On average across Russia, the preschool education enrollment rate reached almost 75 percent in 2023. In other words, the number of children enrolled in daycare institutions and kindergartens occupied three quarters of all children aged one to six years. Among the country's regions, the highest preschool education enrollment was recorded in the Northwestern Federal District at over 90 percent. The lowest figure was observed in the North Caucasian Federal District, where over half of children one to six years old were enrolled in preschool education.

This indicator provides information about the percentage of children ages 3-4 years enrolled in preschool based on caregiver report.Access to early childhood education (i.e., education before the age of 5 years) is associated with numerous health benefits later in life. For instance, young children who are enrolled in high quality preschool programs are more likely to graduate from high school, have higher paying jobs, own homes, and have improved cognitive function than children who are not enrolled. All these additional advantages can increase average life expectancy.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show counts and percentages for school enrollment by education level by Neighborhood Statistical Areas in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

Pop3P_e

# Population ages 3 and over, 2017

Pop3P_m

# Population ages 3 and over, 2017 (MOE)

InSchool_e

# Population 3 years and over enrolled in school, 2017

InSchool_m

# Population 3 years and over enrolled in school, 2017 (MOE)

InPreSchool_e

# Enrolled in nursery school, preschool, 2017

InPreSchool_m

# Enrolled in nursery school, preschool, 2017 (MOE)

pInPreSchool_e

% Enrolled in nursery school, preschool, 2017

pInPreSchool_m

% Enrolled in nursery school, preschool, 2017 (MOE)

InKindergarten_e

# Enrolled in kindergarten, 2017

InKindergarten_m

# Enrolled in kindergarten, 2017 (MOE)

pInKindergarten_e

% Enrolled in kindergarten, 2017

pInKindergarten_m

% Enrolled in kindergarten, 2017 (MOE)

InElementary_e

# Enrolled in elementary school (grades 1-8), 2017

InElementary_m

# Enrolled in elementary school (grades 1-8), 2017 (MOE)

pInElementary_e

% Enrolled in elementary school (grades 1-8), 2017

pInElementary_m

% Enrolled in elementary school (grades 1-8), 2017 (MOE)

InHS_e

# Enrolled in high school (grades 9-12), 2017

InHS_m

# Enrolled in high school (grades 9-12), 2017 (MOE)

pInHS_e

% Enrolled in high school (grades 9-12), 2017

pInHS_m

% Enrolled in high school (grades 9-12), 2017 (MOE)

InCollegeGradSch_e

# Enrolled in college or graduate school, 2017

InCollegeGradSch_m

# Enrolled in college or graduate school, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Abstract

This study aimed to (a) investigate the impacts of offering an additional year of pre-primary education in Bangladesh on child development outcomes (cognitive and social-emotional) and (b) examine the benefits relative to the costs of the program. The study also examined the mechanisms through which the Early Year Pre-School Program affected the outcomes of interest (e.g., children's school readiness) and the operational and community conditions for program implementation. This study provides evidence for the government of Bangladesh on how and how much the additional year of preschool benefits children, and at what cost. In addition to informing future policy in Bangladesh, this information may be useful for other countries considering similar programming. This survey provides endline findings for the evaluation and incorporates information from the baseline (2017) and midline (2018) surveys.

Geographic coverage

District of Meherpur

Analysis unit

Individuals, schools, and communities

Kind of data

Sample survey data [ssd]

Sampling procedure

We conducted a randomized controlled trial (RCT) of the EYPP to determine its impacts on children's learning and development. In 2016, we randomly assigned 100 schools in the Meherpur district of Bangladesh to either a treatment group receiving the EYPP (n = 50) or a no-program control group (n = 50). In October 2017, we conducted a census of the area around all 100 schools to identify children who lived within a 15-minute walk of the school and were in the target age range-that is, children expected to enroll in a typical government pre-primary in 2019 and enter Grade 1 in 2020. In the 50 treatment school catchment areas, children selected for the study were invited to participate in the EYPP at their local school during the 2018 school year and then would go on to government pre-primary as usual in 2019. In the 50 control school catchment areas, children selected for the study would be eligible to enroll in the government pre-primary as usual in 2019 but did not have the EYPP available to them the year before.

Sampling of Children: The target sample for our study included all children in the census areas born from January 1, 2013 - December 31, 2013 (because on-time enrollment in government pre-primary school for these children would be in January 2019). In most cases (exact figure unknown but in a substantial majority), children's dates of birth were verified with the Extended Program of Immunization (EPI) card or a birth certificate. If these documents were unavailable (even after parents were encouraged to search), enumerators recorded what the parent reported as the child's date of birth. We identified a total of 1,986 children born in 2013. We did not exclude any age-eligible children based on any other criteria (for example, children with disabilities were included in our sample pool).

AIR agreed with the World Bank that we would sample an average of 20 children in each of the 100 study communities. Many communities had fewer than 20 eligible children. Because EYPP centers will typically enroll up to 25 children, for both treatment and control communities with 25 or fewer children, we included all eligible children in the study (with parental consent). In the 20 communities (14 treatment and 6 control) with over 25 children in the target age range, we drew a random subsample of 25 for inclusion in this sample.

For this longitudinal study, we collected baseline, midline, and endline data. The midline and endline samples included schools, children, and families enrolled in the study at baseline; we did not add any new participants after baseline. Of the 1,856 enrolled children and families, 1,801 (97%) participated at all three timepoints.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

We administered the family questionnaire at baseline, midline, and endline. Its purpose was to gather information on the characteristics of the study children and their home environments and, at midline and endline, to determine whether and how the intervention affected the home learning environment. Nearly all items on this questionnaire were already used widely in Bangladesh as part of national household surveys. To administer this tool, enumerators read questions and response options aloud to respondents (parents or guardians of the study children). For some questions about family background, we asked the question only at baseline because the answers were unlikely to change across time and were unrelated to the intervention.

At each timepoint, we measured children's school readiness with the IDELA, which has been used widely in Bangladesh. A trained enumerator administered the assessment to children one on one. At endline, we also added subtasks from the Early Grade Reading Assessment (EGRA) and the Early Grade Mathematics Assessment (EGMA) as used in Bangladesh. Because the EGRA and EGMA were designed for children in Grade 1 and higher, we did not expect the study children to perform well, but wanted to ensure that we were prepared should we have ceiling issues with children's performance on the IDELA.

The endline parent questionnaire can be found under the 'Documentation' tab. To obtain a free copy of the IDELA questionnaire please go to https://idela-network.org/the-idela-tool/ and register.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: - Office editing and coding - During data entry - Structure checking and completeness - Secondary editing - Structural checking of STATA data files

Response rate

97% (n = 1801 children)

Vietnam VN: School Enrollment: Preprimary: Male: % Gross data was reported at 88.396 % in 2016. This records an increase from the previous number of 83.973 % for 2015. Vietnam VN: School Enrollment: Preprimary: Male: % Gross data is updated yearly, averaging 50.103 % from Dec 1977 (Median) to 2016, with 27 observations. The data reached an all-time high of 88.396 % in 2016 and a record low of 20.721 % in 1977. Vietnam VN: School Enrollment: Preprimary: Male: % Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vietnam – Table VN.World Bank.WDI: Education Statistics. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

This layer shows public vs. private school enrollment by sex by grade group. This 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. Any schools that receives public funding are considered public, including continuation schools and some charter & online schools. This layer is symbolized to show the percentage of students in kindergarten through 12th grade who are enrolled in a private school. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B14002 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National 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 2023 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.

"Enrollment counts are based on the October 31 Audited Register for the 2017-18 to 2019-20 school years. To account for the delay in the start of the school year, enrollment counts are based on the November 13 Audited Register for 2020-21 and the November 12 Audited Register for 2021-22. * Please note that October 31 (and November 12-13) enrollment is not audited for charter schools or Pre-K Early Education Centers (NYCEECs). Charter schools are required to submit enrollment as of BEDS Day, the first Wednesday in October, to the New York State Department of Education." Enrollment counts in the Demographic Snapshot will likely exceed operational enrollment counts due to the fact that long-term absence (LTA) students are excluded for funding purposes. Data on students with disabilities, English Language Learners, students' povery status, and students' Economic Need Value are as of the June 30 for each school year except in 2021-22. Data on SWDs, ELLs, Poverty, and ENI in the 2021-22 school year are as of March 7, 2022. 3-K and Pre-K enrollment totals include students in both full-day and half-day programs. Four-year-old students enrolled in Family Childcare Centers are categorized as 3K students for the purposes of this report. All schools listed are as of the 2021-22 school year. Schools closed before 2021-22 are not included in the school level tab but are included in the data for citywide, borough, and district. Programs and Pre-K NYC Early Education Centers (NYCEECs) are not included on the school-level tab. Due to missing demographic information in rare cases at the time of the enrollment snapshot, demographic categories do not always add up to citywide totals. Students with disabilities are defined as any child receiving an Individualized Education Program (IEP) as of the end of the school year (or March 7 for 2021-22). NYC DOE "Poverty" counts are based on the number of students with families who have qualified for free or reduced price lunch, or are eligible for Human Resources Administration (HRA) benefits. In previous years, the poverty indicator also included students enrolled in a Universal Meal School (USM), where all students automatically qualified, with the exception of middle schools, D75 schools and Pre-K centers. In 2017-18, all students in NYC schools became eligible for free lunch. In order to better reflect free and reduced price lunch status, the poverty indicator does not include student USM status, and retroactively applies this rule to previous years. "The school’s Economic Need Index is the average of its students’ Economic Need Values. The Economic Need Index (ENI) estimates the percentage of students facing economic hardship. The 2014-15 school year is the first year we provide ENI estimates. The metric is calculated as follows: * The student’s Economic Need Value is 1.0 if: o The student is eligible for public assistance from the NYC Human Resources Administration (HRA); o The student lived in temporary housing in the past four years; or o The student is in high school, has a home language other than English, and entered the NYC DOE for the first time within the last four years. * Otherwise, the student’s Economic Need Value is based on the percentage of families (with school-age children) in the student’s census tract whose income is below the poverty level, as estimated by the American Community Survey 5-Year estimate (2020 ACS estimates were used in calculations for 2021-22 ENI). The student’s Economic Need Value equals this percentage divided by 100. Due to differences in the timing of when student demographic, address and census data were pulled, ENI values may vary, slightly, from the ENI values reported in the School Quality Reports. In previous years, student census tract data was based on students’ addresses at the time of ENI calculation. Beginning in 2018-19, census tract data is based on students’ addresses as of the Audited Register date of the g

Abstract

The objectives of the evaluated intervention are to expand access to quality Early Childhood Education (ECE) for 3-5-year-olds (through the construction of facilities, provision of materials, training of staff), as well as to build the demand for Early Childhood Care and Development ECCD services among families from disadvantaged backgrounds.

The study aims to find out whether the provision of simple community preschools increases enrollment and retention rates in ECCD services. Particularly with an eye towards primary school readiness, effects on the cognitive and socio-emotional development of young children are measured. Using longitudinal data, it is tested whether complementary demand-side interventions increased enrollment, especially among the poorest households, and if demand-side interventions have an effect on the impact of the intervention.

Geographic coverage

Rural villages in 13 provinces:

• Kampong Chhnang

• Kampong Speu

• Kampot

• Kandal

• Koh Kong

• Kratie

• Mondulkiri

• Preah Sihanuk

• Prey Veng

• Ratanakiri

• Steung Treng

• Svay Rieng

• Takeo

Analysis unit

The ECCD describes children and caregivers, households and villages.

Universe

Households with at least one child of age 2 - 4 at baseline.

Kind of data

Sample survey data [ssd]

Sampling procedure

The goal was to sample 26 eligible households in each of the 305 assigned villages. Eligibility criterion: at least one child of age 2-4 (at baseline) lives in the household.

Because the household listings held by local authorities are unreliable and often outdated, the data collection firm conducted a complete listing of all households in each village. Before interviewing households, the staff conducted a village mapping exercise. With the cooperation of the local authorities (village chief), the field staff have drawn a map of the village, showing all roads/intersections and households within the village and on its boundaries. Using this list, they managed to calculate the sampling interval (SI).

Field staff then followed a modified version of the EPI-walk methodology to select households: starting from a randomly selected village intersection, the interviewer:

1) Implemented the sampling interval to select the first household

2) Assessed the household eligibility (household includes at least one child aged 2 - 4 years) and

3) Conducted the household and child surveys.

If the household is not eligible for this study, the interviewer will implement a systematic sampling strategy until he/she finds an eligible household. In other words, a sampling interval of “1” was implemented until an eligible household was selected. Whenever an interview was completed with an eligible household, the interviewer implemented the previously calculated SI to reach the next household.

A maximum of 26 eligible households was selected in each targeted village. Interviewers kept following the sampling methodology described here previously until they reached the sample target (26 eligible households) or until all households were approached. If all households in the village were approached but less than 26 of them were found to be eligible, then the field team moved to the next target village.

Sampling deviation

In some village, less than 26 eligible households could be found. The goal of 26 households per village was reached in 67% of the villages. The average number of sampled households per village is 23.1.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Structured household interviews included the:

• Household and caregiver instrument

• Child testing

• Caregiver and child anthropometric data measurement

• Village chief interviews: During the administration of the household survey, the interview with the village chief was conducted.

Cleaning operations

Data management procedures for this study included:

• Testing of all instruments on Survey CTO v2.10. This included checking all of the items validity and logic patterns (skips, relevance rules, etc.). This stage also ensured that items used as key identifiers (for future merging of datasets) are clearly defined.

• Setting up on-field and in-office QC procedures (re-interviews, comparison of key items in different datasets, etc.). Implementing in-office procedures in order to identify and correct data inconsistencies and errors. Error reports were produced to this end.

• Setting up daily upload/download procedures and making sure that data is well received every day. Field work progress reports were produced to this end.

• Cleaning/labeling final dataset

Data validation and quality control took place during every step of the survey process, with mechanisms built into the survey design itself, data collection and data reporting processes. Thus, a range of tools was built into the data entry system to enable validation of data both at point of entry and during reporting stage. Unique identifiers were developed for each questionnaire in advance, to assist with datasets merging and longitudinal tracking.

Quality control procedures: During data collection, frequent checks of the data entry system were undertaken, as well as ongoing comparison of re-interviews, to ensure that data is accurately recorded by interviewers and the system is functioning as designed. After completion of data collection, the complete database was “cleaned” to check for inconsistent or missing values, incorrect skips, and validation errors. In addition to the in-system validation rules mentioned here before, the following QC procedures were implemented:

• Interview Summary: A summary of critically important items was displayed as in-system feature at the end of the interview. These items were mostly used for the dataset merging and needed to be exactly identical between the household/caregiver and the child systems. Interviewers and Field Editors made sure that the same merging information was recorded for both systems.

• Interview observations: The Field Editor or the Field Supervisor randomly selected an Interviewer in their team and joined the interview at any random point. They observed whether the Interviewer actually implemented the interview techniques agreed upon during the training: respect of the question exact and accurate wording, following conversational or standardized techniques on the relevant items (defining or not difficult notions/words to help the respondent answering the question), accurately recording respondent answers into the tablet system (Survey CTO v2.10), following ethical policies, etc.

• Re-interviews: The Field Editor randomly selected a household, from those with who an interview was completed. He/she then re-interviewed this household, asking once again a selection of 15-20 items (chosen because of their critical importance: items used for merging, items used to assess household eligibility, filter questions, etc.) for a maximum of five minutes. Stored in a separate dataset, this data was then compared against the actual initial interview data, back in office, by the Data QC Supervisor. Discrepancies were inquired into and the Data QC Supervisor called the respondent back to obtain the final correct answer. Observations and re-interviews concerned a minimum of 20% of the cases (around 5 households per village).

• Time stamps: In-system section time stamps were analyzed. They allowed the Data QC Supervisor and the Data Manager to identify potential issues (if a section/interview took seemingly significantly more time than the average) and to help assessing fieldwork progress and forecast fieldwork completion.

At completion of the data collection, (potential) data entry of paper forms and reconciliation processes, a draft database was produced, accompanied by a codebook and will be cleaned and labelled using Stata 13.1 do-files. The database was finalized based on feedback from the WB research team.

Qualitative data (as necessary: Concurrent with the quantitative data processing, any qualitative data (open-ended data from household and individual interviews) was translated into English by experienced Khmer-English translators.

Response rate

Within the 305 villages targeted for the baseline study, a total of 7,058 households were selected using the EPI walk and interviews attempted, containing a total of 7,642 children within the target age range (2 - 4 years old). Of these interviews, a total of 6,972 interviews were successfully completed (a completion rate of 98.8%). An additional 78 interviews were reported as “complete with some missing values” by the interviewers (because the respondent did not know how to answer a given question, or refused to answer, etc.).

Ecuador Consumer Price Index (CPI): Education: PP: EE: Preschool Education Enrollment data was reported at 161.573 2004=100 in Dec 2014. This stayed constant from the previous number of 161.573 2004=100 for Nov 2014. Ecuador Consumer Price Index (CPI): Education: PP: EE: Preschool Education Enrollment data is updated monthly, averaging 133.903 2004=100 from Jan 2005 (Median) to Dec 2014, with 120 observations. The data reached an all-time high of 161.816 2004=100 in Sep 2014 and a record low of 101.449 2004=100 in Mar 2005. Ecuador Consumer Price Index (CPI): Education: PP: EE: Preschool Education Enrollment data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Ecuador – Table EC.I016: Consumer Price Index: 2004=100.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from American Community Survey 5-year estimates for 2011-2015 to show school enrollment by level of school, by state Senate district for the state of Georgia.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2011-2015). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here.

Attributes:

DISTRICT = GA Senate District

POPULATION = District Population (2010 Census)

Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)

last_edited_date = Last date feature was edited by ARC

profile_url = Web address of ARC district profile

Pop_3yrsOlder_Enrolld_in_School = #, Population 3 years and over enrolled in school

Nursery_school_preschool = #, Enrolled in nursery school, preschool

Pct_Nursery_Preschool = %, Enrolled in nursery school, preschool

Kindergarten = #, Enrolled in kindergarten

Percent_Kindergarten = %, Enrolled in kindergarten

Elementary_school_grades_1_8 = #, Enrolled in elementary school (grades 1-8)

Percent_ElemSchool_grades_1_8 = %, Enrolled in elementary school (grades 1-8)

High_school_grades_9_12 = #, Enrolled in high school (grades 9-12)

Percent_HS_grades_9_12 = %, Enrolled in high school (grades 9-12)

College_or_graduate_school = #, Enrolled in college or graduate school

Percent_College_or_grad_school = %, Enrolled in college or graduate school

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2011-2015

Credits

U.S. Census Bureau, Atlanta Regional Commission

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from American Community Survey 5-year estimates for 2010-2014 to show school enrollment status, by city for the State of Georgia.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2010-2014). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here.

Attributes:

NAME = Name of city or municipality

Acres = Area in acres

Sq_Miles = Area in square miles

County20 = Within ARC 20-county region

County10 = Within ARC 10-county region

In_School = In School

In_Pre_School = In Pre-School

Pct_In_Pre_School = % In Pre-School

In_Kindergarten = In Kindergarten

Pct_In_Kindergarten = % In Kindergarten

In_Elementary = In Elementary

Pct_In_Elementary = % In Elementary

In_High_School = In High School

Pct_In_High_School = % In High School

In_College_or_Grad_School = In College or Grad School

Pct_In_College_or_Grad_School = % In College or Grad School

last_edited_date = Last date feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2010-2014

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

Ecuador Consumer Price Index (CPI): Education: PP: Enrollment of Preschool and Primary Education (EE) data was reported at 142.694 2004=100 in Dec 2014. This stayed constant from the previous number of 142.694 2004=100 for Nov 2014. Ecuador Consumer Price Index (CPI): Education: PP: Enrollment of Preschool and Primary Education (EE) data is updated monthly, averaging 121.230 2004=100 from Jan 2005 (Median) to Dec 2014, with 120 observations. The data reached an all-time high of 142.888 2004=100 in Sep 2014 and a record low of 94.212 2004=100 in Mar 2005. Ecuador Consumer Price Index (CPI): Education: PP: Enrollment of Preschool and Primary Education (EE) data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Ecuador – Table EC.I016: Consumer Price Index: 2004=100.

The Head Start Family and Child Experiences Survey (FACES) is a periodic, ongoing longitudinal study of program performance. Successive nationally representative samples of Head Start children, their families, classrooms, and programs provide descriptive information on the population of children and families served; staff qualifications, credentials, and opinions; Head Start classroom practices and quality measures; and child and family outcomes. FACES includes a battery of child assessments across multiple developmental domains (cognitive, social, emotional, and physical). FACES 2009 is the latest FACES cohort study and followed children from Head Start entry in fall 2009 through one or two years of program participation and to kindergarten.

For nearly a decade, the Office of Head Start, the Administration for Children and Families, other federal agencies, local programs, and the public have depended on FACES for valid and reliable national information on (1) the skills and abilities of Head Start children, (2) how Head Start children's skills and abilities compare with preschool children nationally, (3) Head Start children's readiness for and subsequent performance in kindergarten, and (4) the characteristics of the children's home and classroom environments. The FACES study is designed to enable researchers to answer a wide range of research questions that are crucial for aiding program managers and policymakers. Some of the questions that are central to FACES include:

1. What are the demographic characteristics of the population of children and families served by Head Start? How has the population served by Head Start changed?
2. What are the experiences of families and children in the Head Start program? How have they changed?
3. What are the cognitive and social skills of Head Start children at the beginning and end of their first year in the program? Has Head Start program performance improved over time?
4. Do the gains in cognitive and social skills that Head Start children achieve carry over into kindergarten? Do larger gains (or greater declines in problem behavior) translate into higher achievement at the end of kindergarten?
5. What are the qualifications of Head Start teachers in terms of education, experience, and credentials? Are average teacher education levels rising in Head Start?
6. What is the observed quality of Head Start classrooms as early learning environments, including the level and range of teaching and interactions, provisions for learning, emotional and instructional support, and classroom organization? How has quality changed over time? What program- and classroom-level factors are related to observed classroom quality? How is observed quality related to children's outcomes and developmental gains?

In response to recent trends and mandates, FACES 2009 expanded the information collected on families and children who speak a primary language other than English and the information collected on children who are homeless. Earlier cohorts of FACES gathered information on the languages spoken in the home and used for classroom instruction. Given the growth in the population of Hispanic/Latino preschoolers (Hernandez 2006), FACES 2009 placed additional emphasis on Dual Language Learners (DLLs). In addition, given the 2007 Head Start Act's focus on children and families who are homeless, FACES 2009 expanded coverage on the enrollment of such children, how the program ensures that they enroll in Head Start, and the special services available to such children and their families.

FACES 2009 carefully balanced the need for consistent measurement of outcomes against the need for improvements in instrumentation and techniques. In some instances, new instruments were added to obtain more comprehensive information on Head Start children. For example, the Expressive One-Word Picture Vocabulary Test was added to assess children's expressive language, which is related to later reading achievement even more so than receptive language (National Early Literacy Panel 2008). A measure of phonemic awareness from the Early Childhood Longitudinal Study-Birth Cohort (ECLS-B) preschool wave was also added to assess children's knowledge of beginning and ending sounds in words. Further, FACES 2009 included a direct assessment of executive functioning-a

ObjectiveChildren with preschool asthma suffer disproportionally more often from severe asthma exacerbations with emergency visits and hospital admissions than school children. However, there are only a few reports on characteristics, hospitalization, phenotypes and symptoms in this age cohort.Patients and methodsThis analysis of an ongoing prospective trial of Tiotropium bromide in preventing severe asthma exacerbations (the TIPP study) assessed baseline characteristics, hospitalizations and symptoms in 100 children with severe preschool asthma. Children aged 1–5 years were analyzed at study enrollment and daily symptoms were recorded by an electronic diary [Pediatric Asthma Caregiver Diary (PACD)] for the following four weeks until randomization.ResultsAt enrollment, the total number of severe asthma exacerbations, defined as three days systemic steroid use or hospitalization in the last 24 months, was mean (±SD) 5.8 ± 5.7 and the test for respiratory and asthma control in kids (TRACK) was mean 46.9 ± 19.0. Daily recording of symptoms by the PACD revealed that only 7 patients were controlled at randomization, whereas 35 were partially and 58 were uncontrolled according to GINA.ConclusionDespite protective therapy with inhaled corticosteroids (ICS), most children of this severe asthma cohort were only partially or uncontrolled according to GINA guidelines.

In 2023, the average fee for a full-day child care center program amounted to ***** Singapore dollars. The fee had been fluctuating throughout the period surveyed.

BackgroundExecutive functions is a crucial ability in the early development of preschool children. While numerous studies have found that music training has a favorable effect on children’s executive functions, there is a lack of a consistent perspective on this topic, particularly with regard to the dose–response relationship.MethodsSystematic searches were conducted of Web of Science, PubMed, Scopus, and China National Knowledge Infrastructure. A random-effects meta-analysis was used to compute standardized mean differences (SMD) and 95% confidence intervals (CI).ResultsIn all, 10 studies were included in the meta-analysis, in which children’s music training groups showed significantly improved inhibitory control (SMD = 0.38, 95% CI: 0.16–0.6), working memory (SMD = 0.35, 95% CI: 0.16–0.54), and cognitive flexibility (SMD = 0.23, 95% CI: 0.04–0.42) in comparison with control groups. Subgroup analyses indicated significant improvements relative to the control groups for inhibitory control following music training having a duration of ≥12 weeks (SMD = 0.51, 95% CI: 0.22–0.8), occurring ≥3 times per week (SMD = 0.48, 95% CI: 0.2–0.75), and lasting 20–30 min per session (SMD = 0.42, 95% CI: 0.2–0.63). Significant improvements were seen for working memory following music training having a duration of ≥12 weeks (SMD = 0.42, 95% CI: 0.18–0.65), occurring 40 min per session (SMD = 0.74, 95% CI: 0.22–1.26).ConclusionMusic training has a positive effect on inhibitory control, working memory, and cognitive flexibility in preschool children aged 3–6 years. This effect is influenced by certain training factors, including the duration of the intervention period, frequency per week, and length of each session.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/#aboutregpage, CRD42024513482.

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.

This data shows February census enrolment figures. All enrolments are self-reported in full-time equivalent (FTE) units and include both full-time and part-time students.\r \r Data Notes:\r \r * Data is self reported by schools at the start of Term 1 each year and is sourced through the School Entitlement System. Enrolment numbers are a major component linked to government funding.\r \r * Student enrolments are generally reported in FTE units. The FTE for students studying less than 10 units, the minimum workload, is determined by the formula: 0.1 x the number of units studied and represented as a proportion of the full-time enrolment of 1.0 FTE.\r \r * Students in Years 11 or 12 can enrol to study as a part-time student or a full-time student. For the annual census, a part-time student is enrolled in less than 10 units, regardless of where those units are studied. All Kindergarten to Year 10 students are considered to be full-time. Pre-school students can be enrolled on a part or full time basis.\r \r * All special education support class and Intensive English class enrolments are full-time, whilst distance education enrolments can be part-time.\r \r * Principals must ensure that each student is only counted once. For example, support class enrolments cannot be counted under mainstream as well.\r \r * A full-time Year 11 student studies 12 or more school delivered units, or a combined total of 12 or more school delivered units and non-school delivered VET units.\r \r * A full-time Year 12 student studies 10 or more school delivered units, or a combined total of 10 or more school delivered units and non-school delivered VET units.\r \r * Operational directorates given with school names are the current 2023 directorates. Due to changes in directorates over time this may mean enrolments by operational directorate is not comparable over time.\r \r Data Source:\r \r * Online Management of School Enrolment and Entitlement system (OMSEE).

We present the analysis dataset, script, as well as the data collection forms from an evaluation of an 11-week, remote early learning program (RELP) delivered via WhatsApp calls and messages to families in areas of Lebanon with little or no access to ECE. The program was created in response to COVID-19 with a focus on Syrian refugee and vulnerable Lebanese families and tested in hard-to-access areas in order to capture impacts on those most likely to need remote ECE post-pandemic. It is shorter than typical school programming because in under-resourced, conflict-affected, and displaced settings it is not always possible to find the political will and resources, or to maintain families, for longer programming (see Bonilla et al., 2019). Thus, in seeking to provide ECE in such settings, it is critical that we understand the impact of programs that are shorter, cheaper, and potentially more scalable than full-year, in-person options. In this evaluation, we examine the impacts of both i) RELP alone and ii) RELP provided alongside a remote parenting support program (RPSP). Impacts presented are in comparison to a wait list control group who received RELP immediately following endline data collection. Analyses examine experimental impacts on child development, parenting, and caregiver well-being. The files provided include: 1) The analysis dataset titled "RELP_clean_deidentified". This dataset includes all outcome variables as well as covariates. Use "Models_dofile" to replicate the analyses. 2) The STATA .do file used to run the analyses, titled "Models_dofile" 3) The CAPI xls forms that were used to collect baseline and endline data for both the IDELA measure of child development as well as the caregiver surveys (baseline and endline) 4) The STATA .do file used to clean the data, run psychometrics and obtain outcome scores, and run the multiple imputation. This file is called "RELP_raw_to_analytic" and it is meant to provide transparency around data transformations that took place starting with the raw data and ending with the analysis dataset "RELP_clean_deidentified". 5) A pdf copy of the published pre-registration document. The study and analysis plan were pre-registered in REES (ID:13920).

BackgroundMore than a third of the world's children are infected with intestinal nematodes. Current control approaches emphasise treatment of school age children, and there is a lack of information on the effects of deworming preschool children.MethodologyWe studied the effects on the heights and weights of 3,935 children, initially 1 to 5 years of age, of five rounds of anthelmintic treatment (400 mg albendazole) administered every 6 months over 2 years. The children lived in 50 areas, each defined by precise government boundaries as urban slums, in Lucknow, North India. All children were offered vitamin A every 6 months, and children in 25 randomly assigned slum areas also received 6-monthly albendazole. Treatments were delivered by the State Integrated Child Development Scheme (ICDS), and height and weight were monitored at baseline and every 6 months for 24 months (trial registration number NCT00396500). p Value calculations are based only on the 50 area-specific mean values, as randomization was by area.FindingsThe ICDS infrastructure proved able to deliver the interventions. 95% (3,712/3,912) of those alive at the end of the study had received all five interventions and had been measured during all four follow-up surveys, and 99% (3,855/3,912) were measured at the last of these surveys. At this final follow up, the albendazole-treated arm exhibited a similar height gain but a 35 (SE 5) % greater weight gain, equivalent to an extra 1 (SE 0.15) kg over 2 years (99% CI 0.6–1.4 kg, p = 10−11).ConclusionsIn such urban slums in the 1990s, five 6-monthly rounds of single dose anthelmintic treatment of malnourished, poor children initially aged 1–5 years results in substantial weight gain. The ICDS system could provide a sustainable, inexpensive approach to the delivery of anthelmintics or micronutrient supplements to such populations. As, however, we do not know the control parasite burden, these results are difficult to generalize.Trial RegistrationClinicalTrials.gov NCT00396500