The World Religion Project (WRP) aims to provide detailed information about religious adherence worldwide since 1945. It contains data about the number of adherents by religion in each of the states in the international system. These numbers are given for every half-decade period (1945, 1950, etc., through 2010). Percentages of the states' populations that practice a given religion are also provided. (Note: These percentages are expressed as decimals, ranging from 0 to 1, where 0 indicates that 0 percent of the population practices a given religion and 1 indicates that 100 percent of the population practices that religion.) Some of the religions (as detailed below) are divided into religious families. To the extent data are available, the breakdown of adherents within a given religion into religious families is also provided.

The project was developed in three stages. The first stage consisted of the formation of a religion tree. A religion tree is a systematic classification of major religions and of religious families within those major religions. To develop the religion tree we prepared a comprehensive literature review, the aim of which was (i) to define a religion, (ii) to find tangible indicators of a given religion of religious families within a major religion, and (iii) to identify existing efforts at classifying world religions. (Please see the original survey instrument to view the structure of the religion tree.) The second stage consisted of the identification of major data sources of religious adherence and the collection of data from these sources according to the religion tree classification. This created a dataset that included multiple records for some states for a given point in time. It also contained multiple missing data for specific states, specific time periods and specific religions. The third stage consisted of cleaning the data, reconciling discrepancies of information from different sources and imputing data for the missing cases.

The Global Religion Dataset: This dataset uses a religion-by-five-year unit. It aggregates the number of adherents of a given religion and religious group globally by five-year periods.

Religious Populations Worldwide

Religious Populations Worldwide by Year and Category

By Throwback Thursday [source]

About this dataset

The dataset includes data on Christianity, Islam, Judaism, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism and various other syncretic and animist religions. For each religion or denomination category, it provides both the total population count and the percentage representation in relation to the overall population.

Additionally, - Columns labeled with Population provide numeric values representing the total number of individuals belonging to a particular religion or denomination. - Columns labeled with Percent represent numerical values indicating the percentage of individuals belonging to a specific religion or denomination within a given population. - Columns that begin with ** indicate primary categories (e.g., Christianity), while columns that do not have this prefix refer to subcategories (e.g., Christianity - Roman Catholics).

In addition to providing precise data about specific religions or denominations globally throughout multiple years,this dataset also records information about geographical locations by including state or country names under StateNme.

This comprehensive dataset is valuable for researchers seeking information on global religious trends and can be used for analysis in fields such as sociology, anthropology studies cultural studies among others

How to use the dataset

Introduction:

• Understanding the Columns:

• Year: Represents the year in which the data was recorded.

• StateNme: Represents the name of the state or country for which data is recorded.

• Population: Represents the total population of individuals.

• Total Religious: Represents the total percentage and population of individuals who identify as religious, regardless of specific religion.

• Non Religious: Represents the percentage and population of individuals who identify as non-religious or atheists.

• Identifying Specific Religions: The dataset includes columns for different religions such as Christianity, Judaism, Islam, Buddhism, Hinduism, etc. Each religion is further categorized into specific denominations or types within that religion (e.g., Roman Catholics within Christianity). You can find relevant information about these religions by focusing on specific columns related to each one.

• Analyzing Percentages vs. Population: Some columns provide percentages while others provide actual population numbers for each category. Depending on your analysis requirement, you can choose either column type for your calculations and comparisons.

• Accessing Historical Data: The dataset includes records from multiple years allowing you to analyze trends in religious populations over time. You can filter data based on specific years using Excel filters or programming languages like Python.

• Filtering Data by State/Country: If you are interested in understanding religious populations in a particular state or country, use filters to focus on that region's data only.

Example - Extracting Information:

Let's say you want to analyze Hinduism's growth globally from 2000 onwards:

• Identify Relevant Columns:
• Year: to filter data from 2000 onwards.

• Hindu - Total (Percent): to analyze the percentage of individuals identifying as Hindus globally.

• Filter Data:

• Set a filter on the Year column and select values greater than or equal to 2000.

• Look for rows where Hindu - Total (Percent) has values.

• Analyze Results: You can now visualize and calculate the growth of Hinduism worldwide after filtering out irrelevant data. Use statistical methods or graphical representations like line charts to understand trends over time.

Conclusion: This guide has provided you with an overview of how to use the Rel

Research Ideas

• Comparing religious populations across different countries: With data available for different states and countries, this dataset allows for comparisons of religious populations across regions. Researchers can analyze how different religions are distributed geographically and compare their percentages or total populations across various locations.
• Studying the impact of historical events on religious demographics: Since the dataset includes records categorized by year, it can be used to study how historical events such as wars, migration, or political changes have influenced religious demographics over time. By comparing population numbers before and after specific events, resea...

World Religions Across Regions

Analyzing Adherence Across Regions, States and the Global System

By Correlates of War Project [source]

About this dataset

The World Religion Project (WRP) is an ambitious endeavor to conduct a comprehensive analysis of religious adherence throughout the world from 1945 to 2010. This cutting-edge project offers unparalleled insight into the religious behavior of people in different countries, regions, and continents during this time period. Its datasets provide important information about the numbers and percentages of adherents across a multitude of different religions, religion families, and non-religious affiliations.

The WRP consists of three distinct datasets: the national religion dataset, regional religion dataset, and global religion dataset. Each is focused on understanding individually specific realms for varied analysis approaches - from individual states to global systems. The national dataset provides data on number of adherents by state as well as percentage population practicing a given faith group in five-year increments; focusing attention to how this number evolves from nation to nation over time. Similarly, regional data is provided at five year intervals highlighting individual region designations with one modification – Pacific Ocean states have been reclassified into their own Oceania category according to Country Code Number 900 or above). Finally at a global level – all states are aggregated in order that we may understand a snapshot view at any five-year interval between 1945‐2010 regarding relationships between religions or religio‐families within one location or transnationally.

This project was developed in three stages: firstly forming a religions tree (a systematic classification), secondly collecting data such as this provided by WRP according to that classification structure – lastly cleaning the data so discrepancies may be reconciled and imported where needed with gaps selected when unknown values were encountered during collection process . We would encourage anyone wishing details undergoing more detailed reading/analysis relating various use applications for these rich datasets - please contact Zeev Maoz (University California Davis) & Errol A Henderson _(Pennsylvania State University)

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How to use the dataset

The World Religions Project (WRP) dataset offers a comprehensive look at religious adherence around the world within a single dataset. With this dataset, you can track global religious trends over a period of 65 years and explore how they’ve changed during that time. By exploring the WRP data set, you’ll gain insight into cross-regional and cross-time patterns in religious affiliation around the world.

Research Ideas

• Analyzing historical patterns of religious growth and decline across different regions
• Creating visualizations to compare religious adherence in various states, countries, or globally
• Studying the impact of governmental policies on religious participation over time

Acknowledgements

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

License

License: Dataset copyright by authors - 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. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: WRP regional data.csv | Column name | Description | |:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | Year | Reference year for data collection. (Integer) | | Region | World region according to Correlates Of War (COW) Regional Systemizations with one modification (Oceania category for COW country code ...

The World Religion Project (WRP) aims to provide detailed information about religious adherence worldwide since 1945. It contains data about the number of adherents by religion in each of the states in the international system. These numbers are given for every half-decade period (1945, 1950, etc., through 2010). Percentages of the states' populations that practice a given religion are also provided. (Note: These percentages are expressed as decimals, ranging from 0 to 1, where 0 indicates that 0 percent of the population practices a given religion and 1 indicates that 100 percent of the population practices that religion.) Some of the religions are divided into religious families. To the extent data are available, the breakdown of adherents within a given religion into religious families is also provided.

The project was developed in three stages. The first stage consisted of the formation of a religion tree. A religion tree is a systematic classification of major religions and of religious families within those major religions. To develop the religion tree we prepared a comprehensive literature review, the aim of which was (i) to define a religion, (ii) to find tangible indicators of a given religion of religious families within a major religion, and (iii) to identify existing efforts at classifying world religions. (Please see the original survey instrument to view the structure of the religion tree.) The second stage consisted of the identification of major data sources of religious adherence and the collection of data from these sources according to the religion tree classification. This created a dataset that included multiple records for some states for a given point in time. It also contained multiple missing data for specific states, specific time periods and specific religions. The third stage consisted of cleaning the data, reconciling discrepancies of information from different sources and imputing data for the missing cases.

The National Religion Dataset: The observation in this dataset is a state-five-year unit. This dataset provides information regarding the number of adherents by religions, as well as the percentage of the state's population practicing a given religion.

Global Religious Demographics

Global religious demographics over time

By Throwback Thursday [source]

About this dataset

The dataset contains information on a wide range of religions, including Christianity, Judaism, Islam, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism, Zoroastrianism, Syncretic Religions (religious practices that blend elements from multiple faiths), Animism (belief in spiritual beings in nature), Non-Religious individuals or those without any religious affiliation.

For each religion and region/country combination recorded in the dataset we have the following information:

• Total population: The total population of the region or country.
• Religious affiliation percentages: The percentages of the population that identify with specific religious affiliations.
• Subgroup populations/percentages: The populations or percentages within specific denominations or sects of each religion.

The dataset also provides additional variables like Year and State Name (for regional data) for further analysis.

How to use the dataset

• Understanding the Columns

  The dataset contains several columns with different categories of information. Here's a brief explanation of some important columns:

  • Year: The year in which the data was recorded.
  • Total Population: The total population of a country or region.
  • State Name (StateNme): The name of the state or region.

  Each religion has specific columns associated with it, such as Christianity, Buddhism, Islam, Hinduism, Judaism, Taoism, Shintoism etc., representing its percentage and population for each category/denomination within that religion.

• Selecting Specific Data

  If you are interested in exploring data related to a particular religion or geographic location:

  • To filter data by Religion: Identify relevant columns associated with that religion such as 'Christianity', 'Buddhism', 'Islam', etc., and extract their respective percentage and population values for analysis.

    Example: If you want to analyze Christianity specifically, extract columns related to Christianity like 'Christianity (Percent)', 'Christianity (Population)', etc.

    Note: There might be multiple columns related to a specific religion indicating different categories or denominations within that religion.

  • To filter data by Geographic Location: Utilize the 'State Name' column ('StateNme') to segregate data corresponding to different states/regions.

    Example: If you want to analyze religious demographics for a particular state/region like California or India:

    i) Filter out rows where State Name is equal to California or India.

    ii) Extract relevant columns associated with your selected religion as mentioned above.

• Finding Trends and Insights

  Once you have selected the specific data you are interested in, examine patterns and trends over time or across different regions.

  • Plotting data using visualizations: Use graphical tools such as line charts, bar charts, or pie charts to visualize how religious demographics have changed over the years or vary across different regions.

  • Analyzing population proportions: By comparing the percentage values of different religions for a given region or over time, you can gather insights into changes in religious diversity.

• Comparing Religions

  If you wish to compare multiple religions:

Research Ideas

• Comparing religious affiliations across different countries or regions: With data on various religions such as Christianity, Islam, Buddhism, Judaism, Hinduism, etc., researchers can compare the religious affiliations of different countries or regions. This can help in understanding the cultural and religious diversity within different parts of the world.
• Exploring the growth or decline of specific religions: By examining population numbers for specific religions such as Jainism, Taoism, Zoroastrianism, etc., this dataset can be used to investigate the growth or decline of these religious groups over time. Researchers can analyze factors contributing to their popularity or decline in particular regions or countries

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: ThrowbackDataThursday 201912 - Religion.csv | Column name...

Overview

The World Religion Project (WRP) aims to provide detailed information about religious adherence worldwide from 1945 to 2010. It contains data about the number of adherents by religion in each of the states in the international system. These numbers are given for every half-decade period (1945, 1950,…, 2010). The data record percentages of the state’s population that practice a given religion. Some of the religions (as detailed in the Codebook) are divided into religious families. To the extent data are available, the breakdown of adherents within a given religion into religious families is also specified in the Codebook.

The project was developed in three stages. The first stage consisted of the formation of a religions tree. A religion tree is a systematic classification of major religions and of religious families within those major religions. The second stage consisted of the identification of major data sources of religious adherence and the collection of data from these sources according to the religion tree classification. The third stage consisted of cleaning the data, reconciling discrepancies of information from different sources, and imputing data for the missing cases.

Please see column description in the PDF file

Citations

Zeev Maoz and Errol A. Henderson. 2013. “The World Religion Dataset, 1945-2010: Logic, Estimates, and Trends.” International Interactions, 39: 265-291.

Data Sets

The WRP contains three datasets: the national religion dataset, the regional religion dataset, and the global religion dataset.

The National Religion Dataset. The unit of analysis in this dataset is the individual state, observed at five-year intervals. This dataset provides information regarding the number of adherents by religion, as well as the percent of the state’s population practicing a given religion.

The Regional Religion Dataset. The unit of analysis in this dataset is the region, observed at five-year intervals. This dataset utilizes the COW regional designations with one modification: the Oceania category for COW country code numbers 900 and above.

The Global Religion Dataset. The unit of analysis in this dataset is the global system, observed at five-year intervals. This dataset aggregates the number of adherents of a given religion and religious group for all states, globally.

Acknowlegement

Foto von James Coleman auf Unsplash

This dataset describes the world’s religious makeup in 2020 and 2010. We focus on seven categories: Christians, Muslims, Hindus, Buddhists, Jews, people who belong to other religions, and those who are religiously unaffiliated. This analysis is based on more than 2,700 sources of data, including national censuses, large-scale demographic surveys, general population surveys and population registers. For more information about this data, see the associated Pew Research Center report "How the Global Religious Landscape Changed From 2010 to 2020."

In 2020, around 28.8 percent of the global population were identified as Christian. Around 25.6 percent of the global population identify as Muslims, followed by 14.9 percent of global populations as Hindu. The number of Muslims increased by 347 million, when compared to 2010 data, more than all other religions combined.

World religion data in this dataset is from the World Religion Database.The map shows the percentage of the majority religion by provinces/states and also included in the database is Christian percentage by provinces/states. Boundaries are based on Natural Earth, August, 2011 modified to match provinces in the World Religion Database.*Originally titled

This dataset contains the estimated religious composition of 198 countries and territories for 2010 to 2050.
The data is sourced from PEW RESEARCH CENTER . In original dataset the number and the percentage share of followers for some religions as "<10000" and "<10%". Because of technical limitations of the visualization tool, these values had to be changed into "10000".

data/rounded_population.csv

This file contains the number followers by religions and region for 2010 to 2050 | Column Name | Description | |-----------------|-------------------------------------------------------------------------------------------------| | Year | The year for which the data is recorded. | | Region | The region to which the country belongs. | | Country | The name of the country. | | Buddhists | The estimated number of Buddhists in the country for the given year. | | Christians | The estimated number of Christians in the country for the given year. | | Folk Religions | The estimated number of followers of folk religions in the country for the given year. | | Hindus | The estimated number of Hindus in the country for the given year. | | Jews | The estimated number of Jews in the country for the given year. | | Muslims | The estimated number of Muslims in the country for the given year. | | Other Religions | The estimated number of followers of other religions in the country for the given year. | | Unaffiliated | The estimated number of people with no religious affiliation in the country for the given year. |

data/rounded_percentage.csv

This file contains the percentage share of followers by religions and region for 2010 to 2050 | Column Name | Description | |------------------|------------------------------------------------------------------------------------------------| | Year | The year for which the data is recorded. | | Region | The region to which the country belongs. | | Country | The name of the country. | | Christians | The percentage share of Christians in the country's population for the given year. | | Muslims | The percentage share of Muslims in the country's population for the given year. | | Unaffiliated | The percentage share of people with no religious affiliation in the country's population. | | Hindus | The percentage share of Hindus in the country's population for the given year. | | Buddhists | The percentage share of Buddhists in the country's population for the given year. | | Folk Religions | The percentage share of followers of folk religions in the country's population. | | Other Religions | The percentage share of followers of other religions in the country's population. | | Jews | The percentage share of Jews in the country's population for the given year. | | All Religions | The percentage share of all religious groups combined in the country's population for the year. |

This is not a public opinion survey, but a massive, ongoing data codification project by the Pew Research Center that measures how governments and societies around the world restrict religious beliefs and practices. By analyzing hundreds of sources for 198 countries and territories, Pew creates two seminal annual indexes: the Government Restrictions Index (GRI) and the Social Hostilities Index (SHI). This data set provides a quantitative, comparable benchmark to track trends in religious freedom, persecution, and the complex intersection of religion, law, and conflict on a global scale from 2007 to the present.

"Between October 2011 and November 2012, Pew Research Center, with generous funding from The Pew Charitable Trusts and the John Templeton Foundation, conducted a public opinion survey involving more than 30,000 face-to-face interviews in 26 countries in Africa, Asia, the Middle East and Europe. The survey asked people to describe their religious beliefs and practices, and sought to gauge respondents; knowledge of and attitudes toward other faiths. It aimed to assess levels of political and economic satisfaction, concerns about crime, corruption and extremism, positions on issues such as abortion and polygamy, and views of democracy, religious law and the place of women in society.

"Although the surveys were nationally representative in most countries, the primary goal of the survey was to gauge and compare beliefs and attitudes of Muslims. The findings for Muslim respondents are summarized in the Religion & Public Life Project's reports The World's Muslims: Unity and Diversity and The World's Muslims: Religion, Politics and Society, which are available at www.pewresearch.org. [...] This dataset only contains data for Muslim respondents in the countries surveyed. Please note that this codebook is meant as a guide to the dataset, and is not the survey questionnaire." (2012 Pew Religion Worlds Muslims Codebook)

Content

The World Religion Project aims to provide detailed information about religious adherence worldwide since 1945. It contains data about the number of adherents by religion in each of the states in the international system for every half-decade period. Some of the religions are divided into religious families, and the breakdown of adherents within a given religion into religious families is provided to the extent data are available.

The project was developed in three stages. The first stage consisted of the formation of a religions tree. A religion tree is a systematic classification of major religions and of religious families within those major religions. To develop the religion tree we prepared a comprehensive literature review, the aim of which was to define a religion, to find tangible indicators of a given religion of religious families within a major religion, and to identify existing efforts at classifying world religions. The second stage consisted of the identification of major data sources of religious adherence and the collection of data from these sources according to the religion tree classification. This created a dataset that included multiple records for some states for a given point in time, yet contained multiple missing data for specific states, specific time periods, and specific religions. The third stage consisted of cleaning the data, reconciling discrepancies of information from different sources, and imputing data for the missing cases.

Acknowledgements

The dataset was created by Zeev Maoz, University of California-Davis, and Errol Henderson, Pennsylvania State University, and published by the Correlates of War Project.

This folder consists of files for a case study of the methods used by Pew Research Center to make direct and indirect estimates for our report on The Religious Composition of the World's Migrants. Two subfolders demonstrate the procedures of the algorithm using two statistical programs, which mirror one another.

In December 2009, Pew Research Center released "Global Restrictions on Religion," the first in a series of annual reports on a data-coding project that seeks to measure levels of government restrictions on religion and social hostilities involving religion around the world. As of February 2015, Pew Research had published six reports on global restrictions on religion, analyzing a total of seven years' worth of data (the first two reports covered a total of three years, from 2007 to 2009). [...] In order to provide social science researchers and the general public with easier access to the data, Pew Research Center has released the full dataset.

The data are presented as a long-format dataset, in which each row is a country-year observation (for example, "Afghanistan, 2007"). The columns contain all of the variables presented in Pew Research Center's annual reports on restrictions on religion, as well as some additional variables analyzed in separate studies. The dataset contains data from 2007 through 2013; as additional years of data are coded, the dataset will be updated.

The codebook proceeds in three parts. First, it explains the methodology and coding procedures used to collect the data. Second it discusses the Government Restrictions Index and Social Hostilities Index, including what they measure and how they are calculated. Finally, it describes each of the variables included in the dataset, along with answer values and definitions of key terms.

This repository contains historical data collected in the digital humanities project Dhimmis & Muslims – Analysing Multireligious Spaces in the Medieval Muslim World. The project was funded by the VolkswagenFoundation within the scope of the Mixed Methods initiative. The project was a collaboration between the Institute for Medieval History II of the Goethe University in Frankfurt/Main, Germany, and the Institute for Visualization and Interactive Systems at the University of Stuttgart, and took place there from 2018 to 2021. The objective of this joint project was to develop a novel visualization approach in order to gain new insights on the multi-religious landscapes of the Middle East under Muslim rule during the Middle Ages (7th to 14th century). In particular, information on multi-religious communities were researched and made available in a database accessible through interactive visualization as well as through a pilot web-based geo-temporal multi-view system to analyze and compare information from multiple sources. The code for this visualization system is publicly available on GitHub under the MIT license. The data in this repository is a curated database dump containing data collected from a predetermined set of primary historical sources and literature. The core objective of the data entry was to record historical evidence for religious groups in cities of the Medieval Middle East. In the project, data was collected in a relational PostgreSQL database, the structure of which can be reconstructed from the file schema.sql. An entire database dump including both the database schema and the table contents is located in database.sql. The PDF file database-structure.pdf describes the relationship between tables in a graphical schematic. In the database.json file, the contents of the individual tables are stored in JSON format. At the top level, the JSON file is an object. Each table is stored as a key-value pair, where the key is the database name, and the value is an array of table records. Each table record is itself an object of key-value pairs, where the keys are the table columns, and the values are the corresponding values in the record. The dataset is centered around the evidence, which represents one piece of historical evidence as extracted from one or more sources. An evidence must contain a reference to a place and a religion, and may reference a person and one or more time spans. Instances are used to connect evidences to places, persons, and religions; and additional metadata are stored individually in the instances. Time instances are connected to the evidence via a time group to allow for more than one time span per evidence. An evidence is connected via one or more source instances to one or more sources. Evidences can also be tagged with one or more tags via the tag_evidence table. Places and persons have a type, which are defined in the place type and person type tables. Alternative names for places are stored in the name_var table with a reference to the respective language. For places and persons, references to URIs in other data collections (such as Syriaca.org or the Digital Atlas of the Roman Empire) are also stored, in the external_place_uri and external_person_uri tables. Rules for how to construct the URIs from the fragments stored in the last-mentioned tables are controlled via the uri_namespace and external_database tables. Part of the project was to extract historical evidence from digitized texts, via annotations. Annotations are placed in a document, which is a digital version of a source. An annotation can be one of the four instance types, thereby referencing a place, person, religion, or time group. A reference to the annotation is stored in the instance, and evidences are constructed from annotations by connecting the respective instances in an evidence tuple.

Dataset Description

This dataset compiles harmonized country-level aggregates from the World Values Survey (WVS, waves 1-7) covering sexual norms, religious values, and social attitudes, integrated with health and economic indicators for cross-national analysis of HIV/STI epidemiology.

Data Sources and Coverage

The dataset merges individual-level responses from WVS (1981-2022) aggregated to country-year observations with:

• HIV prevalence (ages 15-49) and sexually transmitted infection burden indicators from the Global Burden of Disease Study 2021 (IHME)
• GDP per capita from World Bank World Development Indicators (WDI)
• GINI coefficients from World Bank SI.POV.GINI indicator, supplemented with alternative sources (World Bank Poverty and Inequality Platform, OECD Income Distribution Database, World Inequality Database, and national statistical offices) for countries with missing data

Variable Domains

The dataset includes measures across five theoretical domains:

• Sexual norms: Attitudes toward homosexuality, sex work, casual sex, and premarital sex (10-point acceptance scales)
• Religious salience: Importance of religion in life, belief in God, and spousal religious concordance preferences
• Social values: Self-reported happiness and health status
• Stigma indicators: Unwillingness to have neighbors with AIDS
• Economic controls: GDP per capita (current US$) and GINI coefficient of income/consumption inequality

Sample Composition

The final analytical dataset contains 68 countries with observations from their most recent WVS wave (2017-2022 period), representing 834,680 individual survey responses aggregated to country level. Countries span seven World Bank regions with particular representation from Europe & Central Asia (40.9%), Latin America & Caribbean (18.2%), and East Asia & Pacific (14.8%).

Data Structure and Missingness

The dataset provides complete data for 10 core variables across all 68 countries. Three additional variables exhibit partial coverage: casual sex acceptance (n=45), premarital sex acceptance (n=56), and GINI coefficient (completed to n=68 through alternative sources). The dataset includes both raw and transformed variables, including logarithmic transformations for skewed distributions and GDP-adjusted GINI residuals to address multicollinearity.

Usage Notes

This dataset was developed to examine relationships between cultural values, economic conditions, and sexual health outcomes while addressing common methodological challenges in cross-national research including temporal consistency, missing data patterns, and measurement heterogeneity across data sources.

This file assembles data from multiple sources on 256 countries and territories, and also aggregates this data globally and by 22 world regions. The file presents most of the data available on the ARDA National Profiles as of 2018 in a single downloadable dataset. Many of the measures are from the ARDA's coding of the 2008 US State Department's International Religious Freedom (IRF) Reports. This coding produced data on 198 different countries and territories (see the Summary file for the International Religious Freedom Data, 2008 for a list of countries coded, available for download from the ARDA), but excluded the United States. In addition, this project assembled (with permission) other cross-national measures of interest to researchers on religion, economics, and politics. They include adherent information from the World Christian Database, scales from Freedom House, the Religion and State Project, the Center for Systemic Peace, the Heritage Foundation, the Correlates of War Project, the Varieties of Democracy Project, the CIRI Human Rights Data Project, and various socio-economic measures from the United Nations, World Bank, and the CIA's World Factbook. The source of each variable in this dataset is acknowledged in the variable's description, except in the case of those variables generated by ARDA researchers' coding of the Department of State's IRF Reports.

The European Values Study (EVS) and the World Values Survey (WVS) are two large-scale, cross-national and longitudinal survey research programmes. They include a large number of questions on moral, religious, social, political, occupational and family values which have been replicated since the early eighties.

Both organizations agreed to cooperate in joint data collection from 2017. EVS has been responsible for planning and conducting surveys in European countries, using the EVS questionnaire and EVS methodological guidelines. WVSA has been responsible for planning and conducting surveys in countries in the world outside Europe, using the WVS questionnaire and WVS methodological guidelines. Both organisations developed their draft master questionnaires independently. The joint items define the Common Core of both questionnaires.

The Joint EVS/WVS is constructed from the two EVS and WVS source datasets: - European Values Study 2017 Integrated Dataset (EVS 2017), ZA7500 Data file Version 5.0.0, doi:10.4232/1.13897 (https://doi.org/10.4232/1.13897). Haerpfer, C., Inglehart, R., Moreno,A., Welzel,C., Kizilova,K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2024. World Values Survey: Round Seven–Country-Pooled Datafile. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. Version. 6.0.0, doi:10.14281/18241.24.

Initial data source was UNESCO web site, supplemented by individual work on different countires/regions;A database of cultural heritage sites assembled by volunteers at the Archaeological Computing Laboratory, University of Sydney;Database is now availabe online through ECAI and can be updated through a password-controlled web browser interface