Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan—another element of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work.

This reflects a collection of tabular, geospatial and textual information from 3 CD-ROMs published in 1995 and 1996 from the USGS in support of the 1995 National Oil and Gas Assessment Project. This includes USGS DDS Series 30, 35 and 36. This collection was available online through various web platforms hosted by USGS Central Energy Resources Science Center / Central Energy Team since initial recovery from the CD's in early 2000's. This contains the data collection from the original data archives. Over 11,000 files are part of this collection, with 1,524 shapefiles, 648 PDFs and 189 Tab-delimited data files. Limited qa/qc was performed on this due to time constraints and acknowledging that this is a representation of a product over 20 years old.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Geographic coverage

Urban and rural areas of Papua New Guinea

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.

A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.

The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Vanuatu and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For Vanuatu, data for December 2023 – January 2025 was collected with each month having approximately 1000 households in the sample and is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in Vanuatu. There is one date file for household level data with a unique household ID. And a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households, where the data is panel data.

Geographic coverage

National, urban and rural. Six provinces were covered by this survey: Sanma, Shefa, Torba, Penama, Malampa and Tafea.

Analysis unit

Household and individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Vanuatu High Frequency Phone Survey (HFPS) sample is drawn from the list of customer phone numbers (MSIDNS) provided by Digicel Vanuatu, one of the country’s two main mobile providers. Digicel’s customer base spans all regions of Vanuatu. For the initial data collection, Digicel filtered their MSIDNS database to ensure a representative distribution across regions. Recognizing the challenge of reaching low-income respondents, Digicel also included low-income areas and customers with a low-income profile (defined by monthly spending between 50 and 150 VT), as well as those with only incoming calls or using the IOU service without repayment. These filtered lists were then randomized, and enumerators began calling the numbers.

This approach was used to complete the first round of 1,000 interviews. The respondents from this first round formed a panel to be surveyed monthly. Each month, phone numbers from the panel are contacted until all have been interviewed, at which point new phone numbers (fresh MSIDNS from Digicel’s database) are used to replace those that have been exhausted. These new respondents are then added to the panel for future surveys.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was developed in both English and Bislama. Sections of the Questionnaire:

-Interview Information -Household Roster (separate modules for new households and returning households) -Labor (separate modules for new households and returning households) -Food Security
-Household Income -Agriculture
-Social Protection
-Access to Services -Assets -Perceptions -Follow-up

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the survey firm and the World Bank team. Data cleaning mainly included formatting, relabeling, and excluding survey monitoring variables (e.g., interview start and end times). Data was edited using the software STATA.

The data are presented in two datasets: a household dataset and an individual dataset. The total number of observations is 13,779 in the household dataset and 77,501 in the individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (hhid_mem) can be found in the individual dataset.

Response rate

In November 2024, a total of 7,874 calls were made. Of these, 2,251 calls were successfully connected, and 1,000 respondents completed the survey. By February 2024, the sample was fully comprised of returning respondents, with a re-contact rate of 99.9 percent.

During a survey carried out among decision-makers in charge of customer engagement/retention strategy from 20 countries worldwide, ** percent of respondents stated that they thought it was important or critical to collect customer channel engagement data; ************* named real-time experience in this context.

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

This Excel file contains early childhood and prekindergarten student enrollment data for all states. The file contains three spreadsheets: total children, male children, and female children.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Solomon Islands and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For Solmon Islands, after five rounds of data collection from 2020-2020, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. Fieldwork took place in two non-consecutive weeks of each month. Data for April 2023-December 2023 were a repeated cross section, while January 2024 established the first month of a panel, the was continued to September 2024. Each month has approximately 550 households in the sample and is representative of urban and rural areas, but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in Solomon Islands. There is one date file for household level data with a unique household ID. and a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households, where the data is panel data.

Geographic coverage

Urban and rural areas of Solomon Islands.

Analysis unit

Household, individual.

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The initial sample was drawn from information provided by a major phone service provider in Solomon Islands, covering all the provinces in the country. It had a probability-based weighted design, with a proportionate stratification to achieve geographical representation. The geographical distribution compared to the 2019 Census is listed below for the first month of the HFPS monthly survey:

Choiseul : Census: 4.3%, HFPS: 5.2% Western : Census: 14.4%, HFPS: 13.7% Isabel : Census: 4.8%, HFPS: 4.7% Central : Census: 3.6%, HFPS: 5.2% Ren Bell : Census: 0.6%, HFPS: 1.4% Guadalcanal: Census: 19.8%, HFPS: 21.1% Malaita : Census: 23.1%, HFPS: 18.7% Makira : Census: 5.6%, HFPS: 5.6% Temotu: Census: 3.0%, HFPS: 3% Honiara: Census: 20.7%, HFPS: 21.3%

Source: Census of Population and Housing 2019

Note: The values in the HFPS column represent the proportion of survey participants residing in each province, based on the raw HFPS data from April.

In April 2023, the geographic distribution of World Bank HFPS participants was generally similar to that of the census data at the province level, though within provinces, areas with less mobile phone connectivity are likely to be underrepresented. One indication of this is that urban areas constituted 38.2 percent of the survey sample, which is a slight overrepresentation, compared to 32.5 percent in the Census 2019.

A monthly panel was established in January 2024, that is ongoing as of March 2025. In each subsequent month after January 2024, the survey firm would first attempt to contact all households from the previous month and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households. Across all months of the survey a total of, 9,926 interviews were completed.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire, which can be found in the External Resources of this documentation, is available in English, with Solomons Pijin translation. There were few changes to the questionnaire across the survey months, but some sections were only introduced in 2024, namely energy access questions and questions to inform the baseline data of the Solomon Islands Government Integrated Economic Development and Climate Resilience (IEDCR) project.

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The total number of observations is 9,926 in the household dataset and 62,054 in the individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

Abstract

The survey provides basic data needed for the development of national policies. The main objectives of the Time Use Survey were as follows:

1. Measurement and analysis of quality of life or general well-being.
2. Identifying demographic and socio-economic characteristics of individuals in Palestinian society.
3. Measurement and valuation of unpaid work (domestic and volunteer work) and development of household production accounts.
4. Improving estimates of paid and unpaid work.
5. Assisting planners and policy makers to develop strategies and policies that may contribute to developmental planning issues.

It is also a rich source of information about the use of time to learn about the nature and structure of individuals in Palestinian society during the year 2012/2013, in different age groups, including children, women, youth and the elderly, and to illuminate the path for decision makers and policy makers in the process of comprehensive national development in this country.

Time Use Survey is a basic tool to determine gender issues. The data enable analysis of the quality of life and an assessment of the extent of female participation in paid and unpaid work (housework and volunteer work) and women's contribution to national accounts.

Geographic coverage

1- Governorate (16 governorates in west bank and Gaza strip) 2- Locality type (urban, rural, camps)

Analysis unit

Individual

Universe

The Target population of the survey consists of all Palestinian individuals of age group 10 years and over, who are living normally with their households in Palestine in 2012/2013 .

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Design After determining the sample size, the sample type is three-stage stratified cluster sample as following:

1- First stage: selecting systematic sample of 220 clusters (enumeration areas). 2- Second stage: selection sample of 21 responded households from each EA selected in the first stage (we use the area sampling to get this number of responded households). 3- Third stage: selection two individuals male and female (10 years and more) from each household selected in second stage using random kish tables.

The population was divided to strata by:

Governorate (16 governorates in west bank and Gaza strip) Locality type (urban, rural, camps)

Sampling deviation

The sample size of the survey is 5,903 Palestinian households.

After determining the sample size, the sample type is three-stage stratified cluster sample as following:

1- First stage: selecting systematic sample of 220 clusters (enumeration areas). 2- Second stage: selection sample of 21 responded households from each EA selected in the first stage (we use the area sampling to get this number of responded households). Third stage: selection two individuals male and female (10 years and more) from each household selected in second stage using random kish tables

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire The survey questionnaire is the main tool for data collection and was designed on the basis of international surveys specially designed for time use surveys, as well as on the basis of the recommendations of the workshop on time use surveys held in Jordan in 2010. This was organized by ESCWA in cooperation with UNSD to develop a questionnaire for a time use survey and coding manual, along with adding activities related to the Palestinian context compatible with the coding manual of the United Nations of 2006. The questionnaire meets the technical specifications for the field work phase and data processing and analysis requirements. The questionnaire included several sections:

1. Identification Data This identifies a unified means of determining data that define a household, including the divisions of sample design: the number in the enumeration area, governorate and locality, building identification number, number of household, and the name of head of household.

2. Quality Control This is the development of controls of field and office operations and the sequencing in questionnaire stages, usually beginning with data collection through to field and office auditing, data coding, data entry, checks after data entry, and ending with the storage process.

3. Household Members Background Details These include household members, relationship to the head of household, gender, date of birth and age, in addition to other demographic and economic data for the household as a whole.

4. Household Questionnaire This includes questions related to the household in terms of type of housing unit, material used as flooring in the housing unit, primary fuel type used in cooking, goods and services available, monthly household income, and other indicators.

5. Daily Record Questionnaire This part of the questionnaire comprised two time records: in the first record, one male member of the household aged 10 years and above is selected at random and in the second record, one female household member aged 10 years and above is selected at random. The day was divided into periods of time of up to 30 minutes each from midnight until six am and 10 minutes for each period during the day from six am until twelve o'clock at night. The record also contains information that shows whether the activity was performed for a fee or financial return or not. Any secondary activity is also recorded. This information identifies the respondent performing these activities, with whom and the means of transportation or venue where the individual performed the various activities throughout the day (during a 24-hour period).

Cleaning operations

Data verification: comprehensive automated rules of data verification in between questions ensured consistency and identification of answers that were out of range or irrational. This was carried out by a special program performed on a regular basis. The team reviewed error messages and modification of errors based on observations or returned the questionnaire to the field for double checking. The auditing mechanism was prepared by the project management and applied to the data entry program by a programmer where necessary. Appropriate data auditing tests proposed by the project management during the auditing procedure were inclusive and covered all questions in the questionnaire. The questionnaires were drawn from extracted lists and checked automatically, corrected and adjusted on the computer. Then a second list was extracted for the same questionnaires to ensure that the amendment was valid and that all questionnaires had been modified.

Response rate

The sample size of the survey was 5,903 households and 4,605 households were completed. Weights were adjusted to compensate for the non-response cases. The response rate in the survey in Palestine was 79.6% for households

Sampling error estimates

Survey data may be affected by statistical errors as a result of the use of a sample rather than a comprehensive survey covering all units of the study population. Thus, differences may be anticipated from the real values that emerge from a census and variations were calculated for the most important indicators.

The results indicated that there was no problem in the dissemination of data applicable to Palestine as a whole or on a regional basis (the West Bank and the Gaza Strip).

Data appraisal

The concept of data quality includes multiple aspects, starting from initial planning for the survey and ending with data dissemination and interpretation of data for optimal use. The most important components of statistical quality include accuracy, comparability, and quality control procedures. Statistical quality also includes checking and auditing data accuracy in multiple aspects of the survey, particularly statistical errors due to the use of a sample, plus non-statistical errors by staff and the use of survey tools. Response rates may also have a crucial impact on estimates

Each dataset provides monthly data at the national level for initial Social Security Disability Insurance (SSDI) applications filed via the Internet. The dataset includes only SSDI initial receipts. SSDI initial receipt represent a worker in covered employment long enough and recently enough to be "insured", earning paid Social Security taxes. Social Security work credits are based on total yearly wages or self-employment income. The amount needed for a credit change from year to year. The number of work credits needed to qualify for disability benefits depends on age at disability onset. Generally, 40 credits are needed, 20 of which were earned in the last 10 years ending with the year of disability onset.

Abstract

The rapid and massive dissemination of mobile phones in the developing world is creating new opportunities for the discipline of survey research. The World Bank is interested in leveraging mobile phone technology as a means of direct communication with poor households in the developing world in order to gather rapid feedback on the impact of economic crises and other events on the economy of such households.

The World Bank commissioned Gallup to conduct the Listening to LAC (L2L) pilot program, a research project aimed at testing the feasibility of mobile phone technology as a way of data collection for conducting quick turnaround, self-administered, longitudinal surveys among households in Peru and Honduras.

The project used face-to-face interviews as its benchmark, and included Short Message Service (SMS), Interactive Voice Response (IVR) and Computer Assisted Telephone Interviews (CATI) as test methods of data collection.

The pilot was designed in a way that allowed testing the response rates and the quality of data, while also providing information on the cost of collecting data using mobile phones. Researchers also evaluated if providing incentives affected panel attrition rates. The Honduras design was a test-retest design, which is closely related to the difference-in-difference methodology of experimental evaluation.

The random stratified multistage sampling technique was used to select a nationally representative sample of 1,500 households. During the initial face-to-face interviews, researchers gathered information on the socio-economic characteristics of households and recruited participants for follow-up research. Questions wording was the same in all modes of data collection.

In Honduras, after the initial face-to-face interviews, respondents were exposed to the remaining three methodologies according to a randomized scheme (three rotations, one methodology per week). Panelists in Honduras were surveyed for four and a half months, starting in February 2012.

Geographic coverage

Includes the entire national territory, with the exception of neighborhoods where access of interviewers is extremely difficult, due to lack of transportation infrastructure or for situations that threaten the physical integrity of the interviewers and supervisors (i.e. extremely high crime rate, warfare, etc.)

Analysis unit

• Households

Universe

All the households that exist in the neighborhoods of Honduras, as reported by the 2001 Census. Institutions such as military, religious or educational living quarters are not included in the universe.

Kind of data

Sample survey data [ssd]

Sampling procedure

Honduras did not have an income oversample because the poverty rate is 60 percent, so oversampling 20 percent above the poverty rate would include a large portion of the middle class, which are not the most vulnerable in times of crisis.

The Honduras panel was built on a nationally representative sample of 1,500 households. The sample was drawn by means of a random, stratified, multistage design. The pilot used Gallup World Poll sampling frame.

Census-defined municipalities were classified into five strata according to population size: I. Municipalities with 500,000 to 999,000 inhabitants II. Municipalities with 100,000 to 499,000 inhabitants III. Municipalities with 50,000 to 99,000 inhabitants IV. Municipalities with 10,000 and 49,000 inhabitants V. Municipalities with less than 10,000 inhabitants

Interviews were then proportionally allocated to these five strata according to their share among the country's population.

• The first stage of the design consisted of a random selection of Primary Sampling Units (PSU's) within each of the five strata previously defined.

• In the second stage, in each PSU, one or more Secondary Sampling Units (SSU's) were then selected.

• Once SSU's were selected, interviewers were sent to the field to proceed with the third stage of the sample design, which consisted of selecting households using a systematic "random route" procedure. Interviewers started from the previously selected "random origin" and walked around the block in clockwise direction, selecting every third household on their right hand side. They were also trained to handle vacant, nonresponsive, non-cooperative households, as well as other failed attempts, in a systematic manner.

Mode of data collection

Other [oth]

Research instrument

The following survey instruments were used in the project:

1) Initial face-to-face questionnaire

In Peru, the starting point was the ENAHO (National Household Survey) questionnaire. Step-wise regressions were done to select the set of questions that best predicted consumption. For the purposes of robustness, the regressions were also done with questions that best predicted income, which yielded the same results. A similar procedure was done in Honduras, using the latest household survey deployed by the Honduran Statistics Institute, except that only best predictors of income were chosen, because Honduras did not have a recent consumption aggregate.

The survey gathered information on households' demographics, household infrastructure, employment, remittances, income, accidents, food security, self-perceptions on poverty, Internet access and cellphones use.

2) Monthly questionnaires (SMS, IVR, CATI)

The questionnaires were worded exactly the same way, regardless of the mode, which meant short questions, since SMS is limited to 160 characters. A maximum of 10 questions had to be chosen for the monthly questionnaire. In addition, two questions sought to ensure the validity of the responses by testing if the respondent was a member of the household. Most questions were time-variant and each questionnaire was repeated to observe if answers changed over time. All questions related to variables that strongly affect household welfare and are likely to change in times of crisis.

3) Final face-to-face questionnaire

Gallup conducted face-to-face closing surveys among 700 panelists. The researchers asked about issues the respondets had with mobile phones and coverage during the test. Panelists were also asked what would motivate them to keep on participating in a project like this in the future.

The questionnaires were worded exactly the same way, regardless of the mode, which meant short questions, since SMS is limited to 160 characters, unlike IVR and CATI.

Response rate

In Honduras, 41% of recruited households failed to answer the first round of follow-up surveys. The attrition rate from the initial face-to-face interview to the end of panel study was 50%.

As part of the survey administration process Gallup implemented a number of mechanisms to maximize the response rate and panelist retention. The following strategies were applied to respondents who did not replay first time:

• The surveys were left open for responses for up to 2 weeks after the original transmission of the survey (from original call in the case of IVR and CATI).
• First reminder was sent within 72 hours of first attempt (SMS and IVR).
• Second reminder was sent within 144 hours of first attempt (SMS and IVR).
• Call backs were made within 72 and 144 hours of first attempt (CATI); or
• Up to 2 call backs were made per appointment with respondent (CATI).

Also, in order to minimize non-response, three types of incentives were given. First, households that did not own a mobile phone were provided one for free. Approximately 127 phones were donated in Honduras. Second, all communications between the interviewers and the households were free to the respondents. Finally, households were randomly assigned to one of three incentive levels: one-third of households received US$1 in free airtime for each questionnaire they answered, one-third received US$5 in free airtime, and one-third received no financial incentive (the control group).

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Tonga and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details. For Tonga, after two rounds of data collection from in 2022, monthly HFPS data collection commenced in April 2023 and continued until November 2024 (but with some gaps in the months of collection). The survey collected socio-economic data on topics including employment, income, food security, health, food prices, assets and well-being. Each month of collection has approximately 415 households in the sample and is representative of urban and rural areas. This dataset contains combined monthly survey data for all months of the continuous HFPS in Tonga.

Geographic coverage

National urban and rural areas (5 islands): Tongatapu, Vava'u, Ha'apai, Eua, Ongo Niua

Analysis unit

Individual and household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tonga High Frequency Phone Survey (HFPS) monthly sample was generated in three ways. The first method is Random Digit Dialing (RDD) process covering all cell telephone numbers active at the time of the sample selection. The RDD methodology generates virtually all possible telephone numbers in the country under the national telephone numbering plan and then draws a random sample of numbers. This method guarantees full coverage of the population with a phone.

First, a large first-phase sample of cell phone numbers was selected and screened through an automated process to identify the active numbers. Then, a smaller second-phase sample was selected from the active residential numbers identified in the first-phase sample and was delivered to the data collection team to be called by the interviewers. When a cell phone was called, the call answerer was interviewed as long as he or she was 18 years of age or above and knowledgeable about the household activities.

It was initially planned to stratify the sample by island group based on the phone number prefixes. However, this was not feasible given the high internal migration across islands and the atypical assignment of phone number prefixes across islands in Tonga. The raw sample is overrepresenting urban areas and the population of Tongatapu.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was developed in both English and Tongan and can be found in this documentation in Excel format. Sections of the Questionnaire are provided below: 1. Interview information and Basic information 2. Household roster 3. Labor 4. Food security and food prices 5. Household income 6. Agriculture 7. Social protection 8. Access to services 9. Assets 10. Education 11. Follow up

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the survey firm and the World Bank team. Data cleaning mainly included formatting, relabeling, and excluding survey monitoring variables (e.g., interview start and end times). Data was edited using the software Stata.

Abstract

The phone survey was conducted to gather data on the socio-economic impact of COVID-19 crisis in Vanuatu. Community transmission of COVID-19 in Vanuatu started only in March 2022 followed by the nation-wide lockdown and other restrictions. Round 1 HFPS survey was a timely process to observe the effect of the crisis on the country. Round 1 interviewed 2,515 households both in urban and rural regions of the country from July 2022 to September 2022.

Survey topics included employment and income, food security, coping strategies, access to health services, and asset ownership - all on household level. Additionally, two individual-level datasets explore adult employment and child education. The former selects a randomly chosen adult in the household - could be the respondent of a household-level data, head of the household or another individual - and inquires about their employment status. For the latter, the respondent is being asked about education of a randomly chosen child in the household with more than one child.

While these findings are not without their caveats due to the lack of baseline data, constraints of the mobile phone survey methodology, and data quality constraints, they represent the best estimates to date and supplement other data on macroeconomic conditions, exports, firm-level information, etc. to develop an initial picture of the impacts of the crises on the population.

Geographic coverage

National urban and rural (6 provinces) coverage: Sanma, Shefa, Torba, Penama, Malampa, Tafea

Analysis unit

Household and Individual.

Universe

All respondents must be aged 18 and over and have a phone.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Vanuatu HFPS Round 1 sample was generated in three ways. The first method is Random Digit Dialing (RDD) process covering all cell telephone numbers active at the time of the sample selection. Majority of the sample was generated through RDD in this round - approximately 84%.

The RDD methodology generates virtually all possible telephone numbers in the country under the national telephone numbering plan and then draws a random sample of numbers. This method guarantees full coverage of the population with a phone.

First, a large first-phase sample of cell phone numbers was selected and screened through an automated process to identify the active numbers. Then, a smaller second-phase sample was selected from the active residential numbers identified in the first-phase sample and was delivered to the data collection team to be called by the interviewers. When a cell phone was called, the call answerer was interviewed as long as he or she was 18 years of age or above and knowledgeable about the household activities.

The remaining 16% of Round 1 respondents was retrieved from Vanuatu's National Sustainable Development Plan (NSDP) Baseline Survey 2019/20.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was developed in both English and Bislama. Sections of the Questionnaire are listed below: 1. Interview Information 2. Basic Information 3. Vaccine Information 4. Health 5. Education 6. Food Insecurity 7. Employment 8. Income 9. Coping Strategies 10. Assets 11. Digital 12. Recontact

The questionnaire is provided in this documentation as an external resource.

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the survey firm and the World Bank team. Data cleaning mainly included formatting, relabeling, and excluding survey monitoring variables (e.g., interview start and end times). Data was edited using the software Stata.

Response rate

Total of 9,674 calls were attempted for Round 1. Response rate - where the phone was picked up - was 40%. Out of these, 66% completed the full survey.

This dataset represents land cover mapping, physical habitat measurements, continuous hydrology measurements, salt tracer measurements, and benthic macroinvertebrate sample scores from nine small streams in unincorporated King County within the Puget Sound region of Washington State. These data were collected during two periods, 2008 – 2012/2013 and 2018 – 2022, as part of a study to evaluate the performance of King County’s land use regulations at protecting stream ecosystems. Six of the streams drained watersheds that were developed or developable and were subject to King County’s land use regulations. Three of the streams drained watersheds that were largely protected from development and served as references for comparison. The initial data collection (2008 – 2012/2013) is described in a report titled, “Assessing Land Use Effects and Regulatory Effectiveness on Streams in Rural Watersheds of King County, Washington,” published in 2014. An analysis of the two combined datasets is described in a report titled, “The Effects of the Land Use Regulatory Framework on Stream Ecosystems in Unincorporated King County Watersheds,” published in 2025. See these reports for details about the sampling methods, study results, and what these data represent. Below we briefly describe the types of data included in this dataset. For questions about these data, please contact James Bower (james.bower@kingcounty.gov), Aaron David (adavid@kingcounty.gov), Ian Higgins (ihiggins@kingcounty.gov), or Rebekah Stiling (rstiling@kingcounty.gov). All data were collected by the King County Water and Land Resources Division, Science and Technical Support Section. Land cover mapping of the nine study watersheds was conducted once at the beginning and end of the first period (2007 and 2012) and once at the beginning and end of the second period (2017 and 2022). The land cover data are represented by ‘Land_cover.csv’. Physical habitat measurements were collected once a year within a defined and consistent section of each stream. Physical habitat measurements are represented by ‘Pools.csv’, ‘Reach_lengths.csv’, ‘Substrate.csv’, ‘Thalweg_depths.csv’, and ‘Wood.csv’. Continuous hydrology measurements of stream discharge, water temperature, and conductivity were collected in each stream throughout most years of the study. Continuous hydrology measurements were summarized into daily values and are represented by ‘Hydrology_daily.csv’. Samples of the benthic macroinvertebrate community were collected in each stream during late summer or early fall across all study years. These samples were used to calculate Puget Sound lowlands Benthic-Index of Biotic Integrity scores for each stream and year. Benthic macroinvertebrate sample scores are represented by ‘BIBI.csv’. Salt tracer measurements were conducted in each stream across multiple flows within each year. Salt tracer measurements are represented by ‘Tracer_measurements.csv’. The ‘Variable_names.csv’ file contains a list of each of the variable/field names within each data file, the variable type for each field, and a brief description of what each variable/field represents.

This project was initiated in response to regulation 50 CFR 660.114, which mandates that economic data be collected from every participant in the trawl rationalization program. The data are collected annually from catcher vessels, catcher processors, motherships, first receivers, and shorebased processors through paper-based forms. The four forms (specific to entity type) are mailed annually in May, and collect data about the fishing, buying, and processing information from the previous year. The entity must submit their data by September 1 in order to renew their limited entry trawl permits, reissue their quota share, vessel accounts, and receive their first receiver site licenses. The data, reports, tech memos, and academic papers are used by Northwest Region staff, Pacific Fishery Management Council (PFMC), and headquarters staff to inform management decisions, and to monitor the effects of the program. Cost, earnings, and production data for first receiver and shorebased processor data.

Abstract

In 2007-2008 a multi-topic household survey, the Timor Leste Living Standards Survey (LSS-2) was conducted in East Timor with the main objectives of developing a system of poverty monitoring and supporting poverty reduction, and to monitor human development indicators and progress toward the Millennium Development Goals. The LSS-3 extension survey was designed to re-visit one third of the households interviewed under the LSS-2 to explore different facets of household welfare and behaviour in the country, while also being able to make use of information collected in the LSS-2 survey for analytic purposes. The four new topics investigated in the extension survey are:

• Risk and Vulnerability: This section is designed to help us understand the dimensions and sources of household-level vulnerability to uninsured risks in Timor Leste, and the efficacy and welfare effects of various risk-management strategies (prevention, mitigation, coping) and mechanisms (private as well as public, formal as well as informal) households do (or do not) have access to. The work in Timor Leste is part of a program of analytic work and policy dialogue throughout the EAP region, more information on which can be found on the World Bank website.
• Land Degradation and Poverty: This section of the questionnaire is designed to identify proximate causes of deforestation through land use patterns and links with poverty; understand strengths and failures of common land resource management institutions (property rights, enforcement); understand the impact of the Siam Weed problem on household welfare.
• Justice for Poor: The Justice for the Poor/Access to Justice (J4P/A2J) module of the survey will serve mainly as an initial diagnostic for project development in the country. The topics we would be interested in covering would be Dispute Processing/Resolution; Social Legal Norms and Perceptions of Efficiency in Government (Local, Sub-District, District and National level).
• Access to Financial Services: The financial service work has the following two objectives: (i) to collect data on access to and use financial services (savings and credit), both formal and informal, and (ii) assess the quality of information on access to financial services obtained from head of households vs. from all adults - i.e. is there a bias introduced by not asking all household members, do the characteristics of the head or the household affect this (gender, age, nuclear family, urban, education levels, wealth, etc.).

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN FOR THE 2008 EXTENSION SURVEY

Sampling for the LSS-3 Extension survey was a sub-sample of the original LSS-“ sample. The LSS-2 field work was divided into 52 "weeks", with each week being a random subset of the total sample. The sub-sample was chosen by randomly selecting 19 weeks from the original field work schedule. Each week contained seven Primary Sampling Units (PSUs) for a total of 133 PSUs. In each PSU the teams were to interview 12 of the original 15 households, with the remaining three to serve as replacements. The total nominal sample size was thus 1596.

Additional interviews: Following the collection and initial analysis of the data, it was determined that data from one district, Manatuto, and partially from another district, Oecussi, were of insufficient quality in certain modules. Therefore, it was decided to repeat the survey in another 25 PSUs of these two districts - six in Manatuto, and 19 in Oecussi. The additional PSUs chosen were randomly selected within the two districts from the remaining non-panel PSUs in the original LSS-2 sample.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

DATA CLEANING

The LSS-3 had a significant number of responses in which the response is "other". In general, if the response clear fit into a pre-coded response category, it was recoded into that category during the cleaning and compilation process. Some responses where additional information was provided were not recoded even though they clearly fit into pre-coded categories. For example, agriculture project" would be recoded into the "agriculture" category, while "community garden" would not. Data users can either use the additional information, or re-code into categories as they see fit. Potential Data Quality Issues in 2008 Extension survey

Data appraisal

Potential Data Quality Issues in 2008 Extension survey

Agriculture: Similarly, to the individual roster of the previous section, the plots listed in the previous survey are listed on the pre-printed cover page and all changes noted. The agricultural section, similarly, to the other sections, suffers from problems with open-ended questions. This is particularly the case for the question asking what community restrictions are placed on the clearing of forest land (section 2d). The translation from the original question was vague (using the Tetun word for "boundary" for "restriction,") and therefore many of the responses relate to physical boundaries on the land, such as stone walls and tree lines. Additionally, the translation of all answers from Tetun into English is imperfect, and those wishing to use this information for analytical purposes are advised to also refer to the original Tetun. Analysts should be careful in using the data from the open ended questions because of translation problems. Also, it was noted during the training and field work that many interviewers had significant difficulties understanding definitions with some of the land management and investment questions. In general, however, all agricultural data may be used for analysis, sampling weights w3.

Finance: It should be noted that the quality of the data for the finance experiment (comparing the knowledge of the household head to that of other household members) was not sufficient for the experiment to be deemed a success. Subsequent spot-checking revealed that in many cases, interviewers asked the household head about the financial activities of various household members instead of asking them directly. Therefore, this data should only be used to measure the access to finance at the household level. The finance sections were not repeated during the additional interviews in the replacement PSUs. Sampling weights w1 should be used when doing any analysis with this data.

Shocks and Vulnerability: It was determined following the initial round of data collection that the shocks and vulnerability module had some issues with uneven interview quality. Two reasons were listed as potential causes of the data quality issues: (1) fundamental inability to adequately translate both the word and concept of a "shock" into the Timorese context, and (2) incomplete / questionable responses to the health shock questions in particular. Analysis for health shocks should drop the "questionable" households and use the "re-interview" households, sampling weights w2.

Justice for the Poor: Similar to the shocks and vulnerability module, the justice module included a long series of follow up questions if the household indicated having experienced a dispute during the recall period. Again, the number of disputes experienced by the household seemed extremely low compared to expectations. This was particularly a problem with the Manatuto district in which no disputes were recorded during the first set of TLSLS2-X interviews. Analysis for the disputes section of the justice module should drop the "questionable" households and use the "re-interview" households, sampling weights w2. The justice model also has a number of instances in which the specifications for "other" were not recorded. Every effort was made to ensure this data was as complete as possible, but gaps do remain. Also, data users should use caution when using the imputed rank variable in section 5D. The rank in terms of importance was not explicitly captured in the data entry software, and the rankings therefore had to be imputed from the order they were listed in the original data entry. Inconsistencies may exist in this variable.

The United Nations began its World Crime Surveys in 1978. The first survey collected statistics on a small range of offenses and on the criminal justice process for the years 1970-1975. The second survey collected data on a wide range of offenses, offenders, and criminal justice process data for the years 1975-1980. Several factors make these two collections difficult to use in combination. Some 25 percent of those countries responding to the first survey did not respond to the second and, similarly, some 30 percent of those responding to the second survey did not respond to the first. In addition, many questions asked in the second survey were not asked in the first survey. This data collection represents the efforts of the investigators to combine, revise, and recheck the data of the first two surveys. The data are divided into two parts. Part 1 comprises all data on offenses and on some criminal justice personnel. Crime data are entered for 1970 through 1980. In most cases 1975 is entered twice, since both surveys collected data for this year. Part 2 includes data on offenders, prosecutions, convictions, and prisons. Data are entered for 1970 through 1980, for every even year.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The cities that are covered are La Paz, El Alto, Cochabamba and Santa Cruz de la Sierra.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members 15 to 64 years old. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The STEP target population is the population 15-64 years old, living in urban areas, as defined by each country's statistical office. The following are excluded from the sample: - Residents of institutions (prisons, hospitals, etc.) - Residents of senior homes and hospices - Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc. - Persons living outside the country at the time of data collection

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified 3-stage sample design was implemented in Bolivia. The stratification variable is city-wealth category. There are 20 strata created by grouping the primary sample units (PSUs) into the 4 cities, i.e.,1- La Paz, 2-El Alto, 3-Cochabamba, 4-Santa Cruz de la Sierra, and 5 wealth categories, i.e., 1-Poorest, 2-Moderately Poor, 3-Middle Wealth, 4-Moderately Rich, 5-Rich.

The source of the sample frame of the first stage units is the 2001 National Census of Population and Housing carried out by the National Institute of Statistics. The primary sample unit (PSU) is a Census Sector. A sample of 218 PSUs was selected from the 10,304 PSUs on the sample frame. This sample of PSUs was comprised of 160 'initial' PSUs and 58 'reserve' PSUs. Of the 218 sampled PSUs, there were 169 activated PSUs consisting of 155 Initial Sampled PSUs and 14 Reserve sampled PSUs. Among the 160 'initial' PSUs, 5 PSUs were replaced due to security concerns; also, 14 reserve PSUs were activated to supplement the sample for initial PSUs where the target sample of 15 interviews was not achieved due to high levels of non-response; thus, only 169 PSUs were actually activated during data collection. The PSUs were grouped according to city-wealth strata, and within each city-wealth stratum PSUs were selected with probability proportional to size (PPS), where the measure of size was the number of households in a PSU.

The second stage sample unit (SSU) is a household. The sampling objective was to obtain interviews at 15 households within each of the initial PSU sample, resulting in a final initial sample of 2,400 interviews. At the second stage of sample selection, 45 households were selected in each PSU using a systematic random method. The 45 households were randomly divided into 15 'Initial' households, and 30 'Reserve' households that were ranked according to the random sample selection order. Note: Due to higher than expected levels of non-response in some PSUs, additional households were sampled; thus, the final actual sample in some PSUs exceeded 45 households.

The third stage sample unit was an individual 15-64 years old (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include:

• The background questionnaire developed by the World Bank (WB) STEP team
• Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP technical standards: two independent translators adapted and translated the STEP background questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator.

The survey instruments were piloted as part of the survey pre-test.

The background questionnaire covers such topics as respondents' demographic characteristics, dwelling characteristics, education and training, health, employment, job skill requirements, personality, behavior and preferences, language and family background.

The background questionnaire, the structure of the Reading Literacy Assessment and Reading Literacy Data Codebook are provided in the document "Bolivia STEP Skills Measurement Survey Instruments", available in external resources.

Cleaning operations

STEP data management process:

1) Raw data is sent by the survey firm 2) The World Bank (WB) STEP team runs data checks on the background questionnaire data. Educational Testing Services (ETS) runs data checks on the Reading Literacy Assessment data. Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check if the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the background questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information on data processing in STEP surveys is provided in "STEP Guidelines for Data Processing" document, available in external resources. The template do-file used by the STEP team to check raw background questionnaire data is provided as an external resource, too.

Response rate

An overall response rate of 43% was achieved in the Bolivia STEP Survey. All non-response cases were documented (refusal/not found/no eligible household member, etc.) and accounted for during the weighting process. In such cases, a reserve household was activated to replace the initial household. Procedures are described in "Operation Manual" that is provided as an external resource.

This data collection is the Original Product Resolution (OPR) Digital Elevation Model (DEM) as provided to the USGS. This source DEM is delivered in the original resolution, units and horizontal and vertical spatial references. These data may be used as the source of updates to the 3D Elevation Program (3DEP), which serves as the elevation layer of The National Map.

Abstract (en): The Study of Women's Health Across the Nation (SWAN) is a multisite longitudinal, epidemiologic study designed to examine the health of women during their middle years. The study examines the physical, biological, psychological and social changes during this transitional period. The goal of SWAN's research is to help scientists, health care providers, and women learn how mid-life experiences affect health and quality of life during aging. The study is co-sponsored by the National Institute on Aging (NIA) and the National Institute of Health (NIH), Office of Research on Women's Health. The study began in 1995 and is in its seventeenth year. Between 1998 and 2001, 2,829 of the 3,302 women that joined SWAN participated in a collection of family history data. The research centers are located in the following communities: Ypsilanti and Inkster, MI (University of Michigan); Boston, MA (Massachusetts General Hospital); Chicago, IL (Rush Presbyterian-St. Luke's Medical Center); Almeda and Contra Costa County, CA (University of California, Davis and Kaiser Permanente); Los Angeles, CA (University of California, Los Angeles); Hackensack, NJ (Hackensack University Medical Center); and Pittsburgh, PA (University of Pittsburgh). SWAN participants represent five racial/ethnic groups and a variety of backgrounds and cultures. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Checked for undocumented or out-of-range codes.. Response Rates: 16,065 completed the screening interview. 3,302 were enrolled in the longitudinal study. 2,829 completed the family medical history. Women age 40 through 55, living in designated geographic areas, with the ability to speak English or other designated languages (Japanese, Cantonese, or Spanish), who had the cognitive ability to provide verbal informed consent, and had membership in a specific site's targeted ethnic group were included within the first SWAN data collection. For the initial data collection, 202,985 sampling units were screened for participation. 34,985 were identified as eligible. 16,065 completed the survey. 3,302 enrolled in the longitudinal study. 2,829 completed the family medical history at either visits 2, 3, or 4. Smallest Geographic Unit: county Site-specific sampling frames were used and encompassed a range of types, including lists of households, telephone numbers, and individual names of women. 2014-02-13 This data collection is now publicly available. Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health (NR004061). United States Department of Health and Human Services. National Institutes of Health. National Institute on Aging (AG012495, AG012505, AG012539, AG012546, AG012553, AG012554). United States Department of Health and Human Services. National Institutes of Health. National Institute of Nursing Research (AG012535). United States Department of Health and Human Services. National Institutes of Health. Office of Research on Women's Health (AG012531). face-to-face interviewUsing the variable SWANID, this dataset can be linked with the SWAN Cross-Sectional Screener Data (ICPSR 04368), Baseline Data (ICPSR 28762), Visit 1 Data (ICPSR 29221), Visit 2 Data (ICPSR 29401), Visit 3 Data (ICPSR 29701), and Visit 4 Data (ICPSR 30142).