Abstract

The study included four separate surveys:

1. The LSMS survey of general population of Serbia in 2002

2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together separately from the 2003 datasets.

3. The LSMS survey of general population of Serbia in 2003 (panel survey)

4. The survey of Roma from Roma settlements in 2003 These two datasets are published together.

Objectives

LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003.

The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed).

Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.]

Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Geographic coverage

The surveys were conducted on the whole territory of Serbia (without Kosovo and Metohija).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample frame for both surveys of general population (LSMS) in 2002 and 2003 consisted of all permanent residents of Serbia, without the population of Kosovo and Metohija, according to definition of permanently resident population contained in UN Recommendations for Population Censuses, which were applied in 2002 Census of Population in the Republic of Serbia. Therefore, permanent residents were all persons living in the territory Serbia longer than one year, with the exception of diplomatic and consular staff.

The sample frame for the survey of Family Income Support recipients included all current recipients of this program on the territory of Serbia based on the official list of recipients given by Ministry of Social affairs.

The definition of the Roma population from Roma settlements was faced with obstacles since precise data on the total number of Roma population in Serbia are not available. According to the last population Census from 2002 there were 108,000 Roma citizens, but the data from the Census are thought to significantly underestimate the total number of the Roma population. However, since no other more precise data were available, this number was taken as the basis for estimate on Roma population from Roma settlements. According to the 2002 Census, settlements with at least 7% of the total population who declared itself as belonging to Roma nationality were selected. A total of 83% or 90,000 self-declared Roma lived in the settlements that were defined in this way and this number was taken as the sample frame for Roma from Roma settlements.

Planned sample: In 2002 the planned size of the sample of general population included 6.500 households. The sample was both nationally and regionally representative (representative on each individual stratum). In 2003 the planned panel sample size was 3.000 households. In order to preserve the representative quality of the sample, we kept every other census block unit of the large sample realized in 2002. This way we kept the identical allocation by strata. In selected census block unit, the same households were interviewed as in the basic survey in 2002. The planned sample of Family Income Support recipients in 2002 and Roma from Roma settlements in 2003 was 500 households for each group.

Sample type: In both national surveys the implemented sample was a two-stage stratified sample. Units of the first stage were enumeration districts, and units of the second stage were the households. In the basic 2002 survey, enumeration districts were selected with probability proportional to number of households, so that the enumeration districts with bigger number of households have a higher probability of selection. In the repeated survey in 2003, first-stage units (census block units) were selected from the basic sample obtained in 2002 by including only even numbered census block units. In practice this meant that every second census block unit from the previous survey was included in the sample. In each selected enumeration district the same households interviewed in the previous round were included and interviewed. On finishing the survey in 2003 the cases were merged both on the level of households and members.

Stratification: Municipalities are stratified into the following six territorial strata: Vojvodina, Belgrade, Western Serbia, Central Serbia (Šumadija and Pomoravlje), Eastern Serbia and South-east Serbia. Primary units of selection are further stratified into enumeration districts which belong to urban type of settlements and enumeration districts which belong to rural type of settlement.

The sample of Family Income Support recipients represented the cases chosen randomly from the official list of recipients provided by Ministry of Social Affairs. The sample of Roma from Roma settlements was, as in the national survey, a two-staged stratified sample, but the units in the first stage were settlements where Roma population was represented in the percentage over 7%, and the units of the second stage were Roma households. Settlements are stratified in three territorial strata: Vojvodina, Beograd and Central Serbia.

Mode of data collection

Face-to-face [f2f]

Research instrument

In all surveys the same questionnaire with minimal changes was used. It included different modules, topically separate areas which had an aim of perceiving the living standard of households from different angles. Topic areas were the following: 1. Roster with demography. 2. Housing conditions and durables module with information on the age of durables owned by a household with a special block focused on collecting information on energy billing, payments, and usage. 3. Diary of food expenditures (weekly), including home production, gifts and transfers in kind. 4. Questionnaire of main expenditure-based recall periods sufficient to enable construction of annual consumption at the household level, including home production, gifts and transfers in kind. 5. Agricultural production for all households which cultivate 10+ acres of land or who breed cattle. 6. Participation and social transfers module with detailed breakdown by programs 7. Labour Market module in line with a simplified version of the Labour Force Survey (LFS), with special additional questions to capture various informal sector activities, and providing information on earnings 8. Health with a focus on utilization of services and expenditures (including informal payments) 9. Education module, which incorporated pre-school, compulsory primary education, secondary education and university education. 10. Special income block, focusing on sources of income not covered in other parts (with a focus on remittances).

Response rate

During field work, interviewers kept a precise diary of interviews, recording both successful and unsuccessful visits. Particular attention was paid to reasons why some households were not interviewed. Separate marks were given for households which were not interviewed due to refusal and for cases when a given household could not be found on the territory of the chosen census block.

In 2002 a total of 7,491 households were contacted. Of this number a total of 6,386 households in 621 census rounds were interviewed. Interviewers did not manage to collect the data for 1,106 or 14.8% of selected households. Out of this number 634 households

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

This is an integration of 10 independent multi-country, multi-region, multi-cultural social surveys fielded by Gallup International between 2000 and 2013. The integrated data file contains responses from 535,159 adults living in 103 countries. In total, the harmonization project combined 571 social surveys.

These data have value in a number of longitudinal multi-country, multi-regional, and multi-cultural (L3M) research designs. Understood as independent, though non-random, L3M samples containing a number of multiple indicator ASQ (ask same questions) and ADQ (ask different questions) measures of human development, the environment, international relations, gender equality, security, international organizations, and democracy, to name a few [see full list below].

The data can be used for exploratory and descriptive analysis, with greatest utility at low levels of resolution (e.g. nation-states, supranational groupings). Level of resolution in analysis of these data should be sufficiently low to approximate confidence intervals.

These data can be used for teaching 3M methods, including data harmonization in L3M, 3M research design, survey design, 3M measurement invariance, analysis, and visualization, and reporting. Opportunities to teach about para data, meta data, and data management in L3M designs.

The country units are an unbalanced panel derived from non-probability samples of countries and respondents> Panels (countries) have left and right censorship and are thusly unbalanced. This design limitation can be overcome to the extent that VOTP panels are harmonized with public measurements from other 3M surveys to establish balance in terms of panels and occasions of measurement. Should L3M harmonization occur, these data can be assigned confidence weights to reflect the amount of error in these surveys.

Pooled public opinion surveys (country means), when combine with higher quality country measurements of the same concepts (ASQ, ADQ), can be leveraged to increase the statistical power of pooled publics opinion research designs (multiple L3M datasets)…that is, in studies of public, rather than personal, beliefs.

The Gallup Voice of the People survey data are based on uncertain sampling methods based on underspecified methods. Country sampling is non-random. The sampling method appears be primarily probability and quota sampling, with occasional oversample of urban populations in difficult to survey populations. The sampling units (countries and individuals) are poorly defined, suggesting these data have more value in research designs calling for independent samples replication and repeated-measures frameworks.

**The Voice of the People Survey Series is WIN/Gallup International Association's End of Year survey and is a global study that collects the public's view on the challenges that the world faces today. Ongoing since 1977, the purpose of WIN/Gallup International's End of Year survey is to provide a platform for respondents to speak out concerning government and corporate policies. The Voice of the People, End of Year Surveys for 2012, fielded June 2012 to February 2013, were conducted in 56 countries to solicit public opinion on social and political issues. Respondents were asked whether their country was governed by the will of the people, as well as their attitudes about their society. Additional questions addressed respondents' living conditions and feelings of safety around their living area, as well as personal happiness. Respondents' opinions were also gathered in relation to business development and their views on the effectiveness of the World Health Organization. Respondents were also surveyed on ownership and use of mobile devices. Demographic information includes sex, age, income, education level, employment status, and type of living area.

Description and PurposeThese data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2022):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethodsThe survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and LimitationsThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. This data is the weighted data provided by the ETC Institute, which is used in the final published PDF report.The 2022 Annual Community Survey report is available on data.tempe.gov. The individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Wydale HolmesContact E-Mail (author): wydale_holmes@tempe.govContact (maintainer): Wydale HolmesContact E-Mail (maintainer): wydale_holmes@tempe.govData Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

Evaluating the factors that drive patterns of population differentiation in plants is critical for understanding several biological processes such as local adaptation and incipient speciation. Previous studies have given conflicting results regarding the significance of pollination mode, seed dispersal mode, mating system, growth form, and latitudinal region in shaping patterns of genetic structure, as estimated by FST values, and no study to date has tested their relative importance together across a broad scale. Here we assembled a 337-species dataset for seed plants from publications with data on FST from nuclear markers and species traits, including variables pertaining to the sampling scheme of each study. We used species traits, while accounting for sampling variables, to perform phylogenetic multiple regressions. Results demonstrated that FST values were higher for tropical, mixed-mating, non-woody species pollinated by small insects, indicating greater population differentiation, and lower for temperate, outcrossing trees pollinated by wind. Among the factors we tested, latitudinal region explained the largest portion of variance, followed by pollination mode, mating system and growth form, while seed dispersal mode did not significantly relate to FST. Our analyses provide the most robust and comprehensive evaluation to date of the main ecological factors predicted to drive population differentiation in seed plants, with important implications for understanding the basis of their genetic divergence. Our study supports previous findings showing greater population differentiation in tropical regions and is the first that we are aware of to robustly demonstrate greater population differentiation in species pollinated by small insects.

Methods The main dataset was collected from a systematic literature review in google scholar. From the manuscript:

"We constructed an FST dataset through a systematic search in google scholar (key words: “plant” AND —the following words, each in a separate search— “genetic structure”, “population differentiation”, “population genetics”, “genetic diversity”, “population gene flow”) for articles published up until June 2018. The search yielded thousands of studies, which we reduced to 356 peer-reviewed publications on seed plants that determined population genetic structure (FST) based on nuclear markers. When multiple studies reported FST values for the same species, we recorded the FST from the study with the largest geographic range, as this may better represent the genetic diversity found in the species (Cavers et al., 2005). By this criterion, we compiled a dataset that included 337 unique species. We extracted information for the predictor variables directly from the publications, and infrequently complemented this, where necessary, with information from peer-reviewed literature on the studied species (see Appendix S1 and Table S1 in Supporting Information). Predictor variables were included in multiple regressions to explain variation in FST values (see section FST models). We also included three factors that pertained to the sampling scheme of each study and that can potentially affect FST (Nybom, 2004; Nybom & Bartish, 2000): genetic marker used, maximum distance between populations, mean sample size per population. We used them to construct a null model to be compared against models with our factors of interest. Factors of interest consisted of five categorical variables with 2–4 levels: mating system (outcrossing, mixed-mating), growth form (non-woody, shrub, tree), pollination mode (large insects, small insects, vertebrates, wind), seed dispersal mode (animal, gravity, wind), and latitudinal region (tropics, sub-tropics, temperate)."

The American Community Survey Education Tabulation (ACS-ED) is a custom tabulation of the ACS produced for the National Center of Education Statistics (NCES) by the U.S. Census Bureau. The ACS-ED provides a rich collection of social, economic, demographic, and housing characteristics for school systems, school-age children, and the parents of school-age children. In addition to focusing on school-age children, the ACS-ED provides enrollment iterations for children enrolled in public school. The data profiles include percentages (along with associated margins of error) that allow for comparison of school district-level conditions across the U.S. For more information about the NCES ACS-ED collection, visit the NCES Education Demographic and Geographic Estimates (EDGE) program at: https://nces.ed.gov/programs/edge/Demographic/ACSAnnotation values are negative value representations of estimates and have values when non-integer information needs to be represented. See the table below for a list of common Estimate/Margin of Error (E/M) values and their corresponding Annotation (EA/MA) values.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data. -9 An '-9' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small. -8 An '-8' means that the estimate is not applicable or not available. -6 A '-6' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. -5 A '-5' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. -3 A '-3' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate. -2 A '-2' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame and Units As in all probability sample surveys, it is important that each sampling unit in the surveyed population has a known, non-zero probability of selection. To achieve this, there has to be an appropriate list, or sampling frame of the primary sampling units (PSUs).The universe defined for the GLSS 5 is the population living within private households in Ghana. The institutional population (such as schools, hospitals etc), which represents a very small percentage in the 2000 Population and Housing Census (PHC), is excluded from the frame for the GLSS 5.

The Ghana Statistical Service (GSS) maintains a complete list of census EAs, together with their respective population and number of households as well as maps, with well defined boundaries, of the EAs. . This information was used as the sampling frame for the GLSS 5. Specifically, the EAs were defined as the primary sampling units (PSUs), while the households within each EA constituted the secondary sampling units (SSUs).

Stratification In order to take advantage of possible gains in precision and reliability of the survey estimates from stratification, the EAs were first stratified into the ten administrative regions. Within each region, the EAs were further sub-divided according to their rural and urban areas of location. The EAs were also classified according to ecological zones and inclusion of Accra (GAMA) so that the survey results could be presented according to the three ecological zones, namely 1) Coastal, 2) Forest, and 3) Northern Savannah, and for Accra.

Sample size and allocation The number and allocation of sample EAs for the GLSS 5 depend on the type of estimates to be obtained from the survey and the corresponding precision required. It was decided to select a total sample of around 8000 households nationwide.

To ensure adequate numbers of complete interviews that will allow for reliable estimates at the various domains of interest, the GLSS 5 sample was designed to ensure that at least 400 households were selected from each region.

A two-stage stratified random sampling design was adopted. Initially, a total sample of 550 EAs was considered at the first stage of sampling, followed by a fixed take of 15 households per EA. The distribution of the selected EAs into the ten regions or strata was based on proportionate allocation using the population.

For example, the number of selected EAs allocated to the Western Region was obtained as: 1924577/18912079*550 = 56

Under this sampling scheme, it was observed that the 400 households minimum requirement per region could be achieved in all the regions but not the Upper West Region. The proportionate allocation formula assigned only 17 EAs out of the 550 EAs nationwide and selecting 15 households per EA would have yielded only 255 households for the region. In order to surmount this problem, two options were considered: retaining the 17 EAs in the Upper West Region and increasing the number of selected households per EA from 15 to about 25, or increasing the number of selected EAs in the region from 17 to 27 and retaining the second stage sample of 15 households per EA.

The second option was adopted in view of the fact that it was more likely to provide smaller sampling errors for the separate domains of analysis. Based on this, the number of EAs in Upper East and the Upper West were adjusted from 27 and 17 to 40 and 34 respectively, bringing the total number of EAs to 580 and the number of households to 8,700.

A complete household listing exercise was carried out between May and June 2005 in all the selected EAs to provide the sampling frame for the second stage selection of households. At the second stage of sampling, a fixed number of 15 households per EA was selected in all the regions. In addition, five households per EA were selected as replacement samples.The overall sample size therefore came to 8,700 households nationwide.

Mode of data collection

Face-to-face [f2f]

Key Table Information.Table Title.Population 60 Years and Over in Puerto Rico.Table ID.ACSST1Y2024.S0102PR.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Subject Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.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.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.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.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 an...

2013-2023 Virginia Population by Urban Area. Contains estimates.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B01001 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

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. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

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. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

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.

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 https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will

Description: The 2005 HSRC Master Sample was used for SABSSM 2008 and 2012, the SANHANES study in 2012 and SASAS 2007-2010 (adjacent EAs) to obtain an understanding of geographical spread of HIV/AIDS, perceptions and attitudes of people and other health related studies over time. Abstract: A sample can be defined as a subset containing the characteristics of a larger population. Samples are used in statistical testing when population sizes are too large for the test to include all possible members or observations. A sample should represent the whole population and not reflect bias toward a specific attribute.[1] One of the most crucial aspects of sample design in household surveys is its frame. The sampling frame has significant implications on the cost and the quality of any survey, household or otherwise.[2] The sampling frame .... in a household survey must cover the entire target population. When that frame is used for multiple surveys or multiple rounds of the same survey it is known as a master sample frame or .... master sample.[3] A master sample is a sample drawn from a population for use on a number of future occasions, so as to avoid ad hoc sampling on each occasion. Sometimes the master sample is large and subsequent inquiries are based on a sub-sample from it.[4] The HSRC compiles master samples in order to construct samples for various HSRC research studies. The 2005 HSRC Master Sample was used for SABSSM 2008 and 2012, SASAS 2007-2010 and the SANHANES study in 2012 to obtain an understanding of geographical spread of HIV/AIDS, perceptions and attitudes of people and other health related studies over time. The 2005 HSRC Master Sample was created in the following way: South Africa was delineated into EAs according to municipality and province. Municipal boundaries were obtained from the Municipal Demarcation Board. An Enumeration area (EA) is the smallest geographical unit (piece of land) into which the country is divided for census or survey enumeration.[5] The concepts and definitions of terms used for Census 2001 comply in most instances with United Nations standards for censuses. A total of 1,000 census enumeration areas (EAs) from the 2001 population census were randomly selected using probability proportional to size and stratified by province, locality type and race in urban areas from a database of 80 787 EAs that were mapped using aerial photography to develop an HSRC master sample for selecting households. The ideal frame would be complete with respect to the target population if all of its members (the universe) are covered by the frame. Ideal characteristics of a master sample: The master frame should be as complete, accurate and current as practicable. A master sample frame for household surveys is typically developed from the most recent census, just as a regular sample frame is. Because the master frame may be used during an entire intercensal (between census) period, however, it will usually require periodic and regular updating such as every 2-3 years. This is in contrast to a regular frame which is more likely to be up-dated on an ad hoc basis and only when a particular survey is being planned[6] [1] http://www.investopedia.com/terms/s/sample.asp [2] http://unstats.un.org/unsd/demographic/meetings/egm/sampling_1203/docs/no_3.pdf [3] http://unstats.un.org/unsd/demographic/meetings/egm/sampling_1203/docs/no_3.pdf [4] A Dictionary of Statistical Terms, 5th edition, prepared for the International Statistical Institute by F.H.C. Marriott. Published for the International Statistical Institute by Longman Scientific and Technical. http://stats.oecd.org/glossary/detail.asp?ID=3708 [5] http://africageodownloads.info/128_mokgokolo.pdf [6] http://unstats.un.org/unsd/demographic/meetings/egm/sampling_1203/docs/no_3.pdf All enumeration areas (80 787 EAs) within the South African borders during the 2001 Census. The whole country was delimited into EAs according to municipality and province. Municipal boundaries were obtained from the Municipal Demarcation Board. A total of 1,000 census enumeration areas (EAs) from the 2001 population census were randomly selected using probability proportional to size and stratified by province, locality type and race in urban areas from a database of 80 787 EAs that were mapped in all surveys using aerial photography to develop all HSRC master sample for selecting households. The first digit represents the province The second and third digits represent the municipality

2013-2023 Virginia Population by Means of Transportation to Work by Number of Vehicles Available by Census Tract. Contains estimates and margins of error.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B08141 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

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. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

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. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

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.

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 https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

Abstract

The objective of this three-year panel survey is to provide the Government of Nepal with empirical evidence on the patterns of exposure to shocks at the household level and on the vulnerability of households’ welfare to these shocks. It covers 6,000 households in non-metropolitan areas of Nepal, which were interviewed in mid 2016. Being a relatively comprehensive and representative (rural) sample household survey, it can also be used for other research into living conditions of Nepali households in rural areas. This is the entire dataset for the first wave of the survey. The same households will be reinterviewed in mid 2017 and mid 2018. The survey dataset contains a multi-topic survey which was completed for each of the 6,000 households, and a community survey fielded to a senior community representative at the village development committee (VDC) level in each of the 400 PSUs.

Geographic coverage

All non-metropolitan areas in Nepal. Non-metropolitan areas are as defined by the 2010 Census.

Analysis unit

Household, following the NLSS definition.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample frame was all households in non-metropolitan areas per the 2010 Census definition, excluding households in the Kathmandu valley (Kathmandu, Lalitpur and Bhaktapur districts). The country was segmented into 11 analytical strata, defined to correspond to those used in the NLSS III (excluding the three urban strata used there). To increase the concentration of sampled households, 50 of the 75 districts in Nepal were selected with probability proportional to size (the measure of size being the number of households). PSUs were selected with probability proportional to size from the entire list of wards in the 50 selected districts, one stratum at a time. The number of PSUs per stratum is proportional to the stratum's population share, and corresponds closely to the allocations used in the LFS-II and NLSS-III (adjusted for different overall numbers of PSUs in those surveys).

In each of the selected PSUs (administrative wards), survey teams compiled a list of households in the ward based on existing administrative records, and cross-checked with local leaders. The number of households shown in the list was compared to the ward population in the 2010 Census, adjusted for likely population growth. Where the listed population deviated by more than 10% from the projected population based on the Census data, the team conducted a full listing of households in the ward. 15 households were selected at random from the ward list for interviewing, and a further 5 households were selected as potential replacements.

Sampling deviation

During the fieldwork, one PSU in Lapu VDC was inaccessible due to weather, and was replaced by a ward in Hastichaur VDC using PPS sampling on that stratum (excluding the already selected PSUs). All other sampled PSUs were reached, and a full sample of 6,000 households was interviewed in the first wave.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The household questionnaire contained 16 modules: the household roster; education; health; housing and access to facilities; food expenses and home production; non-food expenditures and inventory of durable goods; jobs and time use; wage jobs; farming and livestock; non-agriculture enterprises/activities; migration; credit, savings, and financial assets; private assistance; public assistance; shocks; and anthropometrics (for children less than 5 years). Where possible, the style of questions was kept similar to those used in the NLSS-III questionnaire for comparability reasons. In some cases, new modules needed to be developed. The shocks questionnaire was developed by the World Bank team. A food security module was added based on the design recommended by USAID, and a psychosocial questionnaire was also developed by social development specialists in the World Bank. The section on government and other assistance was also redesigned to cover a broader range of programs and elicit information on details such as experience with enrollment and frequency of payment.

The community questionnaire was fielded to a senior community representative at the VDC level in each of the 400 PSUs. The purpose of the community questionnaire was to obtain further details on access to services in each PSU, to gather information on shocks at the community level, and to collect market price data. The questionnaire had six modules: respondent details; community characteristics; access to facilities; educational facilities; community shocks, household shocks; and market price.

Cleaning operations

These are the raw data entered and checked by the survey firm, formatted to conform to the original questionnaire numbering system and confidentialized. The data were cleaned for spelling errors and translation of Nepali phrases, and suspicious values were checked by calling respondents. No other transformations have taken place.

Response rate

Of the 6,000 originally sampled households, 5,191 agreed to be interviewed. Of the 13.5% of households that were not interviewed, 11.1% were resident but could not be located by the team after two attempts, 0.9% were found to have outmigrated, and 1.4% refused. The 809 replacement households were drawn in order from the randomized list created during sampling (see above).

Abstract

China Living Standards Survey (LSS) consists of one household survey and one community (village) survey, conducted in Hebei and Liaoning Provinces (northern and northeast China) in July 1995 and July 1997 respectively. Five villages from each three sample counties of each province were selected (six were selected in Liaoyang County of Liaoning Province because of administrative area change). About 880 farm households were selected from total thirty-one sample villages for the household survey. The same thirty-one villages formed the samples of community survey. This document provides information on the content of different questionnaires, the survey design and implementation, data processing activities, and the different available data sets.

Geographic coverage

Regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The China LSS sample is not a rigorous random sample drawn from a well-defined population. Instead it is only a rough approximation of the rural population in Hebei and Liaoning provinces in North-eastern China. The reason for this is that part of the motivation for the survey was to compare the current conditions with conditions that existed in Hebei and Liaoning in the 1930's. Because of this, three counties in Hebei and three counties in Liaoning were selected as "primary sampling units" because data had been collected from those six counties by the Japanese occupation government in the 1930's. Within each of these six counties (xian) five villages (cun) were selected, for an overall total of 30 villages (in fact, an administrative change in one village led to 31 villages being selected). In each county a "main village" was selected that was in fact a village that had been surveyed in the 1930s. Because of the interest in these villages 50 households were selected from each of these six villages (one for each of the six counties). In addition, four other villages were selected in each county. These other villages were not drawn randomly but were selected so as to "represent" variation within the county. Within each of these villages 20 households were selected for interviews. Thus, the intended sample size was 780 households, 130 from each county. Unlike county and village selection, the selection of households within each village was done according to standard sample selection procedures. In each village, a list of all households in the village was obtained from village leaders. An "interval" was calculated as the number of the households in the village divided by the number of households desired for the sample (50 for main villages and 20 for other villages). For the list of households, a random number was drawn between 1 and the interval number. This was used as a starting point. The interval was then added to this number to get a second number, then the interval was added to this second number to get a third number, and so on. The set of numbers produced were the numbers used to select the households, in terms of their order on the list. In fact, the number of households in the sample is 785, as opposed to 780. Most of this difference is due to a village in which 24 households were interviewed, as opposed to the goal of 20 households

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) DATA ENTRY All responses obtained from the household interviews were recorded in the household questionnaires. These were then entered into the computer, in the field, using data entry programs written in BASIC. The data produced by the data entry program were in the form of household files, i.e. one data file for all of the data in one household/community questionnaire. Thus, for the household there were about 880 data files. These data files were processed at the University of Toronto and the World Bank to produce datasets in statistical software formats, each of which contained information for all households for a subset of variables. The subset of variables chosen corresponded to data entry screens, so these files are hereafter referred to as "screen files". For the household survey component 66 data files were created. Members of the survey team checked and corrected data by checking the questionnaires for original recorded information. We would like to emphasize that correction here refers to checking questionnaires, in case of errors in skip patterns, incorrect values, or outlying values, and changing values if and only if data in the computer were different from those in the questionnaires. The personnel in charge of data preparation were given specific instructions not to change data even if values in the questionnaires were clearly incorrect. We have no reason to believe that these instructions were not followed, and every reason to believe that the data resulting from these checks and corrections are accurate and of the highest quality possible.

(b) DATA EDITING The screen files were then brought to World Bank headquarters in Washington, D.C. and uploaded to a mainframe computer, where they were converted to "standard" LSMS formats by merging datasets to produce separate datasets for each section with variable names corresponding to the questionnaires. In some cases, this has meant a single dataset for a section, while in others it has meant retaining "screen" datasets with just the variable names changed. Linking Parts of the Household Survey Each household has a unique identification number which is contained in the variable HID. Values for this variable range from 10101 to 60520. The first number is the code for the six counties in which data were collected, the second and third digits are for the villages within each county. Finally, the last two digits of HID contain the household number within the village. Data for households from different parts of the survey can be merged by using the HID variable which appears in each dataset of the household survey. To link information for an individual use should be made of both the household identification number, HID, and the person identification number, PID. A child in the household can be linked to the parents, if the parents are household members, through the parents' id codes in Section 01B. For parents who are not in the household, information is collected on the parent's schooling, main occupation and whether he/she is currently alive. Household members can be linked with their non-resident children through the parents' id codes in Section 01C. Linking the Household to the Community Data The community data have a somewhat different set of identifying variables than the household data. Each community dataset has four identifying variables: province (code 7 for Hebei and code 8 for Liaoning); county (six two digit codes, of which the first digit represents province and the second digit represents the three counties in each province); township (3 digit code, first digit is county, second digit is county and third digit is township); and village (4 digit code, first digit is county, second digit is county, third digit is township, and third fourth digit is village). Constructed Data Set Researchers at the World Bank and the University of Toronto have created a data set with information on annual household expenditures, region codes, etc. This constructed data set is made available for general use with the understanding that the description below is the only documentation that will be provided. Any manipulation of the data requires assumptions to be made and, as much as possible, those assumptions are explained below. Except where noted, the data set has been created using only the original (raw) data sets. A researcher could construct a somewhat different data set by incorporating different assumptions. Aggregate Expenditure, TOTEXP. The dataset TOTEXP contains variables for total household annual expenditures (for the year 1994) and variables for the different components of total household expenditures: food expenditures, non-food expenditures, use value of consumer durables, etc. These, along with the algorithm used to calculate household expenditures are detailed in Appendix D. The dataset also contains the variable HID, which can be used to match this dataset to the household level data set. Note that all of the expenditure variables are totals for the household. That is, they are not in per capita terms. Researchers will have to divide these variables by household size to get per capita numbers. The household size variable is included in the data set.

1: This value is the absolute value of the ratio of the estimated to the known (i.e. Column 2/Column 1) which is transformed with a logarithm (base 2). Successive columns (5, 7, 9, 11, 13, 15, 17) use the preceding estimation value.Recursive Back Estimation Process to Identify and Eliminate Poor Predictors Using the Original Estimator Without Weights.

This layer shows Population by Age and Sex. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the Total population ages 65 and over. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B01001, B01002, DP05Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Physiological dysregulation may underlie aging and many chronic diseases, but is challenging to quantify because of the complexity of the underlying systems. Recently, we described a measure of physiological dysregulation, DM, that uses statistical distance to assess the degree to which an individual’s biomarker profile is normal versus aberrant. However, the sensitivity of DM to details of the calculation method has not yet been systematically assessed. In particular, the number and choice of biomarkers and the definition of the reference population (RP, the population used to define a “normal” profile) may be important. Here, we address this question by validating the method on 44 common clinical biomarkers from three longitudinal cohort studies and one cross-sectional survey. DMs calculated on different biomarker subsets show that while the signal of physiological dysregulation increases with the number of biomarkers included, the value of additional markers diminishes as more are added and inclusion of 10-15 is generally sufficient. As long as enough markers are included, individual markers have little effect on the final metric, and even DMs calculated from mutually exclusive groups of markers correlate with each other at r~0.4-0.5. We also used data subsets to generate thousands of combinations of study populations and RPs to address sensitivity to differences in age range, sex, race, data set, sample size, and their interactions. Results were largely consistent (but not identical) regardless of the choice of RP; however, the signal was generally clearer with a younger and healthier RP, and RPs too different from the study population performed poorly. Accordingly, biomarker and RP choice are not particularly important in most cases, but caution should be used across very different populations or for fine-scale analyses. Biologically, the lack of sensitivity to marker choice and better performance of younger, healthier RPs confirm an interpretation of DM physiological dysregulation and as an emergent property of a complex system.

These data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. This dataset has two layers and includes both the weighted data and unweighted data. Weighting data is a statistical method in which datasets are adjusted through calculations in order to more accurately represent the population being studied. The weighted data are used in the final published PDF report.These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2023):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethods:The survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. The weighted data are used by the ETC Institute, in the final published PDF report.The 2023 Annual Community Survey report is available on data.tempe.gov or by visiting https://www.tempe.gov/government/strategic-management-and-innovation/signature-surveys-research-and-dataThe individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

2013-2023 Virginia Population by Race by Census Block Group. Contains estimates and margins of error.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B03002 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

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. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

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. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

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.

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 https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Dominican Republic in 2010 and 2016, as part of Latin America and the Caribbean Enterprise Surveys rollout, an initiative of the World Bank. The objective of the Enterprise Survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Dominican Republic ES 2010 was conducted in March - September 2011, ES 2016 was carried out in August 2016 - April 2017. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 719 establishments was analyzed: 257 businesses were from 2010 ES only, 256 - from 2016 only, and 206 firms were from 2010 and 2016.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively measure characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size and region.

Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries - Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72).

Size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

In 2016, regional stratification was done across three regions: Santo Domingo, Santiago-Puerto Plata-Espaillat and the Rest of the country.

The sample frame consisted of listings of firms from three sources: for panel firms the list of 360 firms from the Dominican Republic 2010 ES was used and for fresh firms (i.e., firms not covered in 2010) a listing of firms obtained from El Directorio de Empresas y Establecimientos (DEE) 2015 and Oficina Nacional de Estadística (ONE), were used.

In 2010, regional stratification was defined in two locations: Santo Domingo and the rest of the country (constituted by urban centers around Santiago and Higuey). For the purposes of sampling, the rest of the country was treated as one area.

The sample frame for 2010 ES was provided by the Oficina Nacional de Estadistica (ONE), dated 2009.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.