The World Values Survey (WVS) is an international research program devoted to the scientific and academic study of social, political, economic, religious and cultural values of people in the world. The project’s goal is to assess which impact values stability or change over time has on the social, political and economic development of countries and societies. The project grew out of the European Values Study and was started in 1981 by its Founder and first President (1981-2013) Professor Ronald Inglehart from the University of Michigan (USA) and his team, and since then has been operating in more than 120 world societies. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years. Extensive geographical and thematic scope, free availability of survey data and project findings for broad public turned the WVS into one of the most authoritative and widely-used cross-national surveys in the social sciences. At the moment, WVS is the largest non-commercial cross-national empirical time-series investigation of human beliefs and values ever executed. World Values Survey Interview Mode of collection: mixed mode Face-to-face interview: CAPI (Computer Assisted Personal Interview) Face-to-face interview: PAPI (Paper and Pencil Interview) Telephone interview: CATI (Computer Assisted Telephone Interview) Self-administered questionnaire: CAWI (Computer-Assisted Web Interview) Self-administered questionnaire: Paper In all countries, fieldwork was conducted on the basis of detailed and uniform instructions prepared by the WVS scientific advisory committee and WVSA secretariat. The main data collection mode in WVS 2017-2021 is face to face (interviewer-administered). Several countries employed mixed-mode approach to data collection: USA (CAWI; CATI); Australia and Japan (CAWI; postal survey); Hong Kong SAR (PAPI; CAWI); Malaysia (CAWI; PAPI). The WVS Master Questionnaire was provided in English and each national survey team had to ensure that the questionnaire was translated into all the languages spoken by 15% or more of the population in the country. A central team monitored the translation process. The target population is defined as: individuals aged 18 (16/17 is acceptable in the countries with such voting age) or older (with no upper age limit), regardless of their nationality, citizenship or language, that have been residing in the [country] within private households for the past 6 months prior to the date of beginning of fieldwork (or in the date of the first visit to the household, in case of random-route selection). The sampling procedures differ from country to country; probability Sample: Multistage Sample Probability Sample, Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 2017-2020. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. Countries with great population size and diversity (e.g. India, China, USA, Russia, Brazil etc.) are requirred to reach an effective sample of N=1500 or larger. Only 2 countries (Argentina, Chile) deviated from the guidelines and planned with an effective sample size below the set threshold. Sample design and other relevant information about sampling were reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling was documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

This survey is a not up-to-date version. Please, use the updated version included in the EVS integrated data files. This national dataset is only available for replication purposes and analysis with additional country-specific variables (see ´Further Remarks´).

Two online overviews offer comprehensive metadata on the EVS datasets and variables.

The extended study description for the EVS 2008 provides country-specific information on the origin and outcomes of the national surveys The variable overview of the four EVS waves 1981 1990 1999/2000 and 2008 allows for identifying country specific deviations in the question wording within and across the EVS waves.

These overviews can be found at: Extended Study Description Variable Overview

Moral, religious, societal, political, work, and family values of Europeans.

Topics: 1. Perceptions of life: importance of work, family, friends and acquaintances, leisure time, politics and religion; frequency of political discussions with friends; happiness; self-assessment of own health; memberships and unpaid work (volunteering) in: social welfare services, religious or church organisations, education, or cultural activities, labour unions, political parties, local political actions, human rights, environmental or peace movement, professional associations, youth work, sports clubs, women´s groups, voluntary associations concerned with health or other groups; tolerance towards minorities (people with a criminal record, of a different race, left/right wing extremists, alcohol addicts, large families, emotionally unstable people, Muslims, immigrants, AIDS sufferers, drug addicts, homosexuals, Jews, gypsies and Christians - social distance); trust in people; estimation of people´s fair and helpful behaviour; internal or external control; satisfaction with life.

1. Work: reasons for people to live in need; importance of selected aspects of occupational work; employment status; general work satisfaction; freedom of decision-taking in the job; importance of work (work ethics, scale); important aspects of leisure time; attitude towards following instructions at work without criticism (obedience work); give priority to nationals over foreigners as well as men over women in jobs.

2. Religion: Individual or general clear guidelines for good and evil; religious denomination; current and former religious denomination; current frequency of church attendance and at the age of 12; importance of religious celebration at birth, marriage, and funeral; self-assessment of religiousness; churches give adequate answers to moral questions, problems of family life, spiritual needs and social problems of the country; belief in God, life after death, hell, heaven, sin and re-incarnation; personal God versus spirit or life force; own way of connecting with the divine; interest in the sacred or the supernatural; attitude towards the existence of one true religion; importance of God in one´s life (10-point-scale); experience of comfort and strength from religion and belief; moments of prayer and meditation; frequency of prayers; belief in lucky charms or a talisman (10-point-scale); attitude towards the separation of church and state.

3. Family and marriage: most important criteria for a successful marriage (scale); attitude towards childcare (a child needs a home with father and mother, a woman has to have children to be fulfilled, marriage is an out-dated institution, woman as a single-parent); attitude towards marriage, children, and traditional family structure (scale); attitude towards traditional understanding of one´s role of man and woman in occupation and family (scale); attitude towards: respect and love for parents, parent´s responsibilities for their children and the responsibility of adult children for their parents when they are in need of long-term care; importance of educational goals; attitude towards abortion.

4. Politics and society: political interest; political participation; preference for individual freedom or social equality; self-assessment on a left-right continuum (10-point-scale); self-responsibility or governmental provision; free decision of job-taking of the unemployed or no permission to refuse a job; advantage or harmfulness of competition; liberty of firms or governmental control; equal incomes or incentives for indivi...

Dataset

This dataset was created by Nouran Amr Mohamed

Released under Apache 2.0

Contents

The Annual Survey of Manufactures (ASM) provides key intercensal measures of manufacturing activity, products, and location for the public and private sectors. The ASM provides the best current measure of current U.S. manufacturing industry outputs, inputs, and operating status, and is the primary basis for updates of the Longitudinal Research Database (LRD). Census Bureau staff and academic researchers with sworn agent status use the LRD for micro data analysis.

Population Health (BRFSS: HRQOL)

Examining Trends, Disparities and Determinants of Health in the US Population

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) offers an expansive collection of data on the health-related quality of life (HRQOL) from 1993 to 2010. Over this time period, the Health-Related Quality of Life dataset consists of a comprehensive survey reflecting the health and well-being of non-institutionalized US adults aged 18 years or older. The data collected can help track and identify unmet population health needs, recognize trends, identify disparities in healthcare, determine determinants of public health, inform decision making and policy development, as well as evaluate programs within public healthcare services.

The HRQOL surveillance system has developed a compact set of HRQOL measures such as a summary measure indicating unhealthy days which have been validated for population health surveillance purposes and have been widely implemented in practice since 1993. Within this study's dataset you will be able to access information such as year recorded, location abbreviations & descriptions, category & topic overviews, questions asked in surveys and much more detailed information including types & units regarding data values retrieved from respondents along with their sample sizes & geographical locations involved!

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How to use the dataset

This dataset tracks the Health-Related Quality of Life (HRQOL) from 1993 to 2010 using data from the Behavioral Risk Factor Surveillance System (BRFSS). This dataset includes information on the year, location abbreviation, location description, type and unit of data value, sample size, category and topic of survey questions.

Using this dataset on BRFSS: HRQOL data between 1993-2010 will allow for a variety of analyses related to population health needs. The compact set of HRQOL measures can be used to identify trends in population health needs as well as determine disparities among various locations. Additionally, responses to survey questions can be used to inform decision making and program and policy development in public health initiatives.

Research Ideas

• Analyzing trends in HRQOL over the years by location to identify disparities in health outcomes between different populations and develop targeted policy interventions.
• Developing new models for predicting HRQOL indicators at a regional level, and using this information to inform medical practice and public health implementation efforts.
• Using the data to understand differences between states in terms of their HRQOL scores and establish best practices for healthcare provision based on that understanding, including areas such as access to care, preventative care services availability, etc

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------| | Year | Year of survey. (Integer) | | LocationAbbr | Abbreviation of location. (String) | | LocationDesc | Description of location. (String) | | Category | Category of survey. (String) | | Topic | Topic of survey. (String) | | Question | Question asked in survey. (String) | | DataSource | Source of data. (String) | | Data_Value_Unit | Unit of data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Footnote symbol for data value. (String) | | Data_Value_Std_Err | Standard error of the data value. (Float) | | Sample_Size | Sample size used in sample. (Integer) | | Break_Out | Break out categories used. (String) | | Break_Out_Category | Type break out assessed. (String) | | **GeoLocation*...

This survey is a not up-to-date version. Please, use the updated version included in the EVS integrated data files. This national dataset is only available for replication purposes and analysis with additional country-specific variables (see ´Further Remarks´).

Two online overviews offer comprehensive metadata on the EVS datasets and variables.

The extended study description for the EVS 2008 provides country-specific information on the origin and outcomes of the national surveys The variable overview of the four EVS waves 1981 1990 1999/2000 and 2008 allows for identifying country specific deviations in the question wording within and across the EVS waves.

Moral, religious, societal, political, work, and family values of Europeans.

Topics: 1. Perceptions of life: importance of work, family, friends and acquaintances, leisure time, politics and religion; frequency of political discussions with friends; happiness; self-assessment of own health; memberships and unpaid work (volunteering) in: social welfare services, religious or church organisations, education, or cultural activities, labour unions, political parties, local political actions, human rights, environmental or peace movement, professional associations, youth work, sports clubs, women´s groups, voluntary associations concerned with health or other groups; tolerance towards minorities (people with a criminal record, of a different race, left/right wing extremists, alcohol addicts, large families, emotionally unstable people, Muslims, immigrants, AIDS sufferers, drug addicts, homosexuals, Jews, gypsies and Christians - social distance); trust in people; estimation of people´s fair and helpful behaviour; internal or external control; satisfaction with life.

1. Work: reasons for people to live in need; importance of selected aspects of occupational work; employment status; general work satisfaction; freedom of decision-taking in the job; importance of work (work ethics, scale); important aspects of leisure time; attitude towards following instructions at work without criticism (obedience work); give priority to nationals over foreigners as well as men over women in jobs.

2. Religion: Individual or general clear guidelines for good and evil; religious denomination; current and former religious denomination; current frequency of church attendance and at the age of 12; importance of religious celebration at birth, marriage, and funeral; self-assessment of religiousness; churches give adequate answers to moral questions, problems of family life, spiritual needs and social problems of the country; belief in God, life after death, hell, heaven, sin and re-incarnation; personal God versus spirit or life force; own way of connecting with the divine; interest in the sacred or the supernatural; attitude towards the existence of one true religion; importance of God in one´s life (10-point-scale); experience of comfort and strength from religion and belief; moments of prayer and meditation; frequency of prayers; belief in lucky charms or a talisman (10-point-scale); attitude towards the separation of church and state.

3. Family and marriage: most important criteria for a successful marriage (scale); attitude towards childcare (a child needs a home with father and mother, a woman has to have children to be fulfilled, marriage is an out-dated institution, woman as a single-parent); attitude towards marriage, children, and traditional family structure (scale); attitude towards traditional understanding of one´s role of man and woman in occupation and family (scale); attitude towards: respect and love for parents, parent´s responsibilities for their children and the responsibility of adult children for their parents when they are in need of long-term care; importance of educational goals; attitude towards abortion.

4. Politics and society: political interest; political participation; preference for individual freedom or social equality; self-assessment on a left-right continuum (10-point-scale); self-responsibility or governmental provision; free decision of job-taking of the unemployed or no permission to refuse a job; advantage or harmfulness of competition; liberty of firms or governmental control; equal incomes or incentives for individual efforts; attitude concerning capitalism versus government ownership; postmaterialism (scale); expectation of future development (less emphasis on money and material possessions, greater respect for auth...

Full edition for scientific use. The COVID-19 pandemic offers unique opportunity - a natural experiment indeed - to study how people’s moral values change during times of crises. In the face of lacking evidence, we cannot take it for granted that the stability of values observed in normal times continues throughout the Corona crisis. This dataset represents the Austrian data of the first wave of a longitudinal study which is conducted in several countries all over the world. A second wave is planned in 2021, a third wave about one year after the crisis. Under the current contact restrictions, using an online panel is the only option to achieve potentially representative data of the Austrian population. The study investigates basic values (measured with classical value concepts such as the Inglehart Index and the short Portraits Values Questionnaire (by Shalom Schwartz) which is also implemented in the European Social Survey). Additional item batteries refer to concepts which are grounded in personality research (e.g. Big Five and Empathy), exposure to the crisis and perceptions of economic consequences. In the Austrian dataset several items of the Social Survey Austria about social, political and environmental attitudes are repeated as well and new concepts about visions of the future after COVID-19 are included as well. The main aim of the study is to figure out how respondents’ perception of the crisis transforms and how these value changes are linked to moral values and social and political attitudes.

Quantitative survey along the small ruminant value chains in Ethiopia Current set contains 43 Traders. WARNING: Data cleaning is on going. We remind users that data downloadable from the portal is for analysis ONLY. Any cleaning happening to these files WILL NOT affect the database. Errors and inconsistencies MUST be reported to the project staff in charge of cleaning.

The World Values Survey (WVS) series was designed to enable a crossnational, crosscultural comparison of values and norms on a wide variety of topics and to monitor changes in values and attitudes across the globe. This dataset contains the survey data from the Australian component of the fifth wave of the World Values Surveys carried out in 2005. It also includes some Australia specific variables that will not be included in the multi-national integrated data file that will become available through the World Values Survey Association. Broad topics covered in the 2005 survey include perception of life, family, work, traditional values, personal finances, religion and morality, the economy, politics and society, the environment, allocation of resources, contemporary social issues, national identity, and technology and its impact on society. Demographic information includes household income, size of locality, region of residence, occupation of the head of household, and the respondent's age, sex, occupation, education, religion, religiosity, political party, and left-right political self-placement.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Household datasets
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.

Change to coding of missing values for household series
From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

End User Licence and Secure Access QLFS Household datasets
Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Changes to variables in QLFS Household EUL datasets
In order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

This dataset contains monthly retail price data for a wide range of consumer products sold in various Canadian provinces over several years. It has been enriched with tax, category, and classification metadata for deeper insights.

Usefulness of the Dataset

This dataset can be used for:

Use Case	Description
Price Trend Analysis	Track price movements over time, province, and product category.
Inflation Studies	Examine inflation on essentials vs non-essentials over time.
Regional Price Comparison	Analyze cost disparities for the same goods across provinces.
Tax Policy Impact	Understand how tax laws affect consumer pricing by region.
Budget Optimization	Identify high-cost vs low-cost essentials for better planning.
Machine Learning Integration	Use in models for price prediction or consumer segmentation.

Purpose and Use Cases

This dataset is ideal for:

🏛️ Policy Analysis

Understand how federal and provincial taxes shape price access — especially for essentials like milk, bread, or medications.

🧍‍♀️ Consumer Insights

See how costs for personal care, food, and baby goods evolve month-over-month in each region.

💸 Inflation & Seasonality

Analyze how monthly or yearly trends (e.g., holiday spikes or inflation events) affect product pricing.

🌍 Social Impact Studies

Measure product accessibility gaps between provinces for low-income consumers or high-tax regions.

🛍️ Retail & Budget Planning

Guide families, retailers, or policymakers on where and when to buy or subsidize certain products.

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers. The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters. The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules. The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

About this dataset

Context

This list ranks the 22 cities in the Price County, WI by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Black Population: This column displays the rank of cities in the Price County, WI by their Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Black Population: The Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Price County Black Population: This tells us how much of the entire Price County, WI Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

Point feature class and related table containing the Precise Surveys measurement time series. Measurements include elevations, Northings and Eastings, distances, and point-to-point measurements. Northing and Easting measurements are in CA State Plane Coordinate systems, Elevations measurements are provided in NAVD88 or NGVD29. This dataset is for data exploration only. These measurements and point locations are not considered survey-grade since there may be nuances such as epochs, adjustments, and measurement methods that are not fully reflected in the GIS data. These values are not considered authoritative values and should not be used in-lieu of actual surveyed values provided by a licensed land surveyor. Related data and time series are stored in a table connected to the point feature class via a relationship class. There may be multiple table entries and time series associated to a single mark. Data was assembled through an import of Excel tables and import of mark locations in ArcGIS Pro. Records were edited by DOE, Geomatics, GDSS to resolve any non-unique mark names. This dataset was last updated 4/2024.

Our codes provide a tool for researchers using any part of the integrated datasets of the European Social Survey (European Social Survey Cumulative File, ESS 1-9, 2020) project to easily differentiate between respondents based on their political affiliation, dividing them into pro-government and pro-opposition groups. Individuals are coded as “government supporters”, “opposition supporters” and “non-identifiers” according to their survey response, while we excluded refusals. The database includes data for 422 985 respondents from eight data rounds between 2002 and 2020 from 33 European countries, organized all in all in 215 country-years.

There are two data files attached.

1. The “European Government-Opposition Voters (EGOV) Data Set” is a comma-separated values table (.csv format file) that includes three variables.

a. The variable “votedforwinner” differentiates between government voters (1), opposition voters (0) and non-voters (missing values); thus it defines the government-opposition status of European voters based on their last vote on the previous election.

b. The variable “closetowinner” differentiates between government partisans (1), opposition partisans (0) and non-partisans (missing values); thus it defines the government-opposition status of European party identifiers based on their partisan attachment.

c. The variable “cseqno” is a unique identification number for European Social Survey (ESS) respondents included in the integrated data sets of the ESS project.

1. The “EGOV – do file” is a do file that can be used to reproduce the content of the above table. These codes are annotated, that is, unusual changes in government composition and overlaps of elections and fieldwork periods are indicated.

The European Government-Opposition Voters Data Set has been produced by using the following pieces of information coming from the (European Social Survey Cumulative File, ESS 1-9, 2020), Comparative Political Data Sets (Armingeon, Isler, Knöpfel, Weisstanner, et al., 2016) and ParlGov (Döring and Manow, 2019) data sets.

•    partisan
  

  preferences, that is, respondents’ vote on the last general election (164 variables, ESS) and respondents’ partisan identity (167 variables, ESS)

•    date of
  

  the interview (year, month, day, ESS)

•    date of
  

  national elections and investitures in each country-case (CPDS and ParlGov)

•    cabinet
  

  composition (CPDS and ParlGov)

•    official
  

  sites on information on national elections for clarification, if necessary

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers. The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters. The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules. The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 0-4 years (39) | Female # 55-59 years (29). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

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

Scope of gender :

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

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

Items in SVS questionnaire and their associated human values.

Four human values are considered to underlie individuals’ environmental beliefs and behaviors: biospheric (i.e., concern for environment), altruistic (i.e., concern for others), egoistic (i.e., concern for personal resources) and hedonic values (i.e., concern for pleasure and comfort). These values are typically measured with an adapted and shortened version of the Schwartz Value Survey (SVS), to which we refer as the Environmental-SVS (E-SVS). Despite being well-validated, recent research has indicated some concerns about the SVS methodology (e.g., comprehensibility, self-presentation biases) and suggested an alternative method of measuring human values: The Portrait Value Questionnaire (PVQ). However, the PVQ has not yet been adapted and applied to measure values most relevant to understand environmental beliefs and behaviors. Therefore, we tested the Environmental-PVQ (E-PVQ) – a PVQ variant of E-SVS –and compared it with the E-SVS in two studies. Our findings provide strong support for the validity and reliability of both the E-SVS and E-PVQ. In addition, we find that respondents slightly preferred the E-PVQ over the E-SVS (Study 1). In general, both scales correlate similarly to environmental self-identity (Study 1), energy behaviors (Studies 1 and 2), pro-environmental personal norms, climate change beliefs and policy support (Study 2). Accordingly, both methodologies show highly similar results and seem well-suited for measuring human values underlying environmental behaviors and beliefs.