About this dataset

Context

The dataset tabulates the population of Manns Choice by race. It includes the population of Manns Choice across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Manns Choice across relevant racial categories.

Key observations

The percent distribution of Manns Choice population by race (across all racial categories recognized by the U.S. Census Bureau): 96.77% are white and 3.23% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Manns Choice
• Population: The population of the racial category (excluding ethnicity) in the Manns Choice is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Manns Choice total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Manns Choice Population by Race & Ethnicity. You can refer the same here

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value is reported as “Unknown” on their first test and then on a subsequent test they report “Asian;” "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" or "White”, then this subsequent reported race/ethnicity will overwrite the previous recording of “Unknown”. If a person has only ever selected “Unknown” as their race/ethnicity, then it will be recorded as “Unknown.” This change provides more specific and actionable data on who is tested in San Francisco.

The second exception is if a person ever marks “Hispanic or Latino/a, all races” for race/ethnicity then this choice will always overwrite any previous or future response. This is because it is an overarching category that can include any and all other races and is mutually exclusive with the other responses.

A person's race/ethnicity will be recorded as “Multi-racial” if they select two or more values among the following choices: “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other.” If a person selects a combination of two or more race/ethnicity answers that includes “Hispanic or Latino/a, all races” then they will still be recorded as “Hispanic or Latino/a, all races”—not as “Multi-racial.”

C. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

D. UPDATE PROCESS Updates automatically at 5:00AM Pacific Time each day. Redundant runs are scheduled at 7:00AM and 9:00AM in case of pipeline failure.

E. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24, 2020 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a user can analyze this data by sorting or filtering by the "specimen_collection_date" field.

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ethnicity by the total number of residents who identify as that race/ethnicity (according to the 2016-2020 American Community Survey (ACS) population estimate), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

F. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing data cleaning efforts. An update to the race or ethnicity designation among a subset of testing records was simultaneously released.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 2/10/2022 - race/ethnicity categorization was changed. See section NOTE ON RACE/ETHNICITY for additional information.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

Pursuant to Local Laws 126, 127, and 128 of 2016, certain demographic data is collected voluntarily and anonymously by persons voluntarily seeking social services. This data can be used by agencies and the public to better understand the demographic makeup of client populations and to better understand and serve residents of all backgrounds and identities. The data presented here has been collected through either electronic form or paper surveys offered at the point of application for services. These surveys are anonymous. Each record represents an anonymized demographic profile of an individual applicant for social services, disaggregated by response option, agency, and program. Response options include information regarding ancestry, race, primary and secondary languages, English proficiency, gender identity, and sexual orientation. Idiosyncrasies or Limitations: Note that while the dataset contains the total number of individuals who have identified their ancestry or languages spoke, because such data is collected anonymously, there may be instances of a single individual completing multiple voluntary surveys. Additionally, the survey being both voluntary and anonymous has advantages as well as disadvantages: it increases the likelihood of full and honest answers, but since it is not connected to the individual case, it does not directly inform delivery of services to the applicant. The paper and online versions of the survey ask the same questions but free-form text is handled differently. Free-form text fields are expected to be entered in English although the form is available in several languages. Surveys are presented in 11 languages. Paper Surveys 1. Are optional 2. Survey taker is expected to specify agency that provides service 2. Survey taker can skip or elect not to answer questions 3. Invalid/unreadable data may be entered for survey date or date may be skipped 4. OCRing of free-form tet fields may fail. 5. Analytical value of free-form text answers is unclear Online Survey 1. Are optional 2. Agency is defaulted based on the URL 3. Some questions must be answered 4. Date of survey is automated

This layer shows population broken down by race and Hispanic origin. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population that is Hispanic or Latino. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2018-2022ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community SurveyData Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 15, 2023National Figures: data.census.gov

This layer shows population broken down by race and Hispanic origin. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population that is Hispanic or Latino. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2017-2021ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 8, 2022National Figures: data.census.govAdditional Census data notes and data processing notes are available at the Esri Living Atlas Layer:https://tempegov.maps.arcgis.com/home/item.html?id=23ab8028f1784de4b0810104cd5d1c8f&view=list&sortOrder=desc&sortField=defaultFSOrder#overview(Esri's Living Atlas always shows latest data)

This layer shows population broken down by race and Hispanic origin. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population that is Hispanic or Latino. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2019-2023ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community SurveyData Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 12, 2024National Figures: data.census.gov

Survey completion by survey mode and self-reported race/ethnicity.

Transportation to Work by Race/Ethnicity

Risk Factors and Inequalities in California

By Health [source]

About this dataset

This table contains important data on the mode of transportation used by California residents aged 16 years and older. This information is sourced from the U.S. Census Bureau Decennial Census and American Community Survey and given as part of a series of indicators as part of the Healthy Communities Data and Indicators Project created by the Office of Health Equity.

Commuting to work makes up a large portion - 19% -of overall travel miles in the United States, with automobiles being overwhelmingly preferred by commuters over other methods like walking or biking. Automobiles show an impressive level of personal mobility, however they are associated with certain hazards such as air pollution, car crashes, pedestrian injuries, sedentary lifestyles linked to stress-related health problems and more. Alternatives such as walking alone or combined with public transport offer physical activity which has been linked to lower rates for diseases like heart disease, stroke, diabetes colon cancer breast cancer dementia depression etc., however these forms do come with their own risks; urban areas especially feature higher collision risks seeking pedestrians due to increased vehicle density while bus/rail passengers face less risk than motorcyclists pedestrians or bicyclists.

But this isn't just any average statistic; certain disadvantaged minority communities bear a disproportionate share when it comes to pedestrian-car fatalities: Native American males have an astonishingly 4 times higher death rate compared to Whites or Asians whereas African-Americans & Latinos face double risk than their respective counterparts; factors like stereotypes regarding race based driving behavior can be partially responsible for this discrepancy further marching for more research into this area our part towards embracing greater equality for all races/ethnicities . As such this data acquired from HealthData & CHHS Open Data is presented in hopes that greater awareness can be generated on current situation leading ultimately towards improving safety & providing better mobility options uniformly across all communities

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How to use the dataset

This dataset contains information on the mode of transportation to work for California residents aged 16 and older by race/ethnicity. It provides an excellent opportunity to compare commute data across different regions, counties, geographies, and ethnicities. This dataset can be used in many ways and can give insights into how different communities utilize different modes of transportation.

To get started using this dataset, begin by filtering the data to narrow down the criteria you are looking for (e.g., region_code or county_fips). Once you have narrowed down your selection of data points, you can use a variety of visualizations to gain insights into population segments who use various means of transport. For example, you could create charts such as bar graphs, line graphs or pie charts that display population patterns across year groups within a given area or particular demographic groupings (race/ethnicity). Additionally, this information could be used for public policy related applications such as informing zones about allocating resources to increase accessibility or safety related concerns with certain modes etc.

By examining this dataset further it is also possible to make comparative analyses between several years which may shed light on social trends over time in regards to commuting behaviors which could potentially reveal potential opportunities when planning infrastructure projects or commuter-friendly services such as ridesharing groups etc., through identifying current commuting gaps in given areas relative two other nearby regions based on mode usage shifts throughout various timespans within the years included in this dataset's range (2000-2010).

In conclusion; whether studying historical trends or analyzing present activity –this Transportation To Work 2000-2006-2010 Dataset holds invaluable insight on travel trends among California’s populous providing great potential for expansive research endeavors as well as guiding decision makers from city councils toward more effective policies & projects delivering positive community impact & productivity benefits

Research Ideas

• Investigating the relationship between mode of transportation and health among different racial/ethnic groups in California and also comparisons across regions.
• ...

Release Date: 2018-08-10.[NOTE: Includes firms with payroll at any time during 2016. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2016 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status for at least one owner and were not publicly held or not classifiable by gender, ethnicity, race, and veteran status. The 2016 Annual Survey of Entrepreneurs asked for information for up to four persons owning the largest percentage(s) of the business. Percentages are for owners of respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. 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. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for Owners of Respondent Employer Firms by How the Owner Initially Acquired the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016. ..Release Schedule. . This file was released in August 2018.. ..Key Table Information. . These data are related to all other 2016 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2016 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2016 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. 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. total.. For Characteristics of Business Owners (CBO) data, all estimates are of owners of firms responding to the ASE. That is, estimates are based only on firms providing gender, ethnicity, race, or veteran status; or firms not classifiable by gender, ethnicity, race, and veteran status that returned an ASE online questionnaire with at least one question answered. The ASE online questionnaire provided space for up to four owners to report their characteristics.. CBO data are not representative of all owners of all firms operating in the United States. The data do not represent all business owners in the United States.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for Owners of Respondent Employer Firms by How the Owner Initially Acquired the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2016 contains data on:. . Number of owners of respondent firms with paid employees. Percent of number of owners of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of owners of respondent firms. . All owners of respondent firms. Female. Male. Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Nonminority. Veteran. Nonveteran. . . Years in business. . All firms. Firms 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. . . How the owner initially acquired the business. . Founded or s...

In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the 20th round of the survey (April 2021) include self-reported health status, health insurance coverage, access to health care, awareness of Marketplace and Medicaid coverage options, use of public benefits, telehealth, COVID-19 vaccine attitudes, forgone care because of the COVID-19 pandemic, and unfair treatment in health care settings. Additional information collected by the survey includes age, gender, sexual orientation, marital status, education, race and ethnicity, United States citizenship, housing type, home ownership, internet access, income, and employment status.

The two surveys that constitute this study -- the 1985 Survey of Officer and Enlisted Personnel (1985 Member Survey) and the 1985 Survey of Military Spouses (1985 Spouse Survey) -- were conducted in order to study various issues relating to military personnel. Areas of investigation included (1) the response of personnel to changes in military compensation and benefits enacted in previous years, (2) factors affecting readiness and retention of active duty personnel, (3) projected behavior of military personnel in response to possible changes in personnel management, (4) differences in career orientations, attitudes, and experiences among members of different subgroups, e.g., minorities, men, and women, (5) the demographic, household, familial, and other characteristics of military personnel, couples, and families, including special groups such as dual-career couples and single-parent families, (6) the impact of military policies on aspects of military and family life such as residential arrangements, continuing education, and spouse employment, (7) family well-being, including economic issues facing military families, and (8) demand for, use, and perceived adequacy of programs providing family services. Data collected by the Member Survey include branch of service, pay grade, military occupation, length of stay at current location, problems encountered at current location and in moving to the location, expected pay grade upon leaving the military, probable behavior under different personnel management options, civilian work experience and earnings, and the degree of satisfaction or dissatisfaction with various aspects of military life such as pay and allowances, personal freedom, acquaintances and friendships, work group and co-workers, assignment stability, environment for families, frequency of moves, retirement benefits, promotion opportunities, job training and in-service education, job security, medical care, and dental care. The Spouse Survey covered some of the subjects included in the Member Survey, but with differing levels of detail and emphasis. Attitudes gauged by the Spouse Survey include satisfaction/dissatisfaction with military housing, rights of civilian spouses, levels of demands made on civilian spouses, availability of job opportunities for civilian spouses, and childcare centers at military bases. Additional information gathered by the surveys includes sex, age, race, Hispanic origin, income and debt, marital status, educational attainment, number and ages of dependents, whether or not dependents were handicapped, and main language spoken at home. Data for the Member Survey and the Spouse Survey are supplied in separate files. A Couple File, comprising husband/wife pairs, contains merged data from both surveys.

Perceived discrimination in medical settings and related factors by race/ethnicity, Sinai Community Health Survey 2.0 (2015–2016, ten Community Areas in Chicago, IL)a.

A 2022 survey found that ** percent of Black individuals had access to full-time remote working opportunities. In comparison ** percent of Asian American workers reported access to full-time remote working options. During the COVID-19 pandemic, many workers across the U.S. began working remotely for the first time. The popularity of remote work has continued as pandemic restrictions have relaxed.

Abstract

The State of Giving project, established by the Centre for Civil Society (CCS) at the University of KwaZulu-Natal (UKZN), the Southern African Grantmakers’ Association (SAGA) and the National Development Agency (NDA), was initiated to generate information on and analyse the resource flows to poverty alleviation and development in South Africa. One component of the broader project was a focus on individual-level giving, which involved the design, implementation and analysis of a national sample survey on individual level giving behaviour. It thus speaks to both the urban and rural and the formal and informal dimensions of our social context. The survey collected data on who gives, why and how much they give, as well as what they give and the recipients of their giving.

Geographic coverage

The sample, a random stratified one comprising 3000 respondents, is representative of all South Africans aged 18 and above.

Analysis unit

Individuals

Universe

The population of interest in the survey was all South Africans aged 18 and above.

Kind of data

Sample survey data

Sampling procedure

A random stratified survey sample was drawn by Ross Jennings at S&T. The sample was stratified by race and province at the first level, and then by area (rural/urban/etc.) at the second level. The sample frame comprised 3000 respondents, yielding an error bar of 1.8%. The results are representative of all South Africans aged 18 and above, in all parts of the country, including formal and informal dwellings. Unlike many surveys, the project partners ensured that the rural component of the sample (commonly the most expensive for logistical reasons) was large and did not require heavy weighting (where a small number of respondents have to represent the views of a far larger community).

Randomness was built into the selection of starting points (from which fieldworkers begin their work) - every 5th dwelling was selected, after a randomly selected starting point had been identified - and into the selection of respondents, where the birthday rule was applied. That is, a household roster was completed, all those aged 18 and above were listed, and the householder whose birthday came next was identified as the respondent. Three call-backs were undertaken to interview the selected respondent; if s/he was unavailable, the household was substituted.

A second sample was drawn, specifically to boost the minority religious groups – namely Hindus, Jews and Muslims. They are separately analysed and reported as part of the broader project, since area sampling was used, disallowing us from incorporating them into the national survey dataset.

Mode of data collection

Face-to-face [f2f]

Research instrument

A set of focus groups were staged across the country in order to inform questionnaire design. Groups were recruited across a range of criteria, including demographic and religious differences, in order to ensure a wide range of views were canvassed. Direct input from focus group participants informed a series of robust design sessions with all the project partners, from which a draft questionnaire was designed. The questionnaire was piloted in two provinces, involving urban and rural respondents and covering all four race groups. The pilot included testing specific questions, and the overall methodological approach, namely our ability to quantify giving. After the pilot results had been assessed, the questionnaire was revised before going into field.

Sampling error estimates

1. "0" values in some variables Many of the variables have a "0" value in addition to the values for responses, e.g. variables with yes/no responses are coded "0" "1""2". There is no indication that the 0 represents "missing" (only Q75 specifies the use of "0" for none/nobody).

2. Variable Q9 (Question 9) Q8 lists the number of resident children under the age of 18. Q9 refers to this question with: "of these children aged below 16 living in your household". This should probably be "aged below 18", in line with Q8 The data only reflects children under 16, so the question should probably have been "of these children, how many below the age of 16 are (Q9A) children of the head of the household and (Q9B) children not born to the head of household, i.e. children born to others. It seems though, that Q8 and Q9 should match, with Q8 identifying children and Q9 identifying children of the household head. If specifying 16 rather than 18 in Q9 is an error, then this has been reflected in the data. This means that household members 17-18 years are listed, but the data does not record whether they are children of the household head.

3. Variable Q21 (Question 21) “What do you think is the most deserving cause that you support or would support if you could?” There are 14 values for Q21 (1-14).According to the report (Everatt, D. and G. Solanki. 2005. A Nation of givers: Social giving amongst South Africans) this and other open-ended questions were later categorised and given numeric codes. However, a codebook was not included with the documentation provided to DataFirst

4. Variable Q22 (Question 22) “Is there one cause or charity or organisation you would definitely NOT give money to?” There are 14 values for Q22 (1-14). Again, this requires a code list for explanation.

5. Variable Q29 (Question 29) Q28 deals with the giving of goods/food/clothes. Q29 provides a breakdown of these items, and Q28Q29L lists time/labour as one of these. It seems that Q29L is incorrectly listed as a sub-set of goods/food/clothes. Also, giving time to causes is dealt with extensively in Q30A-Q and Q31A-Q, so this variable seems out of place.

6. Variable Q39 (Question 36) This concerns the giving of food, goods, or other forms of help to beggars/street children/people asking for help, but the question text does not specifically mention these forms of help, so can be misleading.

7. Variable Q44 (Question 44) Q44 asks the respondent to complete the sentence "Help the poor because…." There are 8 values for this variable (0-7 and 11). Again, a code list is required to explain these values.

8. Variable Q59 (Question 59) This question has three coded responses (1-3) so should have three values (or 4, with a “missing” value). There are 12 values for this variable, though (59A-59L). It is possible that this variable has been swopped with Q60 (However, Q60 only has 11 options in the questionnaire)

9. Variable Q60 (Question 60) The variable from this question only has 4 values, but there are 11 possible responses to this question (60A-60K). This variable could have been swopped with Q59 (In which case, the extra value needs explanation, as Q59 only has 11 options in the questionnaire.

10. Variables Q67 - Q82 From this point on the order of variables seems wrong, as the responses don't match the number of values listed in the questionnaire. The variables seem to refer to the next question along, e.g. Variable Q67 seems to have data emanating from Question 68, and so on. The data in the revised dataset has been corrected to reflect this.

11. There is no variable Q83 in the dataset, although there is a question 83 in the questionnaire. This seems to support the above explanation. Data users are requested to provide any additional findings on this that come to light in their research.

The Los Angeles Metropolitan Area Surveys [LAMAS] 7, 1973 collection reflects data gathered in 1973 as part of the Los Angeles Metropolitan Area Studies (LAMAS). The LAMAS, beginning in the spring of 1970, are a shared-time omnibus survey of Los Angeles County community members, usually repeated twice annually. The LAMAS were conducted ten times between 1970 and 1976 in an effort to develop a set of standard community profile measures appropriate for use in the planning and evaluation of public policy. The LAMAS instruments, indexes, and scales were used to track the development and course of social indicators (including social, psychological, health, and economic variables) and the impact of public policy on the community. Questions in this survey cover respondents' attitudes toward the following topics: community and public services, local government politics, political efficacy, residential mobility, and integration of their neighborhood. In addition, participating researchers were given the option of submitting questions to be asked in addition to the core items. These additional question topics include: travel time to work, number of vehicles, means of transportation, and alcohol use, as well as drinking and driving. Demographic variables in this collection include sex, age, race, ethnicity, education, occupation, income, religion, marital status, birth place, and housing type.

Survey Demographic Questions and Response Options.

Survey domains and measurements obtained in the ALTERNATIVE survey cohort.

Summary scores of the patient-reported outcomes and health literacy.

This report presents findings from the third (wave 3) in a series of follow up reports to the 2017 Mental Health of Children and Young People (MHCYP) survey, conducted in 2022. The sample includes 2,866 of the children and young people who took part in the MHCYP 2017 survey. The mental health of children and young people aged 7 to 24 years living in England in 2022 is examined, as well as their household circumstances, and their experiences of education, employment and services and of life in their families and communities. Comparisons are made with 2017, 2020 (wave 1) and 2021 (wave 2), where possible, to monitor changes over time.

The Private School Universe Survey, 2013-14 (PSS 2013-14), is a study that is part of the Private School Universe program. PSS 2013-14 (https://nces.ed.gov/surveys/pss/) is a cross-sectional survey that collects data on private elementary and secondary schools, including religious orientation, level of school, length of school year, length of school day, total enrollment (K-12), race/ethnicity of students, number of high school graduates, number of teachers employed, program emphasis, and existence and type of kindergarten program. The study is conducted using mail questionnaires, an internet response option and telephone and personal follow-up of all private schools in the United States. The PSS universe consists of a diverse population of schools. It includes both schools with a religious orientation (e.g., Catholic, Lutheran, or Jewish) and nonsectarian schools with programs ranging from regular to special emphasis and special education. Keys statistics produced from PSS 2013-14 are on the number of private schools, students, and teachers, the number of high school graduates, the length of the school year and school day.