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”.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data for the households, families, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines refer to the specified race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the householder shown in the table. Data in the "Total population" column are shown regardless of the race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the person..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Industry titles and their 4-digit codes are based on the 2017 North American Industry Classification System. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Data about computer and Internet use were collected by asking respondents to select "Yes" or "No" to each type of computer and each type of Internet subscription. Therefore, respondents were able to select more than one type of computer and more than one type of Internet subscription..The category "with a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; a fixed wireless subscription; or other non-dial up subscription types..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle..With a computer includes those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer..Caution should be used when comparing data for computer and Internet use before and after 2016. Changes in 2016 to the questions involving the wording as well as the response options resulted in changed respon...

Raw data for a 2018 demographic survey fielded by the Society for Marine Mammalogy. Responses have been censored for privacy such that any specific responses given to a question by 3 or fewer respondents have been recoded as "other". The prompts for the questions in the survey are as follows, with column headings found in the .csv files in parentheses.A unique respondent identifier is in the first column, 'id', in each file. files ending in "-affiliation", "-ethnicity", "-membership", "-occupation", and "-societies" have multiple choice responses for each respondent for those respective questions.(age) What is your age?(gender) What is your gender?(transgender) Do you consider yourself to be transgender?(sexual.orientation) Do you consider yourself to be...? (options: Heterosexual or straight; homosexual, bisexual, asexual, other)(learning) Do you consider yourself to have learning difficulties or disabilities?(sensory) Do you have a long-lasting condition resulting in severe vision, hearing, or speech impairment (not including the wearing of glasses, contact lenses, or hearing aids that correct the condition to the point that normal activities are not severely impacted)?(physical) Do you have a long-lasting condition that substantially limits one or more basic physical activities such as walking, climbing stairs, reaching, lifting, or carrying? (birth) What is your country of birth?(citizenship) What do you consider to be your primary country of citizenship?(residence) What is your current country of residence?(language) What is your first language?(ethnicity) Peoples’ ethnicity describes their feeling of belonging and attachment to a distinct group of a larger population that shares their ancestry, colour, language or religion. With which ethnic group do you identify? The list below is not intended to be globally comprehensive, so please select all that apply, or describe the ethnic group that you identify with the most in the choice labelled "Other". (options: Caucasian; Latinx/Hispanic/Spanish origin; Middle Eastern; African; African descent (e.g., African American); Caribbean; South Asian; East Asian; Pacific Islander)(degree) What is the highest degree or level of school you have completed? (options: some high school; high school degree or equivalent; some college, no degree; associate degree; Bachelor’s degree; Master’s degree; Professional degree; Doctorate; other.(occupation) Do you consider yourself to be a...? (please select all that apply; options: natural scientist, social scientist, interdisciplinary scientist, educator, whale-watching naturalist; administration/manager; science communicator; technology developer/maker)(affiliation) What is your institutional affiliation? (please select all that apply)(society) Please select all societies of which you are currently a member. Societies or organizations not on this list can be entered in "Other".(membership) Please select all membership levels that you have in the above societies. (options: student, full, associate, emeritus, life, honorary, other)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data for the households, families, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines refer to the specified race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the householder shown in the table. Data in the "Total population" column are shown regardless of the race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the person..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Industry titles and their 4-digit codes are based on the 2017 North American Industry Classification System. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..The category "with a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; a fixed wireless subscription; or other non-dial up subscription types..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle..With a computer includes those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer..Caution should be used when comparing data for computer and Internet use before and after 2016. Changes in 2016 to the questions involving the wording as well as the response options resulted in changed response patterns in the data. Most not...

This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation together with the Kyiv International Institute of Sociology from 03 to 17 July 2023.

A description of the methodology is given on p.2 of the "selected results" file, which is part of this data collection.

The poll covers the following thematic fields: jobs + entrepreneurship, corruption, economic situation, healthcare sector, war, people under Russian occupation.

This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation are also included in this data collection as separate pdf-files.

Additionally, the data collection contains one file with "selected results" which document some major results of the survey in the form of a analytical summaries and descriptive statistics and another file with a clarification concerning the interpretation of question 5.24 about the president's "personal responsibility" for corruption in the country. These files are in Ukrainian only.

New in version 1.1: An English translation of the questionnaire has been added under "files".

The survey on the professional integration of graduates is part of the system of surveys on the transition from education to employment. As its primary objective, these surveys want to detect the employment status of young people in a predetermined distance from an educational qualification (respectively, diploma, degree, doctorate). The choice to analyze the phenomenon at a certain distance from the qualification, traditionally detected in "three years", is motivated both by the need to take account of the conclusion of further qualification activities and by the fact that the time of insertion in the labour market is not short, especially if you also want to investigate the quality of their job. Despite the previous editions, the survey of 2015 on graduates in 2011 used mixed tecniques Cati/Cawi. The 2015 survey on the professional integration of graduates was conducted at a greater distance from degree (four years) and for the first time it has investigated on the employment outcomes of graduates in 2011 biennial specialized courses (the "+2" introduced with the reform of the academic cycles in Italy). In fact, the greatest distance from graduation has allowed us to follow, longitudinally, graduate people in "three-year" courses in 2011 (Bachelor's degree) to the possible consecutive specialist degree, obtaining early feedback on how many workers also have obtained a specialist degree since 2011. This edition of the survey includes: - graduates in "three-year" courses (Bachelor's degree); - graduates in "single-cycle" courses (which includes, in addition to specialist degrees/single-cycle degrees, the 4-6 years "traditional" degrees); - graduates in specialist degree/master (two-year duration). The survey questionnaire is divided into five sections. the first section, which covers all respondents, deals with the information on studying/training activities carried out from the high school diploma to the time of the interview, with particular attention to the degree program concluded in 2011: any other academic degrees before graduating in 2011; reasons for the choice of the University; possible recognition of course credits; course attendance; satisfaction with their choices of study; etc.. The second section is devoted to work and it is only addressed to people claiming to perform a work activity. Among others, these pieces of information are asked: employment, sector of economic activity, employment status, type of contract, occupation, work schedule, earnings, current job satisfaction. In particular, with reference to the earnings, for the first time in the 2011 edition of the survey, different questions were adopted depending on whether the respondent was a self-employed person (in this case it was asked for annual net salary) or an employee or a project worker (in these cases it was asked for the monthly net salary). The third section is devoted to the job search, and it is only addressed to respondents that they were looking for job. These pieces of information are asked: the last action regarding the job search; the preferred type of work and schedule; the propensity to moving from your country/city; etc... The fourth section deals with mobility, and it is designed to collect information on: residence prior to enrollment at the university; any transfers for study purposes; place where he usually lives at the time of the interview and motivations, etc.. Finally, the last section of the questionnaire is addressed to all respondents, collecting information on: parents' education level and job; interviewee's marital status, children (if any) and living situation (whom s/he lives with) of the interviewee.

Abstract

The main objective of the Household Labour Force Survey is to obtain information on the structure of the labour force in the country. This includes information on economic activity, occupation, status in employment and hours worked for employed persons; and information on the duration of unemployment and occupation sought by the unemployed.

Revisions made in 2009

The Labour Force Survey questionnaire was re-examined by working together with an expert from ILO. In this study, some questions which were found unsuitable for country situation were dropped, some of them were revised and improvements were made in the wording of some questions in order to draw attention for the reference period expressions.

The most important revision in 2009 is transition to the "time-related underemployment" and "inadequate employment" definition instead of the underemployment concept that had been used until 2009. In the Sixteenth International Conference of Labour Statisticians, organized by ILO, the existing definition of underemployment was changed mainly considering the measuring problems and new concepts called as "time-related underemployment" and "inadequate employment" were introduced in order to measure underemployment more accurately. Together with the new underemployment definitions, related questions and options were revised in the questionnaire accordingly.

Sampling addresses have gradually been started to be selected from national address data base since 2009 and, national address data base has been used entirely at sampling since 2011.

In year 2009, economic activities in labour force survey were double coded by International Classification of Economic Activities in the European Union (NACE) both by Rev 1 and by Rev 2. From 2010 onwards NACE Rev 2 has started to be used. In 2009 micro data set economic activity codes are given by both classifications.

Since 2009, economic activity and occupation codes have been given as 2 digits in micro data CD different from previous years.

Geographic coverage

Geographical area covered: All settlements in Turkey have been covered in sample selection.

Urban areas: Settlements with a population of 20,001 and over are defined as urban.

Rural areas: Settlements with a population of 20,000 or less are defined as rural.

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

2009 Household Labor Force Survey is designed to produce estimations on annually, quarterly (3 months) and monthly basis over 3 months moving average by carrying out the survey at each month in the country.

Sample size of the survey is calculated in order to have annual estimations on Nuts1 x urban-rural and Nuts2 level.

For the determination of the sample size, two studies were carried out:

In the first study, the initial selection probabilities, f0, were calculated in parallel with the year of 2004. The number of households was allocated to the Nuts2xurban-rural groups (52) proportionally. Then, in order to achieve the sufficient sample size in each group, the number of households in the urban groups were weighted by 1.5*f0 and in the rural groups by f0. By this weighting, some groups had still under or over sample sizes. These groups were reweighted by f0 or 2*f0. Hence the final sample sizes from the first study were obtained.

In the second study, the requirement of Eurostat 577/98 regulation was taken into account. The instructions in this regulation were applied on the 2007 data set and the sample sizes in each stratum were calculated independently. Following the regulation, firstly, %5 of the working age population was calculated and the corresponding groups belonging to this %5 of the working age population were determined. The groups were chosen from age, gender and education level groups. Then the sample sizes for each stratum (52) were calculated depending on both the %8 coefficient of variation criteria and the values of unemployment rate, design effect, overlapping factors between quarters and correlation coefficient values in each of the selected age, gender and education level groups.

The achieved sample sizes from the two studies were examined and the maximum ones in each stratum were chosen as the final sample size of the survey. Annual sample size of 2009 LFS was determined as approximately 168000 households. Accordingly, the quarterly sample size consists of approximately 42000 households.

Annual sample design of LFS allows:

• Producing quarterly estimations • Measuring variation between consecutive quarters • Cumulating quarterly estimations for annual estimations • Measuring variation between same quarters of the consecutive years • Monthly estimations over 3 month moving average approach.

The rotation pattern is applied by the use of 8 subsamples in each quarter. Each subsample constitutes 350 clusters. The addresses to be surveyed from each selected cluster are divided into two sets namely A and B. In each quarter, only one of these sets is included in the survey. Hence the P overlapping ratio between consecutive quarters and same quarters between consecutive years are guaranteed. Number of addresses in each cluster is 15. This value was determined by taken into account the rate of homogeneity value.

Household Labour Force Survey Rotation Pattern

According to the scheme above, in the first quarter of 2009, 1 of the 8 subsamples comes from the new design (2009), while the others are from the previous design. In this way, the transition from the old design to the new one is spread over time. In the year 2010, all subsamples come from the new design

The address frame of 2009 survey is National Address Data Base (UAVT) which is updated regularly and linked with Address Based Population Register System (ADNKS). Each new subsample to be included in the survey is selected from the updated UAVT. Therefore listing study in the field is not needed which was the case of the survey before.

Sampling Methodology: Two stage stratified cluster sampling.

First stage sampling unit: Blocks, which constitutes approximately 100 household addresses. While forming the sampling frame of the block, updated UAVT is used. Each of villages that don't have municipalities is defined as one block. The blocks are selected by proportional to size. Second stage sampling unit: Addresses. Number of 30 addresses is selected at once. The selection is done systematically then the selected addresses are divided into two sets (A and B). In each quarter, only one of these sets from the same block is included in the survey.

Stratification: Nuts2, urban-rural

Sampling Error Estimation: Sampling errors related to proportion and total estimates of the survey are calculated based on Taylor Series approximation using SAS module.

Mode of data collection

Computer Assisted Personal Interview [capi]

Abstract

The DHS is intended to serve as a primary source for international population and health information for policymakers and for the research community. In general, DHS has four objectives: - To provide participating countries with a database and analysis useful for informed choices, - To expand the international population and health database, - To advance survey methodology, and - To help develop in participating countries technical skills and resources necessary to conduct demographic and health surveys.

Apart from estimating fertility and contraceptive prevalence rates, DHS also covers the topic of child health, which has become the focus of many development programs aimed at improving the quality of life in general. The Indonesian DHS survey did not include health-related questions because this information was collected in the 1987 SUSENAS in more detail and with wider geographic coverage. Hence, the Indonesian DHS was named the "National Indonesian Contraceptive Prevalence Survey" (NICPS).

The National Indonesia Contraceptive Prevalence Survey (NICPS) was a collaborative effort between the Indonesian National Family Planning Coordinating Board (NFPCB), the Institute for Resource Development of Westinghouse and the Central Bureau of Statistics (CBS). The survey was part of an international program in which similar surveys are being implemented in developing countries in Asia, Africa, and Latin America.

The 1987 NICPS was specifically designed to meet the following objectives: - To provide data on the family planning and fertility behavior of the Indonesian population necessary for program organizers and policymakers in evaluating and enhancing the national family planning program, and - To measure changes in fertility and contraceptive prevalence rates and at the same time study factors which affect the change, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and availability of contraception.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

The 1987 NICPS sample was drawn from the annual National Socioeconomic Survey (popularly called SUSENAS) which was conducted in January and February 1987. Each year the SUSENAS consists of one set of core questions and several modules which are rotated every three years. The 1987 SUSENAS main modules covered household income, expenditure, and consumption. In addition, in collaboration with the Ministry of Health, information pertaining to children under 5 years of age was collected, including food supplement patterns, and measurement of height, weight, and arm circumference. In this module, information on prenatal care, type of birth attendant, and immunization was also asked.

This national survey covered over 60,000 households which were scattered in almost all of the districts. The data were collected by the "Mantri Statistik", a CBS officer in charge of data collection at the sub-district level. All households covered in the selected census blocks were listed on the SSN 87-LI form. This form was then used in selecting samples for each of the modules included in the SUSENAS. This particular form was also used to select the sample households in the 1987 NICPS.

Sample selection in the 1987 SUSENAS utilized a multistage sampling procedure. The first stage consisted of selecting a number of census blocks with probability proportional to the number of households in the block. Census blocks are statistical areas formed before the 1980 Population Census and contain approximately 100 households. At the second stage, households were selected systematically from each sampled census block.

Selection of the 1987 NICPS sample was also done in two stages. The first stage was to select census blocks from the those selected in the 1987 SUSENAS. At the second stage a number of households was selected systematically from the selected census block.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire was used to record all members of the selected households who usually live in the household. The questionnaire was utilized to identify the eligible respondents in the household, and to provide the numerator for the computation of demographic measurements such as fertility and contraceptive use rates.

The individual questionnaire was used for all ever-married women aged 15-49, and consisted of the following eight sections:

Section 1 Respondent's Background

This part collected information related to the respondent and the household, such as current and past mobility, age, education, literacy, religion, and media exposure. Information related to the household includes source of water for drinking, for bathing and washing, type of toilet, ownership of durable goods, and type of floor.

Section 2 Reproduction

This part gathered information on all children ever born, sex of the child, month and year of birth, survival status of the child, age when the child died, and whether the child lived with the respondent. Using the information collected in this section, one can compute measures of fertility and mortality, especially infant and child mortality rates. With the birth history data collected in this section, it is possible to calculate trends in fertility over time. This section also included a question about whether the respondent was pregnant at the time of interview, and her knowledge regarding women's fertile period in the monthly menstrual cycle.

Section 3 Knowledge and Practice of Family Planning

This section is one of the most important parts of the 1987 NICPS survey. Here the respondent was asked whether she had ever heard of or used any of the family planning methods listed. If the respondent had used a contraceptive method, she was asked detailed questions about the method. For women who gave birth to a child since January 1982, questions on family planning methods used in the intervals between births were also asked. The section also included questions on source of methods, quality of use, reasons for nonuse, and intentions for future use. These data are expected to answer questions on the effectiveness of family planning use. Finally, the section also included questions about whether the respondent had been visited by a family planning field worker, which community-level people she felt were most appropriate to give family planning information, and whether she had ever heard of the condom, DuaLima, the brand being promoted by a social marketing program.

Section 4 Breastfeeding

The objective of this part was to collect information on maternal and child health, primarily that concerning place of birth, type of assistance at birth, breastfeeding practices, and supplementary food. Information was collected for children born since January 1982.

Section 5 Marriage

This section gathered information regarding the respondent's age at first marriage, number of times married, and whether the respondent and her husband ever lived with any of their parents. Several questions in this section were related to the frequency of sexual intercourse to determine the respondent's risk of pregnancy. Not all of the data collected in this section are presented in this report; some require more extensive analysis than is feasible at this stage.

Section 6 Fertility Preferences

Intentions about having another child, preferred birth interval, and ideal number of children were covered in this section.

Section 7 Husband's Background and Respondent's Work

Education, literacy and occupation of the respondent's husband made up this section of the questionnaire. It also collected information on the respondent's work pattern before and after marriage, and whether she was working at the time of interview.

Section 8 Interview Particulars

This section was used to record the language used in the interview and information about whether the interviewer was assisted by an interpreter. The individual questionnaire also included information regarding the duration of interview and presence of other persons at particular points during the interview. In addition to the questionnaires, two manuals were developed. The manual for interviewers contained explanations of how to conduct an interview, how to carry out the field activity, and how to fill out the questionnaires. Since information regarding age was vital in this survey, a table to convert months from Javanese, Sundanese and Islamic calendar systems to the Gregorian calendar was attached to the 1987 NICPS manual for the interviewers.

Response rate

The NICPS covered a sample of nearly 15,000 households to interview 11,884 respondents. Respondents for the individual interview were ever-married women aged 15-49. During the data collection, 14,141 out of the 14,227 existing households and 11,884 out of 12,065 eligible women were successfully interviewed. In general, few problems were encountered during interviewing, and the response rate was high--99 percent for households and 99 percent for individual respondents.

Note: See APPENDIX A in the report for more information.

Sampling error estimates

The results from sample surveys are affected by two types of errors: (1) non-sampling error and (2) sampling error. Non-sampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way questions are asked, misunderstanding of the questions on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and

Anonymised raw data and questionnaire for the Nature Careers graduate survey 2022. The final survey sample after data cleaning consisted of 3,253 responses globally and provides insight into:

current employment and study choices satisfaction with current study perceptions of their future career options experiences with mental health and discrimination desired career support reflections on research culture and their study decisions

Abstract (en): The Artists Training and Career Project, conducted by the Research Center for Arts and Culture (RCAC), studied the training and career choices and patterns of craftspeople and painters through national surveys of a sampling of artists in each discipline. Topics include training and preparation for painting and craft careers, acceptance in the marketplace, critical evaluation, public response, involvement in professional organizations, and career satisfaction. As a complement to the surveys, RCAC also conducted personal narrative interviews with artists and related experts. The survey of craftspeople was conducted in 1990 and included 1,257 respondents. The survey of painters was conducted in 1991 and included 889 respondents. As well, the survey of actors was conducted in 1992. Funding for the study was provided by the Andrew W. Mellon Foundation and the New York Foundation for the Arts (NYFA). The Artists Training and Career Project: Painters: Questionnaires were mailed to 2,000 US painters in 1991, selected randomly from lists provided by organizations with painter members. A postage-paid return envelope was included with the questionnaire, and a reminder postcard was sent ten days after the questionnaire. The Artists Training and Career Project: Craftspeople: Questionnaires were mailed to 3,942 United States craft artists in 1990, selected randomly from lists provided by organizations with craftsperson members. The questionnaire was eight pages long and contained 151 questions plus a request for additional comments "to learn something about the many things respondents want to say that do not fit onto little lines or in boxes and categories." A postage-paid return envelope was included with the questionnaire, and a reminder postcard was sent ten days after the questionnaire. Response Rates: The response rate for the Artists Training and Career Project: Craftspeople is 33 percent, and for the Artists Training and Career Project: Painters is 46 percent. Craftspeople and painters in the United States. For both data files, random sampling was used and was based on merged lists of artists obtained from their corresponding organizations around the United States. For both data files, the margin of error is plus or minus 3 percent, and in certain selected questions, plus or minus 0.5 percent. The Artists Training and Career Project: Painters: The names of 20,035 painters were submitted by organizations. After the duplicates are removed, the sample was 18,329. Of the 18,329, a random sample of 2,000 was drawn and was mailed questionnaires. The Artists Training and Career Project: Craftspeople: There were 41,705 craft artists on the lists submitted by organizations. The lists were then merged and purged to avoid duplicates, and a random sample of 4,195 craft artists was drawn. A second check of the lists revealed that the sample needed further purging to eliminate missed duplicates, business or organizational listings, and incomplete mailing addresses. In the end, the questionnaire was sent to 3,942 individual craft artists. mail questionnaire Others contributed to this data collection. Robert Greenblatt, computer consultant; Mary Greeley, project coordinator; and Zoe Freedman, project coordinator, contributed to The Artists Training and Career Project: Painters. Robert Greenblatt and Mary Greeley contributed to The Artists Training and Career Project: Craftspeople. Data collection for this study was conducted by Research Center for Arts and Culture. Funding for this study was provided by the Andrew W. Mellon Foundation and the New York Foundation for the Arts. This data collection was previously distributed by the Cultural Policy and the Arts National Data Archive (CPANDA). The CPANDA Identification Number (study number) for the entire data collection is c00020. The CPANDA Identification number for the Artists Training and Career Project: Craftspeople is a00029, and for the Artists Training and Career Project: Painters is a00027. CPANDA conducted the following processing steps for release of this collection: produced a codebook, checked for undocumented codes, performed consistency checks, provided frequencies, performed recodes, and reformatted the data.Users of the data are requested to notify the Research Center for Arts and Culture (Research Center for Arts and Culture, Teachers College, Columbia University, Box 78, 525 West 120th Street, New York, NY 10027) about their intended uses of the data. Reports ...

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data for the households, families, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines refer to the specified race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the householder shown in the table. Data in the "Total population" column are shown regardless of the race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the person..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..Industry titles and their 4-digit codes are based on the 2017 North American Industry Classification System. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Data about computer and Internet use were collected by asking respondents to select "Yes" or "No" to each type of computer and each type of Internet subscription. Therefore, respondents were able to select more than one type of computer and more than one type of Internet subscription..The category "with a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; a fixed wireless subscription; or other non-dial up subscription types..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle..With a computer includes those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer..Caution should be used when comparing data for computer and Internet use before and after 2016. Changes in 2016 to the questions involving the wording as well as the response options resulted in changed response patterns in the data. Most noticeable are increases in overall computer ownership or use, the total of Internet subscriptions, satellite subscriptions, and cellular data plans for a smartphone or other mobile device. For more detailed information about these changes, see the 2016 American Community Sur...

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data for the households, families, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines refer to the specified race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the householder shown in the table. Data in the "Total population" column are shown regardless of the race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the person..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..Industry titles and their 4-digit codes are based on the 2017 North American Industry Classification System. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Data about computer and Internet use were collected by asking respondents to select "Yes" or "No" to each type of computer and each type of Internet subscription. Therefore, respondents were able to select more than one type of computer and more than one type of Internet subscription..The category "with a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; a fixed wireless subscription; or other non-dial up subscription types..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle.."With a computer" includes those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer..Caution should be used when comparing data for computer and Internet use before and after 2016. Changes in 2016 to the questions involving the wording as well as the response options resulted in changed response patterns in the data. Most noticeable are increases in overall computer ownership or use, the total of Internet subscriptions, satellite subscriptions, and cellular data plans for a smartphone or other mobile device. For more detailed information about these changes, see the 2016 American Community S...

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data for the households, families, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines refer to the specified race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the householder shown in the table. Data in the "Total population" column are shown regardless of the race, Hispanic or Latino, American Indian or Alaska Native, or ancestry of the person..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..Industry titles and their 4-digit codes are based on the 2017 North American Industry Classification System. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..The category "with a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; a fixed wireless subscription; or other non-dial up subscription types..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle..With a computer includes those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer..Caution should be used when comparing data for computer and Internet use before and after 2016. Changes in 2016 to the questions involving the wording as well as the response options resulted in changed response patterns in the data. Most noticeable are increases in overall computer ownership or use, the total of Internet subscriptions, satellite subscriptions, and cellular data plans for a smartphone or other mobile device. For more detailed information about these changes, see the 2016 American Community Survey Content Test Report for Compu...

Abstract

Opportunity-focused, high-growth entrepreneurship and science-led innovation are crucial for continued economic growth and productivity. Working in these fields offers the opportunity for rewarding and high-paying careers. However, the majority of youth in developing countries do not consider either as job options, affecting their choices of what to study. Youth may not select these educational and career paths due to lack of knowledge, lack of appropriate skills, and lack of role models. We provide a scalable approach to overcoming these constraints through an online education course for secondary school students that covers entrepreneurial soft skills, scientific methods, and interviews with role models.

The study comprises three experimental trials provided Before and during COVID-19 pandemic in different regions of Ecuador. This catalog entry includes data from Experiment 1: Educational Zone 2/Municipality of Quito 2019-2020. The data from the other two experiments are also available in the catalog.

Experiment 1: Educational Zone 2/Municipality of Quito 2019-2020 In course of Showing Life Opportunities project we conducted a randomized control trial in high schools in Educational Zone 2, Ecuador and Municipality of Quito, Ecuador in 2019-2020; Students finish the program in July 2020. The intervention is an online education course that covers entrepreneurial soft skills, scientific methods, and interviews with role models. This course is taken by students at school (some students finish the program at school during COVID-19 outbreak). We work with mostly 14-19 year-old students (16,570 students). The experimental program covers 126 schools in Educational Zone 2 and 11 schools in Municipality of Quito. We randomly assign schools either to treatment (and receiving the entrepreneurship courses online), or placebo-control (receiving a placebo treatment of online courses from standard curricula) groups. We also cross-randomize the role models and evaluate set of nimble interventions to increase take-up.

The details of intervention can be found in AEA registry: Asanov, Igor and David McKenzie. 2020. Showing Life Opportunities: Increasing opportunity-driven entrepreneurship and STEM careers through online courses in schools. AEA RCT Registry. July 19.

Geographic coverage

Experiment 1: Municipality of Quito and Educational Zone 2 Educational Zone 2 has its administrative headquarters in the city of Tena, Napo province. Its covers provinces of Napo, Orellana and Pichincha, 8 districts (15D01, 22D01, 17D10, 17D11, 15D02, 17D12, 22D02, 22D03), its 16 cantons and 68 parishes. It has an area of 39,542.58 km². The educational zone 2 spread from east to the western border of the Ecuador. We cover students of age 14-18 in schools that has sufficient access to the internet and classes of the K10, K11, or K12. We included the municipality of Quito in the study to enrich the coverage of program by having large (capital) city in the sample.

Analysis unit

Student

Kind of data

Sample survey data [ssd]

Sampling procedure

All students in selected schools who were present in classes filled out the baseline questionnaire

Mode of data collection

Internet [int]

Research instrument

Questionnaires We execute three main sets of questioners. A. Internet (Online Based survey)

The survey consists of a multi-topic questionnaire administered to the students through online learning platform in school during normal educational hours before COVID-19 pandemic or at home during the COVID-19 pandemic. We collect next information: 1. Subject specific knowledge tests. Spanish, English, Statistics, Personal Initiative (only endline), Negotiations (only endline). 2. Career intentions, preferences, beliefs, expectations, and attitudes. STEM and entrepreneurial intentions, preferences, beliefs, expectations, and attitudes. 3. Psychological characteristics. Personal Initiative, Negotiations, General Cognitions (General Self-Efficacy, Youth Self-Efficacy, Perceived Subsidiary Self-Efficacy Scale, Self-Regulatory Focus, Short Grit Scale), Entrepreneurial Cognitions (Business Self-Efficacy, Identifying Opportunities, Business Attitudes, Social Entrepreneurship Standards). 4. Behavior in (incentivized) games: Other-regarding preferences (dictator game), tendency to cooperate (Prisoners Dilemma), Perseverance (triangle game), preference for honesty, creativity (unscramble game). 5. Other background information. Socioeconomic level, language spoken, risk and time preferences, trust level, parents background, big-five personality traits of student, cognitive abilities. Background information (5) collected only at the baseline. B. First follow-up Phone-based Survey Zone 2, Summer (Phone Based). The survey replicates by phone shorter version of the internet-based survey above. We collect next information: 1. Subject specific knowledge tests.
2. Career intentions, preferences, beliefs, expectations, and attitudes. 3. Psychological characteristics

C. (Second) Follow-up Phone-Based Survey, Winter, Zone 2, Highlands Educational Regime.

We execute multi-topic questionnaire by phone to capture the first life-outcomes of students who finished the school. We collect next information:

1. Life Outcome 1- Education. The set of questions that aims to measure the learning success, career/study intentions, propensity to plan and approach others with studying tasks, entrepreneurial intentions.
2. Life Outcome 2- Labor. The set of questions that aims to measure employment status and income, job searching behavior, time devoted for working/business, salary expectations and knowledge about the careers, self-initiated contribution to the family.
3. Personal Initiative/Negotiations related and other measures. The set of questions that aim to measure level of personal initiative, negotiation strategies, pregnancy rate, gender stereotypes, math/STEM self-efficacy, gender attitudes, parent-student communication effects.

Cleaning operations

Data Editing A. Internet, Online-based surveys. We extracted the raw data generated on online platform from each experiment and prepared it for research purposes. We made several pre-processing steps of data: 1. We transform the raw data generated on platform in standard statistical software (R/STATA) readable format. 2. We extracted the answer for each item for each student for each survey (Baseline, Midline, Endline). 3. We cleaned duplicated students and duplicated answers for each item in each survey based on administrative data, performance and information given by students on platform. 4. In case of baseline survey, we standardized items/scales but also kept the raw items.

B. Phone-based surveys. The phone-based surveys are collected with help of advanced CATI kit. It contains all cases (attempts to call) and indication if the survey was effective. The data is cleaned to be ready for analysis. The data is anonymized but contains unique anonymous student id for merging across datasets.

The purpose of the study: to explore the views of the Lithuanian employed population on the social consequences of the COVID-19 pandemic and quarantine with a particular focus on changes in employment and working practices. Major investigated questions: respondents who are working were asked how safe they currently feel in general. Given the block of questions, they had to assess how the quarantine has affected their daily routine in general in various areas (financial situation, work - 7 choices in total). They were asked how they assess the risk of contracting COVID-19 personally. They were asked to assess the financial situation of the household over the upcoming 12 months and to provide their work status. Opinions on how life in Lithuania and their personal lives have changed over the last 12 months and when the coronavirus pandemic will end were analysed. Later, respondents were asked whether there was a change in the average number of working hours, earnings, workload and stress at work since the start of the coronavirus pandemic. There was a need to know how work relationships with colleagues, supervisors and clients have changed as a result of the pandemic and whether respondents had received help at work from these individuals since the pandemic. They were asked if they had experienced any inappropriate behaviour at work from supervisors, colleagues or clients (e.g., harassment, intimidation, terror, psychological abuse, insults, threats, physical aggression) and where or to whom they would first turn if they encountered such inappropriate behaviour in a workplace. The survey assessed the views on whether the coronavirus pandemic has increased the personal risk of losing a job. The aim was to find out whether there was a period of self-isolation due to the pandemic and its impact on respondents' incomes. Lithuanian workers who had lost their jobs in the wake of the coronavirus pandemic were asked what economic activity they were engaged in. They were asked whether it be difficult or easy to find a new job that suited them if they were to lose their job now. Later on, the survey went on to find out how the respondents' current employer takes care of the safety of its employees and whether there is an existing trade union or a works council in a workplace, and how the activities of these institutions have changed since the pandemic. The question about an average monthly net income was asked. Those Lithuanian workers who had to work remotely in the wake of the coronavirus pandemic were asked to rate a number of statements related to remote work (I have the right conditions at home for remote work, I have the necessary technical tools for remote working - 8 choices in total). The aim was to find out whether the employer provided the necessary tools for remote work (computer, telephone, printer, etc.) and what impact remote work has on work performance. The statement that "once the pandemic is over, I will no longer have my own workspace at my workplace or I will have to share it with another worker" was assessed. All respondents were then asked whether they would like to work remotely in the future and whether the coronavirus pandemic might require them to change their current qualifications and/or elevate their existing skills. They were asked whether they intend to get vaccinated once the coronavirus vaccine becomes available and whether they have any loans. At the end of the survey, there was a block of statements provided about different experiences and respondents had to answer whether they were suitable to describe their current experiences (there are enough people I can turn to in times of trouble - 6 choices in total). Socio-demographic characteristics: gender, age, place of residence, size of settlement, marital status, education, household size, age of children, nationality, economic activity, category of workers, square metres of living space, number of rooms in the apartment.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years and above who live in South Africa.

Geographic coverage

National Coverage

Analysis unit

Individuals, households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

Survey requirements and design :

The Labour Force Survey frame has been developed as a general purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings and these are divided equally into four rotation groups, i.e. 7 500 dwellings per rotation group. The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for the 2001 census, the country was divided into 80 787 enumeration areas (EAs). Some of these EAs are small in terms of the number of households that were enumerated in them at the time of Census 2001. Stats SA's household-based surveys use a Master Sample which comprises of EAs that are drawn from across the country. For the purposes of the Master Sample the EAs that contained less than 25 households were excluded from the sampling frame, and those that contained between 25 and 99 households were combined with other EAs to form Primary Sampling Units (PSUs). The number of EAs per PSU ranges between one and four. On the other hand, very large EAs represent two or more PSUs. The sample is designed to be representative at the provincial level and within provinces at the metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal. This implies that for example, that within a metropolitan area the sample is designed to be representative at the different geography types that may exist within that metro. The current sample size is 3 080 PSUs. It is equally divided into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group. The sample for the redesigned Labour Force Survey is based on a stratified two-stage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Sample rotation :

The sampled PSUs have been assigned to 4 rotation groups, and dwellings selected from the PSUs assigned to rotation group "1" are rotated in the first quarter. Similarly, the dwellings selected from the PSUs assigned to rotation group "2" are rotated in the second quarter, and so on. Thus, each sampled dwelling will remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, say 2 quarters and a new household moves in then the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (unoccupied). Each quarter, ¼ of the sampled dwellings rotate out of the sample and are replaced by new dwellings from the same PSU or the next PSU on the list. A total of 3 080 PSUs were selected for the redesigned LFS, and 770 have been assigned to each of the four rotation groups.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire consists of the following sections:

Section 1 - Biographical information (marital status, language, migration, education,training, literacy, etc. Section 2 - Economic activities Section 3 - Unemployment and economic inactivity Section 4 - Main work activities in the last week Section 5 - Earnings in the main job All sections - Comprehensive coverage of all aspects of the labour market

Cleaning operations

Data Processing

Introduction : The purpose of data processing is to ensure that the information collected from the sampled primary sampling units, dwelling units and households (i.e. the boxes containing QLFS questionnaires) are physically received, stored and processed. The aim is to produce a clean dataset that has all the information contained in the questionnaires. Except for the scanning system, all other elements of the data processing system were developed in-house. One important innovation that is central to the smooth operation of the entire system is the development of barcodes that are linked to a unique number on each questionnaire. This information provides the link between the information recorded in the Master Sample database and other processes such as editing and imputation as well as weighting and variance estimation.

Processing phases : QLFS data processing is continuous, starting on the second week of every month. Data processing for each quarter must be completed by the first Friday of the subsequent month to ensure that the four-week deadline for publication of the QLFS results is met.

The phases listed below occur sequentially.

Receiving of questionnaires : The contents of the boxes containing questionnaires sent from the regional offices are verified when received at the DPC. The questionnaire barcodes captured in the provinces are captured again at the DPC to ensure that all questionnaires have been received.

Primary preparation : The purpose of primary preparation is to ensure that all questionnaires are correctly stacked and positioned prior to being guillotined.

Guillotining: The purpose of the guillotine process is to cut off the spines of the questionnaires in order to have pages separated for scanning.

Secondary preparation : The purpose of secondary preparation is to ensure that the questionnaires are correctly stacked and positioned for scanning. At the same time, quality assurance takes place on the work done during the primary preparation and guillotining processes.

Scanning : The purpose of scanning and recognition is to convert the questionnaires into an electronic format and Tagged Image File Format (TIFF) images.

Verification : The purpose of scanning verification is to manually correct un-interpretable characters, missing data and errors detected by validation rules.

Electronic coding: Industry and occupation codes are assigned using the electronic coding system which converts the respondents' industry and occupation descriptions into numeric codes based on Standard Industry Classification (SIC) and South African Standard Occupation Classification (SASCO). If the system fails to assign a code for either industry or occupation, the coding is assigned manually.

Automated editing and imputation : QLFS uses the editing and imputation module to ensure that output data is both clean and complete10. There are three basic components, called functions, in the Edit and Imputation Module:

Function A: Record acceptance Function B: Edit and imputation Function C: Clean up, derived variables and preparation for weighting Function A: Record acceptance

This function is divided into three phases:

First phase: Pre-function A : The first phase ensures that the records contain valid information in selected Cover Page questions required during edit and imputation and during the subsequent weighting and variance estimation. Any blanks or other errors that need to be corrected are done here before processing of the record can proceed.

Second phase: Function A record acceptance : The second phase ensures that there is enough demographic and labour market activity information to ensure that editing and imputation can be successfully completed.

Third phase: Post Function A clean up : This phase ensures that certain data are present where there is evidence that they should be. This for example, involves: • Ensuring that if there is written material in the job description questions then there are corresponding industry and occupation codes for them. • Ensuring that partial blanks or non-numeric characters that appear in questions where the Survey Officer is required to enter numbers are validated. • Ensuring that where there is written material in the space provided for "Other - specify" that the corresponding option is marked.

Function B: Edit and imputation : Having determined in Function A that the content of the record would support extensive editing and imputation, this function carries out those activities. Editing is

This survey asked Detroit area residents about satisfaction with their neighborhoods, police relations, racial discrimination, and perceptions of the 1967 riot and its consequences. In addition, the questionnaire measured feelings of political efficacy, political involvement, evaluations of various political personalities and social programs, and respondents' personal values and aspirations. Respondents' attitudes toward race relations were examined in a series of questions dealing with integration and separation of the races and an open-ended question that prompted respondents to define "Black power." Also included in this study are three derived measures: a general trust scale, an index assessing respondents' interpretations of the riot, and a political power index measuring respondents' perceptions of their ability to affect local and national laws. Questions also elicited background information, such as composition of respondents' parental families, level of education of parental figures, father's occupation, and parental influence on the respondents' job choices. Region and size of place of residence during childhood were also ascertained, as well as how long the respondent had lived in Detroit. Demographic data include age, sex, race, marital status, education and technical training, occupation, employment history, union membership, and service in the Armed Forces for the head of household. In all cases Black respondents were interviewed by Black interviewers and white respondents were interviewed by white interviewers.

The survey charted the spiritual, physical and material welfare of people living in Finland. Respondents were asked to state their occupational title, type of work and employer and status in the labour market during the past three years. People who were married or cohabiting were asked about their spouse's employment status. The unemployed or laid off respondents were asked about unemployment benefits and duration of unemployment or layoff. The unemployed answered to questions about experiences of unemployment, job seeking,, the suitability of different options (e.g. retraining and odd jobs) in their present situation and the importance of different things (e.g. fear of coping in the job and improvement of income level) in accepting a job. Respondents evaluated their general health, for example, psychosomatic symptoms, loneliness and the existence of a confidential friend. They were asked whether they have access to different leisure time activities. Households' monthly net income and monthly expenses were charted. Respondents' satisfaction with their living standard and monthly spending money of their household and its sufficiency were probed. They also rated the difficulty/easiness of obtaining income and the level of income in the next five years. Respondents were also asked whether they feel they are living in poverty or are over-indebted. Respondents were asked to evaluate the frequency of financial difficulties and use of strategies to solve them. They were presented with a set of attitudinal statements about social security and public services. Perceptions of the sufficiency of the level of income support and different social security benefits were charted. Respondents assessed the amount of money that one adult needs monthly and the reasonable amount of social security benefits their household would require. Background variables included year of birth, gender, marital status, household composition, type of place of residence, highest level of education and political party preference.

Residents in private households between 16 and 75 years of age

This survey of 1,008 adult residents includes questions from earlier Orange County Annual Surveys. It also includes key indicators from the PPIC Statewide Survey for comparisons with the state and regions of California. It also considers racial/ethnic, income, and political differences. The following issues are explored in this Orange County Survey: Orange County Issues, Housing Issues, and State and National Issues. Orange County Issues include such questions as: What are the trends over time in consumer confidence and the public's ratings of the quality of life and the economy in Orange County? Do residents recall the Orange County government bankruptcy in 1994, how do they perceive its impacts today, and have attitudes toward the county government recovered in the past 10 years? How satisfied are re sidents with their local public services and city governments? What are the most important issues facing the county and how do residents rate the problems in their regions? What are their perceptions of commuting and transportation plans and preferences for local transportation taxes? Housing Issues include such questions as: How satisfied are residents with their homes and neighborhoods and how do they perceive their opportunities for buying a home in Orange County? How many residents feel the financial strain of housing costs, perceive the benefits of rising home values, or are seriously considering moving? What housing and neighborhood options are they willing to consider?