About this dataset

Context

The dataset tabulates the Gays 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 Gays 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 2023, the population of Gays was 214, a 1.38% decrease year-by-year from 2022. Previously, in 2022, Gays population was 217, a decline of 3.12% compared to a population of 224 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Gays decreased by 59. In this period, the peak population was 281 in the year 2010. 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 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Gays is shown in this column.
• Year on Year Change: This column displays the change in Gays 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 Gays Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Gay by race. It includes the distribution of the Non-Hispanic population of Gay across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Gay across relevant racial categories.

Key observations

Of the Non-Hispanic population in Gay, the largest racial group is White alone with a population of 84 (98.82% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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: This column displays the racial categories (for Non-Hispanic) for the Gay
• Population: The population of the racial category (for Non-Hispanic) in the Gay is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Gay total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Gay Population by Race & Ethnicity. You can refer the same here

In 2024, 14.2 percent of Millennials in the United States stated that they identify as LGBTQ+, while in 2012, less than six percent of respondents from the same generation said the same. Members of Generation Z were the most likely to identify as LGBTQ+, at over 23 percent.

As bisexual individuals in the United States (U.S.) face significant health disparities, researchers have posited that these differences may be fueled, at least in part, by negative attitudes, prejudice, stigma, and discrimination toward bisexual individuals from heterosexual and gay/lesbian individuals. Previous studies of individual and social attitudes toward bisexual men and women have been conducted almost exclusively with convenience samples, with limited generalizability to the broader U.S. population. Our study provides an assessment of attitudes toward bisexual men and women among a nationally representative sample of heterosexual, gay, lesbian, and “other”-identified adults in the U.S. Data were collected from the 2015 National Survey of Sexual Health and Behavior (NSSHB), via an online questionnaire with a probability sample of adults (18 and over) from throughout the U.S. We included two modified 5-item versions of the Bisexualities: Indiana Attitudes Scale (BIAS), validated sub-scales that were developed to measure attitudes toward bisexual men and women. Data were analyzed using descriptive statistics, gamma regression, and paired t-tests. Gender, sexual identity, age, race/ethnicity, income, and educational attainment were all significantly associated with participants' attitudes toward bisexual individuals. In terms of responses to individual scale items, participants were most likely to “neither agree nor disagree” with attitudinal statements. Across sexual identities, self-identified "other" participants reported the most positive attitudes, while heterosexual male participants reported the least positive attitudes. Overall, attitudes toward bisexual men were significantly less positive than toward bisexual women across identities. As in previous research on convenience samples, we found a wide range of demographic characteristics were related with attitudes toward bisexual individuals in our nationally-representative study of heterosexual, gay/lesbian, and "other"-identified adults in the U.S. Additionally, as in previous studies, gender emerged as a significant characteristic; female participants’ attitudes were more positive than male participants’ attitudes, and all participants’ attitudes were generally more positive toward bisexual women than bisexual men. While population data suggest a marked shift in more positive attitudes toward gay men and lesbian women in the general population of the U.S., the largest proportions of participants in our study reported a relative lack of agreement or disagreement with the affective-evaluative statements in the BIAS scales. Findings document the absence of positive attitudes toward bisexual individuals among the general population of adults in the U.S. Our findings highlight the need for developing intervention approaches to promote more positive attitudes toward bisexual individuals, targeted toward not only heterosexual and but also gay/lesbian individuals and communities.

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.

Center types, definitions, and criteria for data collection.

Analysis of ‘What Do Men Think It Means To Be A Man?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/masculinity-surveye on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This directory contains data behind the story What Do Men Think It Means To Be A Man?.

masculinity-survey.csv contains the results of a survey of 1,615 adult men conducted by SurveyMonkey in partnership with FiveThirtyEight from May 10-22, 2018. The modeled error estimate for this survey is plus or minus 2.5 percentage points. The percentages have been weighted for age, race, education, and geography using the Census Bureau’s American Community Survey to reflect the demographic composition of the United States age 18 and over. Crosstabs with less than 100 respondents have been left blank because responses would not be statistically significant.

The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.

Source: https://github.com/fivethirtyeight/data

This dataset was created by FiveThirtyEight and contains around 200 samples along with Adult Men, No Children, technical information and other features such as: - Age 35 64 - Race White - and more.

How to use this dataset

• Analyze Sexual Orientation Gay/ Bisexual in relation to Has Children
• Study the influence of Race Non White on Age 18 34
• More datasets

Acknowledgements

If you use this dataset in your research, please credit FiveThirtyEight

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Mapping LGBTQ Equality: 2010 to 2020 presented the status of LGBTQ equality at the U.S. state level by examining a policy tally by the Movement Advancement Project (MAP), and encompassed nearly 40 LGBTQ-related laws and policies across all 50 states, the District of Columbia, and the five U.S. territories as of January 1, 2020. The report also compared the January 1, 2020 status of LGBTQ policy landscape to the status of those same laws as of January 1, 2010. MAP's policy tally aggregated these laws and policies to gauge the LGBTQ-related policy landscape across the country. What emerged in 2020 was a patchwork of positive LGBTQ laws and policies, with variations both by region and area of law, as well as growth in both the policy accomplishments and challenges facing LGBTQ people over the decade of observation. Areas of law and policy included: relationship and parental recognition, nondiscrimination, religious exemptions, LGBTQ youth-related laws, health care, criminal justice, and identity documents.

!!!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 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.

Numerous studies from Europe and North America have documented sexual orientation-based health disparities, but due to data limitations, very little is known about the health of sexual minorities (i.e., lesbians, gay men, bisexual individuals, and other non-heterosexual populations) in developing countries. This research note uses newly available nationally representative data from the Chilean Socio-Economic Characterization Survey (CASEN) to explore sexual orientation-based disparities in self-rated health, health insurance coverage, and healthcare utilization in Chile. Our findings indicate that sexual minority respondents report worse self-rated health and greater health care utilization, and that sexual minority men are more likely to have private health insurance relative to heterosexual men. These findings are important in facilitating continued efforts to reduce health disparities in Latin America.

We anonymously surveyed members and non-members of the LGBTQIA+ community of conservation students and professionals in North America to explore participants’ lived experiences in conservation regarding safety, belonging, and inclusion. Our 737 responses included 10% that identified as genderqueer, gender nonconforming, questioning, nonspecific, genderfluid, transgender woman, agender, transgender man, two spirit Indigenous, or intersex (hereafter gender expansive), and 29% bisexual, queer, lesbian, gay, asexual, pansexual, omnisexual, questioning, or non-heterosexual (hereafter queer+). Data also include results of a non-response survey of 157 individuals who chose not to complete our the full survey, but answered basic demographic questions to determine non-response bias. Methods Responses were solicited from an email list that included natural resource, conservation, ecology, wildlife, and fisheries departments from public and private universities; 4-year colleges; 2-year colleges; professional schools; technical, vocational, or trade schools; Hispanic-serving institutions; historically Black colleges and universities; tribal colleges, and women’s colleges. To include perspectives from non-academic settings and to target LGBTIQA+ individuals, we included listserv members of the “Out in the Field'' LGBTQIA+ and ally working group of the Wildlife Society as part of our survey population. We distributed a Qualtrics suvey and consent letter to ask respondents about their feelings and experiences of safety, belonging, and inclusion working in the field of conservation.

Abstract: The resurgence of the HIV epidemic among gay men and other men who have sex with men (MSM) is indicative of limitations or failures in prevention policies directed to this group. Based on the theoretical approaches of vulnerability and Care, we analyze the panorama of HIV/AIDS prevention policies for gays and other MSM in Brazil using national documents that support prevention policies of HIV/AIDS and documents produced by nongovernmental organizations and by the LGBT National Conferences. We identified, in the documents analyzed, three readings that support prevention policies: a) epidemiological; b) preventive responsibility; c) based on human rights and vulnerability. The dispute, denial and hegemony of each of these perspectives at different times allows us to understand some of the challenges and barriers faced in preventing HIV and AIDS among gays and other MSM. Our analysis shows changes in the intensity and quality of the dialogue between state and society. The fragile formalization and restricted scope of the documents stand out as limitations in the effectuation of a prevention approach based on vulnerability and human rights, as well as the incorporation of the Public Care perspective. We reiterate the importance of a qualified dialogue with the individuals involved in the policies to hear their needs, aspirations and critics.

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.

Victimization characteristics of violent hate crime, LGBT versus non-LGBT, United States, National Crime Victimization Survey 2017–2019.

The type of bias-motivation among hate crime victims, by sexual orientation and gender identity, United States, National Crime Victimization Survey 2017–2019.

Exposure to minority stressors, sexual orientation affiliation, and mental health outcomes by cohort (N = 1,518).

IMDb was the only source from which data was extracted. The sample was constructed using the tool search engines, filtering “feature films” (over 45 min of lentgh), excluding “adult titles” and excluding “released”. In order to obtain a comprehensive length of time, all productions from 1895 to December 2019 were included according to a search carried out in March 2020. To collect only those films that could have detailed information, the number of items was limited to those with over 50 user ratings (N = 119809) as a way of minimally controlling the popularity of the published work.

In the resulting sample (N = 1768), the presence of descriptors in the field “plot” was coded, designating different terms related to the LGBTQ+ community. Those included the following terms and their variants with similar etymology: homosexual (homo/homosexuality), gay, lesbian, trans (transsexual, transgender) and queer. After adding those productions that had a descriptor term from this list in the keywords field, the total of the items corresponding to these categories was 9409 films. This decision responds to the need to reflect in the sample films in which there is representation of the group, but it is not necessarily part of the plot or is revealed through the course of it. It is also supported by the documentary tradition, according to which keywords tend to overlap with the plot or summary (La Barre & de Novais Cordeiro, 2012, p. 241).

The following information was extracted from each item (movie):

• Production by country and production by language in each year. In co-productions, only the first producing country was considered and the other countries discarded.

• Identity of the group (gay, lesbian, trans, etc.) as per the plot keywords. Several identities and expressions such as transsexuality and transgender have been included under the label “trans” as it was impossible to recognize the correct expression from the labels provided by IMDb.

• Cinema genres. Using the first two descriptors, a list of genre pairs was created, which were later grouped into 13 generic categories, as per the formal qualities of the theme: Drama (any combinations of the drama category that were not included in other categories), Comedy (combinations including comedy that were not considered in other categories), Action/Adventure, Melodrama (Drama + Comedy), Horror, Crime (including thrillers), Fantasy (including Science Fiction), Biography (in both fictional and documentary forms), Documentary (excluding biographies and fake documentaries but including News), Romantic Comedy (Romance + Comedy), Animation (excluding documentary formats) and Music/Musical feature films.

• Age ratings. Parental Advisory guidelines have changed significantly over the decades, from the first classifications in the United Kingdom, Germany, and the United States to today. In order to establish a suitable comparison, the descriptor provided by IMDb, which is usually established by the MPAA (Motion Picture Association of America), was used. When this was omitted, the descriptor used was determined as per the recommended age: Universal, Parental Guidance (PG), 12-13, 14-16, 17-18, as well as X and banned, according to the historical equivalence as provided by IMDd (2020).

Data was pooled to consider evolution by historical periods and trends in a single group or correlational ex post facto design. Subsequently, the data was analyzed with the statistical package SPSS v.26. To visualize the main trends from the data, Tableau 2020 software was used.

Historical context for the definition of three cohorts of sexual minorities in the United States.

BackgroundReducing adolescent suicide in the United States is a public health priority, and lesbian, gay, bisexual, transgender, and queer (LGBTQ+) youth are at elevated risk. The Centers for Disease Control and Prevention has identified six evidence-informed school-based practices (EIPs) that enhance health equity and potentially reduce suicide-related behavior for LGBTQ+ students. Guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, we conducted a five-year, community-engaged cluster randomized controlled trial in 42 New Mexican high schools to study the implementation of these six EIPs. This paper assesses the effectiveness, utility, and benefits of the study's implementation strategy—the Dynamic Adaptation Process (DAP), a participatory and multifaceted implementation approach.MethodsOur convergent parallel mixed-method analysis focused on 22 New Mexico high schools randomized into an implementation condition. Data sources included annual structured assessments of EIP implementation, individual and small-group qualitative interviews with school professionals, periodic debriefs and interviews with implementation coaches, and coach activity logs. We analyzed quantitative data using linear regressions and qualitative data using deductive coding techniques, integrating the results through a joint display.ResultsThe schools experienced statistically significant changes compared to their baseline in adopting safe spaces, prohibitions on bullying and harassment based on LGBTQ+ identity, inclusive health education materials, staff professional development, and facilitation of students' access to LGBTQ+ affirming healthcare. We attribute these changes to the impact of the DAP. The DAP facilitated collaboration among school professionals and community organizations to shift knowledge and attitudes and execute contextually responsive implementation strategies. It also fostered relationship-building and leadership, encouraging school leaders to legitimate implementation efforts and champion health equity for LGBTQ+ students.DiscussionParticipatory implementation science models like the DAP can help prioritize health equity for marginalized populations by enabling the uptake of practices likely to contribute to well-being. This mixed-methods study provides a rich example for future research tackling health disparities for LGBTQ+ people in schools and other complex systems.

Employment in the publishing industry in the United States was made up mostly by straight or heterosexual people in 2019, with this group accounting for ** percent of the publishing workforce. Bi and pansexual employees had greater representation than people who identified as gay, lesbian, or asexual, but there remain calls for greater diversity in U.S. publishing, not only when it comes to sexual orientation but also for employees from minority ethnic groups and those with disabilities.