In Austria a population census takes place every 10 years; this census contains a program of important statistical data on population and employment. They roughly corresponds to the information in the Mikrozensus standard survey but are more detailed (for instance with question on the connection of the place of residence and the workplace, questions on education, confession, etc.) Population and Mikrozensus are closely linked which the name already implies: Mikrozensus means a small-scale population census; this should demonstrate that what the population census reports only every 10 years, the Mikrozensus reports through the method of ongoing sampling. These ongoing sample are also collected in the years of the population census. The Mikrozensus however is far more detailed than the survey program of the population census because the Mikrozensus special surveys offer the possibility of asking questions which are fare beyond the scope of the population census. This complementary function of Mikrozensus and population census becomes especially obvious in the June-survey: certain questions that could not be posed in the population census due to the limited program were answered in the Mikrozensus via sampling. These were the topics: questions on the social stratification of the population questions on fertility and succession of birth questions on the silent Human Resources

Population size estimates of people who identify with particular race(s) in Alaskan Communities/Places and aggregation at Boroughs - CDAs and State level for recent 5-year American Community Survey (ACS) intervals. The 5-year interval data sets are published approximately 1/2 a period later than the End Year listed - for instance the interval ending in 2019 is published in mid-2021.Source: US Census Bureau, American Community SurveyThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: US Census Bureau - Why We Ask About RaceUSE CONSTRAINTS: The Alaska Department of Commerce, Community, and Economic Development (DCCED) provides the data in this application as a service to the public. DCCED makes no warranty, representation, or guarantee as to the content, accuracy, timeliness, or completeness of any of the data provided on this site. DCCED shall not be liable to the user for damages of any kind arising out of the use of data or information provided. DCCED is not the authoritative source for American Community Survey data, and any data or information provided by DCCED is provided "as is". Data or information provided by DCCED shall be used and relied upon only at the user's sole risk. For information about the American Community Survey, click here.

Context

Today thought to do some work on Data Visualization, and Income which is motivation for all, so picked Census Income Data set from UCI ML Repository. This is a very good data set for beginners to start with, and for Classification problem. Though UCI has already explained the attributes very well, so will be copy-paste them below for reference, also do refer the link. link to UCI : https://archive.ics.uci.edu/ml/datasets/census+income

Also special thanks to Ronny Kohavi and Barry Becker, for sharing the data set on UCI ML Repo.

Content

Abstract: Predict whether income exceeds $50K/yr based on census data. Also known as "Adult" dataset. Ask : Prediction task is to determine whether a person makes over 50K a year.

Listing of attributes:

• income : >50K, <=50K.
• age: continuous.
• workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
• fnlwgt: continuous.
• education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
• education-num: continuous.
• marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
• occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
• relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
• race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
• sex: Female, Male.
• capital-gain: continuous.
• capital-loss: continuous.
• hours-per-week: continuous.
• native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

About this dataset

Context

The dataset tabulates the population of Pembroke town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Pembroke town. The dataset can be utilized to understand the population distribution of Pembroke town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Pembroke town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Pembroke town.

Key observations

Largest age group (population): Male # 65-69 years (832) | Female # 60-64 years (1,066). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Pembroke town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Pembroke town is shown in the following column.
• Population (Female): The female population in the Pembroke town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Pembroke town for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pembroke town Population by Gender. You can refer the same here

Abstract The report focuses on the analysis of the census of a small town, carried out in the United Kingdom to ask important developmental questions, prepare, process, analyze, and make informed data-driven decisions for future economic, social, and infrastructural planning of the community. Introduction The purpose of the census is to compare different demographics of the nation and provide the government with accurate statistics to enable better planning. Some common factors such as household population, number of commuters, birth rate, death rate, Marital Status, religion, the number of seniors, and population growth were assessed. In recent times, net migration has directly contributed to more than half of population growth (Cangiano, 2019). It also analyzes the current state of health of the nation by its infirmity rate. This report highlights employment and unemployment trends by assessing the working-age population, their occupation, type of occupation, and how it affects the town’s economic output. It also examined and evaluated the current state of the population to identify investment opportunities for growth, deficits, and how to deal with existential challenges

The data on relationship to householder were derived from answers to Question 2 in the 2015 American Community Survey (ACS), which was asked of all people in housing units. The question on relationship is essential for classifying the population information on families and other groups. Information about changes in the composition of the American family, from the number of people living alone to the number of children living with only one parent, is essential for planning and carrying out a number of federal programs.

The responses to this question were used to determine the relationships of all persons to the householder, as well as household type (married couple family, nonfamily, etc.). From responses to this question, we were able to determine numbers of related children, own children, unmarried partner households, and multi-generational households. We calculated average household and family size. When relationship was not reported, it was imputed using the age difference between the householder and the person, sex, and marital status.

Household – A household includes all the people who occupy a housing unit. (People not living in households are classified as living in group quarters.) A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other people in the building and which have direct access from the outside of the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living arrangements.

Average Household Size – A measure obtained by dividing the number of people in households by the number of households. In cases where people in households are cross-classified by race or Hispanic origin, people in the household are classified by the race or Hispanic origin of the householder rather than the race or Hispanic origin of each individual.

Average household size is rounded to the nearest hundredth.

Comparability – The relationship categories for the most part can be compared to previous ACS years and to similar data collected in the decennial census, CPS, and SIPP. With the change in 2008 from “In-law” to the two categories of “Parent-in-law” and “Son-in-law or daughter-in-law,” caution should be exercised when comparing data on in-laws from previous years. “In-law” encompassed any type of in-law such as sister-in-law. Combining “Parent-in-law” and “son-in-law or daughter-in-law” does not represent all “in-laws” in 2008.

The same can be said of comparing the three categories of “biological” “step,” and “adopted” child in 2008 to “Child” in previous years. Before 2008, respondents may have considered anyone under 18 as “child” and chosen that category. The ACS includes “foster child” as a category. However, the 2010 Census did not contain this category, and “foster children” were included in the “Other nonrelative” category. Therefore, comparison of “foster child” cannot be made to the 2010 Census. Beginning in 2013, the “spouse” category includes same-sex spouses.

Replication materials for "Sensitive Questions, Spillover Effects, and Asking About Citizenship on the U.S. Census."

The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM(r) classification, census block group, and latitude-longitude. PRIZM(r) classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM(r) classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because of its higher spatial accuracy than other databases describing US Census geographies, including those provided by the US Census. This database includes data only for environmental problems: In regard to the following environmental and quality of life issues, I'd like you to tell me if you consider it to be a major problem, somewhat of a problem... Visit https://dataone.org/datasets/knb-lter-bes.49.570 for complete metadata about this dataset.

Connecticut Nurses Census 1917 The Connecticut Nurses Census is a part of State Archives Record Group 029: Records of the Military Census Department. The census forms may give basic details such as birthplace, age, marital status, maiden name, and current residence, as well as more specific information such as the name of the nursing school attended, medical specialty, and year of licensure. This census included the registration of both female and male nurses. This index includes the name, birthplace, age, current residence, form number and box number. If a field is left blank, it is because the person who submitted the form did not answer that question (e.g. age, anybody!) People may request a copy of a census form by contacting us by telephone (860) 757-6580 or email. Please include the name of the individual and form number.

This dataset has been clipped to the Broward County extent from the Census dataset available through the United States Department of Treasury Community Development Financial Institutions (CDFI) Fund.

OPPORTUNITY ZONES RESOURCES: downloaded from Census : https://www.cdfifund.gov/Pages/Opportunity-Zones.aspx

The authority to implement IRC 1400Z-1 and 1400Z-2 has been delegated to the IRS. The CDFI Fund is supporting the IRS with the Opportunity Zone nomination and designation process under IRC 1400Z-1 only. In addition to an initial set of proposed regulations and guidance on how the Qualified Opportunity Zone (QOZ) tax benefits under IRC 1400Z-2 (including the certification of Qualified Opportunity Funds (QOFs) and eligible investments in QOZs) will be administered, Treasury and IRS have issued a second set of proposed regulations relating to gains that may be deferred as a result of a taxpayer's investment in a QOF, special rules for an investment in a QOF held by a taxpayer for at least 10 years, and updates to portions of previously proposed regulations under section 1400Z-2 to address various issues, including: the definition of “substantially all.” You may submit comments on the proposed regulations electronically via the Federal Rulemaking Portal at www.regulations.gov (IRS REG-115420-18 or IRS REG 120186-18).Concurrent with the second set of proposed regulations, Treasury and IRS published a request for information (RFI), asking for detailed comments regarding ways to assess QOF investments including asset class, identification of Qualified Opportunity Zones and the impact and outcomes on those Qualified Opportunity Zones. You may submit comments on the RIF electronically via the Federal Rulemaking Portal at www.regulations.gov (TREAS-DO-2019-0004). IRS also has posted a list of Frequently Asked Questions about Opportunity Zones on the irs.gov Tax Reform pages. You will want to monitor the Tax Reform page at the IRS website for additional Opportunity Zone information and other Tax Reform information. For any other questions, please call (800) 829-1040.

List of designated Qualified Opportunity Zones (QOZs): This spreadsheet was updated December 14, 2018, to include two additional census tracts in Puerto Rico that, based on 2012-2016 American Community Survey data, meet the statutory criteria for a Low-Income Community and are deemed as designated QOZs. Based on nominations of eligible census tracts by the Chief Executive Officers of each State, Treasury has completed its designation of Qualified Opportunity Zones. Each State nominated the maximum number of eligible tracts, per statute, and these designations are final. The statute and legislative history of the Opportunity Zone designations, under IRC § 1400Z, do not contemplate an opportunity for additional or revised designations after the maximum number of zones allowable have been designated in a State or Territory. Based on IRC 1400Z-1, designations are based upon the boundaries of the tract at the time of the designation in 2018, and do not change over the period of the designation, even if the boundaries of an individual census tract are redefined in future Census releases.

Source: United States Census Bureau

Effective Date:

Last Update:12/14/2018

Update Cycle: As needed, Census occurs once every decade

Data tables on the social and economic conditions in Pre-Confederation Canada from the first census in 1665 to Confederation in 1867. This dataset is one of three that cover the history of the censuses in Quebec. These tables cover New France for the years 1676-1754. For census data for the years 1825-1861, see the Lower Canada dataset; for census data for the years 1765-1790, see the Province of Quebec dataset. The tables were transcribed from the fourth volume of the 1871 Census of Canada: Reprint of the Censuses of Canada, 1665-1871, available online from Statistics Canada, Canadiana, Government of Canada Publications, and the Internet Archive. Note on terminology: Due to the nature of some of the data sources, terminology may include language that is problematic and/or offensive to researchers. Certain vocabulary used to refer to racial, ethnic, religious and cultural groups is specific to the time period when the data were collected. When exploring or using these data do so in the context of historical thinking concepts – analyzing not only the content but asking questions of who shaped the content and why.

This dataset provides Census 2022 estimates for highest level of qualification of all people aged 16 and over in Scotland.

Highest level of qualification

The highest level of qualification is derived from the question asking people to indicate all types of qualifications held. There were 14 response options (plus “no qualifications”) covering professional and vocational qualifications, apprenticeships and a range of academic qualifications. For the purpose of statistical outputs, these are combined into five categories for the highest level of qualification, plus a category for no qualifications.

The census question on qualifications asked people to tick all the qualifications they had from a list of options.

The highest qualification variables from the 2011 and 2022 Censuses are not fully comparable due to the inclusion of apprenticeship qualifications in 2022 Apprenticeships were not included in 2011.

Details of classification can be found here

The quality assurance report can be found here

CONTEXT

Practice Scenario: The UIW School of Engineering wants to recruit more students into their program. They will recruit students with great math scores. Also, to increase the chances of recruitment,​ the department will look for students who qualify for financial aid. Students who qualify for financial aid more than likely come from low socio-economic backgrounds. One way to indicate this is to view how much federal revenue a school district receives through its state. High federal revenue for a school indicates that a large portion of the student base comes from low incomes families.

The question we wish to ask is as follows: Name the school districts across the nation where their Child Nutrition Programs(c25) are federally funded between the amounts $30,000 and $50,000. And where the average math score for the school districts corresponding state is greater than or equal to the nations average score of 282.

The SQL query below in 'Top5MathTarget.sql' can be used to answer this question in MySQL. To execute this process, one would need to install MySQL to their local system and load the attached datasets below from Kaggle into their MySQL schema. The SQL query below will then join the separate tables on various key identifiers.

DATA SOURCE Data is sourced from The U.S Census Bureau and The Nations Report Card (using the NAEP Data Explorer).

Finance: https://www.census.gov/programs-surveys/school-finances/data/tables.html

Math Scores: https://www.nationsreportcard.gov/ndecore/xplore/NDE

COLUMN NOTES

All data comes from the school year 2017. Individual schools are not represented, only school districts within each state.

FEDERAL FINANCE DATA DEFINITIONS

t_fed_rev: Total federal revenue through the state to each school district.

C14- Federal revenue through the state- Title 1 (no child left behind act).

C25- Federal revenue through the state- Child Nutrition Act.

Title 1 is a program implemented in schools to help raise academic achievement ​for all students. The program is available to schools where at least 40% of the students come from low inccom​​e families.

Child Nutrition Programs ensure the children are getting the food they need to grow and learn. Schools with high federal revenue to these programs indicate students that also come from low income​ families.

MATH SCORES DATA DEFINITIONS

Note: Mathematics, Grade 8, 2017, All Students (Total)

average_scale_score - The state's average score for eighth graders taking the NAEP math exam.

Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This service provides a tract-level dataset illustrating the outcome of analysis techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural.

To create this data, analysts first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. Analysts then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. The approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike.

If aggregating tract-level probabilities to larger areas, users are strongly encouraged to use occupied household counts as weights.

We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may:

prefer to use an uncontrolled classification, or

prefer to create more than three categories.

To accommodate these uses, our final tract-level output dataset includes the "raw" probability an average household would describe their neighborhood as urban, suburban, and rural. These probability values can be used to create an uncontrolled classification or additional categories.

The final classification is controlled to AHS national estimates (26.9% urban; 52.1% suburban, 21.0% rural).

  For more information about the 2017 AHS Neighborhood Description Study click on the following visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. 

Data Dictionary: DD_Urbanization Perceptions Small Area Index.

This dataset provides Census 2022 estimates for highest level of qualification of all people aged 16 and over by sex by age (in 16 categories) in Scotland.

Age

A person's age on Census Day, 20 March 2022. Infants aged under 1 year are classified as 0 years of age.

Sex

This is the sex recorded by the person completing the census. The options were "Female" and "Male". Guidance on answering the question can be found here

Highest level of qualification

The highest level of qualification is derived from the question asking people to indicate all types of qualifications held. There were 14 response options (plus “no qualifications”) covering professional and vocational qualifications, apprenticeships and a range of academic qualifications. For the purpose of statistical outputs, these are combined into five categories for the highest level of qualification, plus a category for no qualifications.

The census question on qualifications asked people to tick all the qualifications they had from a list of options.

The highest qualification variables from the 2011 and 2022 Censuses are not fully comparable due to the inclusion of apprenticeship qualifications in 2022 Apprenticeships were not included in 2011.

Details of classification can be found here

The quality assurance report can be found here

Abstract

The 2022 Nepal Demographic and Health Survey (NDHS) is the sixth survey of its kind implemented in the country as part of the worldwide Demographic and Health Surveys (DHS) Program. It was implemented by New ERA under the aegis of the Ministry of Health and Population (MoHP) of the Government of Nepal with the objective of providing reliable, accurate, and up-to-date data for the country.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2022 NDHS collected information on fertility, marriage, family planning, breastfeeding practices, nutrition, food insecurity, maternal and child health, childhood mortality, awareness and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs), women’s empowerment, domestic violence, fistula, mental health, accident and injury, disability, and other healthrelated issues such as smoking, knowledge of tuberculosis, and prevalence of hypertension.

The information collected through the 2022 NDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of Nepal’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nepal.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2022 NDHS is an updated version of the frame from the 2011 Nepal Population and Housing Census (NPHC) provided by the National Statistical Office. The 2022 NDHS considered wards from the 2011 census as sub-wards, the smallest administrative unit for the survey. The census frame includes a complete list of Nepal’s 36,020 sub-wards. Each sub-ward has a residence type (urban or rural), and the measure of size is the number of households.

In September 2015, Nepal’s Constituent Assembly declared changes in the administrative units and reclassified urban and rural areas in the country. Nepal is divided into seven provinces: Koshi Province, Madhesh Province, Bagmati Province, Gandaki Province, Lumbini Province, Karnali Province, and Sudurpashchim Province. Provinces are divided into districts, districts into municipalities, and municipalities into wards. Nepal has 77 districts comprising a total of 753 (local-level) municipalities. Of the municipalities, 293 are urban and 460 are rural.

Originally, the 2011 NPHC included 58 urban municipalities. This number increased to 217 as of 2015. On March 10, 2017, structural changes were made in the classification system for urban (Nagarpalika) and rural (Gaonpalika) locations. Nepal currently has 293 Nagarpalika, with 65% of the population living in these urban areas. The 2022 NDHS used this updated urban-rural classification system. The survey sample is a stratified sample selected in two stages. Stratification was achieved by dividing each of the seven provinces into urban and rural areas that together formed the sampling stratum for that province. A total of 14 sampling strata were created in this way. Implicit stratification with proportional allocation was achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at the different levels, and by using a probability-proportional-to-size selection at the first stage of sampling. In the first stage of sampling, 476 primary sampling units (PSUs) were selected with probability proportional to PSU size and with independent selection in each sampling stratum within the sample allocation. Among the 476 PSUs, 248 were from urban areas and 228 from rural areas. A household listing operation was carried out in all of the selected PSUs before the main survey. The resulting list of households served as the sampling frame for the selection of sample households in the second stage. Thirty households were selected from each cluster, for a total sample size of 14,280 households. Of these households, 7,440 were in urban areas and 6,840 were in rural areas. Some of the selected sub-wards were found to be overly large during the household listing operation. Selected sub-wards with an estimated number of households greater than 300 were segmented. Only one segment was selected for the survey with probability proportional to segment size.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used in the 2022 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nepal. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Nepali, Maithili, and Bhojpuri. The Household, Woman’s, and Man’s Questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the three languages for each questionnaire. The Biomarker Questionnaire was completed on paper during data collection and then entered in the CAPI system.

Cleaning operations

Data capture for the 2022 NDHS was carried out with Microsoft Surface Go 2 tablets running Windows 10.1. Software was prepared for the survey using CSPro. The processing of the 2022 NDHS data began shortly after the fieldwork started. When data collection was completed in each cluster, the electronic data files were transferred via the Internet File Streaming System (IFSS) to the New ERA central office in Kathmandu. The data files were registered and checked for inconsistencies, incompleteness, and outliers. Errors and inconsistencies were immediately communicated to the field teams for review so that problems would be mitigated going forward. Secondary editing, carried out in the central office at New ERA, involved resolving inconsistencies and coding the open-ended questions. The New ERA senior data processor coordinated the exercise at the central office. The NDHS core team members assisted with the secondary editing. The paper Biomarker Questionnaires were compared with the electronic data file to check for any inconsistencies in data entry. The pictures of vaccination cards that were captured during data collection were verified with the data entered. Data processing and editing were carried out using the CSPro software package. The concurrent data collection and processing offered a distinct advantage because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed by July 2022, and the final cleaning of the data set was completed by the end of August.

Response rate

A total of 14,243 households were selected for the sample, of which 13,833 were found to be occupied. Of the occupied households, 13,786 were successfully interviewed, yielding a response rate of more than 99%. In the interviewed households, 15,238 women age 15-49 were identified as eligible for individual interviews. Interviews were completed with 14,845 women, yielding a response rate of 97%. In the subsample of households selected for the men’s survey, 5,185 men age 15-49 were identified as eligible for individual interviews and 4,913 were successfully interviewed, yielding a response rate of 95%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors result from mistakes made in implementing data collection and in data processing, such as failing to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and entering the data incorrectly. Although numerous efforts were made during the implementation of the 2022 Nepal Demographic and Health Survey (2022 NDHS) 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 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, and so on), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the

Abstract

The primary objective of NRVA 2005 is to collect information at community and household level to better understand livelihoods of Kuchi (nomadic pastoralists), rural and urban households throughout the country, and to determine the types of risks and vulnerabilities they face. National and international stakeholders can benefit from the summarized findings of the report or the data set made available for in-depth analysis to develop strategies to address the short, medium, and long-term needs of the nomadic, rural and urban populations through better informed and timely policy development and intervention strategies.

The 2005 Assessment takes into account a series of recommendations made by several stakeholders during a workshop held in June 2004 when the preliminary NRVA 2003 results were discussed. The assessment includes urban households allowing a more comprehensive appreciation of the status of the country in the summer of 2005.

Geographic coverage

The survey covered 34 provinces excluding 6 districts.

Analysis unit

Community (Shura), Households, and Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

A sample of 30,822 households from 34 provinces (1,735 Kuchi, 23,220 rural and 5,867 urban) was drawn excluding 6 districts that were not enumerated (as CSO household listing data was not available at the time of sampling the Livestock Census [FAO, 2003] data was used). Twelve districts were enumerated only by male surveyors in all Zabul (11 districts) and Maruf district in Kandahar due to security restrictions; however, in the se districts the food consumption part of the female questionnaire was filled out by male enumerators interviewing male respondents.

Rural and Urban Settled Households

The analytical domain, the unit at which the data are statistically representative, is at the level of 34 rural provinces; in contrast to NRVA 2003, the province of Uruzgan was split into smaller Uruzgan and Daykundi; the same happened to Parwan, which was split into Parwan and Panjsher. In addition to these 34 provincial analytical domains, there are 10 urban areas with populations larger than 10,000 households.

The survey has also collected data representative of these 10 urban domains. Thus, there are 44 settled analytical domains. Because Kuchi have been considered as one national analytical domain, there are a total of 45 analytical domains for NRVA 2005. Collecting representative data with a proportional sample at the provincial level creates a challenge because of the large variation in provincial population from the smallest population in the province of Nimroz, with only 13,941 rural households, to Hirat, with 226,650 rural households. To adjust the sampling to the available budget, the province Jawzjan with 50,900 rural households, has been used as the base analytical domain for which the sampling fraction has been determined. For those domains with populations less than Jawzjan, and where the sample fraction delivered less than 350 households, further clusters were added to ensure a minimum sample size of 350 households. The sample is therefore not self-weighting.

For those provinces or districts within provinces where the sample frame was not yet available at the time of sampling (42 districts), the Livestock Census database was used to draw a sample. On arrival at a village, the number of households was determined during the male community interview. As it was difficult for the enumerators to predict the number of households within dwellings, an additional question was asked for the total number of dwellings in the village. This number was divided by 12, to create a sampling interval for households within the community. The enumerators then selected a household each time they counted the sampling interval houses. By using this method, the sampled households were randomly and spread equally throughout the village.

Kuchi households

The household listing conducted by CSO did not effectively include the migratory Kuchi population to the date of the survey; hence there was no effective sampling frame for this population. Apparently, this lack of enumeration of the Kuchi population includes those that have recently settled. This is exactly the same population that was surveyed during winter/spring 2004 by the National Multi-Sectoral Assessment for Kuchi (NMAK), i.e. the Kuchi that is still nomadic and those that have recently settled since the onset of the last drought period. This is the best estimate of the current Kuchi population. The unit of observation for the survey was the Kuchi communities in their winter location, where one or more Kuchi communities may have been located. The sample frame for the survey was created by constructing the predicted Kuchi populations in their summer location, for which information was collected from the NMAK 2004 survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

The core of NRVA 2005 is being formed by the household questionnaire. The household questionnaire consisted of the following 18 sections; the first 14 were answered by the male head of household or male respondent, and the last four by the female members of the household: - Household register and education; - Housing; - Household facilities; - Drinking water; - Assets and credit; - Livestock; - Agriculture and land tenure; - Migration, remittance and social networks; - Sources of income; - Households expenditures; - Cash for work; - Food Aid and iodized salt; - Household shocks and coping strategies; - HIV/AIDS; - Food consumption; - Maternal child health; - Children 0 – 59 months; - HIV/AIDS and literacy test.

The total number of questions that were asked to the sampled households exceeded 260 but not all questions were answered because some of them were eliminated based on the responses provided (with skipping rules). The household is regarded as the unit of analysis. In Afghanistan there is a need to address the questions to males and females depending on their nature. In every sampled community 12 households have been interviewed. On average the time required to answer the household questionnaire was less than two hours. Besides the household questionnaire, information was gathered at community level. Therefore, two community questionnaires were designed – one male and one female. These two questionnaires addressed the following topics:

Male shura questionnaire: - Community information; - Access to infrastructure; - Markets access; - Health access; - Education; - Community roles and governance; - Programme activities; - Community priorities; - Water table.

Female shura questionnaire: - Health access; - Community bodies and governance; - Community priorities.

Cleaning operations

Automated data entry

Teleform Enterprise version 8 (Cardiff software, donated by WFP) was used throughout the process to scan the NRVA 2005 Teleform questionnaires filled in the field. Teleform is an electronic pre-programmed method of gathering data (optical readable software), often used for its speed and accuracy in large surveys and censuses. A scanner capable of processing 60 sheets per minute was used. Unlike NRVA 2003, where Teleform was only used for the shura and wealth group data after being transcribed by VAM and key enumerator staff into scan able formats; finally the information was scanned into a Microsoft Access database using Teleform.

The NRVA 2005 was completely designed in Teleform; then the enumerators filled in the pre-designed questionnaire sheets and the data were directly scanned into the Access database. Scanning 1.3 million data sheets took two to three months more than anticipated; the process was finally finished in February 2006. These delays were partially due to the quality of enumeration of questionnaires, computer hardware that was not powerful enough to sustain the processing required (alleviated by the loan of a high-speed server from UNOPS) and the absence of a stable electricity supply (alleviated by the loan of the power generator from WFP).

Once the data were scanned, the programme logically checked if the number of responses per question was not exceeded. Unfortunately, within NRVA 2005 a decision was taken to insert the number of the response within the answer circles. This resulted in some false positive answers as a high percentage of the answer circles were already coloured. Only when a true answer was also indicated (giving two responses) the programme stopped asking for verification, if there was no response then the false positive was accepted and these responses were taken out during normal cleaning practices. Once a questionnaire was validated, the image file was deleted and the data was written to the Access database. Descriptive statistics were estimated with SPSS and Genstat. Cluster analysis using ADATTI software was used for food security profiling. Provincial statistics produced are included in the Annex; those for national, Kuchi, rural and urban categories are included in the main body of the document.

Data appraisal

Data constraints and limitations

In spite of the time spent on the design of the questionnaire and its implementation in NRVA 2005, the data gathered have the following limitations: - Seasonality. Food security assessment and household perceptions are only valid for the summer season, rather than for the whole year. - Limited data on non-food consumption. Due to the multilateral nature of the assessment most of the non-food consumptions (except communication costs) have been included as groups to avoid an exhaustive questionnaire with a strong risk of lowering the quality of data. - Income.

Dataset population: Persons aged 3 and over

Age

Age is derived from the date of birth question and is a person's age at their last birthday, at 27 March 2011. Dates of birth that imply an age over 115 are treated as invalid and the person's age is imputed. Infants less than one year old are classified as 0 years of age.

Proficiency in English

Proficiency in English language classifies people whose main language is not English (or not English or Welsh in Wales) according to their ability to speak English. A person is classified in one of the categories:

• Can speak English very well
• Can speak English well
• Cannot speak English well
• Cannot speak English

This question was handled slightly differently in the England and Wales censuses.

In the English census a tick box was used in Question 18, asking "What is your main language?", giving the option of 'English' or 'Other'.

In the Welsh census, a tick box was used in Question 18, asking "What is your main language?", giving the option of 'English or Welsh' or 'Other'.

Those who ticked 'Other' would be asked about their ability to speak English.

A consequence of this is that a person who reports their main language to be Welsh and completed the Welsh census, will not be asked about their ability to speak English. Whereas a person who indicates that their main language is Welsh and lives in England would be asked about 'their ability to speak English'.

Copies of the census forms can be found here: UK census forms.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Tags

   survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES




   Summary


   BES Research, Applications, and Education


   Description


   Geocoded for Baltimore County. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 



   The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 


   Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.


   This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because