BackgroundFor people with intellectual and developmental disabilities, other’s perceptions of them based on their condition often begin before birth and go on to impact relationships, opportunities, and self perception across the life course. Search engine results and news media, which may portray these conditions stereotypically or in poor light, are often a key source in these perceptions. Our purpose was to understand how search engine results and available news media can shape perceptions on certain intellectual and developmental disabilities.MethodsWe developed an online Likert-scale survey to measure differences in perceptions based off first available search engine results, images, and news headlines of four intellectual and developmental disabilities: cerebral palsy, Down syndrome, Prader-Willi syndrome, and Angelman syndrome. These four conditions were selected to compare less prevalent (Prader-Willi and Angelman) and more prevalent conditions (Down syndrome and cerebral palsy). Perception questions addressed general impression and aspects of the disability experience expected to be impacted by perception from others. We recruited via multiple social media platforms, flyers posted in the Boston area, and word of mouth to local communities and friends.Findings229 individuals opened the survey, and 125 responses were used in analysis. Mean responses to Prader-Willi syndrome were significantly more negative than responses to cerebral palsy, Down syndrome, and Angelman syndrome across all variables. Responses to Angelman syndrome were also more negative than responses to Down syndrome. Significant differences between conditions found when treating the data as continuous were confirmed when treating the data as ordinal.ConclusionLesser-known intellectual and developmental disabilities, such as Prader-Willi syndrome and Angelman syndrome, are subject to more negative portrayal in media, leading to more negative perception, which may impact social opportunity and quality of life. Combined with our finding that the perception of Prader-Willi syndrome follows the ideals of the medical model of disability more closely than the social model, a need for social model of disability training and education for physicians and other medical providers is clear.

Social Influence on Shopping

Social Survey Data from 300,000 Millennials and Gen Z Members

By Adam Halper [source]

About this dataset

This dataset offers a comprehensive look into the shopping habits of millennials and Gen Z members, including valuable insights about how their choices are influenced by social media. By exploring the responses given to survey questions related to this topic, we can gain an understanding of how these generations' interests, beliefs and desires shape their decisions when it comes to retail experiences. With 150 million survey responses from our 300,000+ millennial and Gen Z participants, we can uncover powerful insights that could help influencers, businesses and marketers more accurately target this demographic. Our data includes important information such as questions asked during the survey, segment types targeted by those questions and corresponding answers gathered with detailed counts/percentages - making this dataset incredibly useful for anyone wanting an in-depth understanding of what drives the purchasing behavior of today's youth

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How to use the dataset

The first step in using this dataset is to take a look at each column: Question, Segment Type, Segment Description, Answer, Count & Percentage. The Question column will provide background on what exactly each survey question was asking - allowing you to get an overall view of what kind of topics were being surveyed in relation to millennials' shopping habits & social media influence. You will then be able to follow up with analysis based on the respective Segment Types & Descriptions given (such as income levels), which leads us into analyzing answers from both Count & Percentage columns combined - providing absolute numbers vs relative ones for further analysis (such as percentages).

Afterwards you'll need an advanced data analysis program such as SPSS or R-Studio - depending on your technical ability - though all most basic spreadsheet programs should suffice, excluding Matlab supported ones due its excessive complexity for something simple like this.. After selecting your preferred program inputting our file with all 150 million survey responses may take some time based on your computers processing capabilities but once loaded you'll be ready for endless possibilities! Now it's time get running with pulling out key insights you require utilizing various different tools found within these platforms whether it be linear regression or guided ANOVA testing which ever technique fits best should help lead navigate through uncovering deeper meaning in your ultra specific question!

As a final precaution while diving through waters filled surprises also keep note any adjustments needed potentially due overfitting or multicollinearity otherwise could cause major issues skew end results unfit requiring start whole process anew! Good luck delving deep discovering millennial behavior related digital world!

Research Ideas

• Identifying which type of segment is most responsive to engaging shopping experiences, such as influencer marketing, social media discounts and campaigns, etc.
• Analyzing the answers given to survey questions in order to understand millennial and Gen Z's opinion about social influence on their shopping habits - what do they view positively or negatively?
• Using the survey responses to uncover any interesting trends or correlations between different segments - is there a particular demographic that values or uses certain types of social influence on their shopping habits more than others?

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: WhatsgoodlyData-6.csv | Column name | Description ...

In August of 2018, FSSA’s Office of Healthy Opportunities deployed a social risk assessment survey. The 10-question survey was made available to anyone applying online through FSSA for health coverage, the Supplemental Nutritional Assistance Program or Temporary Assistance for Needy Families. The results of this survey are aggregated and presented below and can help communities better understand the social risk factors affecting the health of those applying for our services. Please read and review the following information regarding the use of this data prior to viewing the tool. This survey was made available to those individuals who applied online ONLY and does not represent anyone who applied in-person, by telephone, by mail or any other method. In 2018, online applications accounted for 79% of those who applied for SNAP, TANF or health coverage. Survey completion is voluntary and does not impact eligibility for SNAP, TANF or health coverage. Applications are filed at a household level and may represent several individuals. The application process identifies a primary contact person for the household, and that individual’s demographics are represented on the dashboard; for example, person’s gender, race and education level. An individual who completes more than one application and survey over any given time period is represented once for each instance, and the survey answers and demographic details are based on each application’s responses. For example, an applicant’s age, education level and survey answers can change over time, and the reporting reflects any such changes. All information is presented in aggregate to ensure personally identifiable information is protected. To protect the privacy of individuals, data representing 20 or less individuals in any county will not be displayed. I.e. it will show as blank

Digital Habits and Employment Status Survey

American Digital Usage and Employment Trends

By Joshua Shepherd [source]

About this dataset

This comprehensive dataset provides a rich and multi-faceted exploration into the intriguing world of digital habits, employment status, and demographics of Americans. Inspired by evolving modern lifestyle trends, this dataset meticulously draws information from varied topics such as gaming habits, job search techniques and broadband usage.

The first part of the dataset delves into the realm of video games and gaming culture. It explores various aspects related to individual's preferences towards different types of games across diverse platforms. It uncovers insights into how much time users spend on these games, their favoured genres and platforms (such as consoles or PC), along with their perspectives on important issues concerning violence in video games.

Next up is an insightful dataset that revolves around job seeking trends through digital channels. In a fast-paced business world where online resources have started playing an integral role in career progression and job hunt processes, this data provides valuable insights about Americans' reliance on internet services for finding potential jobs.

Hard-hitting questions revolving around workforce automation form yet another component of this extensive database. This section throws light upon the use of computers, robots or artificial intelligence to carry out tasks traditionally performed by human workers.

Probing further into modern relationship dynamics comes queries pertaining to online dating landscape. This segment explores Americans' attitudes towards online dating platforms - their usual go-to applications/web portals for seeking new relationships or love interests.

Lastly but importantly is an exhaustive set containing facts and figures regarding home broadband usage among Americans across all age groups & genders including their access to crucial cable TV services & smartphone possession rates & dependency levels over them in daily life activities ranging from shopping to banking & even learning new skills!

Collectively offering a well-rounded snapshot at contemporary American societies –this explorative data aims at providing stepping stones for researchers trying to understand these realms thereby serving larger cause making our society better

How to use the dataset

This dataset provides a rich collection of information about the digital habits, employment status, and secondary demographic data of respondents from the June-July 2015 Gaming, Job Search, and Broadband Usage Among Americans survey. With multiple sections regarding diverse topics such as gaming, online job searches, internet usage patterns and more fundamental demographics details - this dataset can be used for various kinds of exploratory data analysis (EDA), machine learning models or creating informative visualizations.

Here is how you can get started with this dataset:

1. Exploring Digital Habits:

The questions about video games ask if a respondent ever plays video games on a computer or console. This can be used to identify key trends in digital habits among different demographic groups - for instance correlation between age or gender and propensity towards gaming.

2. Analysing Job Searches:

The job seeking portion has information regarding use of internet in search processes and its effectiveness according to respondents’ opinion. You could perform an analysis on how working status (or even age group) affects the way individuals employ technology during their job searches.

3. Studying Broadband Usage:

Data about broadband usage at home would give insights into internet adoption rates among various demographic groups.

4.Predictive Modelling:

Potential predictive modeling could include predicting someone's employment status based on their digital habits or vice versa.

5.Cross-Referencing Data Points:

Using two or more datapoints can yield some interesting results as well - like finding out if gamers are more likely than non-gamers to frequently change jobs or seeing if there is any correlation with high speed broadband usage and employment type etc.

Before conducting any analysis do keep in mind that it would be beneficial to conduct some basic cleaning tasks such as checking for missing values, removing duplicates etc., suitable encoding discrete variables including education level into numerical ones based upon intuition behind categories ordinality could also provide better model performance.

This is just scratching the surface of p...

The American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social and economic data. The ACS replaces the decennial census long form in 2010 and every year thereafter. The annual ACS sample is smaller than that of previous long form surveys resulting in a larger sampling error. Coefficients of Variation (CVs), which are statistical measures that show the relative amount of sampling error associated with an estimate, are presented here as a measure of reliability and usability of the data. The unit of geography used for the 2010 - 2014 data is the census tract - a small statistical area within a county, which is delineated every 10 years prior to the decennial census.Last Updated: UnknownThis is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/Demographics/MD_AmericanCommunitySurvey/FeatureServer/0

Table from the American Community Survey (ACS) 5-year series on age and gender related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B01001 Sex by Age, B01002 Median Age by Sex. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B01001, B01002Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estima

The City of Norfolk is committed to using data to help inform decisions and allocate resources. One important source of data is input from residents about their priorities and satisfaction with the services we provide. Norfolk last conducted a citywide survey of residents in 2014.

To provide up-to-date information regarding resident priorities and satisfaction, Norfolk contracted with ETC institute to conduct a survey of residents. This survey was conducted in the fall of 2022; surveys were sent via the U.S. Postal Service and respondents were given the choice of responding by mail, online, or by telephone. This survey represents a random and statistically valid sample of residents from across the city. ETC Institute monitored responses and followed up to ensure all sections of the city were represented. An opportunity was also provided for residents not included in the random sample to take the survey and express their views. This dataset includes all survey data, excluding demographic data to protect privacy. This dataset will be updated every two years.

About this dataset

Context

The dataset tabulates the Elsa population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Elsa. The dataset can be utilized to understand the population distribution of Elsa by age. For example, using this dataset, we can identify the largest age group in Elsa.

Key observations

The largest age group in Elsa, TX was for the group of age 20 to 24 years years with a population of 630 (11.08%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Elsa, TX was the 80 to 84 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Elsa is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Elsa 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 Elsa Population by Age. You can refer the same here

In 2018-19 the GLA first undertook a Survey of Londoners. At the time it provided vital evidence on Londoners that had never been collected before in such detail. In 2021-22, the GLA conducted another Survey of Londoners, following the same methodology as the Survey of Londoners 2018-19, an online and paper self-completion survey of adults aged 16 and over in London. The survey, which received responses from 8,630 Londoners, aimed to assess the impact of COVID-19 and associated restrictions on key social outcomes for Londoners, not available from other data sources. It is important to understand the context in which the Survey of Londoners 2021-22 took place. Survey fieldwork began in November 2021; so, up to that point, it had been four months since most legal limits on social contact had been removed. However, after fieldwork had started, some restrictions due to the emergence of the Omicron variant were introduced. This may or may not have had some effect on the data. Given these changing circumstances, caution should be applied when interpreting the results. The Survey of Londoners 2021-22 also took place just before the full effects of the cost-of-living crisis began to set in. It is highly likely that the situations of Londoners have changed while analysis was taking place. On this page there is a headline findings report, published on 30 September 2022, which provides descriptive results for the key headline measures and supporting demographic data collected by the survey. Accompanying this report are more detailed tables documenting the key results of the survey by a range of demographic and other characteristics, a short summary document presenting key findings from the survey, and a technical report for those interested in the survey’s methodology. Further to these, a series of pen portraits, providing snapshots of particular groups of Londoners, as captured at the time of the Survey of Londoners 2021-22, were first added on 31 October 2022. Also on this page, there is an initial findings report, that was published on 2 September 2022. This was published to provide timely evidence from the survey to support the case for further targeted support to help low-income Londoners with the cost-of-living crisis. We have launched an online explorer where users can interrogate the data collected from the two surveys, conducted in 2018-19 and 2021-22. This is the first iteration, so we welcome any feedback on it - GO TO THE EXPLORER The record-level Survey of Londoners dataset can be accessed via the UK Data Service, University of Essex. The dataset is available for not-for-profit educational and research purposes only. Finally, as the North East London (NEL) NHS funded a 'boost' in their sub-region to enable a more detailed analysis to be conducted within, they produced an analytical report in September 2022. This is also available for download from this page.

Overview The Stack Overflow Developer Survey Dataset represents one of the most trusted and comprehensive sources of information about the global developer community. Collected by Stack Overflow through its annual survey, the dataset provides insights into the demographics, preferences, habits, and career paths of developers.

This dataset is frequently used for: - Analyzing trends in programming languages, tools, and technologies. - Understanding developer job satisfaction, compensation, and work environments. - Studying global and regional differences in developer demographics and experience.

The data has of two CSV files, "survey_results_public" that consist of data and "survey_results_schema" that describes each column in detail.

Data Dictionary: All the details are in "survey_results_schema.csv"

Features of the Stack Overflow Developer Survey Dataset

Demographic & Background Information - Respondent: A unique identifier for each survey participant. - MainBranch: Describes whether the respondent is a professional developer, student, hobbyist, etc. - Country: The country where the respondent lives. - Age: The respondent's age. - Gender: The gender identity of the respondent. - Ethnicity: Ethnic background (when available). - EdLevel: The highest level of formal education completed. - UndergradMajor: The respondent's undergraduate major. - Hobbyist: Indicates whether the person codes as a hobby (Yes/No).

Employment & Professional Experience - Employment: Employment status (full-time, part-time, unemployed, student, etc.). - DevType: Types of developer roles the respondent identifies with (e.g., Web Developer, Data Scientist). - YearsCode: Number of years the respondent has been coding. - YearsCodePro: Number of years coding professionally. - JobSat: Job satisfaction level. - CareerSat: Career satisfaction level. - WorkWeekHrs: Approximate hours worked per week. - RemoteWork: Whether the respondent works remotely and how frequently.

Compensation - CompTotal: Total compensation in USD (including salary, bonuses, etc.). - CompFreq: Frequency of compensation (e.g., yearly, monthly).

Learning & Education - LearnCode: How the respondent first learned to code (e.g., online courses, university). - LearnCodeOnline: Online resources used (e.g., YouTube, freeCodeCamp). - LearnCodeCoursesCert: Whether the respondent has taken online courses or earned certifications.

Technology & Tools - LanguageHaveWorkedWith: Programming languages the respondent has used. - LanguageWantToWorkWith: Languages the respondent is interested in learning or using more. - DatabaseHaveWorkedWith: Databases the respondent has experience with. - PlatformHaveWorkedWith: Platforms used (e.g., Linux, AWS, Android). - OpSys: The operating system used most often. - NEWCollabToolsHaveWorkedWith: Collaboration tools used (e.g., Slack, Teams, Zoom). - NEWStuck: How often the respondent feels stuck when coding. - ToolsTechHaveWorkedWith: Frameworks and technologies respondents have worked with.

Online Presence & Community - SOAccount: Whether the respondent has a Stack Overflow account. - SOPartFreq: How often the respondent participates on Stack Overflow. - SOVisitFreq: Frequency of visiting Stack Overflow. - SOComm: Whether the respondent feels welcome in the Stack Overflow community. - OpenSourcer: Level of involvement in open-source contributions.

Opinions & Preferences - WorkChallenge: Challenges faced at work (e.g., unclear requirements, unrealistic expectations). - JobFactors: Important job factors (e.g., salary, work-life balance, technologies used). - MentalHealth: Questions on how mental health affects or is affected by their job.

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

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.The study reported in this paper employed the mixed methods approach comprising a quantitative and qualitative analysis. The quantitative and econometric analysis of the dependent variable, namely, the final marks for the research report and the independent variables that explain it. The results show significance in terms of the assignments and existing knowledge marks in terms of their bachelor's average mark. We extended the analysis to a qualitative and quantitative survey, which indicated that the mean statistical feedback was above average and therefore strongly agreed/agreed except for library use by the student. Students, therefore, need more guidance in terms of library use and the open questions showed a need for a research methods course in the future. Furthermore, supervision tends to be a significant determinant in all cases. It is also here where supervisors can use social media instruments such as WhatsApp and Facebook to inform students further. This study contributes as the first to investigate the preparation and research skills of students for master's and doctoral studies during the COVID-19 pandemic in an online environment.

This dataset contains longitudinal purchases data from 5027 Amazon.com users in the US, spanning 2018 through 2022: amazon-purchases.csv It also includes demographic data and other consumer level variables for each user with data in the dataset. These consumer level variables were collected through an online survey and are included in survey.csv fields.csv describes the columns in the survey.csv file, where fields/survey columns correspond to survey questions. The dataset also contains the survey instrument used to collect the data. More details about the survey questions and possible responses, and the format in which they were presented can be found by viewing the survey instrument. A 'Survey ResponseID' column is present in both the amazon-purchases.csv and survey.csv files. It links a user's survey responses to their Amazon.com purchases. The 'Survey ResponseID' was randomly generated at the time of data collection. amazon-purchases.csv Each row in this file corresponds to an Amazon order. Each such row has the following columns: Survey ResponseID Order date Shipping address state Purchase price per unit Quantity ASIN/ISBN (Product Code) Title Category The data were exported by the Amazon users from Amazon.com and shared by users with their informed consent. PII and other information not listed above were stripped from the data. This processing occurred on users' machines before sharing with researchers.

Researchers at Utah State University created a short survey instrument to gather information about the views and concerns of Utah residents related to water issues. This survey was designed to give the public a chance to share their perceptions and concerns about water supply, water quality, and other related issues. While finding out what the ‘average citizen’ feels about key water issues was one goal of the project, the most interesting and important results are found in exploring ways in which perspectives about water vary across the population based on where people live and their demographic background (gender, age, education, etc.). This survey helps bring a voice to groups of citizens typically not represented in water policy debates. The findings have been and continue to be shared with water managers and decision makers who are planning for local and state water system sustainability.

This survey effort is also a key outreach and education component of the iUTAH project. High school groups, college and university classes, and others are invited to collaborate with iUTAH faculty to conduct public intercept surveys. Co-collection and analysis of survey data provides a hands-on learning opportunity about the principles of social science research. This effort helps increase awareness about the complexity of water issues in Utah, and the methods through which scientists learn about the public’s thoughts and concerns. Between July 2014 and April 2016, the survey has been implemented with collaborating students and faculty from the University of Utah, Utah Valley University, Weber State University, Salt Lake Community College, Southern Utah University, Dixie State University, and Snow College.

The survey involved using a structured protocol to randomly approach adults entering grocery stores in communities across the state, and inviting them to complete a 3-minute questionnaire about thier perceptions and concerns about water issues in Utah. The survey was self-administered on an iPad tablet and uploaded to a web server using the Qualtrics Offline App.

The project generated responses from over 7,000 adults, with a response rate of just over 42% . Comparisons of the respondents with census data suggest that they are largely representative of the communities where data were collected and of the state's adult population.

The data are anonymous and are available as a public dataset here. The data also served as the basis for the development of an open-source web-based survey data viewer that can be found at: http://data.iutahepscor.org/surveys/ and were also reported in Jones et al. (2016). We encourage users to use the viewer to explore the survey results.

The files below include a document describing in detail the method/protocol used in the study, and copies of field materials we used to implement the project. We also include copies of the full dataset and a codebook in various formats.

The Community Life Survey was first commissioned by the Cabinet Office in 2012-2013. It is a household survey conducted in England, tracking the latest trends and developments across areas key to encouraging social action and empowering communities, including:volunteering and charitable giving;views about the local area;community cohesion and belonging;community empowerment and participation;influencing local decisions and affairs; and subjective well-beingUp to 2015-16, the survey used a face-to-face methodology. Following thorough testing, the CLS moved to an online and paper mixed-method approach from 2016-17 onwards (a paper self-completion questionnaire being available as an alternative to the online survey), with an end to previous current face-to-face method. The objectives of the survey are to: The objectives of the survey are to: provide robust, nationally representative data on behaviours and attitudes within communities to inform and direct policy and action in these areas;to provide data of value to all users, including public bodies, external stakeholders and the public; andunderpin further research and debate on building stronger communitiesThe Community Life Survey incorporates a small number of priority measures from the Citizenship Survey, which ran from 2001-2011, conducted by the Department for Communities and Local Government. These measures were incorporated in the Community Life Survey so that trends in these issues could continue to be tracked over time. (The full Citizenship Survey series is held at the UK Data Archive under GNs 33347 and 33474.)

Further information may be found on the gov.uk Community Life Survey website.

The Community Life Survey Experimental Online Data, 2013-2014 includes the Special Licence data from a project testing the viability of an online alternative to the face-to-face survey. This dataset covers the 2013-2014 online survey, with a sample size of 10,215 adults (aged 16 years and over) in England, which ran from June 2013 to March 2014. Data from a postal version of the questionnaire, which was available on request, is also included in the dataset. This questionnaire covered the same topics as the online survey but was reduced in length. Full details can be found in the Web Survey Technical Report which is available in the Documentation section below.

End User Licence and Special Licence data Users should note that there are two versions of each Community Life Survey Experimental Online Data experimental online dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains more detailed variables relating to: social class; ethnicity; religion; sexual identity and lower level geographical classifications.

The SL data have more restrictive access conditions than those made available under the standard EUL. Prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. Therefore, users are strongly advised to order the standard version of the data.

The standard EUL version of the Community Life Survey Experimental Online Data, 2013-2014 is held under SN 7737.

The main topics include: identity and social networks, local community, civic engagement, volunteering, social action, subjective well-being and basic demographics.The Community Life Survey Experimental Online Data, 2013-2014: Special Licence Access data file includes the following extra variables:SOC2010 (SOC2010)SOC2010 Sub-Major Group (SOC10smg)Ethnic group: 6 categories (Ethnic6)Ethnic group: 11 categories (Ethnic11a)Respondents Ethnic origin and age (excludes Mixed/Other) (Rethage9a)Sex within Ethnicity: 11 categories (E11sex1)Sex within Ethnicity: 10 categories (E5sex1)Whether practising for each religion (Actrel)Respondent religion: 7 categories (Jewish included in other)(Relig7)Practice status for each religion (Relstat)Ethno-Religious groups: 11 categories (E11Relig1)Respondent sexual identity: 3 categories (Sid2g)ONS Ward Classification: Subgroup (2001 Wards) (Wrdsubgpg)ONS District Level Classification: Subgroup (2003) (Ladsubgpg)Output Area Classification : Subgroup (52 categories) (Oasubgrp)Rural and Urban Area Classification 2004 (Rural)

The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. Much of the ACS data provided on the Census Bureau's Web site are available separately by age group, race, Hispanic origin, and sex. Summary files, Subject tables, Data profiles, and Comparison profiles are available for the nation, all 50 states, the District of Columbia, Puerto Rico, every congressional district, every metropolitan area, and all counties and places with populations of 65,000 or more. Detailed Tables contain the most detailed cross-tabulations published for areas 65k and more. The data are population counts. There are over 31,000 variables in this dataset.

The American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social and economic data. The ACS replaces the decennial census long form in 2010 and every year thereafter. The annual ACS sample is smaller than that of previous long form surveys resulting in a larger sampling error. Coefficients of Variation (CVs), which are statistical measures that show the relative amount of sampling error associated with an estimate, are presented here as a measure of reliability and usability of the data. The unit of geography used for the 2010 - 2014 data is the ZIP Code Tabulation Area (ZCTA). ZCTAs are statistical geographic areas produced by the Census Bureau by aggregating census blocks to create generalized areas closely resembling the U.S. Postal Service's postal ZIP codes.Last Updated: UnknownThis is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/Demographics/MD_AmericanCommunitySurvey/FeatureServer/1

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Anonymous online follow-up survey of mystical experience in Freediving. The initial MEQ30 survey was repeated with 15 items, plus several personality tests added, along with demographics and a Freediving Federation features survey hiding the main purpose of the questionnaire. Data from a short 4+4 items test survey is also provided. Excel files contains raw data saved from Google Forms data collected. Headers are questionnaire items. This survey was submitted to Russian speaking freedivers only. Most headers in the big file of this dataset contain both Ru and Eng versions of the survey for ease of data use by the research community.

This survey by EFSA provides insights in terms of: • Europeans’ concerns regarding food, and interest in several food safety topics. • Europeans’ knowledge and perception of new genomic techniques (NGTs), including awareness of NGTs, which NGT-related information evokes most interest, perceived effects on the environment, health, etc. of the application of NGTs to food, among others.

    The survey was implemented by the Teleperformance in 24 member states (i.e. all EU27 countries except Cyprus, Luxembourg, and Malta) plus Norway between 17th and 19th of November 2021. A total of 8,900 respondents from different social and demographic groups completed the survey online in their mother tongue, with 300 to 500 respondents per country. These sample sizes provide robust results and ensure that responses are representative in each of the countries to be surveyed.

    The sample was nationally representative with respect to age and gender. Other demographic information collected included education, among others.