The National Household Education Survey (NHES) reports on the condition of education in the United States by collecting data at the household level rather than using a traditional, school-based data collection system. The surveys attempt to address many current issues in education, such as preprimary education, school safety and discipline, adult education, and activities related to citizenship. This survey included two topical survey components. The Parent and Family Involvement in Education (PFI) Survey (Part 1) addressed homeschooling, school choice, types and frequency of family involvement in children's schools, school practices to involve and support families, learning activities with children outside of school, and the involvement of nonresidential parents. The Adult Education for Work-Related Reasons (AEWR) Survey (Part 2) collected information about participation in college and university degree or certificate programs taken for work-related reasons, postsecondary vocational/ technical diploma or degree programs taken for work-related reasons, apprenticeships, work-related courses, and work-related informal learning. In addition, the survey explored factors associated with participation or nonparticipation in adult education activities.

The National Household Education Survey Program, 2012 Parent and Family Involvement in Education Survey (PFI-NHES:2012), is a study that is part of the National Household Education Survey (NHES) program. PFI-NHES:2012 (https://nces.ed.gov/nhes/) is a cross-sectional survey that collects data directly from households on educational issues. This study was conducted using address based sample, self-administered questionnaires of households. Households in 2012 were sampled. Key statistics produced from PFI-NHES:2012 are early childhood care and education, children's readiness for school, parent perceptions of school safety and discipline, before- and after-school activities of school-age children, participation in adult and continuing education, parent involvement in education, school choice, homeschooling, and civic involvement.

The National Home and Hospice Care Survey (NHHCS) for 1998 examines home health agencies and hospices and their current patients and discharges. These surveys were designed to provide information for those who establish standards for, plan, provide, and assess long-term care services, both in the home and in inpatient settings. Data on agency characteristics were obtained through personal interviews with the agency administrators and include the number of home health and hospice patients served in the last 12 months, type of facility ownership (i.e., profit, nonprofit, government, other), Medicare and Medicaid certification, number and type of staff, and the hours they worked. Data on a sample of patients currently receiving home health and hospice care as well as a sample of discharges were obtained by interviewing staff persons most familiar with the care provided to these patients. Respondents were requested to refer to medical or other records whenever necessary. No patient was interviewed directly. Information for patients currently receiving services and discharged patients includes services provided and provider type, type of residence, referral source, medical diagnosis, type of care (home health or hospice), type of aids used (eyeglasses, dentures, walker, cane, oxygen, hospital bed, etc.), daily living activities that required assistance, and billing charges and expected payment source(s). The discharge diagnosis for discharged patients was also recorded. Demographic variables for both types of patients include sex, age, race, Hispanic origin, and marital status.

Abstract copyright UK Data Service and data collection copyright owner.

The English House Condition Survey (EHCS) was a national survey of housing in England, commissioned by the Department for Communities and Local Government (DCLG). The survey was originally called the National House Condition Survey and covered England and Wales. The information obtained through the survey provides an accurate picture of the type, condition and energy efficiency of housing in England, the people living there, and their views on housing and their neighbourhoods. The survey covers all tenure types.

The EHCS ran quinquennially from 1967-2001. From 2002, the survey moved to a continuous basis and the data were provided as a two-year rolling sample of approximately 16,000 cases (i.e. the data for 2003 cover information collected from April 2002 to March 2004, data for 2004 comprise information collected from April 2003 to March 2005). From April 2008, the EHCS merged with the Survey of English Housing (SEH) (available at the UK Data Archive under GN 33277) to form the English Housing Survey (EHS) (available at the Archive under GN 33422).

Further information can be found on the English House Condition Survey and English Housing Survey web pages.

The aim of the 1971 survey was to obtain data on various aspects of the physical condition of the permanent housing stock of England and Wales in order to compare house conditions in 1967 and 1971.

Main Topics:

Variables include tenure and age of dwelling, whether multi-occupied, number of rooms, amenities (whether shared), standard of heating, repair costs. General fitness of dwelling (under section 4 of the 1957 Housing Act). Car space available, whether dwellings front on to roads. For pre-1919 terrace houses, frontage and front/back garden length were also included.

The National Household Education Survey, 2005 After-School Programs and Activities (ASPA-NHES:2005), is a study that is part of the National Household Education Survey (NHES) program. ASPA-NHES:2005 (https://nces.ed.gov/nhes/) is a cross-sectional survey that collects data directly from households on educational issues. This study was conducted using address based sample, self-administered questionnaires of households. Households in 2005 were sampled. The study response rate was 67.5 percent. Key statistics produced from ASPA-NHES:2005 are participation in after-school programs and activities.

A longitudinal study which follows the cohort of current residents and discharged residents sampled from the 1985 National Nursing Home Survey (NNHS), thus permitting study of nursing home and hospital utilization over time. The study was conducted in three waves. To supplement the current and discharged resident components, the 1985 NNHS included a new component - the Next-of-Kin (NOK). The NOK, using a Computer Assisted Telephone Interviewing (CATI) system, was designed to collect information about current and former nursing home residents that is not generally available from patient records or other sources in the nursing home. The NNHSF obtains additional information on a portion of the residents for whom a Current Resident Questionnaire (CRQ) or a Discharged Resident Questionnaire (DRQ) was completed. In September 1994, the NNHSF Mortality Public Use Data Tape was released, covering the years 1984-1990. It contains the multiple cause-of-death information for 6,507 subjects from the NNHSF found to be deceased after linking and matching of files with the National Death Index. Information on the mortality tape includes the date of death, region of occurrence and residence, etc. All NNHSF tapes include a patient identification number common across files to allow linkage among them. Data Availability: Public Use data tapes for each wave and the mortality tape are available through the National Technical Information Office (NTIS), NACDA and the ICPSCR at the University of Michigan. The 1985 survey tape includes eight files: the facility questionnaire, nursing staff questionnaire, current resident questionnaire, discharged resident questionnaire, expense questionnaire, nursing staff sampling list, current resident sampling list, discharged resident sampling list. The next-of-kin questionnaire is available on a separate tape. * Dates of Study: 1987-1990 * Study Features: Longitudinal * Sample Size: ** 1987: 6,001 (Wave I) ** 1988: 3,868 (Wave II) ** 1990: 3,041 (Wave III) Links: * Wave I (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/09813 * Wave II (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/09838 * Wave III (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06142

This profile presents information from the 2011 National Household Survey (NHS) for various levels of geography, including provinces and territories, census metropolitan areas/census agglomerations, census divisions, census subdivisions, census tracts and federal electoral districts. NHS data topics include: Immigration and Ethnocultural Diversity; Aboriginal Peoples; Education and Labour; Mobility and Migration; Language of work; Income and Housing.

The National Household Education Survey, 2007 School Readiness (SR-NHES:2007), is a study that is part of the National Household Education Survey (NHES) program. SR-NHES:2007 (https://nces.ed.gov/nhes/) is a cross-sectional survey that collects data directly from households on educational issues. This study was conducted using address based sample, self-administered questionnaires of households. Households in 2007 were sampled. The study's response rate was 53.2 percent. Key statistics produced from SR-NHES:2007 are children's readiness for school.

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Quarterly

Sampling procedure

Sample size:

Abstract

The GHS is an annual household survey, specifically designed to measure various aspects of the living circumstances of South African households. The key findings reported here focus on the five broad areas covered by the GHS, namely: education, health, activities related to work and unemployment, housing and household access to services and facilities.

Geographic coverage

The scope of the General Household Survey 2006 was national coverage.

Analysis unit

The units of anaylsis for the General Household Survey 2006 are individuals and households.

Universe

The survey covered all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

Enumeration Areas (EAs) that had a household count of less than twenty-five were omitted from the census frame that was used to draw the sample of PSUs for the Master Sample. Other omissions from the frame included all institution EAs except workers’ hostels, convents and monasteries. EAs in the census database that were found to have less than sixty dwelling units during listing were pooled.

The Master Sample is a multi-stage stratified sample. The overall sample size of PSUs was 3 000. The explicit strata were the 53 district councils. The 3 000 PSUs were allocated to these strata using the power allocation method. The PSUs were then sampled using probability proportional to size principles. The measure of size used was the number of households in a PSU as counted in the census.

The sampled PSUs were listed with the dwelling unit as the listing unit. From these listings systematic samples of dwelling units per PSU were drawn. These samples of dwelling units form clusters. The size of the clusters differs depending on the specific survey requirements. The GHS uses one of the clusters that contain ten dwelling units.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GHS 2006 questionnaire collected data on: Household characteristics: Dwelling type, home ownership, access to water and sanitation facilities, access to services, transport, household assets, land ownership, agricultural production Individuals' characteristics: demographic characteristics, relationship to household head, marital status, language, education, employment, income, health, disability, access to social services, mortality. Women's characteristics: fertility

Response rate

86,0% of the expected 32 566 interviews were successfully completed and positive responses were obtained. It was not possible to complete interviews in 4,4 % of the sampled dwelling units. An additional 9,6% of all interviews were not conducted for various reasons such as the sampled dwelling units had become vacant or had changed status (e.g. they were used as shops/small businesses at the time of the enumeration but were originally listed as dwelling units)

Sampling error estimates

Estimation and use of standard error The published results of the General Household Survey are based on representative probability samples drawn from the South African population, as discussed in the section on sample design. Consequently, all estimates are subject to sampling variability. This means that the sample estimates may differ from the population figures that would have been produced if the entire South African population had been included in the survey. The measure usually used to indicate the probable difference between a sample estimate and the corresponding population figure is the standard error (SE), which measures the extent to which an estimate might have varied by chance because only a sample of the population was included. There are two major factors which influence the value of a standard error. The first factor is the sample size. Generally speaking, the larger the sample size, the more precise the estimate and the smaller the standard error. Consequently, in a national household survey such as the GHS, one expects more precise estimates at the national level than at the provincial level due to the larger sample size involved. The second factor is the variability between households of the parameter of the population being estimated, for example, the number of unemployed persons in the household.

Abstract

The National Household Travel Survey (NHTS) 2020 is the third round of the survey series designed to assess domestic transport and tourism travel patterns of South African households as well as their attitudes about transport. The NHTS collects data on general household characteristics, travel patterns of households, and attitudes about transport.

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The target population of the survey consists of all private households in all nine provinces of South Africa and residents in workers' hostels. The survey does not cover other collective living quarters such as students' hostels, old-age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the NHTS was based on a master sample (MS) that used a two-stage, stratified design with probability-proportional-to-size (PPS) sampling of PSUs from within strata, and systematic sampling of dwelling units (DUs) from the sampled primary sampling units (PSUs). A self-weighting design at provincial level was used and MS stratification was divided into two levels, primary and secondary stratification. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarized at PSU level. The following variables were used for secondary stratification, household size, education, occupancy status, gender, industry and income.

Census enumeration areas (EAs) as delineated for Census 2001 formed the basis of the PSUs. The following additional rules were used: • Where possible, PSU sizes were kept between 100 and 500 dwelling units (DUs); • EAs with fewer than 25 DUs were excluded. • EAs with between 26 and 99 DUs were pooled to form larger PSUs and the criteria used was same settlement type; • Virtual splits were applied to large PSUs: 500 to 999 splits into two; 1000 to 1499 splits into three; and 1500 plus split into four PSUs; and • Informal PSUs were segmented.

A Randomized Probability Proportional to Size (RPPS) systematic sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3080 PSUs were selected. In each selected PSU a systematic sample of dwelling units was drawn. The number of DUs selected per PSU varies from PSU to PSU and depends on the Inverse Sampling Ratios (ISR) of each PSU.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consisted of 9 sections: Section 1: General health and functioning, social grants and social relief Section 2: General travel patterns Section 3: Education and education-related travel patterns Section 4: Work-related travel patterns (age 15 years and above) Section 5: Business trips Section 6: Other travel patterns Section 7: General household information Section 8: Household attitudes and perceptions about transport Section 9: Survey officer questions

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Contains 2006 Ward Census data, 2011 Ward Census data, and 2011 Ward National Household Survey (NHS) data.

Source: Statistics Canada, Census and NHS profile data for user-specified ward areas, , 2011 Census and National Household Survey, data received November 2013. Reproduced and distributed on an as is basis with the permission of Statistics Canada.

Accuracy: The 2011 National Household Survey (NHS) may contain bias due to non-response rates, especially for data pertaining to small geographic areas. The average non-response rate for the city of Ottawa was 18.9%. Further information on the NHS can be found on the Statistics Canada web site. http://www12.statcan.gc.ca/nhs-enm/2011/dp-pd/prof/help-aide/aboutdata-aproposdonnees.cfm?Lang=E

Update Frequency:Updated with each five-year national census (next census is undertaken in 2016; updated ward data are expected in 2018).

Attributes:Provides various demographic data broken down by the 23 wards of the City of Ottawa.

Contact: Royce Fu

Abstract

The Household Income and Expenditure Survey is a survey collecting data on income, consumption and expenditure patterns of households, in accordance with methodological principles of statistical enquiries, which are linked to demographic and socio-economic characteristics of households. A Household Income and expenditure Survey is the sole source of information on expenditure, consumption and income patterns of households, which is used to calculate poverty and income distribution indicators. It also serves as a statistical infrastructure for the compilation of the national basket of goods used to measure changes in price levels. Furthermore, it is used for updating of the national accounts.

The main objective of the NHIES 2009/2010 is to comprehensively describe the levels of living of Namibians using actual patterns of consumption and income, as well as a range of other socio-economic indicators based on collected data. This survey was designed to inform policy making at the international, national and regional levels within the context of the Fourth National Development Plan, in support of monitoring and evaluation of Vision 2030 and the Millennium Development Goals. The NHIES was designed to provide policy decision making with reliable estimates at regional levels as well as to meet rural - urban disaggregation requirements.

Geographic coverage

National Coverage

Analysis unit

Individuals and Households

Universe

Every week of the four weeks period of a survey round all persons in the household were asked if they spent at least 4 nights of the week in the household. Any person who spent at least 4 nights in the household was taken as having spent the whole week in the household. To qualify as a household member a person must have stayed in the household for at least two weeks out of four weeks.

Kind of data

Sample survey data [ssd]

Sampling procedure

The targeted population of NHIES 2009/2010 was the private households of Namibia. The population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in the survey. However, private households residing within institutional settings were covered. The sample design for the survey was a stratified two-stage probability sample, where the first stage units were geographical areas designated as the Primary Sampling Units (PSUs) and the second stage units were the households. The PSUs were based on the 2001 Census EAs and the list of PSUs serves as the national sample frame. The urban part of the sample frame was updated to include the changes that take place due to rural to urban migration and the new developments in housing. The sample frame is stratified first by region followed by urban and rural areas within region. In urban areas further stratification is carried out by level of living which is based on geographic location and housing characteristics. The first stage units were selected from the sampling frame of PSUs and the second stage units were selected from a current list of households within each selected PSU, which was compiled just before the interviews.

PSUs were selected using probability proportional to size sampling coupled with the systematic sampling procedure where the size measure was the number of households within the PSU in the 2001 Population and Housing Census. The households were selected from the current list of households using systematic sampling procedure.

The sample size was designed to achieve reliable estimates at the region level and for urban and rural areas within each region. However the actual sample sizes in urban or rural areas within some of the regions may not satisfy the expected precision levels for certain characteristics. The final sample consists of 10 660 households in 533 PSUs. The selected PSUs were randomly allocated to the 13 survey rounds.

Sampling deviation

All the expected sample of 533 PSUs was covered. However a number of originally selected PSUs had to be substituted by new ones due to the following reasons.

Urban areas: Movement of people for resettlement in informal settlement areas from one place to another caused a selected PSU to be empty of households.

Rural areas: In addition to Caprivi region (where one constituency is generally flooded every year) Ohangwena and Oshana regions were badly affected from an unusual flood situation. Although this situation was generally addressed by interchanging the PSUs betweensurvey rounds still some PSUs were under water close to the end of the survey period. There were five empty PSUs in the urban areas of Hardap (1), Karas (3) and Omaheke (1) regions. Since these PSUs were found in the low strata within the urban areas of the relevant regions the substituting PSUs were selected from the same strata. The PSUs under water were also five in rural areas of Caprivi (1), Ohangwena (2) and Oshana (2) regions. Wherever possible the substituting PSUs were selected from the same constituency where the original PSU was selected. If not, the selection was carried out from the rural stratum of the particular region. One sampled PSU in urban area of Khomas region (Windhoek city) had grown so large that it had to be split into 7 PSUs. This was incorporated into the geographical information system (GIS) and one PSU out of the seven was selected for the survey. In one PSU in Erongo region only fourteen households were listed and one in Omusati region listed only eleven households. All these households were interviewed and no additional selection was done to cover for the loss in sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

The instruments for data collection were as in the previous survey the questionnaires and manuals. Form I questionnaire collected demographic and socio-economic information of household members, such as: sex, age, education, employment status among others. It also collected information on household possessions like animals, land, housing, household goods, utilities, household income and expenditure, etc.

Form II or the Daily Record Book is a diary for recording daily household transactions. A book was administered to each sample household each week for four consecutive weeks (survey round). Households were asked to record transactions, item by item, for all expenditures and receipts, including incomes and gifts received or given out. Own produce items were also recorded. Prices of items from different outlets were also collected in both rural and urban areas. The price collection was needed to supplement information from areas where price collection for consumer price indices (CPI) does not currently take place.

Cleaning operations

The questionnaires received from the regions were registered and counterchecked at the survey head office. The data processing team consisted of Systems administrator, IT technician, Programmers, Statisticians and Data typists.

Data capturing

The data capturing process was undertakenin the following ways: Form 1 was scanned, interpreted and verified using the “Scan”, “Interpret” & “Verify” modules of the Eyes & Hands software respectively. Some basic checks were carried out to ensure that each PSU was valid and every household was unique. Invalid characters were removed. The scanned and verified data was converted into text files using the “Transfer” module of the Eyes & Hands. Finally, the data was transferred to a SQL database for further processing, using the “TranScan” application. The Daily Record Books (DRB or form 2) were manually entered after the scanned data had been transferred to the SQL database. The reason was to ensure that all DRBs were linked to the correct Form 1, i.e. each household’s Form 1 was linked to the corresponding Daily Record Book. In total, 10 645 questionnaires (Form 1), comprising around 500 questions each, were scanned and close to one million transactions from the Form 2 (DRBs) were manually captured.

Response rate

Household response rate: Total number of responding households and non-responding households and the reason for non-response are shown below. Non-contacts and incomplete forms, which were rejected due to a lot of missing data in the questionnaire, at 3.4 and 4.0 percent, respectively, formed the largest part of non-response. At the regional level Erongo, Khomas, and Kunene reported the lowest response rate and Caprivi and Kavango the highest. See page 17 of the report for a detailed breakdown of response rates by region.

Data appraisal

To be able to compare with the previous survey in 2003/2004 and to follow up the development of the country, methodology and definitions were kept the same. Comparisons between the surveys can be found in the different chapters in this report. Experiences from the previous survey gave valuable input to this one and the data collection was improved to avoid earlier experienced errors. Also, some additional questions in the questionnaire helped to confirm the accuracy of reported data. During the data cleaning process it turned out, that some households had difficulty to separate their household consumption from their business consumption when recording their daily transactions in DRB. This was in particular applicable for the guest farms, the number of which has shown a big increase during the past five years. All households with extreme high consumption were examined manually and business transactions were recorded and separated from private consumption.

Abstract

The demand for evidence based decision making has reached unprecedented levels today more than ever before. The level of data usage has extended not only to cover basic administrative data but also to include more detailed household level information. Household surveys therefore, have become an invaluable source of information for monitoring outcome and impact indicators of national and international development frameworks.

As a key contributor to the monitoring framework, Uganda Bureau of Statistics (UBOS) has conducted large-scale surveys since 1989. The surveys have had a nationwide coverage with varying core modules and objectives. The 2005/06 round of household surveys was yet another in a series conducted by UBOS. The last household survey was conducted in 2002/03 with a focus on labourforce and informal sector in addition to the standard Socio-economic module. This time round, the survey carries an agriculture module in addition to the Socio-economic module. The surveys primarily collect socio-economic data required for measurement of human development and monitoring social goals with special reference to the measurement of poverty under the Poverty Eradication Action Plan (PEAP) and Millennium Development Goals (MDGs).

The main objective of the survey was to collect high quality and timely data on demographic, social and economic characteristics of the household population for national and international development frameworks.

Specifically, the objectives were to: 1. Provide information on the selected economic characteristics of the population including their economic activity status among others. 2. Design and conduct a country-wide agricultural survey through the household approach and to prepare and provide estimates of area and production of major crops and other characteristics at national and regional levels. 3. Meet special data needs of users for the Ministries of Finance, Planning and Economic Development, Agriculture, Animal Industry and Fisheries, Health, Education and Sports among others, and other collaborating Institutions like Economic Policy Research Centre, together with donors and the NGO community so as to monitor the progress of their activities and interventions. 4. Generate and build social and economic indicators and monitor the progress made towards social and economic development goals of the country; and 5. Consolidate efforts being made in building a permanent national household survey capability at UBOS.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered a sample of household members in each district.

Kind of data

Sample survey data [ssd]

Sampling procedure

Survey Design A two stage sampling design was used to draw the sample. At the first stage, Enumeration Areas (EAs) were drawn with Probability Proportional to Size (PPS), and at the second stage, households which are the Ultimate Sampling Units, were drawn using Simple Random Sampling (SRS).

The sample of EAs for the UNHS 2005/06 was selected using the Uganda Population and Housing Census Frame for 2002. Initially, a total of 600 Enumeration Areas (EAs) was selected. These EAs were allocated to each region on the basis of the population size of the region. However, in the Northern region, the number of EAs drawn was doubled. The extra EAs were to be held in reserve to allow for EA attrition due to insecurity.

After this sample was drawn, it was realized that the sample size in 10 districts needed to be increased to about 30 EAs in each district to have an adequate sample size for separate analysis. These extra EAs were selected using an inter-penetrating sampling method which led to drawing an extra 153 EAs. Moreover, because a considerable proportion of the population in the North was in Internally Displaced People (IDPs) camps, this was treated as a separate selection stratum and an additional sample of 30 EAs was drawn from the IDPs. Thus, a total of 783 EAs representing both the general household population and displaced population was selected for the UNHS 2005/06.

Sample Size The size required for the sample was determined by taking into consideration several factors, the three most important being: the degree of precision (reliability) desired for the survey estimates, the cost and operational limitations, and the efficiency of the design. The UNHS 2005/06 covered a sample size of about 7,400 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five types of questionnaires were administered, namely; socio-economic survey questionnaire, agriculture questionnaire, community questionnaire, price questionnaire and crop harvest cards. The Socio-economic questionnaire collected information on household characteristics including education and literacy, the overall health status, health seeking behavior of household members, malaria, fever and disability, activity status of household members, wage employment, enterprise activities, transfers and household incomes, housing conditions assets, loans, household expenditure, welfare indicators and household shocks. The Agricultural module covered the household crop farming enterprise particulars with emphasis on land, crop area, inputs, outputs and other allied characteristics. The Community Survey questionnaire collected information about the community (LC1). The information related to community access to facilities, community services and other amenities, economic infrastructure, agriculture and markets, education and health infrastructure and agricultural technologies. The Price questioonaire was administered to provide standard equivalents of non standard units through weighing items sold in markets. It was used to collect the different local prices and the non standard units which in many cases are used in selling various items. A crop card was administered to all sampled households with an agricultural activity. Respondents were requested to record all harvests from own produce.

Cleaning operations

Double entry was done to take care of data entry errors. Interactive data cleaning and secondary editing was done. All these processes were done using CSPro ( Census Survey Processing Data Entry application).

To ensure good quality of data, a system of double entry was used. A manual system of editing questionnaires was set-up in June 2005 and two office editors were recruited to further assess the consistency of the data collected. A computer program (hot-deck scrutiny) for verification and validation was developed and operated during data processing.

Range and consistency checks were included in the data-entry program that was developed in CSPro. More intensive and thorough checks were carried out using MS-ACCESS by the processing team.

Sampling error estimates

The estimates were derived from a scientifically selected sample and analysis of survey data was undertaken at national, regional and rural-urban levels. Sampling Errors (SE) and Coefficients of Variations (CVs) of some of the variables have been presented in Appendices of the Socio-Economic Report and Agricultural Module Reports to show the precision levels.

The National Household Survey on Drug Abuse (NHSDA) series measures the prevalence and correlates of drug use in the United States. The surveys are designed to provide quarterly, as well as annual, estimates. Information is provided on the use of illicit drugs, alcohol, and tobacco among members of United States households aged 12 and older. Questions include age at first use as well as lifetime, annual, and past-month usage for the following drug classes: marijuana, cocaine (and crack), hallucinogens, heroin, inhalants, alcohol, tobacco, and nonmedical use of prescription drugs, including pain relievers, tranquilizers, stimulants, and sedatives. The survey covers substance abuse treatment history and perceived need for treatment, and includes questions from the Diagnostic and Statistical Manual (DSM) of Mental Disorders that allow diagnostic criteria to be applied. Respondents are also asked about personal and family income sources and amounts, health care access and coverage, illegal activities and arrest record, problems resulting from the use of drugs, and needle-sharing. Questions introduced in previous NHSDA administrations were retained in the 1999 survey, including questions asked only of respondents aged 12 to 17. These "youth experiences" items covered a variety of topics, such as neighborhood environment, illegal activities, gang involvement, drug use by friends, social support, extracurricular activities, exposure to substance abuse prevention and education programs, and perceived adult attitudes toward drug use and activities such as school work. Also retained were questions on mental health and access to care, perceived risk of using drugs, perceived availability of drugs, driving behavior and personal behavior, and cigar smoking. Questions on the tobacco brand used most often were introduced with the 1999 survey. Demographic data include sex, race, age, ethnicity, marital status, educational level, job status, veteran status, and current household composition. This study has 1 Data Set.

Abstract

The National Household Survey - PNAD investigates annually and permanently, general characteristics of the population, education, labor, income and housing, and others with varying regularity, according to the information needs for the country. Such characteristics include migration, fertility , marriage, health, food security, among other topics. The survey of these statistics is an important instrument for the formulation, validation and evaluation of policies to socio-economic development and the improvement of living conditions in Brazil.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey is conducted by a random sample of households. The information is provided by person resident or non-resident, considered capable of providing information for the whole neighborhood and the home. The interviewer is instructed not to accept a person under 14 years of age as an informant. The sampling plan uses cluster sampling, self-weighted in three stages (respectively municipalities, census tracts and households) with geographical stratification of the units of the first stage set for each state. The large municipalities in terms of population and those belonging to the metropolitan areas were each treated as a stratum and therefore included in the sample with certainty, being called autorrepresentativos. The other municipalities within the same geographic microregion were grouped into strata of approximately equal size, and designated non autorrepresentativos. Strata in these municipalities were selected systematically with probability proportional to size (ppt). Sectors are the unit of selection in the second stage and also are selected systematically and ppt, in which case the size is measured by the number of households. The sectors were stratified according to the situation of urban and rural states of the northern region, except for Tocantins, to allow comparison of indicators from PNADs after 2004 with those performed before insertion of the rural area of the northern states. In other regions this stratification is only implicit, ie, there is an ordering for the situation of the sector before the systematic selection. Municipalities and selected sectors are kept in the sample until they are available new Census data, when they are selected new units for the sample. Each year, in each sector selected for the sample is prepared (or updated) in the field a listing of households, producing an updated register for selection. An important characteristic of this listing operation refers to the Register of New Buildings, which is prepared to contain the buildings account for large changes in the sizes of sectors. The inventory of new construction is done in the municipalities of the sample, both in the sectors selected for the sample as those not selected. An area of new construction is excluded from the area of the original sector and is dealt with separately at the time of selection of households in this case is performed according to the sample fraction of the area. Households, which are units of the third selection stage, are formed by private households and the housing units in collective households occupied during the listing operation. The initial number of households per sector in the sample was set at 16. The sampling fraction indicates the proportion of the population constituting the sample. Currently fractions ranging from 1/50 (rural area of Roraima) to 1/800 (Sao Paulo). How the selection of households in each selected sector for the sample is done systematically to ensure self-weighting sample, the selection range of households remains fixed from year to year. This procedure entails an annual increase in the number of households in the sample, it depends on the number of households upgraded the sector by listing operation. In PNAD 2008, approximately 151,000 households were selected. The final size of the sample of PNAD 2009 was approximately 851 municipalities, 7818 153837 sectors and households. In 2007 PNAD introduced the use of electronic collector ( Personal Digital Assistant - PDA) for carrying out data collection, making it possible to improve the research operating system . Also during PNAD 2007 the DIA system was used, which is an imputation system that automatically detects qualitative data errors. Developed by the National Institute of Statistics - INE of Spain, the software aims to facilitate debugging censuses and large statistical research. In this first year of use of the application, all steps of criticism usually applied to data from the National Household Survey core questionnaire were performed, followed by a process of simultaneous validation of the data collected. In 2008 PNAD used only the Canadian Census Edit and Imputation System - CANCEIS already including the procedures usually applied to critical data from the questionnaires. Starting from PNAD 2011 sample selection of Rondônia, Acre, Amazonas, Roraima, Pará and Amapá followed the same methodology in other units of the Federation.

Mode of data collection

Face-to-face [f2f]

The National Household Survey (NHS) was conceived to replace the mandatory long-form census questionnaire. The content of the NHS 2011 is similar to the past long-form questionnaire, although some questions and sections have changed. This profile presents information from the 2011 National Household Survey (NHS) for various levels of geography, including provinces and territories, census metropolitan areas/census agglomerations, census divisions, census subdivisions, dissemination areas, federal electoral districts, and forward sortation areas. The forward sortation areas profile was created as a custom tabulation by the University of Toronto, and subsequently shared with ODESI and the DLI. NHS data topics include: Immigration and Ethnocultural Diversity; Aboriginal Peoples; Education and Labour; Mobility and Migration; Language of work; Income and Housing. 2011 Census data topics include: Population and dwelling counts; Age and sex; Families, households and marital status; Structural type of dwelling and collectives; and Language. The Aboriginal Population Profile presents information on the Aboriginal identity population from the 2011 National Household Survey (NHS). The profile for the NHS Special Collection for 13 Indian reserves and Indian settlements in Northern Ontario provides information from a special data collection following the 2011 National Household Survey (NHS).

The number of U.S. home sales in the United States declined in 2023, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2023, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes are expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Only 15 percent of U.S. renters could afford to become homeowners and in metros with highly competitive housing markets such as Los Angeles, CA, and Urban Honolulu, HI, this share was below five percent. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 387,000 U.S. dollars in 2023 and was forecast to increase slightly until 2025. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

Abstract

Niger is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) program. This program has developed a household level survey with a view to enhancing our knowledge of agriculture in Sub-Saharan Africa, in particular, its role in poverty reduction and the techniques for promoting efficiency and innovation in this sector. To achieve this objective, an innovative model for agricultural data collection in this region will need to be developed and implemented. To this end, activities conducted in the future will be supported by four main pillars: a multisectoral framework, institutional integration, analytical capacity building, and active dissemination.

First, agricultural statistical data collection must be part of an expanded and multisectoral framework that goes beyond the rural area. This will facilitate generation of the data needed to formulate effective agricultural policies throughout Niger and in the broader framework of the rural economy.

Second, agricultural statistical data collection must be supported by a well-adapted institutional framework suited to fostering collaboration and the integration of data sources. By supporting a multi-pronged approach to data collection, this project seeks to foster intersectoral collaboration and overcome a number of the current institutional constraints.

Third, national capacity building needs to be strengthened in order to enhance the reliability of the data produced and strengthen the link between the producers and users of data. This entails having the capacity to analyze data and to produce appropriate public data sets in a timely manner. The lack of analytical expertise in developing countries perpetuates weak demand for statistical data.

Consequently, the foregoing has a negative impact on the quality and availability of policy-related analyses. Scant dissemination of statistics and available results has compounded this problem.

In all countries where the LSMS-ISA project will be executed, the process envisioned for data collection will be a national household survey, based on models of LSMS surveys to be conducted every three years for a panel of households. The sampling method to be adopted should ensure the quality of the data, taking into account the depth/complexity of the questionnaire and panel size, while ensuring that samples are representative.

The main objectives of the ECVMA are to:

• Gauge the progress made with achievement of the Millennium Development Goals (MDGs);
• Facilitate the updating of the social indicators used in formulating the policies aimed at improving the living conditions of the population;
• Provide data related to several areas that are important to Niger without conducting specific surveys on individual topics ;
• Provide data on several important areas for Niger that are not necessarily collected in other more specific surveys.

Geographic coverage

National Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

The data entry was done in the field simultaneously with the data collection. Each data collection team included a data entry operator who key entered the data soon after it was collected. The data entry program was designed in CSPro, a data entry package developed by the US Census Bureau. This program allows three types of data checks: (1) range checks; (2) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (3) inter-record checks to determine inconsistencies between the different modules of the questionnaire.

The data as distributed represent the best effort to provide complete information. The data were collected and cleaned prior to the construction of the consumption aggregate. Using the same guidelines as were used in 2011, the households that are provided in the data set should have consumption data for both visits. This may not be the case. During the cleaning process, it was found that households had been misidentified which allowed more households to be included in the final consumption aggregate file (see below). The raw data that contains household/item level data that was used to calculate the consumption aggregate has been included in the distribution file.There are 3,614 households and 26,579 individuals in the data.