The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.

South Africa ZA: Unemployment with Advance Education: % of Total Labour Force data was reported at 14.610 % in 2017. This records an increase from the previous number of 11.240 % for 2016. South Africa ZA: Unemployment with Advance Education: % of Total Labour Force data is updated yearly, averaging 9.050 % from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 14.610 % in 2017 and a record low of 6.870 % in 2011. South Africa ZA: Unemployment with Advance Education: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Employment and Unemployment. The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average;

Goal 4Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allTarget 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomesIndicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sexSE_TOT_PRFL: Proportion of children and young people achieving a minimum proficiency level in reading and mathematics (%)Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)SE_TOT_CPLR: Completion rate, by sex, location, wealth quintile and education level (%)Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary educationIndicator 4.2.1: Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, by sexiSE_DEV_ONTRK: Proportion of children aged 36−59 months who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning (% of children aged 36-59 months)Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sexSE_PRE_PARTN: Participation rate in organized learning (one year before the official primary entry age), by sex (%)Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including universityIndicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sexSE_ADT_EDUCTRN: Participation rate in formal and non-formal education and training, by sex (%)Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurshipIndicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skillSE_ADT_ACTS: Proportion of youth and adults with information and communications technology (ICT) skills, by sex and type of skill (%)Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsIndicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedSE_GPI_PTNPRE: Gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio)SE_GPI_TCAQ: Gender parity index of trained teachers, by education level (ratio)SE_GPI_PART: Gender parity index for participation rate in formal and non-formal education and training (ratio)SE_GPI_ICTS: Gender parity index for youth/adults with information and communications technology (ICT) skills, by type of skill (ratio)SE_IMP_FPOF: Immigration status parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio)SE_NAP_ACHI: Native parity index for achievement (ratio)SE_LGP_ACHI: Language test parity index for achievement (ratio)SE_TOT_GPI: Gender parity index for achievement (ratio)SE_TOT_SESPI: Low to high socio-economic parity status index for achievement (ratio)SE_TOT_RUPI: Rural to urban parity index for achievement (ratio)SE_ALP_CPLR: Adjusted location parity index for completion rate, by sex, location, wealth quintile and education levelSE_AWP_CPRA: Adjusted wealth parity index for completion rate, by sex, location, wealth quintile and education levelSE_AGP_CPRA: Adjusted gender parity index for completion rate, by sex, location, wealth quintile and education levelTarget 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracyIndicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sexSE_ADT_FUNS: Proportion of population achieving at least a fixed level of proficiency in functional skills, by sex, age and type of skill (%)Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable developmentIndicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for allIndicator 4.a.1: Proportion of schools offering basic services, by type of serviceSE_ACS_CMPTR: Schools with access to computers for pedagogical purposes, by education level (%)SE_ACS_H2O: Schools with access to basic drinking water, by education level (%)SE_ACS_ELECT: Schools with access to electricity, by education level (%)SE_ACC_HNDWSH: Schools with basic handwashing facilities, by education level (%)SE_ACS_INTNT: Schools with access to the internet for pedagogical purposes, by education level (%)SE_ACS_SANIT: Schools with access to access to single-sex basic sanitation, by education level (%)SE_INF_DSBL: Proportion of schools with access to adapted infrastructure and materials for students with disabilities, by education level (%)Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countriesIndicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of studyDC_TOF_SCHIPSL: Total official flows for scholarships, by recipient countries (millions of constant 2018 United States dollars)Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing StatesIndicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education leveliSE_TRA_GRDL: Proportion of teachers who have received at least the minimum organized teacher training (e.g. pedagogical training) pre-service or in-service required for teaching at the relevant level in a given country, by sex and education level (%)

Abstract

This study provides an update on measures of educational attainment for a broad cross section of countries. In our previous work (Barro and Lee, 1993), we constructed estimates of educational attainment by sex for persons aged 25 and over. The values applied to 129 countries over a five-year intervals from 1960 to 1985.

The present study adds census information for 1985 and 1990 and updates the estimates of educational attainment to 1990. We also have been able to add a few countries, notably China, which were previously omitted because of missing data.

Dataset:

Educational attainment at various levels for the male and female population. The data set includes estimates of educational attainment for the population by age - over age 15 and over age 25 - for 126 countries in the world. (see Barro, Robert and J.W. Lee, "International Measures of Schooling Years and Schooling Quality, AER, Papers and Proceedings, 86(2), pp. 218-223 and also see "International Data on Education", manuscipt.) Data are presented quinquennially for the years 1960-1990;

Educational quality across countries. Table 1 presents data on measures of schooling inputs at five-year intervals from 1960 to 1990. Table 2 contains the data on average test scores for the students of the different age groups for the various subjects.Please see Jong-Wha Lee and Robert J. Barro, "Schooling Quality in a Cross-Section of Countries," (NBER Working Paper No.w6198, September 1997) for more detailed explanation and sources of data.

Geographic coverage

The data set cobvers the following countries: - Afghanistan - Albania - Algeria - Angola - Argentina - Australia - Austria - Bahamas, The - Bahrain - Bangladesh - Barbados - Belgium - Benin - Bolivia - Botswana - Brazil - Bulgaria - Burkina Faso - Burundi - Cameroon - Canada - Cape verde - Central African Rep. - Chad - Chile - China - Colombia - Comoros - Congo - Costa Rica - Cote d'Ivoire - Cuba - Cyprus - Czechoslovakia - Denmark - Dominica - Dominican Rep. - Ecuador - Egypt - El Salvador - Ethiopia - Fiji - Finland - France - Gabon - Gambia - Germany, East - Germany, West - Ghana - Greece - Grenada - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong - Hungary - Iceland - India - Indonesia - Iran, I.R. of - Iraq - Ireland - Israel - Italy - Jamaica - Japan - Jordan - Kenya - Korea - Kuwait - Lesotho - Liberia - Luxembourg - Madagascar - Malawi - Malaysia - Mali - Malta - Mauritania - Mauritius - Mexico - Morocco - Mozambique - Myanmar (Burma) - Nepal - Netherlands - New Zealand - Nicaragua - Niger - Nigeria - Norway - Oman - Pakistan - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Romania - Rwanda - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Solomon Islands - Somalia - South africa - Spain - Sri Lanka - St.Lucia - St.Vincent & Grens. - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syria - Taiwan - Tanzania - Thailand - Togo - Tonga - Trinidad & Tobago - Tunisia - Turkey - U.S.S.R. - Uganda - United Arab Emirates - United Kingdom - United States - Uruguay - Vanuatu - Venezuela - Western Samoa - Yemen, N.Arab - Yugoslavia - Zaire - Zambia - Zimbabwe

South Africa ZA: School Enrollment: Primary: Female: % Net data was reported at 77.611 % in 2015. This records a decrease from the previous number of 85.949 % for 2005. South Africa ZA: School Enrollment: Primary: Female: % Net data is updated yearly, averaging 88.444 % from Dec 1970 (Median) to 2015, with 13 observations. The data reached an all-time high of 93.281 % in 1995 and a record low of 65.268 % in 1970. South Africa ZA: School Enrollment: Primary: Female: % Net data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Education Statistics. Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.; ; 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).

Abstract

Despite the many useful studies on the use of Open Educational Resources (OER) in higher education, most are focused on the activity of students and instructors in the Global North who enjoy comparatively higher levels of economic development, educational provision, policy elaboration, and technological access than those in the Global South – the region where OER is touted as having its potentially greatest impact. This dataset arises from a survey focusing on higher education instructors and students in South America, Sub-Saharan Africa, and South and Southeast Asia. This was a cross-regional survey of 295 instructors at 28 universities in nine countries, Brazil, Chile, Colombia, Ghana, Kenya, South Africa, India, Indonesia, Malaysia. This research seeks to establish a baseline of empirical data for assessing OER awareness and use in the Global South.

The overarching research questions that this study set out to answer are: 1. What proportion of instructors in the Global South have ever used OER? 2. Which variables may account for different OER usage rates between respondents in the Global South?

In order to address these questions, survey responses were correlated against the question (26) of the survey which directly addresses OER usage: "Have you ever used OER that are available in the public domain or has an open license (e.g. Creative Commons) that allows it to be used and/or adapted by others?" A core purpose of the overarching ROER4D project is the development of an empirical baseline of OER and Open Educational Practice (OEP) activity in the Global South. OER itself is a novel concept, and is tied to a broader spectrum of OEP that overlap with, but do not always exactly coincide with, formal OER practice. As such, an investigation into the use, reuse, adaptation, and sharing practices performed by higher education instructors, and the digital infrastructure and foundational literacies that underpin these practices (regardless of their knowledge of formal OER activity) is integral in ascertaining baseline practice. This dataset includes responses by instructors who engage in reuse and sharing activities, irrespective of whether they have consciously used OER in their practice. As such, it offers insights into the practices that exist outside of formally-labelled OER production. Dimension 2 of the survey instrument "Educational Resources" is framed around general practice relating to sharing, use, reuse, creation, and licensing of educational materials, rather than OER per se. Data arising from these responses are to be treated with caution in terms of making inferences around OER, but remain useful in terms of gaining a more informed sense of instructors’ everyday practice. The survey was conducted in four languages (English, Spanish, Portuguese, and Bahasa Melayu); as such, four research instruments were originally produced and four sets of microdata collected. The microdata have been translated into English, and only the English instrument and the aggregated, translated instructor- response microdata is included here. The student-response microdata is not part of this dataset. The dataset is considered to be of interest to OER scholars, practitioners, and policy-makers, as it seeks to provide a useful cross-regional comparison of various aspects of OER adoption.

Geographic coverage

The survey was conducted in nine countries in South America, Sub-Saharan Africa, and South and Southeast Asia.Countries covered were Brazil, Chile, Colombia, Ghana, Kenya, South Africa, India, Indonesia, Malaysia.

Analysis unit

Individuals

Universe

The study engaged instructors in higher education institutions in the nine countries involved in the study.

Kind of data

Qualitative data

Mode of data collection

Face-to-face [f2f]

Response rate

The survey gathered 295 usable responses from instructors.

Forecast: Total First Level of Private Education Enrolment Rate in South Africa 2024 - 2028 Discover more data with ReportLinker!

Despite the many useful studies on the use of Open Educational Resources (OER) in higher education, most are focused on the activity of students and instructors in the Global North who enjoy comparatively higher levels of economic development, educational provision, policy elaboration, and technological access than those in the Global South – the region where OER is touted as having its potentially greatest impact.This dataset arises from a study focusing on higher education instructors and students in South America, Sub-Saharan Africa, and South and Southeast Asia. Based on a cross-regional survey of 295 instructors at 28 universities in nine countries, this research seeks to establish a baseline of empirical data for assessing OER awareness and use in the Global South.The overarching research questions that this study set out to answer are:1. What proportion of instructors in the Global South have ever used OER?2. Which variables may account for different OER usage rates between respondents in the Global South?In order to address these questions, survey responses were correlated against the question (26) of the survey which directly addresses OER usage: “Have you ever used OER that are available in the public domain or has an open license (e.g. Creative Commons) that allows it to be used and/or adapted by others?”A core purpose of the overarching ROER4D project is the development of an empirical baseline of OER and Open Educational Practice (OEP) activity in the Global South. OER itself is a novel concept, and is tied to a broader spectrum of OEP that overlap with, but do not always exactly coincide with, formal OER practice. As such, an investigation into the use, reuse, adaptation, and sharing practices performed by higher education instructors, and the digital infrastructure and foundational literacies that underpin these practices (regardless of their knowledge of formal OER activity) is integral in ascertaining baseline practice.This dataset includes responses by instructors who engage in reuse and sharing activities, irrespective of whether they have consciously used OER in their practice. As such, it offers insights into the practices that exist outside of formally-labelled OER production. Dimension2 of the survey instrument “Educational Resources” is framed around general practice relating to sharing, use, reuse, creation, and licensing of educational materials, rather than OER per se. Data arising from these responses are to be treated with caution in terms of making inferences around OER, but remain useful in terms of gaining a more informed sense of instructors’ everyday practice.The survey was conducted in four languages (English, Spanish, Portuguese, and Bahasa Melayu); as such, four research instruments were originally produced and four sets of microdata collected. The microdata have been translated into English, and only the English instrument and the aggregated, translated instructor-response microdata is included here. The student-response microdata is not part of this dataset.The dataset is considered to be of potential interest to OER scholars, practitioners, and policy-makers, as it seeks to provide a useful cross-regional comparison of various aspects of OER adoption.This dataset was first published by DataFirst.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

The survey had national coverage. The lowest level of geographic aggregation for the data is Province

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The QLFS frame has been developed as a general purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings per quarter.

The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for the 2001 Census, the country was divided into 80 787 enumeration areas (EAs). Stats SA's household-based surveys use a Master Sample of Primary Sampling Units (PSUs) which comprises of EAs that are drawn from across the country.

The sample is designed to be representative at the provincial level and within provinces at the metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal. This implies, for example, that within a metropolitan area the sample is representative at the different geography types that may exist within that metro.

The current sample size is 3 080 PSUs. It is divided equally into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

The sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Mode of data collection

Face-to-face [f2f]

Forecast: Total Second Level of Private Education Enrolment Rate in South Africa 2024 - 2028 Discover more data with ReportLinker!

Abstract

The National Income Dynamics Study (NIDS) is a face-to-face longitudinal survey of individuals living in South Africa as well as their households. The survey was designed to give effect to the dimensions of the well-being of South Africans, to be tracked over time. At the broadest level, these were: Wealth creation in terms of income and expenditure dynamics and asset endowments; Demographic dynamics as these relate to household composition and migration; Social heritage, including education and employment dynamics, the impact of life events (including positive and negative shocks), social capital and intergenerational developments;
Access to cash transfers and social services

Wave 1 of the survey, conducted in 2008, collected the detailed information for the national sample. In 2010/2011 Wave 2 of NIDS re-interviewed these people, gathering information on developments in their lives since they were interviewed first in 2008. As such, the comparison of Wave 1 and Wave 2 information provides a detailed picture of how South Africans have fared over two years of very difficult socio-economic circumstances.

This administrative dataset is for schools attended by NIDS respondents. The dataset was created by matching the names of schools with Department of Education (DoE) registered lists of schools in South Africa. A detailed description of the matching process is provided in the user manual, which includes a description of the inherent limitations associated with conducting such an exercise.

Geographic coverage

The survey had national coverage

Analysis unit

The units of analysis in the dataset are schools

Universe

The target population for NIDS was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

This dataset reflects a comprehensive overview of employees' cybersecurity awareness and behaviour within South African (SA) higher education institutions (HEIs). The data were collected through a structured survey from employees within SA HEIs, including responses about various factors influencing cybersecurity behaviour. The dataset encompasses variables such as:EDU: The participants' level of education.CAPOL: Cybersecurity Policy Awareness.CA1-CA5: Cybersecurity Awareness.ICP1-ICP4: Institutions Cybersecurity Policies.CEI1-CEI6: Cybersecurity Experience.ATT1-ATT5: Attitudes towards cybersecurity.SN1-SN4: Subjective Norms.PBC1-PBC4: Perceived Behavioural Control.TA1-TA4: Threat Appraisal.CRE1-CRE3: Cybersecurity Response Efficacy.CSE1-CSE4: Cybersecurity Self-Efficacy.CCB1-CCB4: Cybersecurity-Compliant Behaviours.CA, ICP, CEI, ATT, SN, PBC, TA, CRE, CSE, CCB: Aggregate scores for each factor. The variables are from institutional cybersecurity environments, theory of planned behaviour and protection motivation. The researcher assessed each factor using up to six questionnaire items developed and adapted from relevant literature. The data employ a five-point Likert scale ranging from 1 ("strongly disagree") to 5 ("strongly agree") to gauge these perceptions. The dataset was analysed using structural equation modelling, ANOVA, and post hoc procedures.The dataset is crucial for analysing the factors contributing to cybersecurity-compliant behaviour among employees and developing strategies to enhance cybersecurity practices in educational institutions. This dataset can be used for various analytical purposes, including regression analysis, structural equation modelling, and hypothesis testing, to explore the relationships between variables and their impact on cybersecurity behaviour.

South Africa ZA: Literacy Rate: Adult: % of People Aged 15 and Above data was reported at 94.368 % in 2015. This records an increase from the previous number of 94.140 % for 2014. South Africa ZA: Literacy Rate: Adult: % of People Aged 15 and Above data is updated yearly, averaging 92.895 % from Dec 1980 (Median) to 2015, with 9 observations. The data reached an all-time high of 94.368 % in 2015 and a record low of 76.200 % in 1980. South Africa ZA: Literacy Rate: Adult: % of People Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; 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).

Abstract

The South African Population Research Infrastructure Network (SAPRIN) is a national research infrastructure funded through the Department of Science and Technology and hosted by the South African Medical Research Council. One of SAPRIN’s initial goals has been to harmonise the legacy longitudinal data from the three current Health and Demographic Surveillance System (HDSS) Nodes. These long-standing nodes are the MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, established in 1993, with a population of 116 000 people; the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, established in 1996, with a current population of 100 000; and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, established in 2000, with a current population of 125 000.

SAPRIN data are processed for longitudinal analysis by organising the demographic data into residence episodes at a geographical location, and membership episodes within a household. Start events include enumeration, birth, in-migration and relocating into a household from within the study population; exit events include death (by cause), out-migration, and relocating to another location in the study population. Variables routinely updated at individual level include health care utilisation, marital status, labour status, education status, as well as recording household asset status. Anticipated outcomes of SAPRIN include: (i) regular releases of up-to-date, longitudinal data, representative of South Africa’s fast-changing poorer communities for research, interpretation and calibration of national datasets; (ii) national statistics triangulation, whereby longitudinal SAPRIN data are triangulated with National Census data for calibration of national statistics and studying the mechanisms driving the national statistics; (iii) An interdisciplinary research platform for conducting observational and interventional research at population level; (iv) policy engagement to provide evidence to underpin policy-making for cost evaluation and targeting intervention programmes, thereby improving the accuracy and efficiency of pro-poor, health and wellbeing interventions; (v) scientific education through training at related universities; and (vi) community engagement, whereby coordinated engagement with communities will enable two-way learning between researchers and community members, and enabling research site communities and service providers to have access to and make effective use of research results.

Geographic coverage

The Agincourt HDSS covers an area of approximately 420km2 and is located in Bushbuckridge District, Mpumalanga in the rural north-east of South Africa close to the Mozambique border. DIMAMO is located in the Capricorn district, Limpopo Province approximately 40 km from Polokwane, the capital city of Limpopo Province and 15-50 km from the University of Limpopo (Turfloop Campus). The site covers an area of approximately 200 km2. AHRI is situated in the south-east portion of the Umkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the south by the Umfolozi river, on the east by the N2 highway (except form portions where the KwaMsane township strandles the highway) and in the north by the Inyalazi river for portions of the boundary. The area is 438km2.

Analysis unit

Exposure episodes

Universe

Households resident in dwellings within the study area will be eligible for inclusion in the household component of SAPRIN. All individuals identified by the household proxy informant as a member of the household will be enumerated. A resident household member is an individual that intends to sleep the majority of time at the dwelling occupied by the household over a four-month period. Households will include resident and non-resident members. An individual is a non-resident member if they have close ties to the household, but do not physically reside with the household most of the time. They can also be called temporary migrants and they are enumerated within the household list. Because household membership is not tied to physical residency, an individual may be a member of more than one household.

Kind of data

Event/transaction data

Sampling procedure

This dataset is not based on a sample but contains information from the complete demographic surveillance areas.

The overall aim of the USAID/SA basic education program is to improve primary grade reading outcomes by building teacher effectiveness and strengthening classroom and school management. This is being accomplished through support to innovative, local interventions that have a demonstrated capacity for scale-up. The main USAID/SA program is the School Capacity and Innovation Program (SCIP), which also leverages significant private sector resources, amplifying the impact of USAID’s investment in the South African education system. SCIP is co-funded by The ELMA Foundation and J.P. Morgan and designed in collaboration with the South African Department of Basic Education. SCIP supports local South African models or interventions that work directly with teachers and school management teams in innovative ways in order to improve their practice as instructional leaders and managers. SCIP is aligned to the USAID Global Education Strategy (2011–2015) which supports interventions to improve learning outcomes with a focus on primary grade reading as a measure of performance. In addition to seeking initiatives that demonstrate innovation and impact, sustainability and scalability are key components of the SCIP program. The goal of the kaMhinga Literacy Project is to demonstrate that the combination of teacher training and community-based teacher support can sustainably achieve primary grade reading levels at a 60% learner literacy level. This will be done through activities aimed at developing the capacity of teachers. Two assessments are reported per year: a baseline assessment completed in February and a final assessment completed in November. To date, a total of three assessments will be reported – Baseline February 2013, Final November 2013 and Baseline February 2014.

This dataset was gathered primarily in the first quarter of 2018 with planning professionals and planning educators in South Africa. The purpose of the project was to assess the changing needs within planning education as South Africa has evolved over the last 20 years to determine whether planning education is fit for purpose in its current configuration. The data demonstrates a high level of confidence by participants in the fundamentals of planning education but that there were still considerable issues to tackle in terms of planning for informality and drawing on theoretical perspectives that have greater relevance for the Global South.
Included in this dataset is an (anonymised) small survey that was undertaken with planning practitioners in South Africa in 2017 examining their attitudes toward the profession and its future in the country. The survey was sent to practitioners identified via membership of the South African Council for Planners (SACPLAN) the body for professional acreditation for planners in South Africa.

International institutions such as the United Nations have highlighted the significance of planning as a discipline in promoting more sustainable environments and dealing with the core economic, social and environmental challenges faced by Africa. Delivering successful urban planning training in SA Higher Education will thus make a key contribution to addressing SA national Government priorities around equity, social justice and democracy. For many years post-colonial and post-apartheid SA has modelled its urban planning practices on Western systems which has been reflected in HE curricula. Concerns have been raised about the relevance and applicability of these Western theories and methods when planning African cities (Watson, 2003, 2009). To date, there has been little or no sustained work that brings together post-colonial and Southern debate theories with an examination of transferring northern planning theories to South Africa. Major uncertainties remain about HE and the appropriateness, usefulness and impact of planning curricula in the last 10 years and their associated teaching methods. The proposed research also aims to reflect more widely on the implications of the SA study for UK planning education; this is especially important given the recent increase in students from the Global South registering for planning-related courses in the UK. O1: To investigate the social and economic value of planning education in SA particularly questions of equity and diversity in HE destination choices, graduation rates and employability outcomes. O2: To deconstruct how the development and delivery of the urban planning undergraduate and postgraduate curriculum addresses issues raised by a changing post-colonial context in SA. O3: Drawing on O2, to assess to what extent issues raised by a changing colonial context is considered and addressed in the UK undergraduate and postgraduate planning curriculum. By doing so and reflecting on lessons from O1, the research will explore the implications for urban planning lecturers in the UK when working with students from Africa and the wider Global South. O4: To create a platform for ideas-sharing between SA academics, professionals and students across the world in order to connect and inform curriculum shaping, teaching methods and wider HE strategies for planning education (especially via SACPLAN) O5: To develop a set of evidence-based resources for HE planning strategies that can address the Global South challenge in SA and across the wider continent. The potential applications and benefits of the proposed research are diverse. There will be an immediate contribution to existing teaching programmes and to SACPLAN/RTPI strategies. There will also be a medium to long-term contribution through the way urban issues are dealt with in SA, what this means for planners when trained and re-trained (CPD) and for the content of planning curricula, teaching methods and thus planning policy.

Abstract

The GHS is an annual household survey which measures the living circumstances of South African households. The GHS collects data on education, health, and social development, housing, access to services and facilities, food security, and agriculture.

Geographic coverage

The General Household Survey has national coverage.

Analysis unit

Households and individuals

Universe

The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa, and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons, and military barracks.

Kind of data

Sample survey data

Sampling procedure

From 2015 the General Household Survey (GHS) uses a Master Sample (MS) frame developed in 2013 as a general-purpose sampling frame to be used for all Stats SA household-based surveys. This MS has design requirements that are reasonably compatible with the GHS. The 2013 Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country, and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the Master Sample, with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs). The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates. The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.

The sample for the GHS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2011 data (secondary stratification).

Mode of data collection

Computer Assisted Personal Interview

Research instrument

Data was collected with a household questionnaire and a questionnaire administered to a household member to elicit information on household members.

Data appraisal

Since 2019, the questionnaire for the GHS series changed and the variables were also renamed. For correspondence between old names (GHS pre-2019) and new name (GHS post-2019), see the document ghs-2019-variables-renamed.

The study examines variables to assess teachers' preparedness for integrating AI into South African schools. The dataset on the Excel sheet consists of 42 columns. The first ten columns comprise demographic variables such as Gender, Years of Teaching Experience (TE), Age Group, Specialisation (SPE), School Type (ST), School Location (SL), School Description (SD), Level of Technology Usage for Teaching and Learning (LTUTL), Undergone Training/Workshop/Seminar on AI Integration into Teaching and Learning Before (TRAIN), and if Yes, Have You Used Any AI Tools to Teach Before (TEACHAI). Columns 11 to 42 contain constructs measuring teachers' preparedness for integrating AI into the school system. These variables are measured on a scale of 1 = strongly disagree to 6 = strongly agree.

AI Ethics (AE): This variable captures teachers' perspectives on incorporating discussions about AI ethics into the curriculum.

Attitude Towards Using AI (AT): This variable reflects teachers' beliefs about the benefits of using AI in their teaching practices. It includes their expectations of having a positive experience with AI, improving their teaching experience, and enhancing their participation in critical discussions through AI applications.

Technology Integration (TI): This variable measures teachers' comfort in integrating AI tools and technologies into lesson plans. It also assesses their belief that AI enhances the learning experience for students, their proactive efforts to learn about new AI tools, and the importance they place on technology integration for effective AI education.

Social Influence (SI): This variable examines the impact of colleagues, administrative support, peer discussions, and parental expectations on teachers' preparedness to incorporate AI into their teaching practices.

Technological Pedagogical Content Knowledge (TPACK): This variable assesses teachers' ability to use technology to facilitate AI learning. It includes their capability to select appropriate technology for teaching specific AI content, and bring real-life examples into lessons.

AI Professional Development (AIPD): This variable evaluates the impact of professional development training on teachers' ability to teach AI effectively. It includes the adequacy of these programs, teachers' proactive pursuit of further professional development opportunities, and schools' provision of such opportunities.

AI Teaching Preparedness (AITP): This variable measures teachers' feelings of preparedness to teach AI. It includes their belief that their teaching methods are engaging, their confidence in adapting AI content for different student needs, and their proactive efforts to improve their teaching skills for AI education.

Perceived Self-Efficacy to Teaching AI (PSE): This variable captures teachers' confidence in their ability to teach AI concepts, address challenges in teaching AI, and create innovative AI-related teaching materials.

Abstract

This dataset was generated from a set of Excel spreadsheets from an Information and Communication Technology Services (ICTS) administrative database on student applications to the University of Cape Town (UCT). This database contains information on applications to UCT between the January 2006 and December 2014. In the original form received by DataFirst the data were ill suited to research purposes. This dataset represents an attempt at cleaning and organizing these data into a more tractable format. To ensure data confidentiality direct identifiers have been removed from the data and the data is only made available to accredited researchers through DataFirst's Secure Data Service.

The dataset was separated into the following data files:

1. Application level information: the "finest" unit of analysis. Individuals may have multiple applications. Uniquely identified by an application ID variable. There are a total of 1,714,669 applications on record.
2. Individual level information: individuals may have multiple applications. Each individual is uniquely identified by an individual ID variable. Each individual is associated with information on "key subjects" from a separate data file also contained in the database. These key subjects are all separate variables in the individual level data file. There are a total of 285,005 individuals on record.
3. Secondary Education Information: individuals can also be associated with row entries for each subject. This data file does not have a unique identifier. Instead, each row entry represents a specific secondary school subject for a specific individual. These subjects are quite specific and the data allows the user to distinguish between, for example, higher grade accounting and standard grade accounting. It also allows the user to identify the educational authority issuing the qualification e.g. Cambridge Internal Examinations (CIE) versus National Senior Certificate (NSC).
4. Tertiary Education Information: the smallest of the four data files. There are multiple entries for each individual in this dataset. Each row entry contains information on the year, institution and transcript information and can be associated with individuals.

Analysis unit

Applications, individuals

Kind of data

Administrative records [adm]

Mode of data collection

Other [oth]

Cleaning operations

The data files were made available to DataFirst as a group of Excel spreadsheet documents from an SQL database managed by the University of Cape Town's Information and Communication Technology Services . The process of combining these original data files to create a research-ready dataset is summarised in a document entitled "Notes on preparing the UCT Student Application Data 2006-2014" accompanying the data.

This data study includes South African Social Accounting Matrix (SAM) for the year 2009. The national SAM is built using official supply-use tables, national accounts, state budgets, and balance of payments, and so provides a detailed representation of the South African economy. It separates 49 activities and 85 commodities; labor is disaggregated by education level; and households by per capita expenditure deciles. Information on labor is d rawn from the 2009 Quarterly Labor Force Survey and on households from the 2005 Income and Expenditure Survey. Finally, the SAM identifies government, investment and foreign accounts. It is therefore an ideal database for conducting economywide impact assessments, including SAM-based multiplier analysis and computable general equilibrium (CGE) modeling.