Summary statistics - weighted sample in the Happiness Monitor survey.

A random sample of households were invited to participate in this survey. In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

Demographic report of weighted survey data (online).

Conventional survey tools such as weighting do not address non-ignorable nonresponse that occurs when nonresponse depends on the variable being measured. This paper describes non-ignorable nonresponse weighting and imputation models using randomized response instruments, which are variables that affect response but not the outcome of interest \citep{SunEtal2018}. The paper uses a doubly robust estimator that is valid if one, but not necessarily both, of the weighting and imputation models is correct. When applied to a national 2019 survey, these tools produce estimates that suggest there was non-trivial non-ignorable nonresponse related to turnout, and, for subgroups, Trump approval and policy questions. For example, the conventional MAR-based weighted estimates of Trump support in the Midwest were 10 percentage points lower than the MNAR-based estimates. Data to replicate estimation described in "Countering Non-Ignorable Nonresponse in Survey Models with Randomized Response Instruments and Doubly Robust Estimation"

The City of Bloomington contracted with National Research Center, Inc. to conduct the 2019 Bloomington Community Survey. This was the second time a scientific citywide survey had been completed covering resident opinions on service delivery satisfaction by the City of Bloomington and quality of life issues. The first was in 2017. The survey captured the responses of 610 households from a representative sample of 3,000 residents of Bloomington who were randomly selected to complete the survey. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the City of Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

Sources of information by personal and service-related characteristics, weighted estimates, Veterans' Survey 2022, UK.

These data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. This dataset has two layers and includes both the weighted data and unweighted data. Weighting data is a statistical method in which datasets are adjusted through calculations in order to more accurately represent the population being studied. The weighted data are used in the final published PDF report.These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2023):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethods:The survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. The weighted data are used by the ETC Institute, in the final published PDF report.The 2023 Annual Community Survey report is available on data.tempe.gov or by visiting https://www.tempe.gov/government/strategic-management-and-innovation/signature-surveys-research-and-dataThe individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

Abstract (en): The American Time Use Survey (ATUS) collects information on how people living in the United States spend their time. Data collected in this study measured the amount of time that people spent doing various activities in 2005, such as paid work, child care, religious activities, volunteering, and socializing. Respondents were randomly selected from households that had completed their final month of the Current Population Survey (CPS), and were interviewed two to five months after their household's last CPS interview. Respondents were interviewed only once and reported their activities for the 24-hour period from 4 a.m. on the day before the interview until 4 a.m. on the day of the interview. Respondents indicated the total number of minutes spent on each activity, including where they were and whom they were with. Except for secondary child care, data on activities done simultaneously with primary activities were not collected. Part 1, Respondent and Activity Summary File, contains demographic information about respondents and a summary of the total amount of time they spent doing each activity that day. Part 2, Roster File, contains information about household members and nonhousehold children under the age of 18. Part 3, Activity File, includes additional information on activities in which respondents participated, including the location of each activity and the total time spent on secondary child care. Part 4, Who File, includes data on who was present during each activity. Part 5, ATUS-CPS 2005 File, contains data on respondents and members of their household collected two to five months prior to the ATUS interviews during their participation in the Current Population Survey (CPS). Parts 6-10 contain supplemental data files that can be used for further analysis of the data. Part 6, Case History File, contains information about the interview process, such as identifiers and interview outcome codes. Part 7, Call History File, gives information about each call attempt, including the call date and outcome. Part 8, Trips File, provides information about the number, duration, and purpose of overnight trips away from home for two or more nights in a row. Part 9, Replicate Weights File I, contains base weights, replicated base weights, and replicate final weights for each case that was selected to be interviewed for ATUS, while Part 10, Replicate Weights File II, contains replicate weights that were generated using the 2006 weighting method. Demographic variables include sex, age, race, ethnicity, education level, income, employment status, occupation, citizenship status, country of origin, relationship to household members, and the ages and number of children in the household. The data contain weight variables which should be used in analyzing the data. Unweighted data are not representative of the population due to differences between population groups in both sampling and nonresponse. ATUS weight variables include the ATUS final weight (TUFINLWGT), which indicates the number of person-days the respondent represents, the ATUS base weight (TUBWGT), and a ATUS final weight based on 2006 weighting methodology (TU06FWGT). ATUS weights were selected from the Current Population Survey (CPS), and CPS weights (after the first-stage adjustment) are the basis for the ATUS weights. These base weights were adjusted to account for the fact that less populous states were not oversampled in ATUS, as they were in the CPS. Further adjustments were made to account for the probability of selecting each household within the ATUS sampling strata and the probability of selecting each person from each sample household. Part 9 contains replicate weights for the variable TUFINLWGT, as well as base weights, while Part 10 contains replicate weights for the variable TU06FWGT. ATUS replicate weights were based on the replicate weights developed for the CPS. ATUS began with the CPS replicate weight after the first-stage ratio adjustment, and each replicate was processed through all of the stages of the ATUS weighting procedure. The CPS replicate weights were based on a modified balanced half-sample method of replication, developed in the 1980s by Robert Fay. For more information about the replicate weights, see the publication, Technical Paper 63RV: Current Population Survey -- Design and Methodology, available via the Bureau of Labor Statistics Web site. More information on the weighting variables used in this study can be found in t...

Fish survey data for four stream in Tioga County Pennsylvania from 2012-2017. Community data based on 2-3 backpack electrofishing passes with individual lengths and weights.

United States - Weighted-Average by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Small Domestic Banks (DISCONTINUED) was 3.10000 Risk Rating in April of 2017, according to the United States Federal Reserve. Historically, United States - Weighted-Average by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Small Domestic Banks (DISCONTINUED) reached a record high of 3.50000 in October of 2003 and a record low of 2.60000 in April of 2008. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Weighted-Average by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Small Domestic Banks (DISCONTINUED) - last updated from the United States Federal Reserve on June of 2025.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Business Survey: Business Activity: Realization: Weighted Net Balance: Manufacturing: Textiles and Wearing Apparel data was reported at 0.000 % in Mar 2025. This records an increase from the previous number of -0.131 % for Dec 2024. Business Survey: Business Activity: Realization: Weighted Net Balance: Manufacturing: Textiles and Wearing Apparel data is updated quarterly, averaging 0.106 % from Dec 2022 (Median) to Mar 2025, with 10 observations. The data reached an all-time high of 0.365 % in Mar 2024 and a record low of -0.131 % in Dec 2024. Business Survey: Business Activity: Realization: Weighted Net Balance: Manufacturing: Textiles and Wearing Apparel data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.SD002: Business Survey: Business Activity.

Indices are created by consolidating multidimensional data into a single representative measure known as an index, using a fundamental mathematical model. Most present indices are essentially the averages or weighted averages of the variables under study, ignoring multicollinearity among the variables, with the exception of the existing Ordinary Least Squares (OLS) estimator based OLS-PCA index methodology. Many existing surveys adopt survey designs that incorporate survey weights, aiming to obtain a representative sample of the population while minimizing costs. Survey weights play a crucial role in addressing the unequal probabilities of selection inherent in complex survey designs, ensuring accurate and representative estimates of population parameters. However, the existing OLS-PCA based index methodology is designed for simple random sampling and is incapable of incorporating survey weights, leading to biased estimates and erroneous rankings that can result in flawed inferences and conclusions for survey data. To address this limitation, we propose a novel Survey Weighted PCA (SW-PCA) based Index methodology, tailored for survey-weighted data. SW-PCA incorporates survey weights, facilitating the development of unbiased and efficient composite indices, improving the quality and validity of survey-based research. Simulation studies demonstrate that the SW-PCA based index outperforms the OLS-PCA based index that neglects survey weights, indicating its higher efficiency. To validate the methodology, we applied it to a Household Consumer Expenditure Survey (HCES), NSS 68th Round survey data to construct a Food Consumption Index for different states of India. The result was significant improvements in state rankings when survey weights were considered. In conclusion, this study highlights the crucial importance of incorporating survey weights in index construction from complex survey data. The SW-PCA based Index provides a valuable solution, enhancing the accuracy and reliability of survey-based research, ultimately contributing to more informed decision-making.

Abstract copyright UK Data Service and data collection copyright owner.

The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).

Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules.

The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain.

From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers.

In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access.

From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable.

The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.

Secure Access Opinions and Lifestyle Survey data

Other Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details.

Main Topics:
Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month.
The non-core questions for this month were:
Company Cars (Module 1a): questions about the number of petrol-fuelled and diesel-fuelled company cars as well as total mileage and total business mileage.
Memory (Module 46): questions to test respondents' memory about events in the past.
Mortgage Arrears (Module 2): source of mortgage, if any, and whether behind in payments. Also 2 questions on whether bought from a Right to Buy scheme.
Stepchildren (Module 5): existence of stepchildren of informant/partner in household, and of dependent children of informant/partner outside the household.
Heights and Weights (Module 42): respondents are asked to estimate their heights and weights and to say how certain they are of the estimates.
Professional Fees (Module 47): methodological experiment to see if HOH or spouse are able to estimate amount spent by all household members on professional fees.
Investment Income (Module 7a): ownership of shares and income from shares and bank accounts.

This data release is associated with the publication "The Quasar Feedback Survey: zooming into the origin of radio emission with e-MERLIN". This work presents 6GHz radio imaging, obtained from e-MERLIN for the 42 quasars from the Quasar Feedback Survey (first presented in Jarvis et al. 2021, MNRAS, 503, 1780 https://doi.org/10.1093/mnras/stab549). These targets are z~0.1 quasar host galaxies, selected to be luminous in [O III] emission (L[O III] > 1042.1 erg/s). For full details of the survey see Jarvis et al. (2021). This data release contains images produced from the e-MERLIN data for each of the 42 sources. The raw e-MERLIN data were obtained under programme ID: CY11205. All normal metadata, associated with astronomical images (observation details, source details, process of imaging), is included with the fits files. These images were produced using Briggs weighting (with robust parameter r=0.5), when applying the TCLEAN imaging algorithm.

Indonesia Business Survey: Investment: Realization: Weighted Net Balance: Public Administration and Defense, Compulsory Social Security data was reported at 0.000 % in Mar 2025. This records a decrease from the previous number of 0.767 % for Dec 2024. Indonesia Business Survey: Investment: Realization: Weighted Net Balance: Public Administration and Defense, Compulsory Social Security data is updated quarterly, averaging 0.512 % from Dec 2022 (Median) to Mar 2025, with 10 observations. The data reached an all-time high of 1.026 % in Dec 2022 and a record low of -0.052 % in Mar 2024. Indonesia Business Survey: Investment: Realization: Weighted Net Balance: Public Administration and Defense, Compulsory Social Security data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SD005: Business Survey: Investment.

Graph and download economic data for Weighted-Average Risk Rating by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Small Domestic Banks (DISCONTINUED) (ERTXSSNQ) from Q2 2003 to Q2 2017 about pricing terms, weighted-average, average, domestic, banks, depository institutions, rate, and USA.

How often UK armed forces veterans felt lonely, and the average combined score on the loneliness scale by personal and service-related characteristics.

Indonesia Business Survey: Investment: Realization: Weighted Net Balance: Quartelly: Total data was reported at 7.677 % in Dec 2022. This records an increase from the previous number of 3.379 % for Sep 2022. Indonesia Business Survey: Investment: Realization: Weighted Net Balance: Quartelly: Total data is updated quarterly, averaging 8.995 % from Mar 2014 (Median) to Dec 2022, with 36 observations. The data reached an all-time high of 11.727 % in Jun 2018 and a record low of -13.060 % in Jun 2020. Indonesia Business Survey: Investment: Realization: Weighted Net Balance: Quartelly: Total data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SD005: Business Survey: Investment. [COVID-19-IMPACT]