'Dataset2' associated with: Who Tweets with Their Location? Understanding the Relationship Between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter

Luke Sloan and Jeffrey Morgan.

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

This data collection consists of modified records from CENSUS OF POPULATION AND HOUSING, 1970 [UNITED STATES]: PUBLIC USE SAMPLES (ICPSR 0018). The original records consisted of 120-character household records and 120-character person records, whereas the new modified records are rectangular (each person record is combined with the corresponding household record) with a length of 188, after the deletion of some items. Additional information was added to the data records, including typical educational requirement for current occupation, occupational prestige score, and group identification code. This version also differs from the original public use census samples in other ways: persons aged 15-75 were included, no majority males were included, but the majority males from CENSUS OF POPULATION AND HOUSING [UNITED STATES], 1970 PUBLIC USE SAMPLE: MODIFIED 1/1000 5% STATE SAMPLES (ICPSR 7922) were included for convenience, 10 percent of the Black population from each file was included, and Mexican Americans (identified by a Spanish surname) from outside the five southwestern states of Arizona, California, Colorado, New Mexico, and Texas were not included in this file. Variables provide information on the housing unit, such as occupancy and vacancy status of house, value of property, commercial use, ratio of rent and property value to family income, availability of plumbing facilities, sewage disposal, complete kitchen facilities, heating facilities, flush toilet, water, television, and telephone. Data are also provided on household characteristics such as household size, family size, and household relationships. Other demographic variables specify age, sex, place of birth, state of residence, Spanish descent, marital status, race, veteran status, income, and ratio of family income to poverty cutoff level. This collection was made available by the National Chicano Research Network of the Institute for Social Research, University of Michigan. See the related collection, CENSUS OF POPULATION AND HOUSING [UNITED STATES], 1970 PUBLIC USE SAMPLE: MODIFIED 1/1000 5% STATE SAMPLES (ICPSR 7922).

The genetic diversity and population structure of Arabidopsis thaliana populations from Norway were studied and compared to a worldwide sample of A. thaliana in order to investigate the demographic history and elucidate possible colonization routes of populations at the northernmost species limit. We genotyped 282 individuals from 31 local populations using 149 single nucleotide polymorphism (SNP) markers. A high level of population subdivision (FST = 0.85 ± 0.007) was found indicating that A. thaliana is highly structured at the regional level. Significant relationships between genetic and geographic distances were found, suggesting an isolation by distance mode of evolution. Genetic diversity was much lower and the level of linkage disequilibrium (LD) higher in populations from the north (65–68oN) compared to populations from the south (59–62oN); this is consistent with a northward expansion pattern. A neighbor-joining (NJ) tree showed that populations from northern Norway form a separate cluster, while the remaining populations are distributed over a few minor clusters. Minimal gene flow seems to have occurred between populations in different regions, especially between the geographically distant northern and southern populations. Our data suggest that northern populations represent a homogenous group that may have been established from a few founders during northward expansions, while populations in the central part of Norway constitute an admixed group established by founders of different origins, most probably as a result of human-mediated gene flow. Moreover, Norwegian populations appeared to be homogenous and isolated compared to a worldwide sample of A. thaliana, but they are still grouped with Swedish populations, which may indicate common colonization histories.

Abstract

The 1993 Turkish Demographic and Health Survey (TDHS) is a nationally representative survey of ever-married women less than 50 years old. The survey was designed to provide information on fertility levels and trends, infant and child mortality, family planning, and maternal and child health. The TDHS was conducted by the Hacettepe University Institute of Population Studies under a subcontract through an agreement between the General Directorate of Mother and Child Health and Family Planning, Ministry of Health and Macro International Inc. of Calverton, Maryland. Fieldwork was conducted from August to October 1993. Interviews were carried out in 8,619 households and with 6,519 women.

The Turkish Demographic and Health Survey (TDHS) is a national sample survey of ever-married women of reproductive ages, designed to collect data on fertility, marriage patterns, family planning, early age mortality, socioeconomic characteristics, breastfeeding, immunisation of children, treatment of children during episodes of illness, and nutritional status of women and children. The TDHS, as part of the international DHS project, is also the latest survey in a series of national-level population and health surveys in Turkey, which have been conducted by the Institute of Population Studies, Haeettepe University (HIPS).

More specifically, the objectives of the TDHS are to:

Collect data at the national level that will allow the calculation of demographic rates, particularly fertility and childhood mortality rates; Analyse the direct and indirect factors that determine levels and trends in fertility and childhood mortality; Measure the level of contraceptive knowledge and practice by method, region, and urban- rural residence; Collect data on mother and child health, including immunisations, prevalence and treatment of diarrhoea, acute respiratory infections among children under five, antenatal care, assistance at delivery, and breastfeeding; Measure the nutritional status of children under five and of their mothers using anthropometric measurements.

The TDHS information is intended to assist policy makers and administrators in evaluating existing programs and in designing new strategies for improving family planning and health services in Turkey.

MAIN RESULTS

Fertility in Turkey is continuing to decline. If Turkish women maintain current fertility rates during their reproductive years, they can expect to have all average of 2.7 children by the end of their reproductive years. The highest fertility rate is observed for the age group 20-24. There are marked regional differences in fertility rates, ranging from 4.4 children per woman in the East to 2.0 children per woman in the West. Fertility also varies widely by urban-rural residence and by education level. A woman living in rural areas will have almost one child more than a woman living in an urban area. Women who have no education have almost one child more than women who have a primary-level education and 2.5 children more than women with secondary-level education.

The first requirement of success ill family planning is the knowledge of family planning methods. Knowledge of any method is almost universal among Turkish women and almost all those who know a method also know the source of the method. Eighty percent of currently married women have used a method sometime in their life. One third of currently married women report ever using the IUD. Overall, 63 percent of currently married women are currently using a method. The majority of these women are modern method users (35 percent), but a very substantial proportion use traditional methods (28 percent). the IUD is the most commonly used modern method (I 9 percent), allowed by the condom (7 percent) and the pill (5 percent). Regional differences are substantial. The level of current use is 42 percent in tile East, 72 percent in tile West and more than 60 percent in tile other three regions. "File common complaints about tile methods are side effects and health concerns; these are especially prevalent for the pill and IUD.

One of the major child health indicators is immunisation coverage. Among children age 12-23 months, the coverage rates for BCG and the first two doses of DPT and polio were about 90 percent, with most of the children receiving those vaccines before age one. The results indicate that 65 percent of the children had received all vaccinations at some time before the survey. On a regional basis, coverage is significantly lower in the Eastern region (41 percent), followed by the Northern and Central regions (61 percent and 65 percent, respectively). Acute respiratory infections (ARI) and diarrhea are the two most prevalent diseases of children under age five in Turkey. In the two weeks preceding the survey, the prevalence of ARI was 12 percent and the prevalence of diarrhea was 25 percent for children under age five. Among children with diarrhea 56 percent were given more fluids than usual.

Breastfeeding in Turkey is widespread. Almost all Turkish children (95 percent) are breastfed for some period of time. The median duration of breastfeeding is 12 months, but supplementary foods and liquids are introduced at an early age. One-third of children are being given supplementary food as early as one month of age and by the age of 2-3 months, half of the children are already being given supplementary foods or liquids.

By age five, almost one-filth of children arc stunted (short for their age), compared to an international reference population. Stunting is more prevalent in rural areas, in the East, among children of mothers with little or no education, among children who are of higher birth order, and among those born less than 24 months after a prior birth. Overall, wasting is not a problem. Two percent of children are wasted (thin for their height), and I I percent of children under five are underweight for their age. The survey results show that obesity is d problem among mothers. According to Body Mass Index (BMI) calculations, 51 percent of mothers are overweight, of which 19 percent are obese.

Geographic coverage

The Turkish Demographic and Health Survey (TDHS) is a national sample survey.

Analysis unit

• Household
• Women age 12-49
• Children under five

Universe

The population covered by the 1993 DHS is defined as the universe of all ever-married women age 12-49 who were present in the household on the night before the interview were eligible for the survey.

Kind of data

Sample survey data

Sampling procedure

The sample for the TDHS was designed to provide estimates of population and health indicators, including fertility and mortality rates for the nation as a whole, fOr urban and rural areas, and for the five major regions of the country. A weighted, multistage, stratified cluster sampling approach was used in the selection of the TDHS sample.

Sample selection was undertaken in three stages. The sampling units at the first stage were settlements that differed in population size. The frame for the selection of the primary sampling units (PSUs) was prepared using the results of the 1990 Population Census. The urban frame included provinces and district centres and settlements with populations of more than 10,000; the rural frame included subdistricts and villages with populations of less than 10,000. Adjustments were made to consider the growth in some areas right up to survey time. In addition to the rural-urban and regional stratifications, settlements were classified in seven groups according to population size.

The second stage of selection involved the list of quarters (administrative divisions of varying size) for each urban settlement, provided by the State Institute of Statistics (SIS). Every selected quarter was subdivided according tothe number of divisions(approximately 100 households)assigned to it. In rural areas, a selected village was taken as a single quarter, and wherever necessary, it was divided into subdivisions of approximately 100 households. In cases where the number of households in a selected village was less than 100 households, the nearest village was selected to complete the 100 households during the listing activity, which is described below.

After the selection of the secondary sampling units (SSUs), a household listing was obtained for each by the TDHS listing teams. The listing activity was carried out in May and June. From the household lists, a systematic random sample of households was chosen for the TDHS. All ever-married women age 12-49 who were present in the household on the night before the interview were eligible for the survey.

Mode of data collection

Face-to-face

Research instrument

Two questionnaires were used in the main fieldwork for the TDHS: the Household Questionnaire and the Individual Questionnaire for ever-married women of reproductive age. The questionnaires were based on the model survey instruments developed in the DHS program and on the questionnaires that had been employed in previous Turkish population and health surveys. The questionnaires were adapted to obtain data needed for program planning in Turkey during consultations with population and health agencies. Both questionnaires were developed in English and translated into Turkish.

a) The Household Questionnaire was used to enumerate all usual members of and visitors to the selected households and to collect information relating to the socioeconomic position of the households. In the first part of the Household Questionnaire, basic information was collected on the age, sex, educational attainment, marital status and relationship to the head of household for each person listed as a household member

Abstract

The 1993 Kenya Demographic and Health Survey (KDHS) was a nationally representative survey of 7,540 women age 15-49 and 2,336 men age 20-54. The KDHS was designed to provide information on levels and trends of fertility, infant and child mortality, family planning knowledge and use, maternal and child health, and knowledge of AIDS. In addition, the male survey obtained data on men's knowledge and attitudes towards family planning and awareness of AIDS. The data are intended for use by programme managers and policymakers to evaluate and improve family planning and matemal and child health programmes. Fieldwork for the KDHS took place from mid-February until mid-August 1993. All areas of Kenya were covered by the survey, except for seven northem districts which together contain less than four percent of the country's population.

The KDHS was conducted by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics of the Government of Kenya. Macro International Inc. provided financial and technical assistance to the project through the intemational Demographic and Health Surveys (DHS) contract with the U.S. Agency for International Development.

OBJECTIVES

The KDHS is intended to serve as a source of population and health data for policymakers and the research community. It was designed as a follow-on to the 1989 KDHS, a national-level survey of similar size that was implemented by the same organisations. In general, the objectives of KDHS are to: - assess the overall demographic situation in Kenya, - assist in the evaluation of the population and health programmes in Kenya, - advance survey methodology, and - assist the NCPD to strengthen and improve its technical skills to conduct demographic and health surveys.

The KDHS was specifically designed to: - provide data on the family planning and fertility behaviour of the Kenyan population to enable the NCPD to evaluate and enhance the National Family Planning Programme, - measure changes in fertility and contraceptive prevalence and at the same time study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding habits and other socioeconomic factors, and - examine the basic indicators of maternal and child health in Kenya.

KEY FINDINGS

The 1993 KDHS reinforces evidence of a major decline in fertility which was first revealed by the findings of the 1989 KDHS. Fertility continues to decline and family planning use has increased. However, the disparity between knowledge and use of family planning remains quite wide. There are indications that infant and under five child mortality rates are increasing, which in part might be attributed to the increase in AIDS prevalence.

Geographic coverage

The 1993 KDHS sample is national in scope, with the exclusion of all three districts in North Eastern Province and four other northern districts (Samburu and Turkana in Rift Valley Province and Isiolo and 4 Marsabit in Eastern Province). Together the excluded areas account for less than 4 percent of Kenya's population.

Analysis unit

• Household
• Women age 15-49
• Men age 20-54
• Children under five

Universe

The population covered by the 1993 KDHS is defined as the universe of all women age 15-49 in Kenya and all husband age 20-54 living in the household.

Kind of data

Sample survey data

Sampling procedure

The sample for the 1993 KDHS was national in scope, with the exclusion of all three districts in Northeastern Province and four other northern districts (Isiolo and Marsabit from Eastern Province and Samburu and Turkana from Rift Valley Province). Together the excluded areas account for less than four percent of Kenya's population. The KDHS sample points were selected from a national master sample maintained by the Central Bureau of Statistics, the third National Sample Survey and Evaluation Programme (NASSEP-3), which is an improved version of NASSEP2 used in the 1989 survey. This master sample follows a two-stage design, stratified by urban-rural residence, and within the rural stratum, by individual district. In the first stage, 1989 census enumeration areas (EAs) were selected with probability proportional to size. The selected EAs were segmented into the expected number of standard-sized clusters to form NASSEP clusters. The entire master sample consists of 1,048 rural and 325 urban ~ sample points ("clusters"). A total of 536 clusters---92 urban and 444 rural--were selected for coverage in the KDHS. Of these, 520 were successfully covered. Sixteen clusters were inaccessible for various reasons.

As in the 1989 KDHS, selected districts were oversampled in the 1993 survey in order to produce more reliable estimates for certain variables at the district level. Fifteen districts were thus targetted in the 1993 KDHS: Bungoma, Kakamega, Kericho, Kilifi, Kisii, Machakos, Meru, Murang'a, Nakuru, Nandi, Nyeri, Siaya, South Nyanza, Taita-Taveta, and Uasin Gishu; in addition, Nairobi and Mombasa were also targetted. Although six of these districts were subdivided shortly before the sample design was finalised) the previous boundaries of these districts were used for the KDHS in order to maintain comparability with the 1989 survey. About 400 rural households were selected in each of these 15 districts, just over 1000 rural households in other districts, and about 18130 households in urban areas, for a total of almost 9,000 households. Due to this oversampling, the KDHS sample is not self-weighting at the national level.

After the selection of the KDHS sample points, fieldstaff from the Central Bureau of Statistics conducted a household listing operation in January and early February 1993, immediately prior to the launching of the fieldwork. A systematic sample of households was then selected from these lists, with an average "take" of 20 households in the urban clusters and 16 households in rural clusters, for a total of 8,864 households selected. Every other household was identified as selected for the male survey, meaning that, in addition to interviewing all women age 15-49, interviewers were to also interview all men age 20-54. It was expected that the sample would yield interviews with approximately 8,000 women age 15-49 and 2,500 men age 20-54.

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the KDHS: a Household Questionnaire, a Woman's Questionnaire, a Man's Questionnaire and a Services Availability Questionnaire. The contents of these questionnaires were based on the DHS Model B Questionnaire, which is designed for use in countries with low levels of contraceptive use. Additions and modifications to the model questionnaires were made during a series of meetings organised around specific topics or sections of the questionnaires (e.g., fertility, family planning). The NCPD invited staff from a variety of organisations to attend these meetings, including the Population Studies Research Institute and other departments of the University of Nairobi, the Woman's Bureau, and various units of the Ministry of Health. The questionnaires were developed in English and then translated into and printed in Kiswahili and eight of the most widely spoken local languages in Kenya (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Meru, and Mijikenda).

a) The Household Questionnaire was used to list all the usual members and visitors of selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods.

b) The Woman's Questionnaire was used to collect information from women aged 15-49. These women were asked questions on the following topics: Background characteristics (age, education, religion, etc.), Reproductive history, Knowledge and use of family planning methods, Antenatal and delivery care, Breastfeeding and weaning practices, Vaccinations and health of children under age five, Marriage, Fertility preferences, Husband's background and respondent's work, Awareness of AIDS. In addition, interviewing teams measured the height and weight of children under age five (identified through the birth histories) and their mothers.

c) Information from a subsample of men aged 20-54 was collected using a Man's Questionnaire. Men were asked about their background characteristics, knowledge and use of family planning methods, marriage, fertility preferences, and awareness of AIDS.

d) The Services Availability Questionnaire was used to collect information on the health and family planning services obtained within the cluster areas. One service availability questionnaire was to be completed in each cluster.

Cleaning operations

All questionnaires for the KDHS were returned to the NCPD headquarters for data processing. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing errors found by the computer programs. One NCPD officer, one data processing supervisor, one questionnaire administrator, two office editors, and initially four data entry operators were responsible for the data processing operation. Due to attrition and the need to speed up data processing, another four data entry operators were later hired

Abstract

The Jerusalem Household Social Survey 2010 is one of the most important statistical activities that have been conducted by PCBS. It is the most detailed and comprehensive statistical activity that PCBS has conducted in Jerusalem. The main objective of the Jerusalem household social survey, 2010 is to provide basic information about: Demographic and social characteristics for the Palestinian society in Jerusalem governorate including age-sex structure, Illiteracy rate, enrollment and drop-out rates by background characteristics, Labor force status, unemployment rate, occupation, economic activity, employment status, place of work and wage levels, Housing and housing conditions, Living levels and impact of Israeli measures on nutrition behavior during Al-Aqsa intifada, Criminal offence, its victims, and injuries caused.

Geographic coverage

Social survey data covering the province of Jerusalem only, the type locality (urban, rural, refugee camps) and Governorate

Analysis unit

households, Individual

Universe

The target population was all Palestinian households living in Jerusalem Governorate.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Sample Frame Were estimated sample size of Jerusalem by 2,075 family, including 1,200 families in the Area J1, and 875 families in the Area of J2 has been the establishment of Sample Frame to Jerusalem (J2) of the General Census of Population and Housing, and Establishment, which was carried out by the PCBS at the end of 2007. And the frame is a list of counting areas, and these areas are used as units an initial preview (PSUs) in the first stage of the process of selecting the sample. Stratified cluster random sample of regular two phases: Phase 1 was selected a stratified random sample of enumeration areas from Jerusalem (J1) and Jerusalem (J2). The number of enumeration areas that have been chosen counting area 75 divided into two Areas : 40 the count of Jerusalem (J1), 35 the count of Jerusalem (J2). Phase 2 Is to choose a random sample (in a field) of the households of the selected enumeration areas are selected so that 30 families from each of the complete count has been selected in the first phase of Jerusalem (J1) and 25 families are selected at random from each Areas regularly count has been selected in the first phase of Jerusalem (J2) on the completion of the data that are a minimum of 20 families from each Areas counted in Jerusalem (J2).

Mode of data collection

Face-to-face [f2f]

Research instrument

A survey questionnaire the main tool for gathering information, so do not need to check the technical specifications for the phase of field work, as required to achieve the requirements of data processing and analysis, has been designed form the survey after examining the experience of other countries on the subject of social surveys, covering the form as much as possible the most important social indicators as recommended by the United Nations, taking into account the specificity of the Palestinian community in this aspect.

Cleaning operations

Data processing:

Input processing programs: Program is designed input beam programming Access, entry screens have been designed and auditing as well as the tests have been developed through automated checking the input and then cleaning the rules of programming questions and to examine variables at the level of form.

Data entry: After the completion of design input, testing and making sure readiness, started work on data entry and after the entrances have been trained to deal with the programs of the entry. Work began on the introduction of survey data as of 26/07/2010 until 28/11/2010. It was the number of entrances who worked on the introduction of statements 5 entrances at a minimum, where the number of employees to enter data commensurate with the flow of forms, note that the flow of the forms on the entry could not be uniform due to the difficulty of transportation because of security conditions, and was checking the forms returned from the entry of by the auditors to complete and re-adjusted and then re-entered its final form after their arrival from the field.

Check and clean the data: Been cleaned data queries run tests and adjust input errors immediately. And re-forms containing errors form to the project manager to deal with them. After the completion of the data entry process began work on the audit and examine the data as follows: 1. Check transitions, and allowed values. 2. Check compatibility and consistency between questions per section and the various departments, and this to us ? E logical relations. 3. Tests based on certain relationships between the different questions, so that was extracted list Balastmarat is matched, review and identify the source of a bug where, if found there are errors in the input was adjusted immediately, and if there are errors, the field was being converted to field work to re-visit again , and correct errors in form, have included the stage of data cleaning in two stages: the stage of cleaning the survey data in terms of consistency and logic and linked to age and date of birth, educational status and other per capita, and consistency of questions of each section of the form of households, while the second stage have included the examination of consistency between the results of Questions Social Survey 2010 and Social Survey of Jerusalem in 2005. Surveys and other surveys such as the impact of expansion and annexation wall on the population, 2008, as well as the Labour Force Survey - third quarter 2010.

Response rate

Were selected (2,374) represented the family of Jerusalem Governorate, a sample size which is equal to the original 2,075 family as well as samples to 299 additional families of Jerusalem (J2) The number of families who were interviewed (1,709) in Jerusalem Governorate, complete Questionnaires 72.0% (1,026) in J1 85.5% (683) in J2 58.2%

Sampling error estimates

Data were collected in a manner that the survey sample and not Balhsr destruction, so she is exposed to two main types of errors. The first sampling errors (statistical errors), and the second non-statistical errors. It is intended that sampling errors of the errors resulting from sample design, so it is easy to measure, the contrast has been calculated and the effect of sample design.

The non-statistical errors are possible to occur in every stage of project implementation, through data collection, inserting, and mistakes can be summarized by the non-response, and response errors (surveyed), and the mistakes of the interview (the researcher) and data-entry errors. To avoid errors and reduce the impact it has made significant efforts through the training of researchers extensive training, and the presence of a group of experts in the concepts and terminology, medical / health, and training on how to conduct interviews, and the things that must be followed during the interview, and the things that should be avoided.

Have been trained on the data entry program entry, program, and were examined in order to see the picture of the situation and reduce any problems, there was constant contact between supervisors and checkers through ongoing visits and periodic meetings. In addition, has been drafting a set of circulars and instructions reminder to the team. Also been circulated answers to questions and problems faced by the researchers during the field work.

As for office work have been trained crew to check the special forms and field detection of errors, which greatly reduces the rates of errors that can occur during field work. In order to reduce the proportion of errors that can occur during entry form to the computer, the software is designed to entry so as not to allow any errors Tnasagah can get during the process of input and contains many of the conditions Logical, where they were loading the program the input of many tests on private answers each question in addition to the relations between the different questions and testing the other logical. This process has led to the disclosure of most of the errors that are not found in previous phases of work, where they were correct all errors that have been discovered.

Data were evaluated according to the following areas: 1. Definition of family members and how to register. 2. Demographic characteristics that have a relationship on Christmas. 3. Breakdown of the profession and activity.

Methods of assessment vary according to the data subject in this survey include the following: 1. Occurrences of missing values and Answers "other" and "Do not know" and examine inconsistencies between different sections or between the date of birth and other sections. Add to examine the internal consistency of the data as part of a logical data and completeness. 2. Compared to survey data with the results of surveys of the relationship and by the Central Bureau of Statistics Palestinian implementation.

Can be summarized as sources of some non-statistical errors that have emerged during the implementation of the survey including the following: Inability to meet the data in some cases the forms because of the lack of a home or be in the housing unit does not exist or are uninhabited and there are families not able to provide some data or refused to do so. Some families did not take the form subject very seriously affecting the quality of the data provided. Errors resulting from the method of asking the question by the researcher in the field. Category understand the question and answer based on his understanding of it. The inability of the technical team overseeing the

In 1971, John Sved derived an approximate relationship between linkage disequilibrium and effective population size for an ideal finite population. This seminal work was extended by Sved and Feldman (1973) and Weir and Hill (1980) who derived additional equations with the same purpose. These equations yield useful estimates of effective population size, as they require a single sample in time. As these estimates of effective population size are now commonly used on a variety of genomic data, from arrays of single nucleotide polymorphisms to whole genome data, some authors have investigated their bias through simulation studies and proposed corrections for different mating systems. However, the cause of the bias remains elusive. Here we show the problems of using linkage disequilibrium as a statistical measure and, analogously, the problems in estimating effective population size from such measure. For that purpose, we compare three commonly used approaches with a transition probability ...

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). For Wave 1 (baseline), the study sampled over 150,000 mailing addresses across the United States to create a national sample of people who do and do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete the Youth Interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Units (PSUs) and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This second replenishment sample was combined for estimation and analysis purposes with Wave 7 adult and youth respondents from the Wave 4 Cohort who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Dataset 0001 (DS0001) contains the data from the Public-Use File Master Linkage File (PUF-MLF). This file contains 93 variables and 82,139 cases. The file provides a master list of every person's unique identification number and what type of respondent they were in each wave for data that are available in the Public-Use Files and Special Collection Public-Use Files. Dataset 0002 (DS0002) contains the data from the Restricted-Use File Master Linkage File (RUF-MLF). This file contains 202 variables and 82,139 cases. The file provides a master list of every person's unique identification number and what type of respondent they were in each wave for data that are available in the Restricted-Use Files, Special Collection Restricted-Use Files, and Biomarker Restricted-Use Files.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations 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 delineations due to differences in the effective dates of the geographic entities..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..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This dataset, prepared by the Inter-university Consortium for Political and Social Research, comprises 1 percent of the cases in the second release of CENSUS OF POPULATION AND HOUSING, 1990 UNITED STATES: PUBLIC USE MICRODATA SAMPLE: 1-PERCENT SAMPLE (ICPSR 9951). As 1 percent of the 1-Percent Public Use Microdata Sample (PUMS), the file constitutes a 1-in-10,000 sample, and contains all housing and population variables in the original 1-Percent PUMS. Housing variables include area type, state and area of residence, farm/nonfarm status, type of structure, year structure was built, vacancy and boarded-up status, number of rooms and bedrooms, presence or absence of a telephone, presence or absence of complete kitchen and plumbing facilities, type of sewage, water source and heating fuel used, property value, tenure, year moved into house/apartment, type of household/family, type of group quarters, language spoken in household, number of persons, related children, own/adopted children, and stepchildren in the household, number of persons and workers in the family, status of mortgage, second mortgage, and home equity loan, number of vehicles available, household income, sales of agricultural products, payments for rent, mortgage, and property tax, condominium fees, mobile home costs, and costs for electricity, water, heating fuel, and flood/fire/hazard insurance. Person variables cover age, sex, and relationship to householder, educational attainment, school enrollment, race, Hispanic origin, ancestry, language spoken at home, citizenship, place of birth, year of immigration, place of residence in 1985, marital status, number of children ever born, presence and age of own children, military service, mobility and personal care limitations, work limitation status, employment status, employment status of parents, occupation, industry, and class of worker, hours worked last week, weeks worked in 1989, usual hours worked per week, temporary absences from work, place of work, time of departure for work, travel time to work, means of transportation to work, number of occupants in vehicle during ride to work, total earnings, total income, wages, and salary income, farm and nonfarm self-employment income, Social Security income, public assistance income, retirement income, and rent, dividend, and net rental income. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06150.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: The dwelling unit is a building or a collection of buildings used for living by a household. - Households: Two types of households are distinguished. The ordinary household is composed of a collection of people, related or not, who recognize the authority of a single individual who is called "head of household", and who live under the same roof or in the same compound and take their daily meals together. A person living alone, who provides for his or her own basic needs consitutues a household. The collective household is composed of a group of persons without a priori family relationship, who live together within a single institution for reasons of health, study, work, travel, punishment or other. - Group quarters: The collective household is composed of a group of persons without a priori family relationship, who live together within a single institution for reasons of health, study, work, travel, punishment or other.

Universe

The entire population living in the country's national territory at a given point in time.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Census Bureau, Republic of Guinea

SAMPLE SIZE (person records): 729071.

SAMPLE DESIGN: Systematic Sample of every 10th dwelling with a random start, drawn by IPUMS

Mode of data collection

Face-to-face [f2f]

Research instrument

"The Household questionnaire" and "The Identification and Numbering of Households and Buildings Form"

Abstract

Monitoring of External Migration Situation in Armenia through Sample Survey Program commissioned by the State Committee of Science of the Republic of Armenia and being currently implemented by Russian–Armenian (Slavonic) University.

The Socio-Demographic Research Center of the Slavonic University (“Research Center”) has been engaged in analyzing migration decisions in Armenia as part of its ongoing Three-Year Program on monitoring migration through collection of household survey data and is therefore uniquely placed to analyze the situation with regards to migration in 2017. The 2017 household survey of migration conducted by “Research Center” is a follow-up survey (repeated cross-section) to those conducted in the years 2015 and 2016.

The survey gives an opportunity to: - Assess the influence of external migration on living conditions of households; - Restructure the whole timetable of trips done by migrant members of households prior to the monitoring; - Measure migration potential of population; - Analyze separate survey questionnaires for returned migrants and migrants staying abroad to reveal the issues they face abroad and after arrival to Armenia, a cause–effect relationship of the phenomenon, etc.

Geographic coverage

National

Analysis unit

Individuals and Households

Kind of data

Sample survey data [ssd]

Sampling procedure

Similar to the studies done in 2015 and 2016, this year methodology of the study has been based on multistage stratified and cluster sampling. At the primary stage of sampling the research group has determined that unit of observation is a household. The sample size: 2100 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument of the study is the survey questionnaire, which consists of the Tittle Page and 5 sections: Section 1. Welfare and remittances Section 2. Socio-demographic and economic characteristics of household members Section 3. The schedule of migration departures and arrivals from the given settlement of present and absent h/h members since 2014 Section 4. Returnees from abroad Section 5. Those who are abroad

Abstract

The Cambodia Demographic and Health Survey 2000 (CDHS) is the first nationally representative survey ever conducted in Cambodia on population and health issues. The primary objective of the survey is to provide the Ministry of Health, Ministry of Planning (MoP), and other relevant institutions and users with updated and reliable data on infant and child mortality, fertility preferences, family planning behavior, maternal mortality, utilization of maternal and child health services, health expenditures, women’s status, domestic violence, and knowledge and behavior regarding AIDS and other sexually transmitted infections (STIs). This information contributes to policy decisions, planning, monitoring, and program evaluation for the development of Cambodia, at both national- and local-government levels.

The long-term objectives of the survey are to technically strengthen the capacity both of the Ministry of Health and the National Institute of Statistics (NIS) of MoP for planning, conducting, and analyzing the results of further surveys.

The CDHS 2000 survey was conducted by the National Institute of Statistics of the Ministry of Planning, and the Ministry of Health. The CDHS executive committee and technical committee were established to oversee all technical aspects of implementation. They consisted of representatives from the Ministry of Health, the Ministry of Planning, the National Institute of Statistics, the United Nations Population Fund (UNFPA), the United Nations Children’s Fund (UNICEF), and the U.S. Agency for International Development (USAID). ORC Macro provided technical assistance including sampling design, survey methodology, interviewer training, and data analysis through the MEASURE DHS+ project. Funding for the survey came from UNFPA, UNICEF, and USAID.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The CDHS survey called for a nationally representative sample of 15,300 women between the ages of 15 and 49. Survey estimates are produced for 12 individual provinces (Banteay Mean Chey, Kampong Cham, Kampong Chhnang, Kampong Spueu, Kampong Thum, Kandal, Kaoh Kong, Phnom Penh, Prey Veaeng, Pousat, Svay Rieng, and Takaev) and for the following 5 groups of provinces: - Bat Dambang and Krong Pailin - Kampot, Krong Preah Sihanouk, and Krong Kaeb - Kracheh, Preah Vihear, and Stueng Traeng - Mondol Kiri and Rotanak Kiri - Otdar Mean Chey and Siem Reab.

The master sample developed in 1998 by the National Institute of Statistics served as the sampling frame for the CDHS survey. The master sample is based on the 1998 Cambodia General Population Census and consists of 600 villages selected with probability proportional to the number of households within the village. Villages are listed with the total population count and the number of enumeration areas (EAs), households, and segments. Enumeration areas were created during the cartography conducted in preparation for the 1998 census. A segment in a village corresponds to a block of about ten households. Segments were created only for villages retained in the master sample and maps showing their boundaries were also available for all of them.

The sample for the CDHS survey is a stratified sample selected in three stages. As for the master sample, stratification was achieved by separating every reporting domain into urban and rural areas. The sample was selected independently in every stratum.

The master sample contains a small number of villages for some of the provinces. For this reason, additional villages were directly selected from the census frame in order to reach the required sample size in these provinces. In the first stage, 471 villages were selected with probability proportional to the number of households in the village. Of these 471 villages, 63 were directly selected from the 1998 census frame. In the second stage, 5 or fewer segments were retained from each of the villages selected from the master sample, while 1 EA was retained from each of the 63 villages directly selected from the 1998 census frame. Each of these EAs consists of several segments.

A household listing was carried out in all selected segments and EAs, and the resulting lists of households served as the sampling frame for the selection of households in the third stage. All women 15-49 were interviewed in selected households.

In addition, a subsample of 50 percent of households was selected for data collection of anthropometry. Anemia testing was implemented in 25 percent of the sample. Only the women identified in the households with anemia testing were eligible for the section related to women's status. In this subsample of households, only one woman was selected in each household to be interviewed on domestic violence.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Two types of questionnaires were used in the CDHS 2000 survey: the Household Questionnaire and the Women’s questionnaire. The contents of these questionnaires were based on the international MEASURE DHS+ model. They were modified according to the situation in Cambodia and were designed to provide information needed by health and family planning program managers and policymakers, mainly the Ministry of Health, the Ministry of Planning, and other relevant institutions and organizations. The agencies involved in developing these questionnaires were the National Institute of Public Health/MoH, the National Institute of Statistics/MoP, UNFPA, UNICEF, USAID, WHO, Hellen Keller International, Marie Stopes International, the Ministry of Women’s Affairs, Project Against Domestic Violence, and the Demographic and Health Surveys (DHS) project of ORC Macro. The questionnaires were developed in English and then translated into Khmer. Back translation of the questionnaires, from Khmer to English, was also conducted.

The Household Questionnaire enumerated all the usual members and visitors of the selected households and collected information on the socioeconomic status of the households. The first part of the questionnaire collected information on the relationship of the persons to the head of household and items such as residence, sex, age, marital status, and level of education. This information was used to identify women who were eligible for the individual interview. The Household Questionnaire also contained information on the prevalence of accidents, physical impairment, illness, and health expenditures. Information was also collected on the dwelling units, including source of water, type of toilet facilities, fuels used for cooking, materials used for the house’s floor and roof, and ownership of a variety of consumer goods. In addition, during the household survey, anthropometry and anemia testing were carried out to determine nutritional status among children less than five years old and women age 15-49.

The Women’s Questionnaire collected information from all women age 15-49 on the following topics:-• Respondent’s background characteristics - Reproduction - Contraceptin (knowledge and use of family planning) - Pregnancy, antenatal care, delivery, and postnatal care - Infant feeding practices, child immunization, and health - Marriage and sexual activity - Fertility preference - Husband’s background characteristics and women’s work - Knowledge of HIV/AIDS and other sexually transmitted infections - Maternal mortality and adult mortality - Women’s status - Domestic violence (household relations module).

Response rate

A total of 12,810 households were selected in the sample, of which 12,475 were occupied at the time the fieldwork was carried out. Of the 12,475 occupied households, 12,236 were successfully interviewed, resulting in a household response rate of 98.1 percent. The main reason for the noninterviewed households was that those households no longer existed in the sampled clusters at the time of the interview.

A total of 15,558 women in these households were identified as women eligible to be interviewed. Questionnaires were then completed for 15,351 of those women, which represented a response rate of 98.7 percent. The principal reason for nonresponse among eligible women was a failure to find them at home despite repeated visits to their household.

Note: See summarized response rates by residence (urban/rural) in Table 1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: 1) nonsampling errors, and 2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2000 Cambodia Demographic and Health Survey (CDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2000 Cambodia Demographic and Health Survey is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples.

The second column contains the -values of the population, which were estimated from samples (10, 50, 100 and 1000 individuals) using the Excap algorithm. The values in parentheses are the standard deviations of the estimated values.

Abstract

The Cambodia Inter-Censal Population Survey, 2004 was designed not only to obtain the much-needed demographic data following the census, but also to serve as a means to train the staff of the NIS and Provincial Planning Offices in demographic data collection.

There are plans to produce in-depth studies on fertility, mortality, migration, literacy and education, labour force, housing and household amenities, and population projections based on the results of the survey.

The Cambodia Inter-Censal Population Survey 2004 (CIPS) is a nationally representative sample survey taken between two censuses, the 1998 census and the proposed 2008 census, in order to update information on population size and growth and other population characteristics as well as household facilities and amenities. Due to the national elections and administrative issues, the CIPS was undertaken in March 2004 instead of 2003, which would have been the five-year midpoint between the 1998 and 2008 censuses.

The conduct of the CIPS 2004 is an important step in the creation of a continuous flow of data that will allow Cambodia to prepare plans and programmes supported by a strong database.

The Cambodia Inter-Censal Population Survey 2004 was conducted with the objective of providing information on the following indicators: - Sex, age and marital status - Births and Deaths - Migration status - Literacy/Educational level - Economic characteristics - Housing and household amenities - Other population and household information

These fresh data will allow for calculations and reliable projections of: - Population size and growth - Fertility - Mortality - Migration

The survey was also intended to train the national staff in sampling, data collection, data processing, analysis and dissemination.

Geographic coverage

National

Analysis unit

Individual, Household

Universe

All Population and housing for all regular households in Cambodia excluding special settlements and institutional households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design for the CIPS 2004 is a three-stage stratified cluster sampling design, it is a probability sample selection of 100 percent of the Cambodian villages coverage areas, the survey covered only regular households and excludes special settlements and institutional households.

The CIPS 2004 was conducted in a nationwide representative sample of 21,000 households within selected 700 villages (primary sampling units) out of 13,886 villages in Cambodia. The 700 villages were selected from updated frame (list of villages for Cambodia).

The General Population Census 1998 databases of the National Institute of Statistics together with the new updated list of villages that were excluded in the general population census of 1998 was used as the sampling frame for the sampling design of the CIPS 2004.

The frame has the following identification particulars: 1- Province code 2- Province name 3- District code 4- District name 5- Commune code 6- Commune name 7- Village Code 8- Village name 9- Size of village (number of households) 10- Area code (1 = Urban, 2 = Rural)

A three-stage sample design has been used for the CIPS. In the first stage a sample of villages was selected. The villages were implicitly stratified into 45 strata (21 provinces each with rural/urban strata i.e. 42 strata plus 3 provinces each totally urban, i.e. 3 urban strata). The villages were selected using linear systematic sampling with probabilities proportionate to size (PPS). The size measure used for the selection was number of households in the village according to the 1998 Census with estimation for a few additional villages not in the 1998 census frame.

In the second stage one Census Enumeration Area was selected randomly (in the head office) in each selected PSU. At the beginning of the fieldwork all households in the EA were listed. A systematic sample of 30 non-vacant households was selected as the third stage of selection.

The listing of households in the EA would become cumbersome if there are many households in the EA. This might be the case when the enumeration area had grown substantially since the census. When the EA was large (population wise) the interviewer was instructed to split the EA into two or more approximately equal-sized segments and to select one segment randomly. All households in the selected segment were listed. Out of the 700 Sample PSUs, 598 were from the rural super stratum and the remaining 102 were from the urban super stratum. For more information on sampling for the survey the general report at national level may be referred to.

Note: All provincial headquarters were treated as urban. In the case of Sihanoukville, Kep and Pailin, the entire province was treated as urban. In Phnom Penh province, the four districts of Doun Penh, Chamkar Mon, 7 Makara and Tuol Kouk were classified as urban. All the remaining areas of the country were rural. Further, urban and rural areas are being reclassified in Cambodia. While these reclassifications have already been drafted, they have not yet been approved by the Royal Government of Cambodia. Upon endorsement and adoption, the new classifications will be used in future census/surveys.

Mode of data collection

Face-to-face [f2f]

Research instrument

The draft questionnaires for the CIPS 2004 were more or less on the 1998 General Census pattern. Some modifications, however, were made by adding new questions on

(i) Whether children aged 0-14 living with own mother (ii) Whether a person's mother is alive and (iii) Details of deaths in households in the last one year with focus on maternal mortality.

Questions mentioned at (i) and (ii) were intended respectively to estimate fertility (by application of own child method) and mortality (by application of orphan hood method). The questions to be included were carefully considered by a Working Group of Cambodia Inter-Censal Population Survey 2004, whose members were mostly from Ministries, NGOs and International Agencies. The Questionnaires were tested twice in the field (both urban and rural) by NIS staff in November 2003. The purpose of the pre-test was to have a full-dressed rehearsal of the whole process and particularly to test the questions in the field so as to make corrections in wording or definitions and to estimate the time taken for enumeration area mapping, house listing, sampling and enumeration of selected household. Based on the pre-test experience the questionnaires were modified and finalized.

Two types of questionnaires were used in the CIPS 2004: Form A House-list and Form B Household Questionnaire.

The Form A was used to collect information on buildings containing one or more households during the preliminary round preceding survey night (March 3, 2004). The information collected related to: construction material of wall, roof and floor, whether it is a wholly or partly residential building, number of households within the building, name and sex of head of household and number of persons usually living in the household.

The Form B, which has five parts, was used for survey enumeration in the period closely following the reference time.

In Part I, information on usual members of the selected household present on survey night, visitors present as well as usual members absent on survey night, was collected.

Part II was used to collect information on each usual member of the household and each visitor present on survey night. The information collected included: full name, relationship to household head, sex, age, natural mother, child aged 0-14 living with own mother, marital status, age at first marriage, mother tongue, religion, place of birth, previous residence, duration of stay, reason for migration, literacy, full time education and economic characteristics.

Part III was used to collect information on females of reproductive age (15-49) as well as children born to these women.

The information collected in part IV related to household conditions and facilities: main source of light, main cooking fuel used, whether toilet facility is available, main source of drinking water and number of living rooms occupied by household.

Part V was used to record the following information in respect of deaths in the household within the last one year:- name of deceased, sex, relationship to head of household, age at death, whether the death has been registered with the civil authorities or not, the cause of death and maternal mortality information.

Cleaning operations

The completed records (Forms A, Form B, Form I, Form II, Map, and other Forms) were systematically collected from the provinces by NIS Survey Coordinators on the due date and submitted to the team receptionist at NIS. NIS Survey Coordinators formed into three teams of two persons were trained during March 7-10 to receive and arrange the completed forms and maps for processing after due checking form the field. Control forms were prescribed by DUC to record every form without any omission. These records were carefully checked, registered and stored in the record room. Editing and coding of the questionnaires were done manually, after which the questionnaires were submitted to the computer section for further processing. The instruction for editing and coding were revised and expanded. Training on editing and coding was conducted for senior staff, who in turn had to train other editors and coders.

The purpose of the editing process was to remove matters of obvious inconsistency, incorrectness and incompleteness, and to improve the quality of data collected. Coding had to be done very carefully in

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.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..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..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..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..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Effective population size (Ne) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum Ne of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential, respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between Ne and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced Ne estimates from 3829 unique populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal, and amphibian populations had a <54% probability of reaching = 50 and a <9% probability of reaching = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median than unlisted populations, and this was consistent across all taxonomic groups. was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds, and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on Ne and help prioritize assessment of populations from taxa most at risk of failing to meet conservation thresholds. Methods Literature search, screening, and data extraction A primary literature search was conducted using ISI Web of Science Core Collection and any articles that referenced two popular single-sample Ne estimation software packages: LDNe (Waples & Do, 2008), and NeEstimator v2 (Do et al., 2014). The initial search included 4513 articles published up to the search date of May 26, 2020. Articles were screened for relevance in two steps, first based on title and abstract, and then based on the full text. For each step, a consistency check was performed using 100 articles to ensure they were screened consistently between reviewers (n = 6). We required a kappa score (Collaboration for Environmental Evidence, 2020) of ³ 0.6 in order to proceed with screening of the remaining articles. Articles were screened based on three criteria: (1) Is an estimate of Ne or Nb reported; (2) for a wild animal or plant population; (3) using a single-sample genetic estimation method. Further details on the literature search and article screening are found in the Supplementary Material (Fig. S1). We extracted data from all studies retained after both screening steps (title and abstract; full text). Each line of data entered in the database represents a single estimate from a population. Some populations had multiple estimates over several years, or from different estimation methods (see Table S1), and each of these was entered on a unique row in the database. Data on N̂e, N̂b, or N̂c were extracted from tables and figures using WebPlotDigitizer software version 4.3 (Rohatgi, 2020). A full list of data extracted is found in Table S2. Data Filtering After the initial data collation, correction, and organization, there was a total of 8971 Ne estimates (Fig. S1). We used regression analyses to compare Ne estimates on the same populations, using different estimation methods (LD, Sibship, and Bayesian), and found that the R2 values were very low (R2 values of <0.1; Fig. S2 and Fig. S3). Given this inconsistency, and the fact that LD is the most frequently used method in the literature (74% of our database), we proceeded with only using the LD estimates for our analyses. We further filtered the data to remove estimates where no sample size was reported or no bias correction (Waples, 2006) was applied (see Fig. S6 for more details). Ne is sometimes estimated to be infinity or negative within a population, which may reflect that a population is very large (i.e., where the drift signal-to-noise ratio is very low), and/or that there is low precision with the data due to small sample size or limited genetic marker resolution (Gilbert & Whitlock, 2015; Waples & Do, 2008; Waples & Do, 2010) We retained infinite and negative estimates only if they reported a positive lower confidence interval (LCI), and we used the LCI in place of a point estimate of Ne or Nb. We chose to use the LCI as a conservative proxy for in cases where a point estimate could not be generated, given its relevance for conservation (Fraser et al., 2007; Hare et al., 2011; Waples & Do 2008; Waples 2023). We also compared results using the LCI to a dataset where infinite or negative values were all assumed to reflect very large populations and replaced the estimate with an arbitrary large value of 9,999 (for reference in the LCI dataset only 51 estimates, or 0.9%, had an or > 9999). Using this 9999 dataset, we found that the main conclusions from the analyses remained the same as when using the LCI dataset, with the exception of the HFI analysis (see discussion in supplementary material; Table S3, Table S4 Fig. S4, S5). We also note that point estimates with an upper confidence interval of infinity (n = 1358) were larger on average (mean = 1380.82, compared to 689.44 and 571.64, for estimates with no CIs or with an upper boundary, respectively). Nevertheless, we chose to retain point estimates with an upper confidence interval of infinity because accounting for them in the analyses did not alter the main conclusions of our study and would have significantly decreased our sample size (Fig. S7, Table S5). We also retained estimates from populations that were reintroduced or translocated from a wild source (n = 309), whereas those from captive sources were excluded during article screening (see above). In exploratory analyses, the removal of these data did not influence our results, and many of these populations are relevant to real-world conservation efforts, as reintroductions and translocations are used to re-establish or support small, at-risk populations. We removed estimates based on duplication of markers (keeping estimates generated from SNPs when studies used both SNPs and microsatellites), and duplication of software (keeping estimates from NeEstimator v2 when studies used it alongside LDNe). Spatial and temporal replication were addressed with two separate datasets (see Table S6 for more information): the full dataset included spatially and temporally replicated samples, while these two types of replication were removed from the non-replicated dataset. Finally, for all populations included in our final datasets, we manually extracted their protection status according to the IUCN Red List of Threatened Species. Taxa were categorized as “Threatened” (Vulnerable, Endangered, Critically Endangered), “Nonthreatened” (Least Concern, Near Threatened), or “N/A” (Data Deficient, Not Evaluated). Mapping and Human Footprint Index (HFI) All populations were mapped in QGIS using the coordinates extracted from articles. The maps were created using a World Behrmann equal area projection. For the summary maps, estimates were grouped into grid cells with an area of 250,000 km2 (roughly 500 km x 500 km, but the dimensions of each cell vary due to distortions from the projection). Within each cell, we generated the count and median of Ne. We used the Global Human Footprint dataset (WCS & CIESIN, 2005) to generate a value of human influence (HFI) for each population at its geographic coordinates. The footprint ranges from zero (no human influence) to 100 (maximum human influence). Values were available in 1 km x 1 km grid cell size and were projected over the point estimates to assign a value of human footprint to each population. The human footprint values were extracted from the map into a spreadsheet to be used for statistical analyses. Not all geographic coordinates had a human footprint value associated with them (i.e., in the oceans and other large bodies of water), therefore marine fishes were not included in our HFI analysis. Overall, 3610 Ne estimates in our final dataset had an associated footprint value.

China Population Statistics: Sample Survey: Sampling Fraction data was reported at 0.105 % in 2023. This records an increase from the previous number of 0.102 % for 2022. China Population Statistics: Sample Survey: Sampling Fraction data is updated yearly, averaging 0.100 % from Dec 1982 (Median) to 2023, with 37 observations. The data reached an all-time high of 100.000 % in 2020 and a record low of 0.063 % in 1994. China Population Statistics: Sample Survey: Sampling Fraction data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Level of Education.

The survey ran within a cycle of five surveys titled: Perception of Art in Slovakia after 2000. The objective was to map the extent of people's relationships to various music genres at several levels. Based on predefined criteria, the introductory question measured the intensity of respondents' interest in individual types of art (intense, average, little, or no interest). The study primarily examined the following areas: the popularity of music genres, frequency of attendance at music events, preference in music perception (in public spaces, at home and elsewhere, listening to music from the media, etc.), level of respondents' musical skills, information sources on music life, respondents' knowledge of music (titles of songs and operas, ability to identify composers and artists), attendance at music festivals, willingness to invest in listening to music (attendance at music events), access to the presentation of music events, reasons for no interest in music. The survey was carried out via a network of interviewers on a sample of 1110 respondents using the quota characteristics of gender, age, education, the size of the settlement, nationality, district, region as well as economic status, and religious denomination.