7,416.000 Unit in 1950. India Number of Schools: Secondary School data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDC001: Number of Schools: Secondary School.

India Number of Teachers: Secondary School data was reported at 3,473,455.000 Person in 2015. This records an increase from the previous number of 3,331,599.000 Person for 2014. India Number of Teachers: Secondary School data is updated yearly, averaging 2,247,960.000 Person from Sep 2001 (Median) to 2015, with 15 observations. The data reached an all-time high of 3,473,455.000 Person in 2015 and a record low of 1,777,495.000 Person in 2001. India Number of Teachers: Secondary School data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDC007: Number of Teachers: Secondary School.

Abstract

UNHCR India implemented a telephone survey to measure the satisfaction with educational services provided by UNHCR and NGO partners. Most reported financial issues as reasons for their children not attending UNHCR partner led schools and lack of devices for not being able to make use of the online program. The survey also covers a few questions on the impact of COVID-19 on school attendance, and the education quality of public schools. The household survey spans a sample of more than 1,500 households and 2,200 children.

Geographic coverage

National coverage

Analysis unit

Households

Universe

Refugees in India

Kind of data

Sample survey data [ssd]

Sampling procedure

Not Applicable

Mode of data collection

Computer Assisted Telephone Interview [cati]

India Expenditure on Education: States: Revenue Account data was reported at 4,919,527,378.000 INR th in 2017. This records an increase from the previous number of 4,381,732,635.000 INR th for 2016. India Expenditure on Education: States: Revenue Account data is updated yearly, averaging 2,423,557,504.000 INR th from Mar 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 7,645,768,000.000 INR th in 2005 and a record low of 1,005,085,412.000 INR th in 2007. India Expenditure on Education: States: Revenue Account data remains active status in CEIC and is reported by Department of Higher Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDE004: Memo Items: Expenditure on Education: Revenue Account: by States.

The Residential School Locations Dataset [IRS_Locations.csv] contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites.

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Abstract

This impact evaluation was conducted by IDinsight for STIR Education in Delhi and Uttar Pradesh in India, and was funded by a World Bank Strategic Impact Evaluation Fund grant. The study seeks to evaluate the impact of STIR's purely motivational, pedagogically neutral, teacher-focused model on student learning levels. STIR works with teachers in low-cost and government schools in order to improve student learning by empowering teachers to act as change-makers and to innovate to overcome challenges in the classroom. IDinsight conducted two three-armed randomized control trials. The study looks at outcomes from 180 Affordable Private Schools (APS) in Delhi and 270 government schools in the Raebareli and Varanasi districts of Uttar Pradesh. The study began in early 2015, and lasted two academic years. In addition to measuring STIR's impact in two different contexts, the study simultaneously tests two iterations of STIR's model in these two contexts.

Geographic coverage

One district in Delhi - East Delhi, and two districts in Uttar Pradesh - Raebareli and Varanasi

Analysis unit

For student learning, the basic unit of analysis is students. For classroom practices, the basic unit of analysis is teachers. For teacher motivation, the basic unit of analysis is teachers.

Universe

• 180 Affordable Private Schools in Delhi, 540 teachers amongst these schools and 5,400 students
• 270 Government Schools in Uttar Pradesh, 810 teachers amongst these schools and 8,100 students

Kind of data

Sample survey data [ssd]

Sampling procedure

Baseline Respondent Identification and Sampling Strategy:

Delhi:

Teacher Motivation: STIR initially did a search process of several hundred Affordable Private Schools (APS) in east Delhi. From these schools, STIR passed school names onto IDinsight where the teachers might be interested in working with IDinsight. IDinsight attempted to sample all schools for the Teacher Motivation survey. In total, IDinsight interviewed 1,259 teachers for the Teacher Motivation survey.

Classroom Observation: From these 1,259 teachers, STIR did an additional round of screening to determine which teachers were the most interested and returned a list of 810 teachers to IDinsight. This list formed the basis of the classroom observation. However, due to attrition and refusals at the school level we were unable to meet our target of teachers and ended up surveying only 342 teachers.

Student Testing: For sampling students in the classroom, IDinsight sampled 10 students per classroom in classes (of all teachers covered for the classroom observation) with more than 10 students using the attendance register for the day the enumerator came to the class. In classes with fewer than 10 students, all children were sampled.

Uttar Pradesh:

Teacher Motivation: In Uttar Pradesh, IDinsight obtained a list of all clusters in Raebareli and Varanasi districts that STIR was working in. From this list, IDinsight selected all clusters with more than 16 schools. This was done to ensure that there would be enough schools in the cluster to assign some to the control group while also maintaining enough treatment schools for STIR to form a network. For the Teacher Motivation survey, IDinsight surveyed all teachers in the school, yielding 1,145 teachers.

Classroom Observation: For the classroom observation, IDinsight sampled roughly 2/3 of the teachers who completed the Teacher Motivation questionnaire, to get a final list of roughly 810 teachers. Teachers were added to this list due to teachers dropping out and the final number was 838 teachers.

Student Testing: For sampling students in the classroom, IDinsight sampled 10 students per classroom in classes with more than 10 students using the attendance register for the day the enumerator came to the class. In classes with fewer than 10 students, all children were sampled.

Midline Respondent Identification and Sampling Strategy:

For midline, which took place at the beginning of the second academic year, we followed up with teachers and students surveyed at baseline. Teachers were added only in the case where the number of teachers still teaching in the school from our baseline lists fell below a certain number. In Delhi, teachers were added if less than two teachers from our list in a given school were available and in Uttar Pradesh, new teachers were added only if all teachers from our baseline lists in a given school dropped out.

The sampling strategy had two clear advantages: 1) It helped us target teachers and students that have been exposed to STIR for as long as possible since the timeline for the overall evaluation is relatively short. 2) The evaluations are already quite complex and this helped have a clear interpretation and narrative surrounding the results.

Delhi:

Teacher Motivation: From the list of 1,259 teachers surveyed at teacher motivation baseline, 453 teachers dropped out of schools during the academic year and hence were not available for surveying during midline. A further 65 teachers refused to participate and 84 teachers were not available during the data collection period. Given this, the total number of teachers surveyed at teacher motivation midline was 657. These teachers formed the sample for analyses.

Classroom Observation: For classroom observations, we attempted to collect data for all 811 teachers on the Delhi original list. For those schools where the number of teachers available from our 811 list fell below two, 148 new teachers were added based on a random selection from those teachers employed at that school as of 1 July 2015. A total of 459 teachers were surveyed as part of the classroom observation midline.

Student Testing: For testing of student learning levels, all students surveyed at baseline formed the potential sample at midline. Among the 3,367 students from baseline, 1,956 students were tracked and surveyed at midline. 1,127 students had dropped out from the schools. 40 students were absent throughout the course of the data collection, and were not found in schools during any of the five revisits. The remaining 244 students were in schools where we could not survey.

Uttar Pradesh:

Teacher Motivation: From the 1,145 teachers surveyed at baseline, 288 teachers dropped out of schools during the course of the academic year and were hence not available for data collection. An additional 61 refused to participate in the data collection and 41 were not available through the course of the data collection. The final number of teachers surveyed at midline were 755. This was the sample for analysis.

Classroom Observation: From the list of 838 teachers surveyed at baseline, we successfully observed the classrooms of 734 of these teachers at midline. Another 13 teachers were added in schools where all teachers from our 838 had dropped out. 12 of these 13 were in Raebareli and 1 was in Varanasi. In total, 747 teachers were surveyed. 82 teachers dropped out of the schools in our sample. 13 teachers refused to participate in the data collection and 14 teachers were absent throughout the survey period and were not available on either of our visits.

Student Testing: Of the 7,386 students tested at baseline, a total of 4,560 students were also tested at midline. 615 students were absent all days of visits to the schools. 149 students were in the four schools that refused data collection. 2,062 dropped out of the schools in our sample.

Endline Respondent Identification and Sampling Strategy:

For endline, which took place after the end of the second academic year, we followed up with teachers and students surveyed at midline. In Delhi, one teacher was added per school to the classroom observation sample where possible. Additional teachers were added to the teacher motivation sample by offering the survey to all the teachers in our sample schools. The sampling strategy had two clear advantages:

1) It helped us target teachers and students that have been exposed to STIR for as long as possible since the timeline for the overall evaluation is relatively short. 2) The evaluations are already quite complex and this helped have a clear interpretation and narrative surrounding the results.

Delhi:

Teacher Motivation: From the list of 657 teachers surveyed at teacher motivation midline, 101 teachers dropped out of schools during the academic year and hence were not available for surveying during endline. A further 25 teachers refused to participate and 50 teachers were not available during the data collection period. Given this, the total number of teachers surveyed at teacher motivation midline was 481. These teachers formed the sample for analyses.

Classroom Observation: For classroom observations, we attempted to collect data for all 459 teachers on the Delhi midline list as well as 102 teachers we surveyed at baseline and couldn't at midline but were hopeful of covering in the last survey. A new teacher was added to each school's sample where possible. A total of 376 teachers were surveyed as part of the classroom observation endline.

Student Testing: For testing of student learning levels, all students surveyed at midline formed the potential sample at endline. Among the 1,956 students from baseline, 1,843 students were tracked and surveyed at midline. 49 students had dropped out from the schools. 45 students were absent throughout the course of the data collection, and were not found in schools during any of the five revisits.

Uttar Pradesh:

Teacher Motivation: From the 967 teachers surveyed at midline, 105 teachers were transfered and 17 retired during the course of the academic year and were hence not available for data collection. An additional 36 refused to participate in the data collection and 26 were not available through

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2018 DC School Report Card. STAR Framework student group scores by school and school framework. The STAR Framework measures performance for 10 different student groups with a minimum n size of 10 or more students at the school. The student groups are All Students, Students with Disabilities, Student who are At Risk, English Learners, and students who identify as the following ESSA-defined racial/ethnic groups: American Indian or Alaskan Native, Asian, Black or African American, Hispanic/Latino of any race, Native Hawaiian or Other Pacific Islander, White, and Two or more races. The Alternative School Framework includes an eleventh student group, At-Risk Students with Disabilities.Some students are included in the school- and LEA-level aggregations that will display on the DC School Report Card but are not included in calculations for the STAR Framework. These students are included in the “All Report Card Students” student group to distinguish from the “All Students” group used for the STAR Framework.Supplemental:Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide).

Number of Schools: Primary School: Delhi data was reported at 2,755.000 Unit in 2015. This records a decrease from the previous number of 2,776.000 Unit for 2014. Number of Schools: Primary School: Delhi data is updated yearly, averaging 2,581.000 Unit from Sep 2001 (Median) to 2015, with 15 observations. The data reached an all-time high of 2,776.000 Unit in 2014 and a record low of 2,111.000 Unit in 2002. Number of Schools: Primary School: Delhi data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDB001: Number of Schools: Primary School.

Mentor International School is one of the best CBSE Schools in Pune is also intended to teach students the importance of responsibility, hard work and citizenship. This will instill character in students and reinforce positive behavior. Our top notch academic studies help out in developing Liberty, Fraternity and Equality in the minds of students. The educational material and teaching methodology of CBSE board is conducive to the national interests of the country. We offer CBSE curriculum which is more student-friendly & very conducive to a positive environment. Referring to best CBSE schools in Pune we prepare our students to pursue future studies from a centralized institution like an IIT or AIIMS. Most modern academic take standards norms to adopt a group of-learning strategy to education. This seems to be a dated approach to learning that continues to hamper our attempts to innovate. The fluency of our world class curriculum match the fluidity of relevant modern knowledge demands.

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Data and insights on the teaching faculty in India's schools, colleges, and universities - qualification, training, pupil-teacher ratio, and more.

India School Drop Out Rate: 6-11 Years Old: Girl data was reported at 18.300 % in 2013. This records a decrease from the previous number of 19.400 % for 2012. India School Drop Out Rate: 6-11 Years Old: Girl data is updated yearly, averaging 36.810 % from Sep 1960 (Median) to 2013, with 24 observations. The data reached an all-time high of 70.900 % in 1970 and a record low of 18.300 % in 2013. India School Drop Out Rate: 6-11 Years Old: Girl data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDA002: School Drop Out Rate: 6-11 Years Old.

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The Unified District Information System for Education (UDISE) is an initiative by the Government of India to collate and manage data related to the education sector at the school level. Launched under the aegis of the Ministry of Education, UDISE serves as a comprehensive database, capturing detailed information about school infrastructure, teacher qualifications, student enrollment, and other crucial educational parameters. The data is collected annually from pre-primary to higher secondary school levels across all Indian states and union territories. UDISE plays a pivotal role in assisting policymakers, educators, and researchers in understanding the state of education in India, enabling informed decision-making and the formulation of targeted interventions. The platform's insights and analytics contribute significantly to enhancing the quality and accessibility of education in the country.

India Number of Students: Secondary School: Girl data was reported at 30,327,000.000 Person in 2015. This records an increase from the previous number of 29,240,000.000 Person for 2014. India Number of Students: Secondary School: Girl data is updated yearly, averaging 19,816,510.500 Person from Sep 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 30,327,000.000 Person in 2015 and a record low of 11,200,000.000 Person in 2000. India Number of Students: Secondary School: Girl data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDC004: Number of Students: Secondary School.

The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2020-2021 school year, from August 2020 – June 2021.

These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

School learning modality types are defined as follows:

• In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
• Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
• Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.

Data Information

• School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
• You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
• The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
  • Public school district that is NOT a component of a supervisory union
  • Public school district that is a component of a supervisory union
  • Independent charter district
• “BI” in the state column refers to school districts funded by the Bureau of Indian Education.

Technical Notes

• Data from September 1, 2020 to June 25, 2021 correspond to the 2020-2021 school year. During this timeframe, all four sources of data were available. Inferred modalities with a probability below 0.75 were deemed inconclusive and were omitted.
• Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable.

Sources

1. K-12 School Opening Tracker. Burbio 2021; https://cai.burbio.com/school-opening-tracker/. June 2021.
2. COVID-19 IMPACT: School District Operational Status. MCH Strategic Data: 2021; https://www.mchdata.com/covid19/schoolclosings. (30 June 2021, date last accessed).
3. Return to Learn Tracker. American Enterprise Institute 2021; https://www.returntolearntracker.net/. (30 June 2021, date last accessed).
4. Colorado School District Reopening Plans. Colorado Department of Education 2021; https://docs.google.com/spreadsheets/d/e/2PACX-1vQipdjO8QWhilhhJ4bX0FBebnHEzK1G3LEDQbE_S-xRvs2t0oHNm--acHwMRFmL9uKw4cXcOUqy1V66/pubhtml. (30 June 2021, date last accessed).
5. Connecticut Report Cards. Connecticut State Department of Education 2021; http://edsight.ct.gov/SASPortal/main.do. (30 June 2021, date last accessed).
6. COVID Planning Tool. Hawaii Department of Education 2021; https://public.tableau.com/views/COVIDPlanningTool-Embed700px/SimpleDashboard. (30 June 2021, date last accessed).
7. Idaho School District Status. Idaho State Department of Education 2021; https://ioem.maps.arcgis.com/apps/opsdashboard/index.html#/379299e56f234fa89cff3c8c6acf3ac8. (30 June 2021, date last accessed).
8. How Illinois Districts Are Providing Instruction: Virtual, In-Person, and Blended Learning. Illinois State Board of Education 2021; https://isp.maps.arcgis.com/apps/opsdashboard/index.html#/fceeacb37da04de4b237ed941dd7d5c4. (30 June 2021, date last accessed).
9. Digital Learning Model Schools. Louisiana Department of Education 2021; https://analytics.la.gov/t/DOE/views/DigitalLearningModel/SchoolLevelModeofLearning. (30 June 2021, date last accessed).
10. Safe Learning Model Dashboard. Minnesota Department of Education 2021; https://analytics.education.state.mn.us/t/MDEPublic/views/MN_Safe_Learning_Model/Dashboard. (30 June 2021, date last accessed).
11. Coronavirus (COVID-19) information. Missouri Department of Elementary & Secondary Education 2021; https://dese.mo.gov/communications/coronavirus-covid-19-information. (30 June 2021, date last accessed).
12. North Carolina School Dashboard. North Carolina Department of Public Instruction 2021. https://docs.google.com/spreadsheets/d/1We8gDpa4Do5NR83Nf8niGE_YxzLDf-KZh-tVWifStxE/edit#gid=0. (30 June 2021, date last accessed).
13. Ring the Bell: Return to In-Person Learning. New Mexico Public Education Department 2021; https://webnew.ped.state.nm.us/reentry-district-and-school-guidance/. (30 June 2021, date last accessed).
14. Reset and Restart Education. Ohio Department of Education 2021; http://education.ohio.gov/Topics/Reset-and-Restart. (30 June 2021, date last accessed).
15. 2020-21 School Status. Oregon Department of Education 2021; https://www.oregon.gov/ode/students-and-family/healthsafety/Pages/2020-21-School-Status.aspx. (30 June 2021, date last accessed).
16. Current Operational Status. South Carolina Department of Education 2021; https://ed.sc.gov/districts-schools/schools/district-and-school-closures/operational-status/#blk2102. (30 June 2021, date last accessed).
17. Tennessee Department of Education COVID-19 District Information. Tennessee Department of Education 2021; https://districtinformation.tnedu.gov/covid-information/search/-1. (30 June 2021, date last accessed).
18. State Snapshot: Virginia School Operational Status. Virginia Department of Education 2021; https://www.doe.virginia.gov/support/health_medical/office/reopen-status.shtml. (30 June 2021, date last accessed).
19. Current Reopening Status of Vermont's K-12 Public Schools. Vermont Department of Financial Regulation 2021; https://dfr.vermont.gov/about-us/covid-19/school-reopening#public-school. (30 June 2021, date last accessed).
20. School Reopening Data. Washington Office of Superintendent of Public Instruction 2021; https://www.k12.wa.us/about-ospi/press-releases/novel-coronavirus-covid-19-guidance-resources/school-reopening-data. (30 June 2021, date last accessed).
21. Public school districts. National Center for Education Statistics 2021; https://nces.ed.gov/ccd/districtsearch/. (30 June 2021, date last accessed).

This web feature service contains location and facility identification information from EPA's Facility Registry Service (FRS) for the subset of facilities that link to the Bureau of Indian Affairs (BIA) school data on Indian land. The BIA is responsible for the administration and management of 55.7 million acres of land held in trust by the United States for American Indians, Indian Tribes, and Alaska natives and provides education services to approximately 48,000 Indian students. FRS identifies and geospatially locates facilities, sites or places subject to environmental regulations or of environmental interest. Using vigorous verification and data management procedures, FRS integrates facility data from EPA's national program systems, other federal agencies, and State and tribal master facility records and provides EPA with a centrally managed, single source of comprehensive and authoritative information on facilities. This data set contains the subset of FRS integrated facilities that link to the BIA school facilities once the BIA Indian School dataset has been integrated into the FRS database. Additional information on FRS is available at the EPA website https://www.epa.gov/enviro/facility-registry-service-frs.

Geographic coverage

The survey covered the whole of the Indian Union except (i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir (for central sample), (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Outline of sample design: A stratified multi-stage design has been adopted for the 64th round survey. The first stage units (FSU) was the 2001 census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. However, for the newly declared towns and out growths (OGs) in census 2001 for which UFS had not yet been done, each individual town/ OG was considered as an FSU. The ultimate stage units (USU) was be households in both the sectors. In case of large FSUs i.e. villages/ towns/ blocks requiring hamlet-group (hg)/ sub-block (sb) formation, one intermediate stage was the selection of two hgs/ sbs from each FSU.

Sampling Frame for First Stage Units: For the rural sector, the list of 2001 census villages (Panchayat wards for Kerala) constitute the sampling frame. For the urban sector, the list of latest available Urban Frame Survey (UFS) blocks and for non-UFS towns list of such towns/ OGs was considered as the sampling frame.

Stratification: Within each district of a State/ UT, generally speaking, two basic strata were formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising of all the urban areas of the district. However, within the urban areas of a district, if there were one or more towns with population 10 lakhs or more as per population census 2001 in a district, each of them formed a separate basic stratum and the remaining urban areas of the district was considered as another basic stratum. For a few districts, particularly in case of Tamil Nadu, if total number of towns in the district for which UFS was not yet done exceeds certain number, all such towns taken together formed another basic stratum. Otherwise, they were merged with the UFS towns for stratification.

Sub-stratification in the Rural sector: If "r" be the sample size allocated for a rural stratum, the number of sub-strata formed is "r/4?. The villages within a district as per frame were first arranged in ascending order of population. Then sub-strata 1 to "r/4" were demarcated in such a way that each sub-stratum comprised a group of villages of the arranged frame and have more or less equal population.

Sub-stratification in the Urban sector: If "u" be the sample size for a urban stratum, "u/4" number of sub-strata were formed. The towns within a district, except those with population 10 lakhs or more and also the non-UFS towns, were first arranged in ascending order of population. Next, UFS blocks of each town were arranged by IV unit no. × block no. in ascending order. From this arranged frame of UFS blocks of all the towns, "u/4? number of sub-strata were formed in such a way that each sub-stratum had more or less equal number of FSUs. For towns with population 10 lakhs or more, the urban blocks were first arranged by IV unit no. × block no. in ascending order. Then "u/4? number of sub-strata were formed in such a way that each sub-stratum had more or less equal number of blocks. All non-UFS towns taken together within the district formed one sub-stratum.

Total sample size (FSUs): 12688 FSUs for central sample and 13624 FSUs for state sample have been allocated at all-India level.

Allocation of total sample to States and UTs: The total number of sample FSUs is allocated to the States and UTs in proportion to population as per census 2001 subject to a minimum sample allocation to each State/ UT. While doing so, the resource availability in terms of number of field investigators had been kept in view.

Allocation of State/ UT level sample to rural and urban sectors: State/ UT level sample was allocated between two sectors in proportion to population as per census 2001 with 1.5 weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. A minimum of 8 FSUs was allocated to each state/ UT separately for rural and urban areas. Further the State level allocation for both rural and urban have been adjusted marginally in a few cases to ensure that each stratum gets a minimum allocation of 4 FSUs.

==========

More information on the sampling methodology is available in the document " Instructions to Field Staff - Volume-I"

Mode of data collection

Face-to-face [f2f]

Research instrument

This schedule is designed to collect the information on (a) participation in education of persons aged 5-29 years in the education system, (b) private expenditure incurred on education and (c) examining the extent of educational wastage and its causes in terms of dropout and discontinuance. The coverage of the ‘education’ includes:

I. School education including those under Education Guarantee Scheme (EGS) commencing from class I to X or XII, as the case may be, irrespective of the recognition status of the educational institution,

II. Higher secondary / Pre-university education leading to certificate/ diploma/degree etc. It also includes enrolment in private unrecognised institutions, which have regular classes and following the syllabus and pattern of the education as in recognised schools or colleges and which sponsor students for public examinations as private or external candidates,

III. General University education, whether full time or part time, leading to certificate/ diploma/ degree etc. The Universities not recognised by University Grant Commission will not be covered,

IV. Correspondence courses conducted by Universities, Deemed Universities or Institutions, authorised by competent authorities for awarding regular degrees or diplomas or certificates,

V. Higher secondary / Pre-university / Under-graduate/ Post-graduate / Professional/ Technical education leading to certificate/diploma/degree etc. conducted by recognised open university/schools,

VI. Technical or Professional courses, leading to degree/diploma/certificates, conducted by Universities, Deemed Universities or institutes like, National Institute of Fashion Technology, National School of Drama, Satyajit Ray Film and Television Institute, Film and Television Institute of India, Lok Nayak Jayaprakash Narayan National Institute of Criminology and Forensic Science, etc. or Institutions, authorised by competent authorities like All India Council of Technical Education (AICTE), Medical Council of India (MCI) etc.,

VII. Professional courses conducted by Institutes like The Institute of Chartered Accountants of India, The Institute of Cost and Works Accountants of India, The Institute of Company Secretaries of India, Actuarial Society of India, etc.,

VIII. All types of vocational courses of duration three months or more, conducted by Institutions like Industrial Training Institute (ITI), National Vocational Training Institute, Regional Vocational Training Institutes,etc., authorised by competent authorities

IX. All the courses at primary level and above, whether recognised or not, conducted by recognised educational institutions and which are not covered under abovementioned categories.

The following courses shall be specifically excluded: - Art, music and similar type of courses conducted by individuals in their houses or unrecognised/ unaffiliated institutions, - Classes taken by Private tutors, - Education in nursery/Kindergartens/Preparatory levels except for their enrolment statuses and dropout / discontinuance statuses. - The non-formal system of education being implemented through various programs by government or other agencies except for their enrolment statuses and dropout / discontinuance statuses.

What is new?

This Schedule is broadly similar to that used in 52nd Round (July 1994 – June 1995) with the following new additions or modifications. - The NSS 52nd round covered only general and technical education whereas 64th round will also cover vocational education, - In the technical/professional category specific information on courses like MBA, Chartered Accountancy etc. will be collected, - Survey will cover persons in the age group 5-29 years as compared to 5-24 years in 52nd round, - Rather than collecting information on distance from nearest primary school information will be collected on the distances from nearest school having primary, upper primary and secondary level classes, - Information on Household Consumption Expenditure (Rs.) during last 30 days will be collected with the help of five questions in block 3 relating to household characteristics in place of detailed worksheet canvassed in 52nd round, - The information about the expenditure on education will be collected for at most two courses rather than one course as was done in 52nd round, - The block for collecting the details about the expenditure on dependents studying away from home in 52nd round has been dropped and two questions i.e. number of dependents studying away from home and the amount sent to them have been included in the block 3 on household characteristics, - A new question on “Changed educational institution during last one year?” has been introduced, - To get an idea about repetition, information about the class/grade/year in the current academic session and in the previous academic session will be collected, - For class-X and below, questions on grade completed before dropping / discontinuance and the type of school last