Open Science in (Higher) Education – data of the February 2017 survey

This data set contains:

• Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.
• Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.
• Readme file (txt)

Survey structure

The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS).

Demographic questions

Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification:

• Natural Sciences
• Arts and Humanities or Social Sciences
• Economics
• Law
• Medicine
• Computer Sciences, Engineering, Technics
• Other

The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification:

• Professor
• Special education teacher
• Academic/scientific assistant or research fellow (research and teaching)
• Academic staff (teaching)
• Student assistant
• Other

We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany.

Remark on OER question

Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER^[2]. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them.

Data collection

The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.

The survey was online from Feb 6^th to March 3^rd 2017, e-mails were mainly sent at the beginning and around mid-term.

Data clearance

We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.

Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question N^o 24 (email address).

References

Allen, E., & Seaman, J. (2016). Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.

First results of the survey are presented in the poster:

Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561

Contact:

Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.

[1] https://www.limesurvey.org

[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”.

This data collection contains information gathered in the Survey of Income and Education (SIE) conducted in April-July 1976 by the Census Bureau for the United States Department of Health, Education, and Welfare (HEW). Although national estimates of the number of children in poverty were available each year from the Census Bureau's Current Population Survey (CPS), those estimates were not statistically reliable on a state-by-state basis. In enacting the Educational Amendments of 1974, Congress mandated that HEW conduct a survey to obtain reliable state-by-state data on the numbers of school-age children in local areas with family incomes below the federal poverty level. This was the statistic that determined the amount of grant a local educational agency was entitled to under Title 1, Elementary and Secondary Education Act of 1965. (Such funds were distributed by HEW's Office of Education.) The SIE was the survey created to fulfill that mandate. Its questions include those used in the Current Population Survey regarding current employment, past work experience, and income. Additional questions covering school enrollment, disability, health insurance, bilingualism, food stamp recipiency, assets, and housing costs enabled the study of the poverty concept and of program effectiveness in reaching target groups. Basic household information also was recorded, including tenure of unit (a determination of whether the occupants of the living quarters owned, rented, or occupied the unit without rent), type of unit, household language, and for each member of the household: age, sex, race, ethnicity, marital history, and education.

School enrollment data are used to assess the socioeconomic condition of school-age children. Government agencies also require these data for funding allocations and program planning and implementation.

Data on school enrollment and grade or level attending were derived from answers to Question 10 in the 2015 American Community Survey (ACS). People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.

School enrollment is only recorded if the schooling advances a person toward an elementary school certificate, a high school diploma, or a college, university, or professional school (such as law or medicine) degree. Tutoring or correspondence schools are included if credit can be obtained from a public or private school or college. People enrolled in “vocational, technical, or business school” such as post secondary vocational, trade, hospital school, and on job training were not reported as enrolled in school. Field interviewers were instructed to classify individuals who were home schooled as enrolled in private school. The guide sent out with the mail questionnaire includes instructions for how to classify home schoolers.

Enrolled in Public and Private School – Includes people who attended school in the reference period and indicated they were enrolled by marking one of the questionnaire categories for “public school, public college,” or “private school, private college, home school.” The instruction guide defines a public school as “any school or college controlled and supported primarily by a local, county, state, or federal government.” Private schools are defined as schools supported and controlled primarily by religious organizations or other private groups. Home schools are defined as “parental-guided education outside of public or private school for grades 1-12.” Respondents who marked both the “public” and “private” boxes are edited to the first entry, “public.”

Grade in Which Enrolled – From 1999-2007, in the ACS, people reported to be enrolled in “public school, public college” or “private school, private college” were classified by grade or level according to responses to Question 10b, “What grade or level was this person attending?” Seven levels were identified: “nursery school, preschool;” “kindergarten;” elementary “grade 1 to grade 4” or “grade 5 to grade 8;” high school “grade 9 to grade 12;” “college undergraduate years (freshman to senior);” and “graduate or professional school (for example: medical, dental, or law school).”

In 2008, the school enrollment questions had several changes. “Home school” was explicitly included in the “private school, private college” category. For question 10b the categories changed to the following “Nursery school, preschool,” “Kindergarten,” “Grade 1 through grade 12,” “College undergraduate years (freshman to senior),” “Graduate or professional school beyond a bachelor’s degree (for example: MA or PhD program, or medical or law school).” The survey question allowed a write-in for the grades enrolled from 1-12.

Question/Concept History – Since 1999, the ACS enrollment status question (Question 10a) refers to “regular school or college,” while the 1996-1998 ACS did not restrict reporting to “regular” school, and contained an additional category for the “vocational, technical or business school.” The 1996-1998 ACS used the educational attainment question to estimate level of enrollment for those reported to be enrolled in school, and had a single year write-in for the attainment of grades 1 through 11. Grade levels estimated using the attainment question were not consistent with other estimates, so a new question specifically asking grade or level of enrollment was added starting with the 1999 ACS questionnaire.

Limitation of the Data – Beginning in 2006, the population universe in the ACS includes people living in group quarters. Data users may see slight differences in levels of school enrollment in any given geographic area due to the inclusion of this population. The extent of this difference, if any, depends on the type of group quarters present and whether the group quarters population makes up a large proportion of the total population. For example, in areas that are home to several colleges and universities, the percent of individuals 18 to 24 who were enrolled in college or graduate school would increase, as people living in college dormitories are now included in the universe.

Most social surveys collect data on respondents’ educational attainment. Current measurement practice involves a closed question with country-specific response options, which are needed because of the differences between educational systems. However, these are quite difficult to compare across countries. This is a challenge for both migrant and international surveys. Therefore, a measurement tool for educational attainment that was initially developed for German migrant surveys in the CAMCES project (Schneider, Briceno-Rosas, Herzing, et al. 2018; Schneider, Briceno-Rosas, Ortmanns, et al. 2018) was extended in the SERISS-project in work package 8, Task 8.3. In deliverable D8.8, we provide a database of educational qualifications and levels for 100 countries, including the definition of a search tree interface to facilitate the navigation of categories for respondents in computer-assisted surveys. All country-specific categories are linked to 3-digit codes of UNESCO's International Standard Classification of Education 2011 for Educational Attainment (ISCED-A), as well as to the education coding scheme used in the European Social Survey (ESS), "edulvlb". A live search of the database via two different interfaces, a search box (for a limited set of countries) and a search tree (for all countries), is available at the surveycodings website at https://surveycodings.org/articles/codings/levels-of-education. The search box and search tree can be implemented in survey questionnaires and thereby be used for respondents’ self-classification in computer-assisted surveys. The live search feature can also be used for post-coding open answers in already collected data.

Abstract

The Education Experience Survey and Literacy Assessment was conducted in Shefa Province, Vanuatu in April, 2011 for Ni-Vanuatu aged from 15 to 60 years. The full report analyses in detail the results of the survey and literacy assessment and highlights correlations between respondents’ educational experience and their literacy levels, employment and income. The survey was aimed at rural Shefa Province so did not cover the capital Port Vila.

The survey and literacy assessment instrument and methodology has been designed to collect accurate and staitsially significant information about education and language experience and also assess acutul literacy levels at the provincial, village and individual level.

The results provide accurate, statistically significant primary data about the education experience of Ni-Vanuatu in Shefa Province.

Geographic coverage

Shefa Province, Vanuatu not including Port Vila. Eight out of the total of 15 islands within the Province mapping were randomly selected.

The villages surveyed were located on islands of Efate, Lelepa, Nguna, Emau, Emae, Buninga, Tongoa, and Laman Island (Epi). The villages in which the survey took place were Mele, Emua, Takara/Sara, Ekipe, Pangpang, Eton, Teoma, Etas, Lelepa,Utanlang, Taloa, Marou, Wiana, Buninga, Tongamea, Sangava, Euta, Matangi/Itakoma, Lumbukiti and Laman.

Analysis unit

Household Individual (Ni-Vanuatu aged from 15 to 60 years).

Universe

The survey covered all people who normally resided in a selected household, between the ages of 15 and 60 years (inclusive).

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was conducted in households in randomly selected rural communities across 8 systematically selected islands out of the 15 islands of Shefa Province. Port Vila was consciously not included, but such a similar exercise in Port Vila would also be very worthwhile. Eight out of the total of 15 islands within the Province mapping were randomly selected. All people who normally resided in a selected household, between the ages of 15 and 60 years (inclusive), were invited to participate in the survey.

The literacy assessment questions were addressed only to respondents who declared an ability to read one of the official languages - English, French or Bislama. With regard to the sampling methodology, great care was taken to ensure that statistically significant results were obtained. The minimum required sample size was calculated using 2009 National Census population figures that indicated the total target population - those people between the ages of 15 to 60 - to be 57,174. The required sample size was 2.36% of the total population, meaning that the number of respondents required was 1,350 people.

This minimum sample size was then used to guide the number of households that needed to be surveyed. It was assumed that a household would typically contain at least three eligible people (15-60 years). As such, it was planned that 20 villages, with 30 households within each village, and an average of three people per household should be interviewed.

Sampling deviation

There was no deviation from sample design.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instrument contains five sections as follows: 1. Individual profile 2. Education experience 3. Language experience 4. Literacy assessment 5. Employment experience

The Individual Profile section of the survey was designed to capture information about the respondents’ gender and age, to allow disaggregation analysis. The first section of the survey also included questions relating to the respondents’ number of children, sources of information used in the previous month, and the respondents’ attitudes to literacy and education.

The second and third parts of the survey were designed to capture information about the respondents’ educational and language experience. The questions in the second part of the survey, explored the education history of the individual, including the highest level of schooling attended and attained, as well as reasons behind non-completion where appropriate.

The third part of the survey questionnaire explored respondents’ language preferences in different situations, and asked respondents to self-declare their literacy status.

The fourth part of the survey is the literacy assessment, which was administered to those participants who self-declared an ability to read one of the three official languages - English, French or Bislama. Therefore, those respondents who indicated in Part 3 that they could read easily, or read some of their preferred official language, participated in the literacy assessment. In contrast, those respondents who indicated that they could not read one of the official languages, did not undertake the literacy assessment and were classified as nonliterate.

The fifth part of the survey looked at the employment experience of respondents. It was designed to extract information about individuals’ participation in the formal economy through cash -paying employment.

Cleaning operations

The survey results were encoded using the Census & Survey Processing System (CSPro) and the data was analysed using the Statistical Package for the Social Sciences (SPSS). For further explanatory notes on the survey analysis, see Appendix C of the External Resource entitled Vanuatu Rural ShefaProvince Education Experience Survey and Literacy Assessment Report

Response rate

100% response rate.

The required sample size was 2.36% of the total population, meaning that the number of respondents required was 1,350 however of the1,350 households selected for the sample, in Shefa Province 1475 interviews were conducted, which is above the minimum sample size of 1,350 people. The survey sample comprised 628 males (42.6%) and 846 females (57.4%). All respondents were between the ages of 15 and 60 years, so as to encompass both the youth and adult demographic.

Description: The Educational Attainment Thematic Report is compiled using data from the Labour Force Survey (LFS). It is a household survey which replaced the Quarterly National Household Survey (QNHS) at the beginning of Q3 2017. The LFS is the official source of quarterly labour force estimates for Ireland including the official rates of employment and unemployment. Questions on educational attainment are included in the core LFS questionnaire each quarter. The Educational Attainment Thematic Report presents the LFS data for adults between 18 and 64 years old with differing levels of educational attainment based on these questions.This data provides a summary of the annual results of education attainment levels across the regional geographies in IrelandGeography available in RDM: State, Regional Assembly and Strategic Planning Area (SPA).Source: CSO Educational Attainment Thematic ReportWeblink: https://www.cso.ie/en/releasesandpublications/ep/p-eda/educationalattainmentthematicreport2021/Date of last source data update: February 2025Update Schedule: Annual

Abstract

The Household Integrated Survey (HIS) in Georgia is conducted regularly from 1996 and has served to assess the level of consumption-based poverty since then. The HIS represents quarterly panel data. The survey covers 13,404 households over the year. Each month 1/12 of the sample is refreshed (about 228 households are changed in 25 census units).

Geographic coverage

National coverage

Universe

The survey covered all household members excluding persons fully supported by the state, for example persons staying in homes for the elderly and the disabled, children in public care institutions, prisoners and etc.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Survey consists in quarterly interviewing households in Tbilisi and 9 Regions of Georgia: 1. Kakheti; 2. Tbilisi; 3. Shida Kartli, including Mtskheta-Mtianeti1; 4. Kvemo Kartli; 5. Mtskheta-Mtianeti; 6. Samtskhe-Javakheti; 7. Adjara; 8. Guria; 9. Samegrelo; 10. Imereti, including Racha-Lechkhumi and Kvemo Svaneti.

The sampling frame of households covers non-institutional part of the population. Those households are subject of observation which live at the sampled addresses. The sample size was selected so that various parameters could be estimated with satisfactory statistical precision not only on the level of the whole country but also on the level of the above listed regions.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Integrated Survey questionnaire consists of 8 sections:

• Shinda 01: General information about living conditions, housing, durables, etc. This section remained unchanged since the household survey was introduced in 1996.

• Shinda 02: Household composition. This section also remained unchanged since the survey inception.

• Shinda 03: Diary expenditure form. This section includes all diary expenditures during one week and it is filled out four times during the households' period of survey.

• Shinda 04: Quarterly expenditures and agricultural activity form. This section covers quarterly expenditures on durables, energy supplies, health care, education, and other services. The questionnaire also collects information about harvest and processing of agricultural products produced by the household, sale and income from selling these products. The questionnaire is filled out four times, simultaneously with diary expenditures form. This section also features “reminder questions”, which help households remember their expenditures.

• Shinda 05: Information about public and private transfers, as well as on changes in household financial and demographic conditions is collected in the section. The substance of the questions was not changed; however their phrasing was adjusted to make them more understandable for respondents.

• Shinda 05-1: Includes information on employment and incomes from employment of adult household members.

• Shinda 07: Refusal form. This section covers information on non-response or non-eligibility. This form helps correct the weights before data processing.

• Shinda 09: Monitoring of Poverty in Georgia.

NOTE: "Shinda" - Georgian abbreviation for "Observation of Households".

The University of Washington - Beyond High School (UW-BHS) project surveyed students in Washington State to examine factors impacting educational attainment and the transition to adulthood among high school seniors. The project began in 1999 in an effort to assess the impact of I-200 (the referendum that ended Affirmative Action) on minority enrollment in higher education in Washington. The research objectives of the project were: (1) to describe and explain differences in the transition from high school to college by race and ethnicity, socioeconomic origins, and other characteristics, (2) to evaluate the impact of the Washington State Achievers Program, and (3) to explore the implications of multiple race and ethnic identities. Following a successful pilot survey in the spring of 2000, the project eventually included baseline and one-year follow-up surveys (conducted in 2002, 2003, 2004, and 2005) of almost 10,000 high school seniors in five cohorts across several Washington school districts. The high school senior surveys included questions that explored students' educational aspirations and future career plans, as well as questions on family background, home life, perceptions of school and home environments, self-esteem, and participation in school related and non-school related activities. To supplement the 2000, 2002, and 2003 student surveys, parents of high school seniors were also queried to determine their expectations and aspirations for their child's education, as well as their own educational backgrounds and fields of employment. Parents were also asked to report any financial measures undertaken to prepare for their child's continued education, and whether the household received any form of financial assistance. In 2010, a ten-year follow-up with the 2000 senior cohort was conducted to assess educational, career, and familial outcomes. The ten year follow-up surveys collected information on educational attainment, early employment experiences, family and partnership, civic engagement, and health status. The baseline, parent, and follow-up surveys also collected detailed demographic information, including age, sex, ethnicity, language, religion, education level, employment, income, marital status, and parental status.

Abstract

In 2004, the Bank, jointly with other donors and the Government of Mozambique, prepared a Poverty and Social Impact Analysis on the issue of fee reform in primary school. Partly as a result of the study findings, the Government took the step of abolishing tuition fees in primary education. In 2006, Ministry of Education and Culture (MEC ) requested a repeat of this analysis, as well as a similar baseline study on barriers to enrollment for the poor in secondary education. In particular the MEC sought World Bank assistance in (a) evaluating the success of the reforms in primary education financing to date, and (b) formulating new policies and initiatives to reduce the barriers the poorest households face in accessing primary and secondary education. This panel survey is part of the Bank's response to this request.

Geographic coverage

Nationally representative

Analysis unit

individuals, households

Universe

The survey was designed to target eligible children/student (i.e. children aged 0-17 y.o. in 2003 or members enrolled in school in 2003) from the IAF sample.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Education Outcomes Panel Survey (NPS) was designed as a panel survey based on a subsample of households interviewed in the 2002/03 Inquérito aos Agregados Familiares (IAF), a national household income and expenditure survey conducted in all provinces of Mozambique from July 2002 to June 2003. The NPS data collection took place from September 2008 to February 2009 and it was performed by a contractor in Mozambique (KPMG), with World Bank and UNICEF field supervision.

The NPS sampling frame consists of enumeration areas (EA) that were drawn to correspond to a particular set of months of the 2002/03 IAF, namely March to May 2003, since it is expected that the IAF has a nationally representative subsample of EAs assigned each quarter. It is important to highlight that the NPS data is nationally representative at the rural and urban areas, but not representative below this level. The main reason is that the IAF sample was clustered to maximize efficiency in the data collection process across a 12 month period, while the NPS sample, due to costs constrains, includes only 3 months. Therefore, the NPS sample does not have enough geographic dispersion to be representative at the province level or below.

All IAF households in the enumeration areas during the months of March-May were included in the NPS sample, resulting in 221 EAs and 2,234 households. This sampling strategy was chosen to reduce the effect of seasonality in the panel analysis when comparing the 2002/03 IAF data to the 2008 NPS data for the same sample households. Originally it was planned to interview all the IAF sample households in these EAs during the same month in which they had been interviewed for the 2002/03 IAF. However, because of delays in the survey planning process, the data collection for the NPS was postponed took place from September 2008 to February 2009. The survey was designed to target eligible children/student (i.e. children aged 0-17 y.o. in 2003 or members enrolled in school in 2003) from the IAF sample. The households in the NPS sample were divided into 2 categories based on their status in 2003:

A. Target 2003 households. These are households that meet at least one of the following criteria: · Households that had at least one child 0-17 years-old in 2003 (see question a13 in the questionnaire ) · Households that had someone in primary or secondary school in 2003, in spite of age (see question a14 in the questionnaire) B. Alternate 2003 Households (14% of original NPS sample)

For the households that did not have any children or student in 2003 but were part of the IAF sample and were in the NPS enumeration area, the following two questions were asked to the first person who was found in the alternate household in 2008: · Does this person's 2008 household currently have anyone who is between 5 and 17 years of age? (see question a15 in the questionnaire) · Does this person's 2008 household currently have anyone who attending primary or secondary school? (see question a16 in the questionnaire)

If the answer was YES to either question (a15 or a16), the interviewer proceeded with the entire questionnaire. If the answer was NO to both questions, the interviewer stopped the interview.

In sum, target households are the source for the panel of children, while alternate households were included to supplement sample size.

There were two types of tracking in the NPS, that of households and that of children/students who split from the original 2003 household and joined new households in 2008. If the entire 2003 household moved in 2008, the field team would gather their new contact information with local leaders, neighbors, friend, etc and follow and interview the household at their new location, provided the household moved within the district (the survey only followed households/children that moved within the district level). New members of the household were also included in the interview.

If the 2003 household was split in 2008 and the members who moved out were a target member (children/student in 2003) who had moved within the district, then the team followed the individuals and interviewed both the original household (if a target member still lived there) and the split household.

The screening for tracking are in section B1 of the questionnaire. A member would be tracked if b100a =1 (this variable is an indicator of whether the member was target member, i.e. less than 17 y.o. in 2003 or attending school in 2003), and b108=2 (the member no longer lives in the household), and b111 <=2 (the member moved to the same village or district). If all these conditions were met, questions B112 (should the member be tracked?) should be 1 (YES) and the household should be followed. The variable "sp" indicates whether the household was the original (sp=0) or a split household (sp>=1).

In case all target members (b100a=1) moved out of the household, the interviewer should end the interview with the original household at question B114.

Mode of data collection

Face-to-face [f2f]

Research instrument

Inquérito aos Agregados Familiares (IAF) 2002 -2003

• caraterísticas gerais do agregado familiar
• questionário comunitário
• questionário das despesas diárias
• questionário das despesas e receitas

NPS Survey 2008-2009

General Household Questionnaire: modules A, B0, B1, B2 (demographics), C0 (education), D0 D1, D2, D3 (employment), E (household characteristics), H (education quality perception), I (transfers) - Consumption module: modules F, GA, GB, GC, GD - Education Event History Module: module C1 - Education Expenditure Modules: module C2

Abstract

The main purpose of a Household Income and Expenditure Survey (HIES) was to present high quality and representative national household data on income and expenditure in order to update Consumer Price Index (CPI), improve statistics on National Accounts and measure poverty within the country.

The main objectives of this survey - update the weight of each expenditure item (from COICOP) and obtain weights for the revision of the Consumer Price Index (CPI) for Funafuti - provide data on the household sectors contribution to the National Accounts - design the structure of consumption for food secutiry - To provide information on the nature and distribution of household income, expenditure and food consumption patterns household living standard useful for planning purposes - To provide information on economic activity of men and women to study gender issues - To generate the income distribution for poverty analysis

The 2010 Household Income and Expenditure Survey (HIES) is the third HIES that was conducted by the Central Statistics Division since Tuvalu gained political independence in 1978.

This survey deals mostly with expenditure and income on the cash side and non cash side (gift, home production). Moreover, a lot of information are collected:

at a household level: - goods possession - description of the dwelling - water tank capacity - fruits and vegetables in the garden - livestock

at an individual level: - education level - employment - health

Geographic coverage

National Coverage: Funafuti and /Outer islands.

Analysis unit

• Household level
• Individual level

Universe

The scope of the 2010 Household Income and Expenditure Survey (HIES) was all occupied households in Tuvalu. Households are the sampling unit, defined as a group of people (related or not) who pool their money, and cook and eat together. It is not the physical structure (dwelling) in which people live. HIES covered all persons who were considered to be usual residents of private dwellings (must have been living in Tuvalu for a period of 12-months, or have intention to live in Tuvalu for a period of 12-months in order to be included in the survey). Usual residents who are temporary away are included as well (e.g., for work or a holiday).

All the private household are included in the sampling frame. In each household selected, the current resident are surveyed, and people who are usual resident but are currently away (work, health, holydays reasons, or border student for example. If the household had been residing in Tuvalu for less than one year: - but intend to reside more than 12 months => he is included - do not intend to reside more than 12 months => out of scope.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tuvalu 2010 Household Income and Expenditure Survey (HIES) outputs breakdowns at the domain level which is Funafuti and Outer Islands. To achieve this, and to match the budget constraint, a third of the households were selected in both domains. It was decided that 33% (one third) sample was sufficient to achieve suitable levels of accuracy for key estimates in the survey. So the sample selection was spread proportionally across all the islands except Niulakita as it was considered too small. The selection method used is the simple random survey, meaning that within each domain households were directly selected from the population frame (which was the updated 2009 household listing). All islands were included in the selection except Niulakita that was excluded due to its remoteness, and size.

For selection purposes, in the outer island domain, each island was treated as a separate strata and independent samples were selected from each (one third). The strategy used was to list each dwelling on the island by their geographical position and run a systematic skip through the list to achieve the 33% sample. This approach assured that the sample would be spread out across each island as much as possible and thus more representative.

Population and sample counts of dwellings by islands for 2010 HIES Islands: -Nanumea: Population: 123; sample: 41 -Nanumaga: Population: 117; sample: 39 -Niutao: Population: 138; sample: 46 -Nui: Population: 141; sample: 47 -Vaitupu: Population: 298; sample: 100 -Nukufetau: Population: 141; sample: 47 -Nukulaelae: Population: 78; sample: 26 -Funafuti: Population: 791; sample: 254 -TOTAL: Population: 1827; sample: 600.

Mode of data collection

Face-to-face [f2f]

Research instrument

3 forms were used. Each question is writen in English and translated in Tuvaluan on the same version of the questionnaire. The questionnaire was highly based on the previous one (2004 survey).

Household Schedule This questionnaire, to be completed by interviewers, is used to collect information about the household composition, living conditions and is also the main form for collecting expenditure on goods and services purchased infrequently.

• composition of the household and demographic profile of each members
• dwelling information
• dwelling expenditure
• transport expenditure
• education expenditure
• health expenditure
• land and property expenditure
• household furnishing
• home appliances
• cultural and social payments
• holydays/travel costs
• Loans and saving
• clothing
• other major expenditure items

Individual Schedule There will be two individual schedules: - health and education - labor force (individual aged 15 and above) - employment activity and income (individual aged 15 and above): wages and salaries working own business agriculture and livestock fishing income from handicraft income from gambling small scale activies jobs in the last 12 months other income childreen income tobacco and alcohol use other activities seafarer

Diary (one diary per week, on a 2 weeks period, 2 diaries per household were required) The diaries are used to record all household expenditure and consumption over the two week diary keeping period. The diaries are to be filled in by the household members, with the assistance from interviewers when necessary. - All kind of expenses - Home production - food and drink (eaten by the household, given away, sold) - Goods taken from own business (consumed, given away) - Monetary gift (given away, received, winning from gambling) - Non monetary gift (given away, received, winning from gambling).

Cleaning operations

Consistency of the data: - each questionnaire was checked by the supervisor during and after the collection - before data entry, all the questionnaire were coded - the CSPRo data entry system included inconsistency checks which allow the National Statistics Office staff to point some errors and to correct them with imputation estimation from their own knowledge (no time for double entry), 4 data entry operators. 1. presence of all the form for each household 2. consistency of data within the questionnaire

at this stage, all the errors were corrected on the questionnaire and on the data entry system in the meantime.

• after data entry, the extreme amount of each questionnaire where selected in order to check their consistency. at this stage, all the inconsistency were corrected by imputation on CSPRO editing.

Response rate

The final response rates for the survey was very pleasing with an average rate of 97 per cent across all islands selected. The response rates were derived by dividing the number of fully responding households by the number of selected households in scope of the survey which weren't vacant.

Response rates for Tuvalu 2010 Household Income and Expenditure Survey (HIES): - Nanumea 100% - Nanumaga 100% - Niutao 98% - Nui 100% - Vaitupu 99% - Nukufetau 89% - Nukulaelae 100% - Funafuti 96%

As can be seen in the table, four of the islands managed a 100 per cent response, whereas only Nukufetau had a response rate of less than 90 per cent.

Further explanation of response rates can be located in the external resource entitled Tuvalu 2010 HIES Report Table 1.2.

Sampling error estimates

The quality of the results can be found in the report provided in this documentation.

This data collection contains information from the Survey of Income and Education (SIE) conducted during the months of April through July of 1976 by the Census Bureau for the Department of Health, Education, and Welfare. The original SIE file, SURVEY OF INCOME AND EDUCATION, 1976 (ICPSR 7634), was modified by the United States Commission of Civil Rights and consists of all the minority records and 1/8 of the majority from the original files. The records were made rectangular by combining three record types (household-level, family-level, and person-level) with lengths of 450 characters into a single record with a length of 846. Three variables have also been added to each record: group identification code, typical educational requirement for current occupation, and occupational prestige code. The survey served as a supplement to the yearly Current Population Survey and was conducted to obtain reliable state-by-state data on the numbers of children in local areas with family incomes below the federal poverty level. The information was used to facilitate Title 1 of the Elementary and Secondary Education Act by the Department of Health, Education, and Welfare. The SIE includes questions used in the Current Population Survey and also contains additional exclusive questions covering school enrollment, disability, health insurance, bilingualism, food stamp recipiency, assets, and housing costs. The SIE modified file was provided by the National Chicano Research Network, which was located at the Survey Research Center of the Institute for Social Research, University of Michigan.

Abstract

Yemen's rapid population growth coupled with its scarce public resources demands more equitable and efficient financial and human resource management system in the basic education sector. Despite overall increase in gross enrollment rates, Yemen still has one of the lowest adult literacy rates in the world. Evaluating how efficiently funds allocated for primary education are spent is one of the steps to improve quality of education in the country.

In 2009, the Government of Yemen with the support of the World Bank launched a project to examine the management of public resources in country's education sector and potential inefficiencies in their use. The overall study consisted of three complimentary surveys. The first survey focused on “in and out” resource flows, expenditures, oversight arrangements and financial management practices. The second survey of 16 schools in 12 districts from three governorates examined how prevailing informal practices deviated from formal rules and regulations with respect to teacher deployment, management, salary payments, and resource allocations to frontline service delivery units. The third study, non-traditional Public Expenditure Tracking Survey, offered findings on leakages in wage and salary expenditures through recording of teacher absenteeism. The latter survey is documented here.

Public Expenditure Tracking Surveys (PETS) of the education sector typically focus on the estimation of fiscal leakages from cash resources allocated at the school level. Unfortunately, such approach was not suitable for Yemen because schools receive few, if any, cash resources, particularly since the recent abolition of school fees. Almost all of the allocations are delivered in-kind (e.g., textbooks, chalks, and equipment) and procured at the central level. This particular nature of the resource allocation system called for non-conventional methods of analysis in identifying fiscal leakages in the system. This non-classical PETS study was designed as an absenteeism survey to detect wage/salary leakages. Anecdotal evidence suggests that teacher absenteeism and the issue of ghost workers particularly stand out as the two most common types of fiscal leakages in Yemen's education system.

The survey was conducted in four governorates, representing Yemen's geographic and political diversity. Hodeidah, Hadramout, Shabwah and Saada governorates were chosen. Researchers paid unannounced visits to 240 randomly selected schools to record how many teachers were absent on the day of the visit without prior approval of leave. Investigators then explored how absence correlated with a wide range of potential determinants of the quality of education at the individual, facility, and national levels. The survey also aimed to expose the methods of keeping ghost workers on payroll.

The number of teachers in the selected schools was 2928; investigators interviewed 1048 of them. The survey instrument included questions about characteristics of teachers, schools, community and students.

Geographic coverage

Hodeidah, Hadramout, Shabwah and Saada governorates

Analysis unit

• Primary schools teachers

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covered 240 schools selected by stratified multi-stage sampling based on the Ministry of Education 2004-2005 Annual School Survey (School Census) data.

Researchers employed purposive selection method to choose governorates. Literacy rate was used as a proxy for the human development index. The selection of governorates for the study represented the geographic and political diversity of Yemen. The sample included governorates from the coastal, mountainous, desert and transitory (mountainous to desert) regions of Yemen as well as from the former North and South. Hadramout, Hodeidah, Shabwah and Saadah governorates were chosen.

In each governorate, five districts were randomly selected. Two criteria were applied for the selection of districts: - number of basic schools in the district must exceed 20 in order to select 12 schools in the district, - the sum of schools in five districts should have enough sample schools for each characteristic - urban, rural, boys, girls, and mixed schools.

Based on these criteria, five districts were selected randomly by using the MS-EXCEL random number generator.

The selection of schools was done in three steps: 1) categorizing schools in a matrix of urban-rural and boys, girls-mixed schools; 2) making proportional adjustments according to each category; 3) selecting schools from each category by applying systematic random sampling method, in which the assigned number of schools is selected from the list of schools in an interval calculated from total number of schools divided by the assigned number of schools. Secondary schools were excluded from the sample.

While there were some difficulties finding schools or reaching remote areas, the fieldwork was completed on time. The total number of teachers in sampled schools was 2928. The number of interviewed teachers was 1048.

Sampling deviation

Due to defects in the original data used for sampling and tribal disputes in certain areas in Saada, a few schools could not be visited. To replace those schools, alternative schools of similar characteristics were selected in the same district. - Hadramout: No replacement of schools - Hodeidah: 1 school was replaced as it has been closed for more than 2 years - Shabwah: 2 schools were replaced as they actually did not exist; 2 questionnaires were filled for one of the schools as that school was using double-shift and had assigned two different names with two distinct principals each shift. - Saada: 3 schools were replaced due to security reasons; 1 school was replaced as it was a secondary school.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire included questions about teacher characteristics, school characteristics, community characteristics, and some information on the students. There were three main parts in the questionnaire: a questionnaire for the principal, headcounts of teachers, and a questionnaire for teachers.

The first part comprised questions about basic school information and teacher records. The teacher records were obtained from the official teacher attendance sheets, unless they were kept separately in the school. If the principal was not available, either the deputy principal or the most senior teacher was designated as the respondent.

Headcounts of teachers and interviews with the teachers were undertaken by the second enumerator in the team, while the first enumerator was responsible for the questionnaire developed for the principal.

The Vocational School Student Survey (VET Student Survey) 2017 is a total study charting experiences of young people studying in Finnish vocational education institutions. The survey was conducted by the Research Foundation for Studies and Education (Otus) in collaboration with the National Union of Vocational Students in Finland (SAKKI), which also funded the study with funding received from the Ministry of Education and Culture and the Ministry of Economic Affairs and Employment. Main themes of the survey included applying for studies, experiences relating to studies and teaching, financial circumstances, plans for the future and working life, and wellbeing and leisure time. Relating to studies at the time of the survey, the respondents were asked, among other things, their field of education, year of study, and distance to the vocational institution. They were also asked whether they had moved to another municipality due to their current studies. Earlier studies and applying for vocational studies were examined with questions regarding, for instance, whether vocational studies had been discussed or recommended in their families or at school, whether their friends or siblings had studied in a vocational institution, and how clear the decision to opt for vocational studies had been. They were also asked whether they had worked or completed other studies before starting vocational studies, and how they had performed in earlier education. Experiences of studies and teaching were examined with questions about the time spent on studies in a week, form and sufficiency of the teaching and guidance received, balancing and managing studies, and money spent on study materials. Possible learning difficulties and support received for these difficulties were also surveyed. With regard to study progress, satisfaction with studies and the institution itself was charted as well as feelings of studying the right field, prospects of graduating, things slowing down study progress, and views on the importance of vocational studies. Working, housing and financial circumstances were investigated by asking about working during studies and in the summer, housing during semesters, financial help from parents and relatives, and sufficiency of money for expenses. Concerning occupational life, opinions were probed on a number of statements about employment, employment prospects after graduation, and the importance of various things for a successful career. Future plans to study were also surveyed. Finally, wellbeing and leisure time were examined with questions concerning e.g. friends, social relationships, bullying and discrimination, sleep, alcohol use, hobbies and Internet use. Background variables included the respondent's year of birth (categorised), gender, and mother tongue. The time the respondent had lived in Finland was further charted, along with languages spoken with parents, and parents' employment status and education level.

Abstract

Household Income and Expenditure Survey (HIES) collects a wealth of information on HH income and expenditure, such as source of income by industry, HH expenditure on goods and services, and income and expenditure associated with subsistence production and consumption. In addition to this, HIES collects information on sectoral and thematic areas, such as education, health, labour force, primary activities, transport, information and communication, transfers and remittances, food expenditure (as a proxy for HH food consumption and nutrition analysis), and gender.

The Pacific Islands regionally standardized HIES instruments and procedures were adopted by the Government of Tokelau for the 2015/16 Tokelau HIES. These standards were designed to feed high-quality data to HIES data end users for:

1. deriving expenditure weights and other useful data for the revision of the consumer price index;
2. supplementing the data available for use in compiling official estimates of various components in the System of National Accounts;
3. supplementing the data available for production of the balance of payments; and
4. gathering information on poverty lines and the incidence of poverty in Tokelau.

The data allow for the production of useful indicators and information on the sectors covered in the survey, including providing data to inform indicators under the UN Sustainable Development Goals (SDGs). This report, the above listed outputs, and any thematic analyses of HIES data, collectively provide information to assist with social and economic planning and policy formation.

Geographic coverage

National coverage.

Analysis unit

Households and Individuals.

Universe

The universe of the 2015/16 Tokelau Household Income and Expenditure Survey (HIES) is all occupied households (HHs) in Tokelau. HHs are the sampling unit, defined as a group of people (related or not) who pool their money, cook and eat together. It is not the physical structure (dwelling) in which people live. The HH must have been living in Tokelau for a period of six months, or have had the intention to live in Tokelau for a period of twelve months in order to be included in the survey.

Household members covered in the survey include: -usual residents currently living in the HH; -usual residents who are temporarily away (e.g., for work or a holiday); -usual residents who are away for an extended period, but are financially dependent on, or supporting, the HH (e.g., students living in school dormitories outside Tokelau, or a provider working overseas who hasn't formed or joined another HH in the host country) and plan to return; -persons who frequently come and go from the HH, but consider the HH being interviewed as their main place of stay; -any person who lives with the HH and is employed (paid or in-kind) as a domestic worker and who shares accommodation and eats with the host HH; and -visitors currently living with the HH for a period of six months or more.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2015/16 Tokelau Household Income and Expenditure Survey (HIES) sampling approach was designed to generate reliable results at the national level. That is, the survey was not designed to produce reliable results at any lower level, such as for the three individual atolls. The reason for this is partly budgetary constraint, but also because the HIES will serve its primary objectives with a sample size that will provide reliable national aggregates.

The sampling frame used for the random selection of HHs was from December 2013, i.e. the HH listing updated in the 2013 Population Count.

The 2015/16 Tokelau HIES had a quota of 120 HHs. The sample covered all three populated atolls in Tokelau (Fakaofo, Nukunonu and Atafu) and the sample was evenly allocated between the three atoll clusters (i.e., 40 HHs per atoll surveyed over a ten-month period). The HHs within each cluster were randomly selected using a single-stage selection process.

In addition to the 120 selected HHs, 60 HHs (20 per cluster) were randomly selected as replacement HHs to ensure that the desired sample was met. The replacement HHs were only approached for interview in the case that one of the primarily selected HHs could not be interviewed.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for this Household Income and Expenditure Survey (HIES) are composed of a diary and 4 modules published in English and in Tokelauan. All English questionnaires and modules are provided as external resources.

Here is the list of the questionnaires for this 2015-2016 HIES: - Diary: week 1 an 2; - Module 1: Demographic information (Household listing, Demographic profile, Activities, Educational status, Communication status...); - Module 2: Household expenditure (Housing characteristics, Housing tenure expenditure, Utilities and communication, Land and home...etc); - Module 3: Individual expenditure (Education, Health, Clothing, Communication, Luxury items, Alcohonl & tobacco); - Module 4: Household and individual income (Wages and salary, Agricultural and forestry activities, Fishing gathering and hunting activities, livestock and aquaculture activities...etc).

Cleaning operations

All inconsistencies and missing values were corrected using a variety of methods: 1. Manual correction: verified on actual questionnaires (double check on the form, questionnaire notes, local knowledge, manual verifications) 2. Subjective: the answer is obvious and be deducted from other questions 3. Donor hot deck: the value is imputed based on similar characteristics from other HHs or individuals (see example below) 4. Donor median: the missing or outliers were imputed from similar items reported median value 5. Record deletion: the record was filled by mistake and had to be removed.

Several questions used the hotdeck method of imputation to impute missing and outlying values. This method can use one to three dimensions and is dependent on which section and module the question was placed. The process works by placing correct values in a coded matrix. For example in Tokelau the “Drink Alcohol” questions used a three dimension hotdeck to store in-range reported data. The constraining dimensions used are AGE, SEX and RELATIONSHIP questions and act as a key for the hotdeck. On the first pass the valid yes/no responses are place into this 3-dimension hotdeck. On the second pass the data in the matrix is updated one person at a time. If a “Drink Alcohol” question contained a missing response then the person's coded age, sex and relationship key is searched in the “valid” matrix. Once a key is found the result contained in the matrix is imputed for the missing value. The first preferred method to correct missing or outlying data is the manual correction (trying to obtain the real value, it could have been miss-keyed or reported incorrectly). If the manual correction was unsuccessful at correcting the values, a subjective approach was used, the next method would be the hotdeck, then the donor median and the last correction is the record deletion. The survey procedure and enumeration team structure allow for in-round data entry, which gives the field staff the opportunity to correct the data by manual review and by using the entry system-generated error messages. This process was designed to improve data quality. The data entry system used system-controlled entry, interactive coding and validity and consistency checks. Despite the validity and consistency checks put in place, the data still required cleaning. The cleaning was a two-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database, consisting of: Person level record - characteristics of every (household) HH member, including activity and education profile; HH level record - characteristics of the dwelling and access to services; Final aggregated income - all HH income streams, by category and type; Final aggregated expenditure - all HH expenditure items, by category and type.

The cleaning was a two-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database.

Response rate

Overall, 99% of the response rate objective was achieved.

Sampling error estimates

Refer to Appendix 2 of the Tokelau 2015/2016 Household Income and Expenditure Survey report attached as an external resource.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES EDUCATIONAL ATTAINMENT - DP02 Universe - Total households Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Educational attainment data are tabulated for people 18 years old and over. Respondents are classified according to the highest degree or the highest level of school completed. The question included instructions for persons currently enrolled in school to report the level of the previous grade attended or the highest degree received.

Abstract

The Tajik Living Standards Survey (TLSS) was conducted jointly by the State Statistical Agency and the Center for Strategic Studies under the Office of the President in collaboration with the sponsors, the United Nations Development Programme (UNDP) and the World Bank (WB). International technical assistance was provided by a team from the London School of Economics (LSE). The purpose of the survey is to provide quantitative data at the individual, household and community level that will facilitate purposeful policy design on issues of welfare and living standards of the population of the Republic of Tajikistan in 1999.

Geographic coverage

National coverage. The TLSS sample was designed to represent the population of the country as a whole as well as the strata. The sample was stratified by oblast and by urban and rural areas.

The country is divided into 4 oblasts, or regions; Leninabad in the northwest of the country, Khatlon in the southwest, Rayons of Republican Subordination (RRS) in the middle and to the west of the country, and Gorno-Badakhshan Autonomous Oblast (GBAO) in the east. The capital, Dushanbe, in the RRS oblast, is a separately administrated area. Oblasts are divided into rayons (districts). Rayons are further subdivided into Mahallas (committees) in urban areas, and Jamoats (villages) in rural areas.

Analysis unit

• Households
• Individuals
• Communites

Kind of data

Sample survey data [ssd]

Sampling procedure

The TLSS sample was designed to represent the population of the country as a whole as well as the strata. The sample was stratified by oblast and by urban and rural areas.

In common with standard LSMS practice a two-stage sample was used. In the first stage 125 primary sample units (PSU) were selected with the probability of selection within strata being proportional to size. At the second stage, 16 households were selected within each PSU, with each household in the area having the same probability of being chosen. [Note: In addition to the main sample, the TLSS also included a secondary sample of 15 extra PSU (containing 400 households) in Dangara and Varzob. Data in the oversampled areas were collected for the sole purpose of providing baseline data for the World Bank Health Project in these areas. The sampling for these additional units was carried out separately after the main sampling procedure in order to allow for their exclusion in nationally representative analysis.] The twostage procedure has the advantage that it provides a self-weighted sample. It also simplified the fieldwork operation as a one-field team could be assigned to cover a number of PSU.

A critical problem in the sample selection with Tajikistan was the absence of an up to date national sample frame from which to select the PSU. As a result lists of the towns, rayons and jamoats (villages) within rayons were prepared manually. Current data on population size according to village and town registers was then supplied to the regional offices of Goskomstat and conveyed to the center. This allowed the construction of a sample frame of enumeration units by sample size from which to draw the PSU.

This procedure worked well in establishing a sample frame for the rural population. However administrative units in some of the larger towns and in the cities of Dushanbe, Khojand and Kurgan-Tubbe were too large and had to be sub-divided into smaller enumeration units. Fortuitously the survey team was able to make use of information available as a result of the mapping exercise carried out earlier in the year as preparation for the 2000 Census in order to subdivide these larger areas into enumeration units of roughly similar size.

The survey team was also able to use the household listings prepared for the Census for the second stage of the sampling in urban areas. In rural areas the selection of households was made using the village registers – a complete listing of all households in the village which is (purported to be) regularly updated by the local administration. When selecting the target households a few extra households (4 in addition to the 16) were also randomly selected and were to be used if replacements were needed. In actuality non-response and refusals from households were very rare and use of replacement households was low. There was never the case that the refusal rate was so high that there were not enough households on the reserve list and this enabled a full sample of 2000 randomly selected households to be interviewed.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was based on the standard LSMS for the CIS countries, and adapted and abridged for Tajikistan. In particular the health section was extended to allow for more in depth information to be collected and a section on food security was also added. The employment section was reduced and excludes information on searching for employment.

The questionnaires were translated into Tajik, Russian and Uzbek.

The TLSS consists of three parts: a household questionnaire, a community level questionnaire and a price questionnaire.

Household questionnaire: the Household questionnaire is comprised of 10 sections covering both household and individual aspects.

Community/Population point Questionnaire: the Community level or Population Point Questionnaire consists of 8 sections. The community level questionnaire provides information on differences in demographic and economic infrastructure. Open-ended questions in the questionnaire were not coded and hence information on the responses to these qualitative questions is not provided in the data sets.

Summary of Section contents

The brief descriptions below provide a summary of the information found in each section. The descriptions are by no means exhaustive of the information covered by the survey and users of the survey need to refer to each particular section of the questionnaire for a complete picture of the information gathered.

Household information/roster This includes individual level information of all individuals in the household. It establishes who belongs to the household at the time of the interview. Information on gender, age, relation to household head and marital status are included. In the question relating to family status, question 7, “Nekared” means married where nekar is the Islamic (arabic) term for marriage contract. Under Islamic law a man may marry more than once (up-to four wives at any one time). Although during the Soviet period it was illegal to be married to more than one woman this practice did go on. There may be households where the household head is not present but the wife is married or nekared, or in the same household a respondent may answer married and another nekared to the household head.

Dwelling This section includes information covering the type of dwelling, availability of utilities and water supply as well as questions pertaining to dwelling expenses, rents, and the payment of utilities and other household expenses. Information is at the household level.

Education This section includes all individuals aged 7 years and older and looks at educational attainment of individuals and reasons for not continuing education for those who are not currently studying. Questions related to educational expenditures at the household level are also covered. Schooling in Tajikistan is compulsory for grades (classes) 1-9. Primary level education refers to grades 1 - 4 for children aged 7 to 11 years old. General secondary level education refers to grades 5-9, corresponding to the age group 12-16 year olds. Post-compulsory schooling can be divided into three types of school: - Upper secondary education covers the grades 10 and 11. - Vocational and Technical schools can start after grade 9 and last around 4 years. These schools can also start after grade 11 and then last only two years. Technical institutions provide medical and technical (e.g. engineering) education as well as in the field of the arts while vocational schools provide training for employment in specialized occupation. - Tertiary or University education can be entered after completing all 11 grades. - Kindergarten schools offer pre-compulsory education for children aged 3 – 6 years old and information on this type of schooling is not covered in this section.

Health This section examines individual health status and the nature of any illness over the recent months. Additional questions relate to more detailed information on the use of health care services and hospitals, including expenses incurred due to ill health. Section 4B includes a few terms, abbreviations and acronyms that need further clarification. A feldscher is an assistant to a physician. Mediniski dom or FAPs are clinics staffed by physical assistants and/or midwifes and a SUB is a local clinic. CRH is a local hospital while an oblast hospital is a regional hospital based in the oblast administrative centre, and the Repub. Hospital is a national hospital based in the capital, Dushanbe. The latter two are both public hospitals.

Employment This section covers individuals aged 11 years and over. The first part of this section looks at the different activities in which individuals are involved in order to determine if a person is engaged in an income generating activity. Those who are engaged in such activities are required to answer questions in Part B. This part relates to the nature of the work and the organization the individual is attached to as well as questions relating to income, cash income and in-kind payments. There are also a few questions relating to additional income generating activities in addition to the main activity. Part C examines employment

Abstract

The Tanzania Demographic and Health Survey (TDHS) is part of the worldwide Demographic and Health Surveys (DHS) programme, which is designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1999 TRCHS was to collect data at the national level (with breakdowns by urban-rural and Mainland-Zanzibar residence wherever warranted) on fertility levels and preferences, family planning use, maternal and child health, breastfeeding practices, nutritional status of young children, childhood mortality levels, knowledge and behaviour regarding HIV/AIDS, and the availability of specific health services within the community.1 Related objectives were to produce these results in a timely manner and to ensure that the data were disseminated to a wide audience of potential users in governmental and nongovernmental organisations within and outside Tanzania. The ultimate intent is to use the information to evaluate current programmes and to design new strategies for improving health and family planning services for the people of Tanzania.

Geographic coverage

National. The sample was designed to provide estimates for the whole country, for urban and rural areas separately, and for Zanzibar and, in some cases, Unguja and Pemba separately.

Analysis unit

• Households
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The TRCHS used a three-stage sample design. Overall, 176 census enumeration areas were selected (146 on the Mainland and 30 in Zanzibar) with probability proportional to size on an approximately self-weighting basis on the Mainland, but with oversampling of urban areas and Zanzibar. To reduce costs and maximise the ability to identify trends over time, these enumeration areas were selected from the 357 sample points that were used in the 1996 TDHS, which in turn were selected from the 1988 census frame of enumeration in a two-stage process (first wards/branches and then enumeration areas within wards/branches). Before the data collection, fieldwork teams visited the selected enumeration areas to list all the households. From these lists, households were selected to be interviewed. The sample was designed to provide estimates for the whole country, for urban and rural areas separately, and for Zanzibar and, in some cases, Unguja and Pemba separately. The health facilities component of the TRCHS involved visiting hospitals, health centres, and pharmacies located in areas around the households interviewed. In this way, the data from the two components can be linked and a richer dataset produced.

See detailed sample implementation in the APPENDIX A of the final report.

Mode of data collection

Face-to-face

Research instrument

The household survey component of the TRCHS involved three questionnaires: 1) a Household Questionnaire, 2) a Women’s Questionnaire for all individual women age 15-49 in the selected households, and 3) a Men’s Questionnaire for all men age 15-59.

The health facilities survey involved six questionnaires: 1) a Community Questionnaire administered to men and women in each selected enumeration area; 2) a Facility Questionnaire; 3) a Facility Inventory; 4) a Service Provider Questionnaire; 5) a Pharmacy Inventory Questionnaire; and 6) a questionnaire for the District Medical Officers.

All these instruments were based on model questionnaires developed for the MEASURE programme, as well as on the questionnaires used in the 1991-92 TDHS, the 1994 TKAP, and the 1996 TDHS. These model questionnaires were adapted for use in Tanzania during meetings with representatives from the Ministry of Health, the University of Dar es Salaam, the Tanzania Food and Nutrition Centre, USAID/Tanzania, UNICEF/Tanzania, UNFPA/Tanzania, and other potential data users. The questionnaires and manual were developed in English and then translated into and printed in Kiswahili.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview and children under five who were to be weighed and measured. Information was also collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, ownership of various consumer goods, and use of iodised salt. Finally, the Household Questionnaire was used to collect some rudimentary information about the extent of child labour.

The Women’s Questionnaire was used to collect information from women age 15-49. These women were asked questions on the following topics: · Background characteristics (age, education, religion, type of employment) · Birth history · Knowledge and use of family planning methods · Antenatal, delivery, and postnatal care · Breastfeeding and weaning practices · Vaccinations, birth registration, and health of children under age five · Marriage and recent sexual activity · Fertility preferences · Knowledge and behaviour concerning HIV/AIDS.

The Men’s Questionnaire covered most of these same issues, except that it omitted the sections on the detailed reproductive history, maternal health, and child health. The final versions of the English questionnaires are provided in Appendix E.

Before the questionnaires could be finalised, a pretest was done in July 1999 in Kibaha District to assess the viability of the questions, the flow and logical sequence of the skip pattern, and the field organisation. Modifications to the questionnaires, including wording and translations, were made based on lessons drawn from the exercise.

Response rate

In all, 3,826 households were selected for the sample, out of which 3,677 were occupied. Of the households found, 3,615 were interviewed, representing a response rate of 98 percent. The shortfall is primarily due to dwellings that were vacant or in which the inhabitants were not at home despite of several callbacks.

In the interviewed households, a total of 4,118 eligible women (i.e., women age 15-49) were identified for the individual interview, and 4,029 women were actually interviewed, yielding a response rate of 98 percent. A total of 3,792 eligible men (i.e., men age 15-59), were identified for the individual interview, of whom 3,542 were interviewed, representing a response rate of 93 percent. The principal reason for nonresponse among both eligible men and women was the failure to find them at home despite repeated visits to the household. The lower response rate among men than women was due to the more frequent and longer absences of men.

The response rates are lower in urban areas due to longer absence of respondents from their homes. One-member households are more common in urban areas and are more difficult to interview because they keep their houses locked most of the time. In urban settings, neighbours often do not know the whereabouts of such people.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TRCHS to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TRCHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the TRCHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the TRCHS is the ISSA Sampling Error Module (SAMPERR). This module used the Taylor linearisation method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rate

Note: See detailed sampling error calculation in the APPENDIX B

Background
The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS) (held at the UK Data Archive under GN 33246), all of its associated LFS boosts and the APS boost. Thus, the APS combines results from five different sources: the LFS (waves 1 and 5); the English Local Labour Force Survey (LLFS), the Welsh Labour Force Survey (WLFS), the Scottish Labour Force Survey (SLFS) and the Annual Population Survey Boost Sample (APS(B) - however, this ceased to exist at the end of December 2005, so APS data from January 2006 onwards will contain all the above data apart from APS(B)). Users should note that the LLFS, WLFS, SLFS and APS(B) are not held separately at the UK Data Archive. For further detailed information about methodology, users should consult the Labour Force Survey User Guide, selected volumes of which have been included with the APS documentation for reference purposes (see 'Documentation' table below).

The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples such as the WLFS and SLFS, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.

Secure Access APS data
Secure Access datasets for the APS include additional variables not included in the standard End User Licence (EUL) versions (see under GN 33357). Extra variables that typically can be found in the Secure Access version but not in the EUL versions relate to:

• geography
  
• types of benefits claimed
• qualifications, education and training, including level of highest qualification, qualifications below highest level, class of first degree, single subject of degree, qualifications from Government schemes, number of O-levels/GCSEs etc passed, type of 'other qualification', type of other work-related or vocational qualifications, qualifications related to work, sources of qualifications, qualifications from school, level of Welsh baccalaureate
• frequency of Welsh speaking
• casual/holiday work
• regular/normal work pattern
• reasons not in work or for leaving work, reasons not looking for work
• payment of own National Insurance and tax
• smoking habits
• single year of age
• health issues
• learning difficulty/disability
• number of bedrooms
• serving in armed forces
• marital status
• main reason for coming to the UK

The EUL version contains less detailed variables. For example, the lowest geography is Government Office Region, only banded age is available, only 3-digit SOC is available for main, second and last job, and only industry division for main, second and last job.

Prospective users of the Secure Access version of the APS will need to fulfil additional requirements, commencing with the completion of extra application forms to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access data users must also complete face-to-face training and agree to the Secure Access User Agreement and Licence Compliance Policy (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access. Further details and links to all APS studies available from the UK Data Archive can be found via the APS Key Data series webpage.

Documentation and coding frames
The APS is compiled from variables present in the LFS. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation (e.g. coding frames for education, industrial and geographic variables, which are held in LFS User Guide Vol.5, Classifications), users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Disability variables from 2013 onwards - LFS and APS
ONS have provided some information on changes since 2013 to the disability variables available on the LFS and APS. The Disability Discrimination Act (DDA) disabled (current disability) category within the historic DISCURR variable no longer corresponds with the advised legal definition of 'current disability'. DISCURR should only be available on LFS microdata from Spring 1998 to January-March 2013 (JM13); beyond that point users should ignore or delete it. In addition, the same 'DDA disabled (current disability)' category within variable DISCURR13 is also not the most appropriate variable to use because a) it is not comparable to the corresponding category in variable DISCURR due to question changes, and b) it no longer measures either the DDA definition of disability or the latest Equality Act definition of disability. However, DISCURR13 is available from the April-March 2013 quarter (AJ13) onwards and was introduced to demonstrate that the variables used to compile DISCURR had also changed from that quarter. Therefore, users are advised to use the disability variable DISEA from AJ13 onwards, which reflects the Equality Act 2010 legal definition of 'disabled', measured according to the GSS Harmonised Standard on health conditions and illnesses. The harmonised disability variables DISEA and DISCURR13 should both be present on the APS person microdata from April 2013-March 2014 (A13M14) onwards. This ensures that APS users have a complete 12 months' data on which to base analysis of the variables. DISCURR should only be present on APS microdata up to and including April 2012-March 2013 (A12M13).

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files
The ONS have identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.

Latest edition information:
For the thirty-first edition (April 2025), a data file for July 2022 to June 2023 has been added to the study.

The Vocational School Student Survey (VET Student Survey) 2019 is a total study charting experiences of young people studying in Finnish vocational education institutions. The survey was conducted by the Research Foundation for Studies and Education (Otus) in collaboration with the National Union of Vocational Students in Finland (SAKKI), which also funded the study with funding received from the Ministry of Education and Culture and the Ministry of Economic Affairs and Employment. Main themes of the survey included applying for studies, experiences relating to studies and teaching, financial circumstances, plans for the future and working life, and wellbeing and leisure time. First, the respondents were asked about their studies at present with questions concerning, for instance, their field of education, how they financed their living costs during studies, and whether they were studying towards a dual or double degree. Questions also surveyed how the respondents had entered their studies (via the joint application system or continuous admission), whether their current field of education had been their first choice when applying, and whether they had began their studies straight away in the autumn after completing comprehensive school. The respondents' decision to apply for vocational studies was further examined with questions regarding, for instance, whether vocational studies had been discussed or recommended in their families or at school, whether their friends or siblings currently studied or had previously studied in a vocational institution, and how clear the decision to opt for vocational studies had been. The respondents were also asked whether they had worked or completed other studies before starting vocational studies, and how they had performed in earlier education. The respondents' experiences of studies and teaching were examined with questions about the time spent on studies in a week, form and sufficiency of the teaching and guidance received, balancing and managing studies, and the atmosphere of their school and study community. Further questions focused on the respondents' opinions on the personalisation of studies and competence-based studying, including, for instance, whether they thought they were able to influence what and how they studied and whether their career plans had been taken into account in their study plan. Opinions were also charted regarding on-the-job learning. Possible learning difficulties and support received for these difficulties were surveyed next. With regard to study progress, satisfaction with studies and the institution itself was charted as well as feelings of studying the right field, prospects of graduating, things slowing down study progress, and views on the importance of vocational studies. Working, housing and financial circumstances were investigated by asking about working during studies and in the summer, housing during the semesters, financial help from parents and relatives, and sufficiency of money for expenses. Concerning occupational life, opinions were probed on a number of statements about employment as well as employment prospects after graduation, and views on the importance of various things for a successful career. Future plans to study were surveyed. Well-being and leisure time were examined with questions about friends, social relationships, bullying and discrimination, sleep, hobbies, and Internet and social media use. Finally, the respondents' values and attitudes were examined with a set of statements including, for example, whether they thought income differences should be reduced, environmental protection should be the first priority, and Finland's EU membership was a good thing. Background variables included the respondent's year of birth, gender, and mother tongue. The time the respondent had lived in Finland was further charted, along with languages spoken with parents, and parents' employment status and education level.

Abstract

The conceptual framework used in this second labour force survey in Samoa aligns closely with the standards and guidelines set out in Resolutions of International Conferences of Labour Statistician

The 2017 Samoa Labour Force Survey was conducted as a joint exercise between the Samoa Bureau of Statistics and the Ministry of Commerce, Industry and Labour and was co-funded by the International Labour Organization and the Trade, Commerce and Manufacturing (TCM) Sector Coordinating Unit of MCIL. Furthermore, the 2017 Samoa Labour Force Survey was implemented simultaneously with the 2017 Samoa School to Work Transition Survey as the two surveys are closely inter-related.

This is the first time ever that the Samoa Bureau of Statistics has used CAPI (computer aided personal interview) where tablets were used to record answers out on the field

Geographic coverage

National

There are four statistical regions in SAMOA namely Apia urban area (AUA), North West Upolu (NWU), Rest of Upolu (RoU) and Savaii. AUA is the urban area while the other three regions are rural areas. Each region is subdivided into political districts, each district into villages and each village into census enumeration areas (EA). The sample for the 2017 Labour Force Survey (LFS) was designed to cover at least 3000 employed population aged 15years and over from all the four regions. This was made mainly to have sufficient cases to provide information on the employed population.

Analysis unit

Individual. Households were targeted during the actual field work where all those aged 15 years and above were interviewed.

Universe

Households were targeted during the actual field work where all those aged 15 years and above were interviewed therefore, information recorded were collected at the household level.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 Labour Force Survey (LFS) sample was drawn from the master sample frame of Household Listing from the most recent Population and Housing Census, 2016. In the 2017 LFS, a representative probability sample of households was selected in two stages. The first stage involved the selection of clusters or primary sampling units using probability proportional to size (PPS) resulting in a total of 259 clusters of which 67 clusters were selected from AUA, 95 in NWU, 49 in ROU and 48 in Savaii. In the second stage of selection, a fixed number of 10 households were selected systematically from the AUA clusters and a fixed number of 12 households were selected from the NWU region and 15 for the other two rural regions namely RoU and Savaii, due to the higher transportation costs in those regions. This resulted in a total of 670 selected households in AUA, 1140 in NWU, 735 in ROU and 720 in Savaii.

During the LFS, in each of the selected households, all persons in the household were interviewed hence the weighting was based on the responding households in the sample (household weights).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2017 Samoa Labour Force Survey questionnaire was similar to the one used in the 2012 Labour Force Survey, with some changes to the questionnaire provided by the ILO. To maintain international comparability, most of the questions were retained such as current activities, characteristics of the main activity and hours of work. However, some questions were modified and altered so that they fit into the local context, such as the classification of education and the participation in the production of goods used by own household.

The twelve sections of the LFS questionnaire were divided into two parts where the first part was designed to obtain data on household characteristics and composition. The following ten sections were designed to collect data on those aged 15 years and above on literacy and education, training, employment, characteristics of the main job/ activity, hours of work, job search, previous work experience, occupational injuries, main activity and own use production. The last section was designed to obtain information on youth school-to-work transition, which was designed in a separate questionnaire in 2012.

The draft questionnaire was pre tested during the supervisors training and during the enumerators training and it was finally tested during the pilot test. The questionnaire was revised rigorously in accordance to the feedback received from each test. At the same time, a field operations manual for supervisors and enumerators was prepared and modified accordingly for field operators to use as a reference during the field work.