As a source of animal and plant population data, the Global Population Dynamics Database (GPDD) is unrivalled. Nearly five thousand separate time series are available here. In addition to all the population counts, there are taxonomic details of over 1400 species. The type of data contained in the GPDD varies enormously, from annual counts of mammals or birds at individual sampling sites, to weekly counts of zooplankton and other marine fauna. The project commenced in October 1994, following discussions on ways in which the collaborating partners could make a practical and enduring contribution to research into population dynamics. A small team was assembled and, with assistance and advice from numerous interested parties we decided to construct the database using the popular Microsoft Access platform. After an initial design phase, the major task has been that of locating, extracting, entering and validating the data in all the various tables. Now, nearly 5000 individual datasets have been entered onto the GPDD. The Global Population Dynamics Database comprises six Tables of data and information. The tables are linked to each other as shown in the diagram shown in figure 3 of the GPDD User Guide (GPDD-User-Guide.pdf). Referential integrity is maintained through record ID numbers which are held, along with other information in the Main Table. It's structure obeys all the rules of a standard relational database.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the 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

All formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of Armenia, registration agency is State Register of Legal Entities (Պետական Ռեգիստր). The universe table is the total number of eligible establishments, and the table is partitioned by the stratification groups (industry classification, establishment size, and subnational region) in a country.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Armenia 2024 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, trade, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

Response rate

Overall survey response rate was 38.2%.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the 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

All formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of Ireland, a firm was classified as registered if recorded in the Companies Registration Office.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Ireland 2024 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, trade, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

Response rate

Overall survey response rate was 9.9%.

NOTE: Data based on a sample except in P3, P4, H3, and H4. For.information on confidentiality protection, sampling error,.nonsampling error, definitions, and count corrections see.http://www.census.gov/prod/cen2000/doc/sf3.pdf

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

These are synthetically generated unit and area level population and sample data that can be used for testing model-based unit-level small area methods. To prevent disclosure issues the datasets have been generated by repeated (Monte-Carlo) sampling of real EU-SILC (Survey of Income and Living Conditions) data in Austria. The data include geographical identifies and can be used for fitting unit-level (Battese-Harter and Fuller type) models and area level models (Fay-Herriott- type) models. The datasets are part of the R package emdi. Examples of the use of the data can be found in the emdi manual available via https://cran.r-project.org/web/packages/emdi/emdi.pdf and in Kreutzmann et al. (2019) Kreutzmann, A. K., Pannier, S., Rojas-Perilla, N., Schmid, T., Templ, M., & Tzavidis, N. (2019). The R package emdi for the estimation and mapping of regional disaggregated indicators. Journal of Statistical Software, 91(7). https://doi.org/10.18637/jss.v091.i07

Abstract (en): The Public Use Microdata Samples (PUMS) contain person- and household-level information from the "long-form" questionnaires distributed to a sample of the population enumerated in the 1980 Census. This data collection, containing 5-percent data, identifies every state, county groups, and most individual counties with 100,000 or more inhabitants (350 in all). In many cases, individual cities or groups of places with 100,000 or more inhabitants are also identified. Household-level variables include housing tenure, year structure was built, number and types of rooms in dwelling, plumbing facilities, heating equipment, taxes and mortgage costs, number of children, and household and family income. The person record contains demographic items such as sex, age, marital status, race, Spanish origin, income, occupation, transportation to work, and education. All persons and housing units in the United States and Puerto Rico. For this data collection, the full 1980 Census sample that received the "long-form" questionnaire (19.4 percent of all households) was sampled again through a stratified systematic selection procedure with probability proportional to a measure of size. This 5-percent sample, i.e., 5 households for every 100 households in the nation, includes over one-fourth of the households that received the long-form questionnaire. 2006-01-12 All files were removed from dataset 81 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 80 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 81 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 80 and flagged as study-level files, so that they will accompany all downloads.1997-08-25 Part 72, Puerto Rico data, has been added to the collection, as well as supplemental documentation for Puerto Rico in the form of a separate PDF file. The household and person records in each hierarchical data file have logical record lengths of 193 characters, but the number of records varies with each file.The record layout for Part 72, Puerto Rico, is different from the state datasets. Refer to the supplemental documentation for this part.The codebook is available in hardcopy form only, while the Puerto Rico supplemental documentation is provided as a Portable Document Format (PDF) file.

States report information from two reporting populations: (1) The Served Population which is information on all youth receiving at least one independent living services paid or provided by the Chafee Program agency, and (2) Youth completing the NYTD Survey. States survey youth regarding six outcomes: financial self-sufficiency, experience with homelessness, educational attainment, positive connections with adults, high-risk behaviors, and access to health insurance. States collect outcomes information by conducting a survey of youth in foster care on or around their 17th birthday, also referred to as the baseline population. States will track these youth as they age and conduct a new outcome survey on or around the youth's 19th birthday; and again on or around the youth's 21st birthday, also referred to as the follow-up population. States will collect outcomes information on these older youth at ages 19 or 21 regardless of their foster care status or whether they are still receiving independent living services from the State. Depending on the size of the State's foster care youth population, some States may conduct a random sample of the baseline population of the 17-year-olds that participate in the outcomes survey so that they can follow a smaller group of youth as they age. All States will collect and report outcome information on a new baseline population cohort every three years. Units of Response: Current and former youth in foster care Type of Data: Administrative Tribal Data: No Periodicity: Annual Demographic Indicators: Ethnicity;Race;Sex SORN: Not Applicable Data Use Agreement: https://res1wwwd-o-tndacand-o-tacfd-o-thhsd-o-tgov.vcapture.xyz/datasets/request-dataset.cfm Data Use Agreement Location: https://res1wwwd-o-tndacand-o-tacfd-o-thhsd-o-tgov.vcapture.xyz/datasets/order_forms/termsofuseagreement.pdf Granularity: Individual Spatial: United States Geocoding: FIPS Code

Extreme phenotype sampling (EPS) is a popular study design used to reduce genotyping or sequencing costs. Assuming continuous phenotype data are available on a large cohort, EPS involves genotyping or sequencing only those individuals with extreme phenotypic values. Although this design has been shown to have high power to detect genetic effects even at smaller sample sizes, little attention has been paid to the effects of confounding variables, and in particular population stratification. Using extensive simulations, we demonstrate that the false positive rate under the EPS design is greatly inflated relative to a random sample of equal size or a “case-control”-like design where the cases are from one phenotypic extreme and the controls randomly sampled. The inflated false positive rate is observed even with allele frequency and phenotype mean differences taken from European population data. We show that the effects of confounding are not reduced by increasing the sample size. We also show that including the top principal components in a logistic regression model is sufficient for controlling the type 1 error rate using data simulated with a population genetics model and using 1,000 Genomes genotype data. Our results suggest that when an EPS study is conducted, it is crucial to adjust for all confounding variables. For genetic association studies this requires genotyping a sufficient number of markers to allow for ancestry estimation. Unfortunately, this could increase the costs of a study if sequencing or genotyping was only planned for candidate genes or pathways; the available genetic data would not be suitable for ancestry correction as many of the variants could have a true association with the trait.

Studies using matrix models to study plant demography. (0.11 MB PDF)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..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..Explanation of Symbols:An ''**'' 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..An ''-'' 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..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' 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..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' 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..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2012 American Community Survey (ACS) data generally reflect the December 2009 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..For more information on understanding race and Hispanic origin data, please see the Census 2010 Brief entitled, Overview of Race and Hispanic Origin: 2010, issued March 2011. (pdf format).The ACS questions on Hispanic origin and race were revised in 2008 to make them consistent with the Census 2010 question wording. Any changes in estimates for 2008 and beyond may be due to demographic changes, as well as factors including questionnaire changes, differences in ACS population controls, and methodological differences in the population estimates, and therefore should be used with caution. For a summary of questionnaire changes see http://www.census.gov/acs/www/methodology/questionnaire_changes/. For more information about changes in the estimates see http://www.census.gov/population/hispanic/files/acs08researchnote.pdf..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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2012 American Community Survey

Abstract (en): This collection contains individual-level and 1-percent national sample data from the 1960 Census of Population and Housing conducted by the Census Bureau. It consists of a representative sample of the records from the 1960 sample questionnaires. The data are stored in 30 separate files, containing in total over two million records, organized by state. Some files contain the sampled records of several states while other files contain all or part of the sample for a single state. There are two types of records stored in the data files: one for households and one for persons. Each household record is followed by a variable number of person records, one for each of the household members. Data items in this collection include the individual responses to the basic social, demographic, and economic questions asked of the population in the 1960 Census of Population and Housing. Data are provided on household characteristics and features such as the number of persons in household, number of rooms and bedrooms, and the availability of hot and cold piped water, flush toilet, bathtub or shower, sewage disposal, and plumbing facilities. Additional information is provided on tenure, gross rent, year the housing structure was built, and value and location of the structure, as well as the presence of air conditioners, radio, telephone, and television in the house, and ownership of an automobile. Other demographic variables provide information on age, sex, marital status, race, place of birth, nationality, education, occupation, employment status, income, and veteran status. The data files were obtained by ICPSR from the Center for Social Analysis, Columbia University. About 600,000 households and group quarters segments, and about 1,800,000 persons in the United States. One sample household for every 100 households, and persons in group quarters in the United States. Records have been sampled on a household-by-household basis so that the characteristics of family members may be interrelated and related to the characteristics of the housing unit. 2006-01-18 File CB7756.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.

Abstract (en): The Public Use Microdata Sample (PUMS) 1-Percent Sample contains household and person records for a sample of housing units that received the "long form" of the 1990 Census questionnaire. Data items include the full range of population and housing information collected in the 1990 Census, including 500 occupation categories, age by single years up to 90, and wages in dollars up to $140,000. Each person identified in the sample has an associated household record, containing information on household characteristics such as type of household and family income. All persons and housing units in the United States. A stratified sample, consisting of a subsample of the household units that received the 1990 Census "long-form" questionnaire (approximately 15.9 percent of all housing units). 2006-01-12 All files were removed from dataset 85 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 83 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 82 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 81 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 80 and flagged as study-level files, so that they will accompany all downloads.1998-08-28 The following data files were replaced by the Census Bureau: the state files (Parts 1-56), Puerto Rico (Part 72), Geographic Equivalency File (Part 84), and Public Use Microdata Areas (PUMAS) Crossing State Lines (Part 99). These files now incorporate revised group quarters data. Parts 201-256, which were separate revised group quarters files for each state, have been removed from the collection. The data fields affected by the group quarters data revisions were POWSTATE, POWPUMA, MIGSTATE and MIGPUMA. As a result of the revisions, the Maine file (Part 23) gained 763 records and Part 99 lost 763 records. In addition, the following files have been added to the collection: Ancestry Code List, Place of Birth Code List, Industry Code List, Language Code List, Occupation Code List, and Race Code List (Parts 86-91). Also, the codebook is now available as a PDF file. (1) Although all records are 231 characters in length, each file is hierarchical in structure, containing a housing unit record followed by a variable number of person records. Both record types contain approximately 120 variables. Two improvements over the 1980 PUMS files have been incorporated. First, the housing unit serial number is identified on both the housing unit record and on the person record, allowing the file to be processed as a rectangular file. In addition, each person record is assigned an individual weight, allowing users to more closely approximate published reports. Unlike previous years, the 1990 PUMS 1-Percent and 5-Percent Samples have not been released in separate geographic series (known as "A," "B," etc. records). Instead, each sample has its own set of geographies, known as "Public Use Microdata Areas" (PUMAs), established by the Census Bureau with assistance from each State Data Center. The PUMAs in the 1-Percent Sample are based on a distinction between metropolitan and nonmetropolitan areas. Metropolitan areas encompass whole central cities, Primary Metropolitan Statistical Areas (PMSAs), Metropolitan Statistical Areas (MSAs), or groups thereof, except where the city or metropolitan area contains more than 200,000 inhabitants. In that case, the city or metropolitan area is divided into several PUMAs. Nonmetropolitan PUMAs are based on areas or groups of areas outside the central city, PMSA, or MSA. PUMAs in this 1-Percent Sample may cross state lines. (2) The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..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..Explanation of Symbols:An ''**'' 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..An ''-'' 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..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' 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..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' 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..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2012 American Community Survey (ACS) data generally reflect the December 2009 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..The ACS questions on Hispanic origin and race were revised in 2008 to make them consistent with the Census 2010 question wording. Any changes in estimates for 2008 and beyond may be due to demographic changes, as well as factors including questionnaire changes, differences in ACS population controls, and methodological differences in the population estimates, and therefore should be used with caution. For a summary of questionnaire changes see http://www.census.gov/acs/www/methodology/questionnaire_changes/. For more information about changes in the estimates see http://www.census.gov/population/hispanic/files/acs08researchnote.pdf..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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2012 American Community Survey

NOTE: Data based on a sample except in P3, P4, H3, and H4. For.information on confidentiality protection, sampling error,.nonsampling error, definitions, and count corrections see.http://www.census.gov/prod/cen2000/doc/cd110s.pdf

Despite discussions about the replicability of findings in psychological research, two issues have been largely ignored: selection mechanisms and model assumptions. Both topics address the same fundamental question: Does the chosen statistical analysis tool adequately model the data generation process? In this article, we address both issues and show, in a first step, that in the face of selective samples and contrary to common practice, the validity of inferences, even when based on experimental designs, can be claimed without further justification and adaptation of standard methods only in very specific situations. We then broaden our perspective to discuss consequences of violated assumptions in linear models in the context of psychological research in general and in generalized linear mixed models as used in item response theory. These types of misspecification are oftentimes ignored in the psychological research literature. It is emphasized that the above problems cannot be overcome by strategies such as preregistration, large samples, replications, or a ban on testing null hypotheses. To avoid biased conclusions, we briefly discuss tools such as model diagnostics, statistical methods to compensate for selectivity and semi- or non-parametric estimation. At a more fundamental level, however, a twofold strategy seems indispensable: (1) iterative, cumulative theory development based on statistical methods with theoretically justified assumptions, and (2) empirical research on variables that affect (self-) selection into the observed part of the sample and the use of this information to compensate for selectivity.

This table shows resident population broken down into country of birth, showing data for London's largest communities (over 10,000 people) in 2004, and 2008 to 2014 from the Annual Population Survey (APS). The 2011 Census data is also provided in the spreadsheet to provide a comparison to the APS data.

The table also shows the percentage of the UK community that live in London.

The Annual Population Survey (APS) sampled around 325,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided.

All populations of fewer than 10,000 have been suppressed.
Numbers are rounded to the nearest thousand.

The APS is the only inter-censal data source that can provide estimates of the population stock by nationality. The data have a range of limitations, particularly in relation to their poor coverage of short-term migrants or recent arrivals. They also struggle to provide estimates for small migrant populations due to small sample sizes.

Information about Londoners by Country of Birth using APS data, can be found in DMAG Briefing 2008-05 http://legacy.london.gov.uk/gla/publications/factsandfigures/dmag-briefing-2008-05.pdf

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the 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 universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination).

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

This table shows resident population of London broken down by nationality, showing data for London's largest communities in 2004, and 2008 to 2012.

Also shows the percentage of the UK community that live in London.
The Annual Population Survey (APS) sampled around 325,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided.

All numbers based on fewer than 50 surveys have been suppressed.
Numbers have been rounded to the nearest thousand.

The APS is the only inter-censal data source that can provide estimates of the population stock by nationality. The data have a range of limitations, particularly in relation to their poor coverage of short-term migrants or recent arrivals. They also struggle to provide estimates for small migrant populations due to small sample sizes.
Information about Londoners by Country of Birth using APS data, can be found in DMAG Briefing 2008-05 http://legacy.london.gov.uk/gla/publications/factsandfigures/dmag-briefing-2008-05.pdf

ONS website

The power of fishery-independent surveys for stock assessments and management decisions is in their consistency over time and space. Although the preference is to limit change to survey execution, such changes may be necessary. In multi-species surveys, changes that improve metrics for one species may be a detriment to survey performance for others. In 2010, the Southeast Reef Fish Survey (SERFS) was formed to better address sampling needs off the U.S. Southeast Atlantic coast by intensifying a historical chevron trap survey (MARMAP), especially at the northern and southern extent of the sampling range. We used several performance metrics (encounter rate, annual coefficient of variability, standard error, and relative abundance index values) to determine the impact of this change in survey coverage on trend estimates for three commonly encountered species with varying centers of distribution in the survey region. Gray Triggerfish (Balistes capriscus) is found throughout the range of both surveys (i.e. centrally-distributed), while White Grunt (Haemulon plumierii) and Red Snapper (Lutjanus campechanus) are centered near the northern and southern extent of the sampling range, respectively. For Gray Triggerfish, the survey intensification had no effect on encounter rate, but reduced the coefficient of variation and indicated that the historical index of relative abundance may have been overestimated. For White Grunt, the survey intensification slightly improved CV but did not affect the index of relative abundance value or encounter rate. For Red Snapper, SERFS increased encounter rates, reduced CV overall, and detected a population increase 5 years earlier than MARMAP. Overall, the intensification of the survey improved at least one performance metric for each species and showed few deleterious effects on performance, suggesting that intensification of the survey was a net-positive for the accurate estimation of population trends in several species of interest.