PRIME Survey Dataset of Minoritised Ethnic People’s Engagement with Online Services

Our dataset is now publicly available via the university's open access repository:

DOI: 10.17861/db813826-e45d-4274-b4c3-7ecdbf2336a5 License: This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0)license.

Note: Please use the DOI link above to access and download the data. This directory is designated for dataset documentation, metadata, and any… See the full description on the dataset page: https://huggingface.co/datasets/JunhaoSong/prime-survey-question-answering.

The National Survey on Drug Use and Health (NSDUH) series (formerly titled National Household Survey on Drug Abuse) primarily 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 included 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 covered substance abuse treatment history and perceived need for treatment, and included questions from the Diagnostic and Statistical Manual (DSM) of Mental Disorders that allow diagnostic criteria to be applied. The survey included questions concerning treatment for both substance abuse and mental health-related disorders. Respondents were 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 administrations were retained in the 2014 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, 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. Several measures focused on prevention-related themes in this section. Also retained were questions on mental health and access to care, perceived risk of using drugs, perceived availability of drugs, driving and personal behavior, and cigar smoking. Questions on the tobacco brand used most often were introduced with the 1999 survey. For the 2008 survey, adult mental health questions were added to measure symptoms of psychological distress in the worst period of distress that a person experienced in the past 30 days and suicidal ideation. In 2008, a split-sample design also was included to administer separate sets of questions (WHODAS vs. SDS) to assess impairment due to mental health problems. Beginning with the 2009 NSDUH, however, all of the adults in the sample received only the WHODAS questions. Background information includes gender, race, age, ethnicity, marital status, educational level, job status, veteran status, and current household composition.

This github repo contains the pre-registration documents (including the survey questions) for a survey-based study examining the effects of race and socio-economic status (SES) on COVID-19. The license requires anyone using materials (mainly the intellectual property: survey questions) credit, in the form of a citation, should they use or refer to the research object. This license lets others distribute, remix, tweak, and build on the work as long as they credit you for the original creation.Summary. SARS-CoV2 attaches to particulate matter and regions with higher pollution loads have higher COVID-19 infection and mortality rates. Racial minorities and socio-economically disadvantaged groups have higher pollution loads and poorer air quality. In the US and UK, these groups also have higher COVID-19 infection and mortality rate and more severe disease progression. Because pollution and poor air quality also negatively impact olfactory ability, we are interested in whether smell and/or taste loss, in the absence of other symptoms, is a key indicator of COVID-19 infection in these groups, who may already have pre-existing smell and taste disorders from poor air quality. There is a data gap to overcome because smell testing only recently became part of national health initiatives in the US and current pandemic clinical practices do not routinely collect race and ethnicity data. Our study is survey-based and aims to collect information about the experience of smell and taste loss in marginalized groups globally.Status. Survey questions were designed to overcome the challenge of global variation in SES and race (Kara C. Hoover, University of Alaska Fairbanks, USA) and focus on household resources and individual assessment's of their 'income/resources' relative to others and their perspective on whether they are viewed as a minoritized/marginalized group in their society. Additional survey questions were designed to address the goals of the study. A small internal award from the University of Alaska Fairbanks College of Liberal Arts was used to purchase (Qualtrics) a balanced sample of American Black, Hispanic and white respondents with household incomes below the United States poverty threshold to guarantee sufficient power to detect an effect (using data from GCCR study). A follow-up survey in FormR that drew on the existing GCCR survey responses and an additional existing dataset from the GCCR were also used to address study questions beyond the US. The study was submitted to the University of Alaska Fairbanks Institutional Review Board for ethical approval on 15 July 2022.

Knowing the racial and ethnic composition of a community is often one of the first steps in understanding, serving, and advocating for various groups. This information can help enforce laws, policies, and regulations against discrimination based on race and ethnicity. These statistics can also help tailor services to accommodate cultural differences.This multi-scale map shows the most common race/ethnicity living within an area. Map opens at tract-level in Los Angeles, CA but has national coverage. Zoom out to see counties and states.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.

Release Date: 2021-01-28.The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applied (Approval ID: CBDRB-FY20-424)...Release Schedule:.Data in this file come from estimates of business ownership by sex, ethnicity, race, and veteran status from the 2019 Annual Business Survey (ABS) collection. Data are also obtained from administrative records, the 2017 Economic Census, and other economic surveys...Note: The collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2019 ABS collection year produces statistics for the 2018 reference year. The "Year" column in the table is the reference year...For more information about ABS planned data product releases, see Tentative ABS Schedule...Key Table Information:.The data include U.S. firms with paid employees operating during the reference year with receipts of $1,000 or more, which are classified in the North American Industry Classification System (NAICS), Sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Employer firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. Employment reflects the number of paid employees during the pay period in the reference year that included March 12...Data Items and Other Identifying Records:.Data include estimates on:.Number of employer firms (firms with paid employees). Sales and receipts of employer firms (reported in $1,000s of dollars). Number of employees (during the March 12 pay period). Annual payroll (reported in $1,000s of dollars)...These data are aggregated by the following demographic classifications of firm for:.All firms. Classifiable (firms classifiable by sex, ethnicity, race, and veteran status). . Sex. Female. Male. Equally male/female. . Ethnicity. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. . Race. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White). Equally minority/nonminority. Nonminority (Firms classified as non-Hispanic and White). . Veteran Status (defined as having served in any branch of the U.S. Armed Forces). Veteran. Equally veteran/nonveteran. Nonveteran. . . . Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status). ...The data are also shown for the number of years the firm has been in operation:.Years in Business:. Firms with less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. ...Data Notes:.. Business ownership is defined as having 51 percent or more of the stock or equity in the business. Data are provided for businesses owned equally (50% / 50%) by men and women, by Hispanics and non-Hispanics, by minorities and nonminorities, and by veterans and nonveterans. Firms not classifiable by sex, ethnicity, race, and veteran status are counted and tabulated separately.. The detail may not add to the total or subgroup total because a Hispanic or Latino firm may be of any race, and because a firm could be tabulated in more than one racial group. For example, if a firm responded as both Chinese and Black majority owned, the firm would be included in the detailed Asian and Black estimates but would only be counted once toward the higher level all firms' estimates.. References such as "Hispanic- or Latino-owned" businesses refer only to businesses operating in the 50 states and the District of Columbia that self-identified 51 percent or more of their ownership in 2018 to be by individuals of Mexican, Puerto Rican, Cuban or other Hispanic or Latino origin. The ABS does not distinguish between U.S. residents and nonresidents. Companies owned by foreign governments or owned by other companies, foreign or domestic, are included in the category "Unclassifiable."...Industry and Geography Coverage:.The data are shown for the total for all sectors (00) and the 2-digit NAICS code levels for:.United States. States and the District of Columbia. Metropolitan Statistical Areas...For more information about NAICS, see NAICS Codes & Understanding Industry Classification Systems. For information about geographies used by economic pro...

The Los Angeles County Social Survey (LACSS) continues the Los Angeles Metropolitan Area Studies (LAMAS) and the Southern California Social Surveys (SCSS). The Log Angeles County Social Survey (LACSS) is part of a continuing annual research project supported by the Institute for Social Science Research at the University of California, Los Angeles (UCLA). Each year a University of California researcher is given an opportunity to be principal investigator and to use a segment of the LACSS for his or her own research. The 1992 principal investigator was Dr. Lawrence Bobo, who was an Associate Professor of Sociology at UCLA. The LACSS 1992 was conducted between February and July 1992. Los Angeles County residents were asked questions concerning ethnic relations, social dominance, social distance, immigration, affirmative action, employment, and government. A split ballot methodology was utilized concerning the topics of immigration and affirmative action. Respondents were randomly selected to answer a series of questions from one of three ballots. In addition, a different series of social distance questions were asked depending on the respondent's ethnicity. Questionnaires were provided in both English and Spanish languages. Demographic information collected includes race, gender, religion, age, education level, occupation, birth place, political party affiliation and ideology, and origin of ancestry.

In developing countries, in-person surveys are frequently conducted in the presence of respondents' family, friends, or neighbors. What effect, if any, does their presence have on survey responses? We use data from an original representative survey in Karachi, Pakistan to examine how such presence affects responses to questions related to ethnic identity and ethnic politics. We find that respondents are systematically more likely to express greater support for ethnic politics and greater feelings of perceived ethnic discrimination in the presence of known others. We present suggestive evidence that this finding is explained by social desirability bias due to a norm of in-group solidarity. Our findings have important implications for the study of ethnic politics and for survey researchers working in contexts where respondent privacy is rarely guaranteed.

This survey of minority groups was part of a larger project to investigate the patterns, predictors, and consequences of midlife development in the areas of physical health, psychological well-being, and social responsibility. Conducted in Chicago and New York City, the survey was designed to assess the well-being of middle-aged, urban, ethnic minority adults living in both hyper-segregated neighborhoods and in areas with lower concentrations of minorities. Respondents' views were sought on issues relevant to quality of life, including health, childhood and family background, religion, race and ethnicity, personal beliefs, work experiences, marital and close relationships, financial situation, children, community involvement, and neighborhood characteristics. Questions on health explored the respondents' physical and emotional well-being, past and future attitudes toward health, physical limitations, energy level and appetite, amount of time spent worrying about health, and physical reactions to those worries. Questions about childhood and family background elicited information on family structure, the role of the parents with regard to child rearing, parental education, employment status, and supervisory responsibilities at work, the family financial situation including experiences with the welfare system, relationships with siblings, and whether as a child the respondent slept in the same bed as a parent or adult relative. Questions on religion covered religious preference, whether it is good to explore different religious teachings, and the role of religion in daily decision-making. Questions about race and ethnicity investigated respondents' backgrounds and experiences as minorities, including whether respondents preferred to be with people of the same racial group, how important they thought it was to marry within one's racial or ethnic group, citizenship, reasons for moving to the United States and the challenges faced since their arrival, their native language, how they would rate the work ethic of certain ethnic groups, their views on race relations, and their experiences with discrimination. Questions on personal beliefs probed for respondents' satisfaction with life and confidence in their opinions. Respondents were asked whether they had control over changing their life or their personality, and what age they viewed as the ideal age. They also rated people in their late 20s in the areas of physical health, contribution to the welfare and well-being of others, marriage and close relationships, relationships with their children, work situation, and financial situation. Questions on work experiences covered respondents' employment status, employment history, future employment goals, number of hours worked weekly, number of nights away from home due to work, exposure to the risk of accident or injury, relationships with coworkers and supervisors, work-related stress, and experience with discrimination in the workplace. A series of questions was posed on marriage and close relationships, including marital status, quality and length of relationships, whether the respondent had control over his or her relationships, and spouse/partner's education, physical and mental health, employment status, and work schedule. Questions on finance explored respondents' financial situation, financial planning, household income, retirement plans, insurance coverage, and whether the household had enough money. Questions on children included the number of children in the household, quality of respondents' relationships with their children, prospects for their children's future, child care coverage, and whether respondents had changed their work schedules to accommodate a child's illness. Additional topics focused on children's identification with their culture, their relationships with friends of different backgrounds, and their experiences with racism. Community involvement was another area of investigation, with items on respondents' role in child-rearing, participation on a jury, voting behavior, involvement in charitable organizations, volunteer experiences, whether they made monetary or clothing donations, and experiences living in an institutional setting or being homeless. Respondents were also queried about their neighborhoods, with items on neighborhood problems including racism, vandalism, crime, drugs, poor schools, teenag

This collection contains a cumulative datafile for The Los Angeles County Social Survey (LACSS) comprised of participants from years 1992 and 1994-1998. The LACSS continues the Los Angeles Metropolitan Area Studies (LAMAS) and the Southern California Social Surveys (SCSS). The Los Angeles County Social Survey (LACSS) is part of a continuing annual research project supported by the Institute for Social Science Research at the University of California, Los Angeles (UCLA). Each year a University of California researcher is given an opportunity to be principal investigator and to use a segment of the LACSS for his or her own research. Data for this collection represents the LACSS conducted between February 1992 and June 1998. No data was included for the year 1993. Each year, Los Angeles County residents were asked questions concerning ethnic relations, social dominance, social distance, immigration, affirmative action, employment, and government. A split ballot methodology was utilized concerning the topics of immigration and affirmative action. Respondents were randomly selected to answer a series of questions from one of three ballots. In addition, a different series of social distance questions were asked depending on the respondent's ethnicity. Demographic information collected includes race, gender, religion, age, education level, occupation, birth place, political party affiliation and ideology, and origin of ancestry.

The Presbyterian Panel began in 1973 and is an ongoing panel study in which mailed and web-based questionnaires are used to survey representative samples of constituency groups of the Presbyterian Church (U.S.A.). These constituency groups include members, elders, pastors serving in a congregation and specialized clergy serving elsewhere. The August 2013 and February 2017 Panel surveys both dealt with race and ethnicity, and the inclusion of a number of identical questions in the two surveys allows for analysis of change over time. This dataset contains data from members and elders of the Presbyterian Church (U.S.A.).

Abstract

This study is an experiment designed to compare the performance of three methodologies for sampling households with migrants:

• a stratified sample using the census to sample census tracts randomly, in which each household is then listed and screened to determine whether or not it has a migrant, with the full length questionnaire then being applied in a second phase only to the households of interest;
• a snowball survey in which households are asked to provide referrals to other households with migrant members;
• an intercept point survey (or time-and-space sampling survey), in which individuals are sampled during set time periods at a prespecified set of locations where households in the target group are likely to congregate.

Researchers from the World Bank applied these methods in the context of a survey of Brazilians of Japanese descent (Nikkei), requested by the World Bank. There are approximately 1.2-1.9 million Nikkei among Brazil’s 170 million population.

The survey was designed to provide detail on the characteristics of households with and without migrants, to estimate the proportion of households receiving remittances and with migrants in Japan, and to examine the consequences of migration and remittances on the sending households.

The same questionnaire was used for the stratified random sample and snowball surveys, and a shorter version of the questionnaire was used for the intercept surveys. Researchers can directly compare answers to the same questions across survey methodologies and determine the extent to which the intercept and snowball surveys can give similar results to the more expensive census-based survey, and test for the presence of biases.

Geographic coverage

Sao Paulo and Parana states

Analysis unit

Japanese-Brazilian (Nikkei) households and individuals

The 2000 Brazilian Census was used to classify households as Nikkei or non-Nikkei. The Brazilian Census does not ask ethnicity but instead asks questions on race, country of birth and whether an individual has lived elsewhere in the last 10 years. On the basis of these questions, a household is classified as (potentially) Nikkei if it has any of the following: 1) a member born in Japan; 2) a member who is of yellow race and who has lived in Japan in the last 10 years; 3) a member who is of yellow race, who was not born in a country other than Japan (predominantly Korea, Taiwan or China) and who did not live in a foreign country other than Japan in the last 10 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

1) Stratified random sample survey

Two states with the largest Nikkei population - Sao Paulo and Parana - were chosen for the study.

The sampling process consisted of three stages. First, a stratified random sample of 75 census tracts was selected based on 2000 Brazilian census. Second, interviewers carried out a door-to-door listing within each census tract to determine which households had a Nikkei member. Third, the survey questionnaire was then administered to households that were identified as Nikkei. A door-to-door listing exercise of the 75 census tracts was then carried out between October 13th, 2006, and October 29th, 2006. The fieldwork began on November 19, 2006, and all dwellings were visited at least once by December 22, 2006. The second wave of surveying took place from January 18th, 2007, to February 2nd, 2007, which was intended to increase the number of households responding.

2) Intercept survey

The intercept survey was designed to carry out interviews at a range of locations that were frequented by the Nikkei population. It was originally designed to be done in Sao Paulo city only, but a second intercept point survey was later carried out in Curitiba, Parana. Intercept survey took place between December 9th, 2006, and December 20th, 2006, whereas the Curitiba intercept survey took place between March 3rd and March 12th, 2007.

Consultations with Nikkei community organizations, local researchers and officers of the bank Sudameris, which provides remittance services to this community, were used to select a broad range of locations. Interviewers were assigned to visit each location during prespecified blocks of time. Two fieldworkers were assigned to each location. One fieldworker carried out the interviews, while the other carried out a count of the number of people with Nikkei appearance who appeared to be 18 years old or older who passed by each location. For the fixed places, this count was made throughout the prespecified time block. For example, between 2.30 p.m. and 3.30 p.m. at the sports club, the interviewer counted 57 adult Nikkeis. Refusal rates were carefully recorded, along with the sex and approximate age of the person refusing.

In all, 516 intercept interviews were collected.

3) Snowball sampling survey

The questionnaire that was used was the same as used for the stratified random sample. The plan was to begin with a seed list of 75 households, and to aim to reach a total sample of 300 households through referrals from the initial seed households. Each household surveyed was asked to supply the names of three contacts: (a) a Nikkei household with a member currently in Japan; (b) a Nikkei household with a member who has returned from Japan; (c) a Nikkei household without members in Japan and where individuals had not returned from Japan.

The snowball survey took place from December 5th to 20th, 2006. The second phase of the snowballing survey ran from January 22nd, 2007, to March 23rd, 2007. More associations were contacted to provide additional seed names (69 more names were obtained) and, as with the stratified sample, an adaptation of the intercept survey was used when individuals refused to answer the longer questionnaire. A decision was made to continue the snowball process until a target sample size of 100 had been achieved.

The final sample consists of 60 households who came as seed households from Japanese associations, and 40 households who were chain referrals. The longest chain achieved was three links.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) Stratified sampling and snowball survey questionnaire

This questionnaire has 36 pages with over 1,000 variables, taking over an hour to complete.

If subjects refused to answer the questionnaire, interviewers would leave a much shorter version of the questionnaire to be completed by the household by themselves, and later picked up. This shorter questionnaire was the same as used in the intercept point survey, taking seven minutes on average. The intention with the shorter survey was to provide some data on households that would not answer the full survey because of time constraints, or because respondents were reluctant to have an interviewer in their house.

2) Intercept questionnaire

The questionnaire is four pages in length, consisting of 62 questions and taking a mean time of seven minutes to answer. Respondents had to be 18 years old or older to be interviewed.

Response rate

1) Stratified random sampling 403 out of the 710 Nikkei households were surveyed, an interview rate of 57%. The refusal rate was 25%, whereas the remaining households were either absent on three attempts or were not surveyed because building managers refused permission to enter the apartment buildings. Refusal rates were higher in Sao Paulo than in Parana, reflecting greater concerns about crime and a busier urban environment.

2) Intercept Interviews 516 intercept interviews were collected, along with 325 refusals. The average refusal rate is 39%, with location-specific refusal rates ranging from only 3% at the food festival to almost 66% at one of the two grocery stores.

Abstract copyright UK Data Service and data collection copyright owner.Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991. This is a teaching resource for those who are new to data analysis. It is a step-by-step guide starting from exploring a survey, understanding the structure of the survey data and then using the data to do some simple exercises to measure differences in health and wellbeing across ethnic groups. The survey used here is Understanding Society: the UK Household Longitudinal Study which interviews individuals in the sampled households every year. To make it easier to use the teaching dataset accompanying this teaching resource only includes responses given by adults (16+ year olds) during the first interview to questions about ethnicity, health and wellbeing and some key socio-demographic characteristics such as age, sex, education, income, labour market status etc. The statistical software used to construct the dataset is Stata, but it is also available to download in SPSS and tab-delimited text formats.Nandi, Alita and Wiltshire, Deborah. (2019). "Teaching Resource: Analysing ethnic differences in health using data from Understanding Society".For information on the main Understanding Society study, see SN 6614, Understanding Society and Harmonised BHPS.Latest edition informationFor the second edition (August 2020), updated data and documentation files were deposited. Main Topics: Social behaviour and attitudesMinoritiesGeneral health and well-being Multi-stage stratified random sample

A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

Abstract copyright UK Data Service and data collection copyright owner. The Centre on the Dynamics of Ethnicity (CoDE), led by the University of Manchester with the Universities of St Andrews, Sussex, Glasgow, Edinburgh, LSE, Goldsmiths, King's College London and Manchester Metropolitan University, designed and carried out the Evidence for Equality National Survey (EVENS), with Ipsos as the survey partner. EVENS documents the lives of ethnic and religious minorities in Britain during the coronavirus pandemic and is, to date, the largest and most comprehensive survey to do so.EVENS used online and telephone survey modes, multiple languages, and a suite of recruitment strategies to reach the target audience. Words of Colour coordinated the recruitment strategies to direct participants to the survey, and partnerships with 13 voluntary, community and social enterprise (VCSE) organisations[1] helped to recruit participants for the survey.The ambition of EVENS was to better represent ethnic and religious minorities compared to existing data sources regarding the range and diversity of represented minority population groups and the topic coverage. Thus, the EVENS survey used an 'open' survey approach, which requires participants to opt-in to the survey instead of probability-based approaches that invite individuals to participate following their identification within a pre-defined sampling frame. This 'open' approach sought to overcome some of the limitations of probability-based methods in order to reach a large number and diverse mix of people from religious and ethnic minorities.EVENS included a wide range of research and policy questions, including education, employment and economic well-being, housing, social, cultural and political participation, health, and experiences of racism and discrimination, particularly with respect to the impact of the COVID-19 pandemic. Crucially, EVENS covered a full range of racial, ethnic and religious groups, including those often unrepresented in such work (such as Chinese, Jewish and Traveller groups), resulting in the participation of 14,215 participants, including 9,702 ethnic minority participants and a general population sample of 4,513, composed of White people who classified themselves as English, Welsh, Scottish, Northern Irish, and British. Data collection covered the period between 16 February 2021 and 14 August 2021.Further information about the study can be found on the EVENS project website.A teaching dataset based on the main EVENS study is available from the UKDS under SN 9249.[1] The VCSE organisations included Business in the Community, BEMIS (Scotland), Ethnic Minorities and Youth Support Team (Wales), Friends, Families and Travellers, Institute for Jewish Policy Research, Migrants' Rights Networks, Muslim Council Britain, NHS Race and Health Observatory, Operation Black Vote, Race Equality Foundation, Runnymede Trust, Stuart Hall Foundation, and The Ubele Initiative. Main Topics: Ethnic minorities, religious minorities, ethnicity, inequality, education, employment and economic well-being, housing, social participation, cultural participation, political participation, health, experiences of racism, experiences of discrimination, impact of COVID-19 pandemic. A number of different methods were used to recruit participants. See documentation for details.

The Fall Enrollment survey is conducted annually by the National Center for Education Statistics (NCES) as part of the Integrated Postsecondary Education Data System (IPEDS). The survey collects data that describe the status of student participation in various types of postsecondary institutions. The data are collected by sex for six racial/ethnic categories as defined by the Office for Civil Rights (OCR). There are two parts included in this survey. Part A, Enrollment Summary by Racial/Ethnic Status, provides enrollment data by race/ethnicity and sex and by level and year of study of the student. Part C, Clarifying Questions, supplies information on students enrolled in remedial courses, extension divisions, and branches of schools, as well as numbers of transfer students from in-state, out of state, and other countries.

Sample data for participants who were volunteers, runners/walkers who volunteer and runners/walkers.

Release Date: 2022-11-10.The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applied (Approval ID: CBDRB-FY22-308)...Release Schedule:.Data in this file come from estimates of business ownership by sex, ethnicity, race, and veteran status from the 2021 Annual Business Survey (ABS) collection. Data are also obtained from administrative records, the 2017 Economic Census, and other economic surveys...Note: The collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2021 ABS collection year produces statistics for the 2020 reference year. The "Year" column in the table is the reference year...For more information about ABS planned data product releases, see Tentative ABS Schedule...Key Table Information:.The data include U.S. firms with paid employees operating during the reference year with receipts of $1,000 or more, which are classified in the North American Industry Classification System (NAICS), Sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Employer firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. Employment reflects the number of paid employees during the pay period in the reference year that included March 12...Data Items and Other Identifying Records:.Data include estimates on:.Number of employer firms (firms with paid employees). Sales and receipts of employer firms (reported in $1,000s of dollars). Number of employees (during the March 12 pay period). Annual payroll (reported in $1,000s of dollars)...These data are aggregated by the following demographic classifications of firm for:.All firms. Classifiable (firms classifiable by sex, ethnicity, race, and veteran status). . Sex. Female. Male. Equally male/female. . Ethnicity. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. . Race. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White). Equally minority/nonminority. Nonminority (Firms classified as non-Hispanic and White). . Veteran Status (defined as having served in any branch of the U.S. Armed Forces). Veteran. Equally veteran/nonveteran. Nonveteran. . . . Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status). ...The data are also shown for the size of sales/receipts/revenue of the business:.Sales, value of shipments, or revenue size of firms:. Firms with sales/receipts of less than $5,000. Firms with sales/receipts of $5,000 to $9,999. Firms with sales/receipts of $10,000 to $24,999. Firms with sales/receipts of $25,000 to $49,999. Firms with sales/receipts of $50,000 to $99,999. Firms with sales/receipts of $100,000 to $249,999. Firms with sales/receipts of $250,000 to $499,999. Firms with sales/receipts of $500,000 to $999,999. Firms with sales/receipts of $1,000,000 or more. ...Data Notes:.. Business ownership is defined as having 51 percent or more of the stock or equity in the business. Data are provided for businesses owned equally (50% / 50%) by men and women, by Hispanics and non-Hispanics, by minorities and nonminorities, and by veterans and nonveterans. Firms not classifiable by sex, ethnicity, race, and veteran status are counted and tabulated separately.. The detail may not add to the total or subgroup total because a Hispanic or Latino firm may be of any race, and because a firm could be tabulated in more than one racial group. For example, if a firm responded as both Chinese and Black majority owned, the firm would be included in the detailed Asian and Black estimates but would only be counted once toward the higher level all firms' estimates.. References such as "Hispanic- or Latino-owned" businesses refer only to businesses operating in the 50 states and the District of Columbia that self-identified 51 percent or more of their ownership in 2020 to be by individuals of Mexican, Puerto Rican, Cuban or other Hispanic or Latino origin. The ABS does not distinguish between U.S. residents and nonresidents. Companies owned by foreign governments or owned by other companies, foreign or domestic, are included in the category "Unclassifiable."...Industry and Geography Coverage:.The data are shown for the total for all sectors (00) and 2-digit NAICS c...

Users can download data regarding the experiences and attitudes of Latinos in the United States. BackgroundThe National Survey of Latinos was conducted by the Pew Hispanic Center. This survey explores the attitudes and experiences of Latinos in the United States. Survey topics include: attitudes towards immigrants, perceptions of discrimination, language ability, language preference, education, experiences with the health care system, fears of deportation, and attitudes about enforcement policy. User FunctionalityUsers can download the dataset directly into SPS S statistical software. Data NotesA nationally representative sample of adult Latinos (age 18 and older) was surveyed in 2002, 2004, 2006 and 2007. Telephone surveys were completed among respondents with a landline or cell phone. Surveys do not include all questions asked in previous surveys. National and state-level information is available.

Survey was conducted before and after the parliamentary election in Romania on 11 December 2016 as a case study on the effect of ethnic party campaigns in communities with different ethnic compositions: ethnic Romanians living in predominantly Hungarian counties, ethnic Romanians in the rest of the country where they are in the majority, and ethnic Hungarians especially from central and western Romania. Respondents were interviewed a first time five to six weeks before the election and another time four to five weeks after the election. The questionnaire consisted of questions on ethnic belonging, trust in individuals and institutions, perceptions of electoral integrity, opinions on parties, and items on life satisfaction and assessments of the current social and political conditions of Romania. While, due to questionnaire constraints, some questions were asked in only one of the interviews, others were recorded in both waves, allowing for analyses of changes over time.The political mobilisation of ethnicity has led to tensions between ethnic groups in, for example, Belgium and Canada, and to violent conflict with disastrous consequences in such diverse cases as Cyprus, Rwanda, and Sri Lanka. Some observers point to the particularistic politics of ethnic parties as fomenting ethnic tensions and call for their regulation. Others argue that ethnic parties may be valuable vehicles in solving such tensions because they contribute to the integration of diverse ethnic groups. However, both views are so far based on assumptions rather than empirical evidence; to date, the effect of ethnic parties on ordinary people within society has not been examined directly. The project fills this gap, contributing to a better understanding of the links between ethnic parties and national unity within the population: Does the presence of ethnic parties affect the way people perceive the ethnic "other" or the nation? Is this effect positive, because ethnic parties as emancipatory vehicles increase the inclusion of ethnic minorities within the population? Or is it negative, because ethnic parties raise awareness of ethnic differences? To answer these questions, the project first conducts a global comparative analysis of 105 diverse countries, using a new multilevel dataset. It will then conduct in-depth studies of two countries to examine the nature of these links. Both the quantitative and in-depth analyses are needed to better understand whether there are general links between the presence of ethnic parties and diminished national unity throughout different contexts and to identify the nature of this link in important cases. Stratified random sampling of three different groups. Sampling of primary sampling units: PSUs (localities) were sampled separately for each group: For Group 1, both urban and rural localities in the counties Harghita and Covasna were randomly sampled in proportion to the overall number of urban–rural localities in each county. For Group 2, counties other than Harghita and Covasna were randomly sampled from all NUTS2 regions. Within each selected county, both urban and rural localities were randomly sampled in proportion to the overall number of urban–rural localities in each county. For Group 3, both urban and rural localities in the counties Harghita, Covasna, Bihor, Mures, Satu Mare, and Cluj were randomly selected in proportion to the overall number of urban–rural localities in each county. Sampling of respondents within PSUs: The sampling of the secondary (households) and tertiary sampling units (individuals) was the same within each locality: a number of starting points were randomly sampled (1–2 per rural locality, more than 1–2 per urban locality). From these starting points interviewers initiated a random walk procedure, selecting the second house to the right and continuing to select every second house after that. In the case of a block of flats, interviewers went to the top floor and conducted the random walk procedure for apartments. At each house or flat, the voting-age person with the most recent birthday was selected for participation. Only one person per household was interviewed. Interviewers made three attempts to interview selected people at different days and times of day before using a pre-selected substitute. A maximum number of 10 interviews was conducted from each starting point. Targeted sample: In total, 1,200 respondents are interviewed per wave, made up of the following three groups: 400 Romanian-speaking citizens aged 18 years or above in Harghita and Covasna counties; 400 Romanian-speaking citizens aged 18 years or above from the other 39 counties and the municipality of Bucharest; 400 Hungarian-speaking citizens aged 18 years or above from throughout Romania – especially from the Central area and West side of the country. Obtained sample: 401 Romanian-speaking citizens aged 18 years or above in Harghita and Covasna counties; 418 Romanian-speaking citizens aged 18 years or above from the other 39 counties and the municipality of Bucharest; 423 Hungarian-speaking citizens aged 18 years or above from throughout Romania – especially from the Central area and West side of the country.

Release Date: 2020-05-19.The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applied (Approval ID: CBDRB-FY20-008)...Release Schedule:.Data in this file come from estimates of business ownership by sex, ethnicity, race, and veteran status from the 2018 Annual Business Survey (ABS) collection. Data are also obtained from administrative records, the 2017 Economic Census and other economic surveys...Note: The collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2018 ABS collection year produces statistics for the 2017 reference year. The "Year" column in the table is the reference year. The ABS has a larger sample size during the benchmark year of 2017. Due to the larger size, more detailed data are shown for reference year 2017...For more information about ABS planned data product releases, see Tentative ABS Schedule...Key Table Information:.Includes U.S. firms with paid employees, operating during the reference year with receipts of $1,000 or more, which are classified in the North American Industry Classification System (NAICS), Sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Employer firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. Employment reflects the number of paid employees during the pay period in the reference year that included March 12...Data Items and Other Identifying Records:.Data include estimates on:.Number of employer firms (firms with paid employees). Sales and receipts of employer firms (reported in $1,000s of dollars). Number of employees (during the March 12 pay period). Annual payroll (reported in $1,000s of dollars)...These data are aggregated by the following demographic classifications of firm for:.All firms. Classifiable (firms classifiable by sex, ethnicity, race, and veteran status). . Sex. Female. Male. Equally male/female. . Ethnicity. Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. . Race. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White). Equally minority/nonminority. Nonminority (Firms classified as non-Hispanic and White). . Veteran Status (defined as having served in any branch of the U.S. Armed Forces). Veteran. Equally veteran/nonveteran. Nonveteran. . . . Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status). ...Moreover, the 2017 reference year statistics include detailed race and ethnicity data tabulated for:.Hispanic subgroups. Mexican, Mexican American, Chicano. Puerto Rican. Cuban. Other Hispanic, Latino, or Spanish. . Asian subgroups. Asian Indian. Chinese. Filipino. Japanese. Korean. Vietnamese. Other Asian. . Native Hawaiian and Other Pacific Islander subgroups. Native Hawaiian. Guamanian or Chamorro. Samoan. Other Pacific Islander. ...Data Notes:.. Business ownership is defined as having 51 percent or more of the stock or equity in the business. Data are provided for businesses owned equally (50% / 50%) by men and women, by Hispanics and non-Hispanics, by minorities and nonminorities, and by veterans and nonveterans. Firms not classifiable by sex, ethnicity, race, and veteran status are counted and tabulated separately.. The detail may not add to the total or subgroup total because a Hispanic or Latino firm may be of any race, and because a firm could be tabulated in more than one racial group. For example, if a firm responded as both Chinese and Black majority owned, the firm would be included in the detailed Asian and Black estimates but would only be counted once toward the higher level all firms' estimates.. References such as "Mexican-owned," "Puerto Rican-owned," "Cuban-owned" or "other Hispanic- or Latino-owned" businesses refer only to businesses operating in the 50 states and the District of Columbia that self-identified 51 percent or more of their ownership in 2017 to be by individuals of Mexican, Puerto Rican, Cuban or other Hispanic or Latino origin. The ABS does not distinguish between U.S. residents and nonresidents. Companies owned by foreign governments or owned by other companies, foreign or domestic, are included in the category "Unclassifiable."...Industry and Geogr...