In 2023, 8.5 percent of female respondents in the United States stated they identify as LGBT, while 4.7 percent of male respondents said the same. This is an increase from 2012, when 3.5 percent of female respondents and 3.4 percent of male respondents identified as LGBT.

According to a global survey conducted in 2021, three in 10 respondents had at least once spoken out against someone who was being prejudiced against LGBT+ people. In addition, some 13 percent attended a public event in support of LGBT+ people, e.g. a Pride march.

About this dataset

Context

The dataset tabulates the Gay household income by age. The dataset can be utilized to understand the age-based income distribution of Gay income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Gay, GA annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Gay, GA household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Gay income distribution by age. You can refer the same here

This statistic shows the share of LGBT population living in states with health insurance protections that include sexual orientation and gender identity in the United States as of **************. At that time, ** percent of the LGBT population lived in U.S. states where health insurance protections did not cover sexual orientation and gender identity.

About this dataset

Context

The dataset tabulates the Gay population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Gay across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Gay was 122, a 7.02% increase year-by-year from 2021. Previously, in 2021, Gay population was 114, an increase of 3.64% compared to a population of 110 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Gay decreased by 31. In this period, the peak population was 153 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Gay is shown in this column.
• Year on Year Change: This column displays the change in Gay population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Gay Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Fort Gay population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Fort Gay across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Fort Gay was 664, a 1.19% decrease year-by-year from 2021. Previously, in 2021, Fort Gay population was 672, a decline of 0.30% compared to a population of 674 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Fort Gay decreased by 137. In this period, the peak population was 804 in the year 2003. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Fort Gay is shown in this column.
• Year on Year Change: This column displays the change in Fort Gay population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Fort Gay Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Gay median household income by race. The dataset can be utilized to understand the racial distribution of Gay income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Gay, GA median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Gay, GA (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Gay median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Fort Gay. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Fort Gay, the median household income for the households where the householder is White increased by $11,655(71.94%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $16,202 in 2013 and $27,857 in 2023.
• Black or African American: As per the U.S. Census Bureau population data, in Fort Gay, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

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

Racial categories include:

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

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Fort Gay.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

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 Fort Gay median household income by race. You can refer the same here

Sexual orientation in the UK by region, sex, age, legal partnership status, and ethnic group. These are official statistics in development.

The LGBTQI+ Dataset 2020-2022_es is a collection of 410,015 original tweets extracted from the social network Twitter between January 1, 2020, and December 31, 2022. To ensure data quality and relevance, retweets, replies, and other duplicate content were excluded, retaining only original tweets. The tweets were collected by Jacinto Mata (University of Huelva, I2C/CITES) with the support of the Python programming language and using the twarc2 tool and the Academic API v2 of Twitter. Tbis data collection is part of the project “Conspiracy Theories and Hate Speech Online: Comparison of patterns in narratives and social networks about COVID-19, immigrants and refugees and LGBTI people [NON-CONSPIRA-HATE!]”, PID2021-123983OB-I00, funded by MCIN/AEI/10.13039/501100011033/ by FEDER/EU.

The search criteria (words and hashtags) used for the data collection followed the objectives of the aforementioned project and were defined by Estrella Gualda, Francisco Javier Santos Fernández and Jacinto Mata (University of Huelva, Spain). Terms and hashtags used for the search and extraction of tweets were: #orgullogay, #orgullotrans, #OrgulloLGTB, #OrgulloLGTBI, #Díadelorgullo, #TRANSFOBIA, #transexuales, #LGTB, #LGTBI, #LGTBIQ, #LGTBQ, #LGTBQ+, anti-gay, "anti gay", anti-trans, "anti trans", "Ley Anti-LGTB", "ley trans", "anti-ley trans".

This dataset collected in the frame of the NON-CONSPIRA-HATE! project had the aim of identifying and mapping online hate speech narratives and conspiracy theories towards LGBTIQ+ people and community. Additionally, the dataset is intended to compare communication patterns in social media (rhetoric, language, micro-discourses, semantic networks, emotions, etc.) deployed in different datasets collected in this project. This dataset also contributes to mapping the actors, communities, and networks that spread hate messages and conspiracy theories, aiming to understand the patterns and strategies implemented by extremist sectors on social media. he dataset includes messages that address a wide range of topics related to the LGBTQI+ community, such as rights, visibility, the fight against discrimination and transphobia, as well as debates surrounding the Trans Law and other related issues. It includes expressions of support and celebration of Pride as well as hate speech and opposition to LGBTQI+ rights, along with debates and controversies surrounding these issues.

This dataset offers a wide range of possibilities for research in various disciplines, as the following examples express:

Social Sciences & Digital Humanities:
- Analysis of opinions, attitudes, and trends toward the LGBTIQ+ people and community.
- Studies on the evolution of public discourse and polarization around issues such as transphobia, hate speech, disinformation, LGBTIQ+ rights and pride, and others.
- Analysis on social and political actors, leaders or organizations disseminating diverse narratives on LGBTIQ+
- Research on the impact of specific events (e.g., Pride Day) on social media conversations.
- Investigations on social and semantic networks around LGBTIQ+ people and community.
- Analysis of narratives, discourses and rethoric around gender identity and sexual diversity.
- Comparative studies on the representation of the LGBTIQ+ people and community in different cultural or geographic contexts.

Computer Science and Artificial Intelligence:
- Development of algorithms for the automatic detection of hate speech, discriminatory language, or offensive content.
- Training natural language processing (NLP) models to analyze sentiments and emotions in texts related to the LGBTIQ+ people and community.

For more information on other technical details of the dataset and the structure of the .jsonl data, see the “Readme.txt” file.

About this dataset

Context

The dataset tabulates the Fort Gay household income by age. The dataset can be utilized to understand the age-based income distribution of Fort Gay income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Fort Gay, WV annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Fort Gay, WV household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Fort Gay income distribution by age. You can refer the same here

Gay or lesbian, Female, 25-34, United Kingdom, Number of people (thousands). Sexual orientation in the UK by sex and age, 2014 to 2020. Source: Annual Population Survey, ONS

In a survey conducted in December 2020, around ** percent of heterosexual Japanese respondents were grouped into a category called "aware, but not taking action". This category groups people who are generally aware of the LGBTQ+ community, but do not consider LGBTQ+ issues, as they are not acquainted with anyone directly affected. Approximately ** percent of respondents were evaluated to be active supporters of sexual minorities.

The acronym LGBTQ+, often just LGBT, is an umbrella term describing members of sexual minorities. The individual letters stand for lesbian, gay, bisexual, transgender, and queer or questioning, with the "+" including an additional and larger spectrum of sexual identities and gender identities.

!!!WARNING~~~This dataset has a large number of flaws and is unable to properly answer many questions that people generally use it to answer, such as whether national hate crimes are changing (or at least they use the data so improperly that they get the wrong answer). A large number of people using this data (academics, advocates, reporting, US Congress) do so inappropriately and get the wrong answer to their questions as a result. Indeed, many published papers using this data should be retracted. Before using this data I highly recommend that you thoroughly read my book on UCR data, particularly the chapter on hate crimes (https://ucrbook.com/hate-crimes.html) as well as the FBI's own manual on this data. The questions you could potentially answer well are relatively narrow and generally exclude any causal relationships. ~~~WARNING!!!For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 8 release notes:Adds 2019 and 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last UCR hate crime data they release. Changes .rda file to .rds.Version 7 release notes:Changes release notes description, does not change data.Version 6 release notes:Adds 2018 dataVersion 5 release notes:Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), made all character values lower case, reordered columns. I also generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

In 2020, around ***** percent of respondents in the Netherlands considered themselves to be homosexual/lesbian, that is to say, only attracted to the same sex. There has been a somewhat increasing trend in the this time range from a low of around *** percent in 2014.

Supporting data for:Tulloch, Ayesha I.T. (2020) Towards more equitable and inclusive conservation and ecology conferences”, Perspective in Nature Ecology and Evolution.4 worksheets:1. Conference Initiatives: Results of review supporting Table 1 and Table S2 in main text of paper. Indicates which of 30 conference events for 10 international conference and ecology conferences implemented different initiatives.To evaluate how ecology and conservation conferences support these principles, the actions and policies of 10 international conferences held by nine academic societies for ecology and conservation were reviewed. Data were collated for the past three events that had been held by each conference targeting an international audience: the biannual International Congress for Conservation Biology (ICCB), International Marine Conservation Congress (IMCC), European Ecological Federation (EEF) Conference and the Society for Ecological Restoration (SER) World Conference on Ecological Restoration, the annual conferences of the Ecological Society of America (ESA), Ecological Society of Australia (ESAus), British Ecological Society (BES) and Association for Tropical Biology and Conservation (ATBC), the conference of the International Association for Ecology (INTECOL), held once every 5 years, and the IUCN World Conservation Congress (WCC) held once every 4 years. Data came from conferences between 2009 and 2020. Data were sourced from conference websites, conference programs and marketing material. Initiatives of interest were those targeted on improving equity and diversity in sex, gender identity and sexual orientation, and associated diversity types and lifestyle choices ̶ marital status, family or carer responsibilities, pregnancy and breastfeeding and physical appearance are categorised according to three broad groups:(a) Minimising discrimination, harassment and implicit bias(b) Minimising barriers to attendance(c) Maximising opportunities for participation & education.2. Conference Affordability: Data on conference registration fees and discounts for students and developing countries.3. Conference Attendance: Data on conference attendee diversity provided by individual conferences and societies on websites and marketing material.4. Conference_equity_forR_200505: Input data (csv file) for GLMM code in R, provided in S3. Code for Statistical Models.

!!!WARNING~~~This dataset has a large number of flaws and is unable to properly answer many questions that people generally use it to answer, such as whether national hate crimes are changing (or at least they use the data so improperly that they get the wrong answer). A large number of people using this data (academics, advocates, reporting, US Congress) do so inappropriately and get the wrong answer to their questions as a result. Indeed, many published papers using this data should be retracted. Before using this data I highly recommend that you thoroughly read my book on UCR data, particularly the chapter on hate crimes (https://ucrbook.com/hate-crimes.html) as well as the FBI's own manual on this data. The questions you could potentially answer well are relatively narrow and generally exclude any causal relationships. ~~~WARNING!!!For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 11 release notes:Adds 2023-2024 dataVersion 10 release notes:Adds 2022 dataVersion 9 release notes:Adds 2021 data.Version 8 release notes:Adds 2019 and 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last UCR hate crime data they release. Changes .rda file to .rds.Version 7 release notes:Changes release notes description, does not change data.Version 6 release notes:Adds 2018 dataVersion 5 release notes:Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), made all character values lower case, reordered columns. I also generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

In a survey conducted in December 2020, a majority of almost ** percent of heterosexual respondents in Japan stated that they had neither a favorable nor a negative attitude towards members of the LGBTQ+ community, viewing sexual minorities in a rather neutral way.

The acronym LGBTQ+, often just LGBT, is an umbrella term describing members of sexual minorities. The individual letters stand for lesbian, gay, bisexual, transgender, and queer or questioning, with the "+" including an additional and larger spectrum of sexual identities and gender identities.

The Generations study is a five-year study designed to examine health and well-being across three generations of lesbians, gay men, and bisexuals (LGB). The study explored identity, stress, health outcomes, and health care and services utilization among LGBs in three generations of adults who came of age during different historical contexts. This collection includes baseline, wave 1, and wave 2 data collected as part of the Generations study. The study aimed to assess whether younger cohorts of LGBs differed from older cohorts in how they viewed their LGB identity and experienced stress related to prejudice and everyday forms of discrimination, as well as whether patterns of resilience differed between different LGB cohorts. Additionally, the study sought to examine how differences in stress experience affected mental health and well-being, including depressive and anxiety symptoms, substance and alcohol use, suicide ideation and behavior, and how younger LGBs utilized LGB-oriented social and health services, relative to older cohorts. In wave 2, respondents were re-interviewed approximately one year after completion of the baseline (wave 1) survey. Only respondents who participated in the original sample of participants were surveyed at wave 2 (i.e., the enhancement oversample was not included in the longitudinal design of this study). In wave 3, respondents were re-interviewed approximately one year after the completion of the wave 2 survey. Demographic variables collected as part of this study include questions related to age, education, race, ethnicity, sexual identity, gender identity, income, employment, and religiosity.

There were estimated to be approximately **** million people in the United Kingdom who identified as being Gay, Lesbian or Bisexual in 2023, compared with ******* in 2014.