Abstract

The Survey of Activities of Young People (SAYP) is a household-based survey that collects data on the activities of young people aged 7-17 years who live in South Africa. The survey covers involvement of children in market production activities, production for own final consumption, household chores as well as activities that children engaged in at school. Statistics South Africa collects SAYP information as part of the module of the Quarterly Labour Force Survey (QLFS) every four years. This information is gathered from respondents who are members of households living in dwellings that have been selected to take part in the QLFS and have children aged 7-17 years.

The aim of the first survey (SAYP 1999) was to collect information on children’s economic activities, including paid and unpaid work. All subsequent survey's (SAYP 2010, 2015 and 2019) are intended to provide updated information on the economic activities of children, including an analysis of child labour in South Africa. The specific objectives of the SAYP are to understand the extent of children’s involvement in economic activities, provide information for the formulation of an informed policy to combat child labour within the country and to monitor the South African Child Programme of Action (CLPA) and Sustainable Development Goal (SDG'S).

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The SAYP covers children aged 7-17 years resident in a household. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Survey of Activities of Young People (SAYP) comprised two stages. The first stage involved identifying households with children aged 7-17 years during the Quarterly Labour Force Survey (QLFS) data collection that took place in the third quarter of 2019 (Q3:2019). The second stage involved a follow-up interview with children in those households to establish what kind of activities they were involved in and several other aspects related to the activities they engaged in.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SAYP collected data in two phases using one questionnaire.

The first phase questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting, and heating, water source for domestic use, land ownership, tenure, and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey.

The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

Many students in secondary school find physical science and mathematics uninteresting and difficult to learn with understanding. This leaves important gaps in their education and narrows the range of careers open to them. This project will redesign key aspects of the teaching and learning of these subjects, devising a principled approach which is more effective in engaging students and guiding them towards understanding. Insights from several social scientific fields – concerned with conceptual growth, identity formation, classroom dialogue, collaborative learning, and relations between everyday and formal understanding – will guide the design of an intervention suitable for widespread use in normal school settings. This research project will generate tried-and-tested resources for training teachers and teaching students, and improve understanding of teaching and learning processes in science and mathematics. Phase 1 will involve collaboration with teacher co-researchers from several schools to devise and pilot the intervention. In Phase 2, classroom implementation by the teacher co-researchers will be analysed, and the intervention refined accordingly. Phase 3 will evaluate repeated implementation by the teacher co-researchers, alongside initial implementation by teachers from a wider range of schools, compared to the established practice of a control group of teachers from similar schools. This dataset was collected during the 2010/11 school year as part of a randomised field trial of the epiSTEMe intervention with Year 7 mathematics and science classes in English secondary schools. An open invitation was sent to schools across the Eastern region and into North London. Schools were invited to participate on the basis that teachers from schools later assigned to the intervention group would follow the associated 2-day training programme for the intervention (and receive the associated classroom materials) prior to the field trial; teachers nominated by schools later assigned to the control group would participate in the field trial using their normal teaching approach, before receiving training/materials after its completion. All schools completing the application process were assigned to an experimental group using an approach in which schools were paired according to school type and contextual value-added score, and then randomly allocated between the intervention or control group. One school withdrew prior to the start of the field trial because of staffing shortages. This yielded 25 participating schools; 12 in the intervention group, 13 in the control. Schools were requested to nominate 2 teachers of mathematics and 2 teachers of science, each with a Year 7 class; in the event, not all schools participated in both subjects or nominated 2 teachers in a subject. Schools were also recommended to choose classes in which a majority of pupils had attained level 4 in the relevant subject in end-of-KS2 assessment (a level achieved by around 80% of pupils nationally in mathematics and science). Because many schools did not timetable their Year 7 classes until close to the start of the school year, the assignment of teachers to classes had to take place vicariously within each school without any involvement of the research team. The field trial was scheduled to be undertaken by 70 teachers with a Year 7 mathematics or science class. After attrition of 10 teachers/classes (i.e. insufficient data returns made), the number of teachers/classes included in the analysis was 60: in Mathematics, 12 intervention, 16 control; in Science, 16 intervention, 16 control. In the case of 5 intervention group teachers, data was also collected in a second Year 7 class, but it did not prove necessary to fall back on this data for the analysis. A 25-item attitude questionnaire (in parallel mathematics and science versions) was administered to each participating class at the start and end of the school year. A series of pre-, immediate post- and deferred post-tests tailored to the particular topic were administered to each class when (and if) it studied each of the target topics over the course of the school year. A 20-item opinion questionnaire (in parallel versions for each topic) was also administered to each class after teaching of the target topic was complete. Background data about students was gathered from school records and/or student questionnaire. A table in the documentation shows the 75 classes that set out to participate in the study. Whatever data were collected from these classes are included in the Original data files. As explained above, 60 classes were retained for analysis. For these classes, the variables on which analysis was based are included in the Composite data files. For the 15 classes excluded from the analysis, the reason is shown in the table.

Abstract

The Survey of Activities of Young People (SAYP) is a household-based survey that collects data on the activities of young people aged 7-17 years who live in South Africa. The survey covers involvement of children in market production activities, production for own final consumption, household chores as well as activities that children engaged in at school. Statistics South Africa collects SAYP information as part of the module of the Quarterly Labour Force Survey (QLFS) every four years. This information is gathered from respondents who are members of households living in dwellings that have been selected to take part in the QLFS and have children aged 7-17 years.

The aim of the first survey (SAYP 1999) was to collect information on childrens economic activities, including paid and unpaid work. All subsequent survey's (SAYP 2010, 2015 and 2019) are intended to provide updated information on the economic activities of children, including an analysis of child labour in South Africa. The specific objectives of the SAYP are to understand the extent of childrent's involvement in economic activities, provide information for the formulation of an informed policy to combat child labour within the country and to monitor the South African Child Programme of Action (CLPA) and Sustainable Development Goal (SDG'S).

Geographic coverage

The survey has national coverage.

Analysis unit

Households and individuals

Universe

The SAYP covers children aged 7-17 years resident in a household. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data

Sampling procedure

The Survey of Activities of Young People (SAYP) comprised two stages. The first stage involved identifying households with children aged 7-17 years during the Quarterly Labour Force Survey (QLFS) data collection that took place in the third quarter of 2019 (Q3:2019). The second stage involved a follow-up interview with children in those households to establish what kind of activities they were involved in and several other aspects related to the activities they engaged in.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SAYP collected data in two phases using one questionnaire.

The first phase questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting and heating,water source for domestic use, land ownership,tenure and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey

The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

The six data sets were created for an undergraduate course at the Babes-Bolyai University, Faculty of Mathematics and Computer Science, held for second year students in the autumn semester. The course is taught both in Romanian and English with the same content and evaluation rules in both languages. The six data sets are the following: - FirstCaseStudy_RO_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the Romanian language - FirstCaseStudy_RO_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the Romanian language - SecondCaseStudy_EN_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the English language - SecondCaseStudy_EN_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the English language - ThirdCaseStudy_Both_traditional_2019-2020.txt - the concatenation of the two data sets for the 2019-2020 academic year (so all instances from FirstCaseStudy_RO_traditional_2019-2020 and SecondCaseStudy_EN_traditional_2019-2020 together) - ThirdCaseStudy_Both_online_2020-2021.txt - the concatenation of the two data sets for the 2020-2021 academic year (so all instances from FirstCaseStudy_RO_online_2020-2021 and SecondCaseStudy_EN_online_2020-2021 together)Instances from the data sets for the 2019-2020 academic year contain 12 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - the grades received by the student for 2 practical exams. If a student did not participate in a practical exam, de grade was 0. Possible values are between 0 and 10. - the number of seminar activities that the student had. Possible values are between 0 and 7. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4Instances from the data sets for the 2020-2021 academic year contain 10 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - a seminar bonus computed based on the number of seminar activities the student had during the semester, which was added to the final grade. Possible values are between 0 and 0.5. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

We provide two sets of data as a result of the Teleprism project (www.teleprism.com). The quantitative dataset includes the data collected from the student and teacher longitudinal surveys. Three different sheets are provided: one for the (master) pupil dataset, one with teacher related information and one with the information provided by the teachers for particular classes. Identifiers are provided to help navigate and match the datasets if needed. Please note that the pupil dataset is given in the long format. The qualitative data folder includes the transcriptions of students and teacher interviews. The questionnaires and interview schedules associated with these datasets are also provided for further information.This project aims to break new ground by mapping secondary students' learning outcomes and choices including dispositions and attitudes together with the teaching they are exposed to. New understandings of how mathematics pedagogy relates to learner engagement and hence outcomes will be important to both mathematics education research, and policy and practice. Capturing five years of progression (Year 7-11) in one year will pose methodological challenges, the resolution of which will contribute to the literature of combining longitudinal and cross-sectional analyses, and dealing with missing data. The main phase of the study will involve a sample of about 50 secondary schools matching the national demographic characteristics. An average of 3 to 5 classes in each year group will be surveyed with a questionnaire capturing students' mathematical dispositions and their perceptions of teaching at three time points establishing longitudinal data: at the start and towards the end of the academic year 2011/12, and at the beginning of the next academic year. A teacher survey will map teaching practices at mathematics, during the first academic year. Additionally, three contrasting case studies will be performed to provide evidence of mathematical teaching practices and narratives of students' developing dispositions and decisions throughout a year. The project employed a mixed methodology to collect the data. Student and teacher surveys with questionnaires were used for the quantitative part, whereas interviews were employed for the qualitative part. The population of interest is students (and their teachers) in secondary education (Year 7 to Year 11) in England. Recruitment of students was done at school-level upon the agreement of the Headmaster. Schools were invited to take part drawing on contacts, and a school database (more details of the sampling can be seen in the Survey Information Document). We approached over 2200 schools and ended up with 40 schools. Schools were required to take part with whole cohorts (at least 2 classes in each year group) however that was not always implemented fully. The survey design was also longitudinal with the same students surveyed at 3 times (ideally): October to December 2011, June-July 2012 and October to December 2012. Their teachers were asked to complete surveys at the first two instances only (during the first academic year of the study). The pupil dataset includes 30388 records of 18170 unique students, from over 700 classes with 280 teachers in the first year. Two case studies took place in two of these 40 schools with interviews with students and their maths teachers.

Abstract

The Survey of Activities of Young People (SAYP) is a household-based survey that collects data on the activities of young people aged 7-17 years who live in South Africa. The survey covers involvement of children in market production activities, production for own final consumption, household chores as well as activities that children engaged in at school. Statistics South Africa collects SAYP information as part of the module of the Quarterly Labour Force Survey (QLFS) every four years. This information is gathered from respondents who are members of households living in dwellings that have been selected to take part in the QLFS and have children aged 7-17 years.

The first survey (SAYP 1999) was commissioned by the Department of Labour (DoL) in 1999, where Stats SA was responsible for data collection and processing, while the analysis and report writing was the responsibility of DoL with the aim of collecting and reporting on information on childrens economic activities, including working. The second survey (SAYP 2010) was conduted in the third quarter of 2010 and is intended to provide updated information on the economic activities of children in South Africa, including their demographic and socio-economic characteristics. However, differences in methodologies followed in the two surveys make comparisons difficult. Additonally, unlike SAYP 1999, SAYP 2010 includes analysis on child labour in South Africa. The specific objectives of the SAYP are to understand the extent of childrent's involvement in economic activities, provide information for the formulation of an informed policy to combat child labour within the country and to monitor the South African Child Programme of Action (CLPA) and Sustainable Development Goal (SDG'S).

Geographic coverage

The survey has national coverage

Analysis unit

Households and individuals

Universe

The SAYP covers children aged 7-17 years resident in a household. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data

Sampling procedure

The Survey of Activities of Young People (SAYP) comprised two stages. The first stage involved identifying households with children aged 7–17 years during the Quarterly Labour Force Survey (QLFS) data collection that took place in the third quarter of 2010 (Q3:2010). The second stage involved a follow-up interview with children in those households to establish what kind of activities they were involved in and several other aspects related to the activities they engaged in.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SAYP collected data in two phases using one questionnaire.

The first phase questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting and heating,water source for domestic use, land ownership,tenure and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey

The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

The joint UNESCO-OECD-Eurostat (UOE) data collection on formal education systems provides annual data on student participation and completion of educational programmes as well as data on personnel, cost and type of resources devoted to education. The reference period for non-monetary education data is the school year and for monetary data it is the calendar year. The International Statistics of Education and Training Systems ÔÇô UNESCO-UIS/OECD/Eurostat (UOE) Questionnaire aims to provide the data required by international bodies, in addition to offering results at the national level. It is a synthesis and analysis operation that appears in the National Statistical Plan 2021-2024 (Prog. 8677) and is carried out by the S.G. of Statistics and Studies of the Ministry of Education and Vocational Training in collaboration with the Ministry of Universities and the National Institute of Statistics. Its purpose is to integrate the statistical information of the activity of the educational-training system in its different levels of education in order to meet the demands of international statistics, of the same name, requested by Eurostat, OECD and UNESCO-UIS. A selection of tables with data derived from this statistic is provided below, together with a presentation summary note:

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The purpose of the National Child Development Study: Activity Histories, 1974-2013 was to merge all data on work and non-work activities in successive sweeps into one longitudinal dataset. Data on work and non-work activities lasting one month or more have been collected in all NCDS sweeps from sweep 4 (age 23) onwards.

The focus of the questions asked at each sweep vary from: work activities engaged in since leaving school (sweep 4 aged 23); work and non-work activities engaged in since leaving school (sweep 5, aged 33); work and non-work activities engaged in since the last sweep (sweep 6, aged 42); work and non-work activities engaged in since the last sweep or aged 16 (sweep 7, aged 46) work and non-work activities engaged in since 2000, or 2004 if included in sweep 7 (sweep 8, aged 50), work and non-work activities engaged in since 2004, or 2008 if included in sweep 8 (sweep 9, aged 55). Therefore the activity histories will start from the time that the cohort member left school and continue until the interview date of the latest data sweep that each cohort member participated in.

The lengths of the activity histories vary depending on the latest sweep that a cohort member was present at. The minimum activity history length recorded is 1 month and the maximum is 480 months (40 years). Gaps in the activity histories occur where a cohort member has not been present at all sweeps and/or where full activity data were not reported.

An employment histories dataset was previously created (Ward, 2007). This work was undertaken as part of the Gender Network Project. The current work on NCDS activity histories builds on this previous activity history and incorporates various cleaning of the data. This previous employment history included data up to sweep 7 (2004) only, did not deal with any non-work activities and did not identify duplicate activities (i.e. where an activity was reported again in a later sweep).

Latest Edition Information
For the second edition (June 2016) the data and documentation were updated to include the latest NCDS wave, extending coverage to 2013.

A set of data includes resources containing statistical data included in the forms of 8-n(year) "Report on the activities of the museum", 7-NC "Report on the activities of club establishments", 7-NC "Consolidated report on the activities of club establishments", 6-nk "Report on the activities of state, public libraries, centralized library systems (CBS), which are classified in the sphere of management of the Ministry of Culture and Tourism of Ukraine", 80-a-RVC "Consolidated Reporting, State, Public and Other Libraries", 1-MSh (annual summary) "Consolidated report of art schools, specialized art schools (boarding schools) of the Ministry of Culture of Ukraine", as well as statistical forms K-2-RVK(2) "Consolidated report on the presence and activities of film demonstrators", 1-PKC (annual) "Report on immovable monuments and objects of cultural heritage (PKC)", 4-f "Report on precious metals and precious stones contained in museum objects"

Abstract

Statistics South Africa (Stats SA) was commissioned by the South African Department of Labour (DoL) to conduct the first Survey of Activities of Young People in 1999. Stats SA was responsible for data collection and processing, while the analysis and report writing was the responsibility of DoL. In thethird quarter of 2010 (Q3:2010) Stats SA conducted the second Survey of Activities of Young People (SAYP) as a supplement to the Quarterly Labour Force Survey (QLFS). However differences in methodologies followed in the two surveys make comparisons difficult. SAYP is a household-based sample survey that collects data on the activities of children aged 7 to 17 years who live in South Africa. This information is gathered from respondents who are members of households living in dwellings that have been selected to take part in the QLFS and have children aged 7–17 years. The survey covers market production activities, production for own final consumption, household chores as well as activities that children engaged in at school. The reference period for some activities is the week preceding the survey interview and for others it is the past twelve months. The specific objectives of SAYP are: • To understand the extent of children’s involvement in economic activities; • To provide users with statistics on the number of working children; • To supply information for the formulation of informed policy to combat child labour within the country; and • To monitor the Child Labour Action Plan of the Department of Labour based on the findings.

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis in the study were households and individuals

Universe

The sampled population was household members in South Africa. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Survey of Activities of Young People (SAYP) involved two stages. The first stage involved identifying households with children aged 7–17 years during the Quarterly Labour Force Survey (QLFS) data collection that took place in the third quarter of 2010 (Q3:2010). The second stage involved a follow-up interview with children in those households to establish what kind of activities they were involved in and several other aspects related to the activities they engaged in. In Q3:2010, all the QLFS questionnaires were checked for any children aged 7–17 years using the question on age in the first part of the QLFS questionnaire. The screening process for the SAYP was performed to ensure that only households with eligible children were revisited.

Sampling deviation

The non-response adjustment is done through the creation of adjustment classes. The adjustment classes are created using Response Homogeneity Groups (RHGs), where respondents have the same characteristics with non-respondents in the group. The response rate (which is the ratio of responses to all eligible units in the sample) is calculated within each class. The inverse of the response rate (adjustment factor) is calculated within each class, and the result is multiplied by the QLFS 2010 person's weights of the responding units to get the adjusted SAYP person weights for responding units. Children identified as ineligible for SAYP were not considered when calculating weights adjustment. In short, the weights of responding children are inflated to account for eligible children that did not respond during SAYP data collection.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Phase one questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting and heating,water source for domestic use, land ownership,tenure and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey

Phase two questionnaire The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

The data files contain data from sections of the questionnaires as follows:

PERSON: Data from Section 1, 2 and 3 of the questionnaire HHOLD : Data from Section 4 ADULT : Data from Section 5 YOUNGP: Data from Section 6, 7, 8 and 9

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, the school learning model indicator metrics will be calculated using a 14-day average rather than a 7-day average. The new school learning model indicators dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County-14-d/e4bh-ax24 As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well. With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county). This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education. Data represent daily averages for each week by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary. These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020. These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures. For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

This data collection contains data from a large battery of mathematics and executive function tasks administered to a sample of 403 participants aged between 5 and 25 years of age. The data collection includes standardised assessments of mathematics, the Numerical Operations and Mathematical Reasoning subtests from the Weschler Individual Achievement Test (WIAT-II UK), in addition to age appropriate experimental mathematics tests of factual knowledge, procedural skill and conceptual understanding in the domain of arithmetic. Executive functions were assessed with experimental tasks measuring verbal and visuospatial short-term and working memory, inhibitory control, cognitive flexibility and selective attention. Questionnaire measures of mathematics anxiety and the Behavior Rating Inventory of Executive Function were also administered. These data underpin the following papers: Gilmore, C., Keeble, S., Richardson, S., & Cragg, L. (2015). The role of cognitive inhibition in different components of arithmetic. ZDM, 1–12. (see Related Resources) Gilmore, C., Keeble, S., Richardson, S., & Cragg, L. (submitted). The interaction of procedural skill, conceptual understanding and executive functions in early mathematics achievement. Cragg, L., Keeble, S., Richardson, S., Roome, H., & Gilmore, C. (in preparation). Direct and indirect influences of executive functions on mathematics achievement. A high proportion of children and adults struggle with learning and doing maths. To help them we need to have a clear understanding of the processes involved in mathematics. There are lots of different skills involved in successful maths performance including maths specific knowledge such as knowledge of facts, procedures, and concepts as well as other more general skills. These include holding and manipulating information in mind (working memory), flexible thinking (shifting), and focusing on relevant information and ignoring distractions (inhibition). These skills are often termed 'executive function' skills and are thought to be particularly important for learning maths. To date, this basic understanding of the importance of executive function skills has not been exploited in the classroom because our understanding is not detailed enough. This project will explore the relationship between maths and executive function skills in greater depth using a variety of different research methods. This research will reveal the ways in which executive function skills are involved in learning and doing maths and help us to understand why some children find maths easy, and other children struggle.These findings will be used to raise teachers’ awareness of the importance of considering executive functions skills when teaching maths. This study used an empirical, experimental data collection method. The sample was recruited from schools and universities in the Nottinghamshire and Leicestershire area. Data were originally collected from seventy-five 5-6-year-olds (Year 1), eighty-four 8-9-year-olds (Year 4), sixty-seven 11-12-year-olds (Year 7), sixty-seven 13-14-year-olds (Year 9) and seventy-five 18-25-year-olds. Thirty-five of the original sample of adults spoke English as a second language (ESL). Initial analyses revealed that this was influencing the results, particularly on the verbal tasks. As a result, data was collected from a further 35 monolingual adults. The Year 1 students attended one of two primary schools. The Year 4 students all attended the same primary school and the Year 7 and 9 students all attended the same secondary school. The 18-25-year-olds were all students at either Loughborough University or the University of Nottingham. Participants were assessed individually at their school or university. Each participant completed a battery of mathematics and executive function tasks lasting approximately 2 hours. For the Year 1 and Year 4 participants this was split across a number of shorter sessions. All of the experimental tasks were programmed using Psychopy software (www.psychopy.org) and presented on HP laptop computers. Further details about the tasks used and measures taken can be found in the task description and read me documents.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

Key Table Information.Table Title.Construction: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2223BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesConstruction workers annual wages($1,000)Construction workers for pay period including March 12Construction workers for pay period including June 12Construction workers for pay period including September 12Construction workers for pay period including December 12Construction, production and/or development and exploration workers annual hours (1,000)Other employees annual wages ($1,000)Other employees for pay period including March 12Other employees for pay period including June 12Other employees for pay period including September 12Other employees for pay period including December 12Total fringe benefits ($1,000)Employers cost for legally required fringe benefits ($1,000)Employers cost for voluntarily provided fringe benefits ($1,000)Total selected costs ($1,000) Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of construction work subcontracted out to others ($1,000)Cost of purchased land ($1,000)Total cost of selected power, fuels, and lubricants ($1,000)Cost of gasoline and diesel fuel ($1,000)Cost of natural gas and manufactured gas ($1,000)Cost of on-highway use of gasoline and diesel fuel ($1,000)Cost of off-highway use of gasoline and diesel fuel ($1,000)Cost of all other fuels and lubricants ($1,000)Cost of purchased electricity ($1,000)Value of construction work ($1,000)Value of construction work on government owned projects ($1,000)Value of construction work on federally owned projects ($1,000)Value of construction work on state and locally owned projects ($1,000)Value of construction work on privately owned projects ($1,000)Value of other business done ($1,000)Value of construction work subcontracted in from others ($1,000)Net value of construction work ($1,000)Value added ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, beginning of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, end of year ($1,000)Gross value of depreciable assets (acquisition costs), beginning of year ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Total retirements ($1,000)Gross value of depreciable assets (acquisition costs), end of year ($1,000)Total depreciation during year ($1,000)Total rental payments or lease payments ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Total other operating expenses ($1,000)Temporary staff and leased employee expenses ($1,000)Expensed computer hardware and other equipment ($1,000)Expensed purchases of software ($1,000)Data processing and other purchased computer services ($1,000)Communication services ($1,000)Repair and maintenance services of buildings and/or machinery ($1,000) Refuse removal (including hazardous waste) services ($1,000)Advertising and promotional services ($1,000)Purchased professional and technical services ($1,000) Taxes and license fees ($1,000)All other operating expenses ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical locati...

This dataset contains data from the National Center for Education Statistics' Academic Library Survey, which was gathered every two years from 1996 - 2014, and annually in IPEDS starting in 2014 (this dataset has continued to only merge data every two years, following the original schedule). This data was merged, transformed, and used for research by Starr Hoffman and Samantha Godbey.This data was merged using R; R scripts for this merge can be made available upon request. Some variables changed names or definitions during this time; a view of these variables over time is provided in the related Figshare Project. Carnegie Classification changed several times during this period; all Carnegie classifications were crosswalked to the 2000 classification version; that information is also provided in the related Figshare Project. This data was used for research published in several articles, conference papers, and posters starting in 2018 (some of this research used an older version of the dataset which was deposited in the University of Nevada, Las Vegas's repository).SourcesAll data sources were downloaded from the National Center for Education Statistics website https://nces.ed.gov/. Individual datasets and years accessed are listed below.[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries Survey (ALS) Public Use Data File, Library Statistics Program, (2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/surveys/libraries/aca_data.asp[dataset] U.S. Department of Education, National Center for Education Statistics, Institutional Characteristics component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Enrollment component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Human Resources component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Employees Assigned by Position component, Integrated Postsecondary Education Data System (IPEDS), (2004, 2002), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Staff component, Integrated Postsecondary Education Data System (IPEDS), (1999, 1997, 1995), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7

This table provides statistical information about people in Canada by their demographic, social and economic characteristics as well as provide information about the housing units in which they live.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.