This dataset is about book series. It has 2 rows and is filtered where the books is From generation to generation : age groups and social structure. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

About this dataset

Context

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

Key observations

The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for United States Population by Age. You can refer the same here

Curious about age demographics of your clientele in The Netherlands? Wondering about which generation can be most often seen flocking to your store? Dive deep into customer insights using our population by age group data of The Netherlands. Whether your customers are down your street or across the globe, we empower you to pinpoint the ideal demographic for your marketing campaigns or projects. Our dataset offers intricate details on this country's age distribution.

These two datasets provide the responses to a survey on food including what influences decisions on what people choose to eat, and what is important to people when selecting food for example price, animal welfare, origin of food. Knowledge of the food system Use of technology when purchasing food and key concerns about food.

The total sample includes all age groups 16+ and has a sample size of 2475.

The Gen Z sample is of generation Z only 16- 25 year olds and has a sample size of 619.

90s Pop Music Recognition

Multigenerational Knowledge Distribution

By Amber Thomas [source]

About this dataset

This dataset was collected to gain insight into the recognition of 90s pop songs between different generations. The data collected reflects how well audiences recognize the music and allows us to see if knowledge of music is passed on from generation to generation. This dataset provides valuable information on how a song's familiarity changes over time and reveals trends in musical recognition across ages.

The data used in this dataset was taken from a music challenge available online which asked users to share their birth year, listen to 30-second snippets of popular 90s songs, and indicate how familiar they are with each song. Respondents could choose from Don't know, Sounds Familiar, Know It, or Singing the Lyrics as answers; by default, users were only asked to respond to 10 tunes, but they had an option for more if desired. The responses showed us that recognition of certain genres may decrease with time while other genres continue to be popular among different generations.

These results can give us an idea about what makes one particular type of genre stand apart from another as it continues throughout time even when people are exposed differently–and use that knowledge in understanding how best brands can market their music and associated products or services–or even what types of content endure beyond any traditional resistance-based life cycle models! We hope you find this information useful in your own research pursuits! If you have any questions or comments regarding this dataset please don't hesitate contacting Matt Daniels

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How to use the dataset

Using this dataset can help you analyze and compare the recognition rates of popular music released in the 90s across different generations. To get started, you'll need to know how the data was collected and organized.

The data was collected by crowd-sourcing through an online music challenge, which asked users to listen to a 30 second snippet of a song and then indicate how familiar they are with it (e.g., 'Don't Know', 'Sounds Familiar', 'Know It', or 'Singing the Lyrics'). From that input, we included columns for artist name, recognition scores from millennials, recognition scores from Gen Z users, and percentages of people from various ages who responded Don't Know to the song.

Research Ideas

• Comparing trends in generational familiarity with 90s music - the data can be used to compare the recognition of the same songs across different age groups, such as millennials and Gen Z. This could allow researchers to explore whether knowledge of and appreciation for these songs has been passed down from one generation to the next.
• Analyzing cultural relevancy of 90s songs - by comparing recognition scores between different age groups, it is possible to use this dataset to analyze which artists/songs continue to be relevant over time and which have faded away into obscurity.
• Conducting longitudinal research on 90s music - this dataset can also be utilized for longitudinal research investigating how familiarity with a particular artist or song has changed over time since its release in the 90s

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: recognition_by_generation.csv | Column name | Description | |:--------------------------------|:---------------------------------------------------------------------| | artist | The artist of the song. (String) | | mean_millennial_recognition | The average recognition score of the song among millennials. (Float) | | mean_gen_z_recognition | The average recognition score of the song among Gen Z users. (Float) |

File: time_series_90s.csv | Column name | Description | |:-----------------------|:-------------------------------------------------------------------------------------------------------| | artist | The artist of the song. (String) | | years_old_13 | The percentage of respondents aged 13 or younger who ...

Definition: The long-term demographic change and the associated changes in the age structure of the population are represented by the numerical ratio of certain age groups. The youth ratio compares the child and youth generation, which is predominantly in the education and training phase, with the middle generation, which is predominantly in the labour market. The age limit for children and adolescents is ‘less than 20 years’ and the age limit for the middle generation is ‘20 to less than 65 years’. The old-age dependency ratio contrasts the older generation, which has largely left the labour force, with the middle generation. For the older generation, the age limit “from 65 years” is chosen.

Data source:
IT.NRW, Population update

Millennials were the largest generation group in the United States in 2024, with an estimated population of ***** million. Born between 1981 and 1996, Millennials recently surpassed Baby Boomers as the biggest group, and they will continue to be a major part of the population for many years. The rise of Generation Alpha Generation Alpha is the most recent to have been named, and many group members will not be able to remember a time before smartphones and social media. As of 2024, the oldest Generation Alpha members were still only aging into adolescents. However, the group already makes up around ***** percent of the U.S. population, and they are said to be the most racially and ethnically diverse of all the generation groups. Boomers vs. Millennials The number of Baby Boomers, whose generation was defined by the boom in births following the Second World War, has fallen by around ***** million since 2010. However, they remain the second-largest generation group, and aging Boomers are contributing to steady increases in the median age of the population. Meanwhile, the Millennial generation continues to grow, and one reason for this is the increasing number of young immigrants arriving in the United States.

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin; for the United States, States, Counties; and for Puerto Rico and its Municipios: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

In 2024, Millennials were the largest generation group in the United States, making up about 21.81 percent of the population. However, Generation Z was not far behind, with Gen Z accounting for around 20.81 percent of the population in that year.

Curious about age demographics of your clientele in Germany? Wondering about which generation can be most often seen flocking to your store? Dive deep into customer insights using our population by age group data of Germany. Whether your customers are down your street or across the globe, we empower you to pinpoint the ideal demographic for your marketing campaigns or projects. Our dataset offers intricate details on this country's age distribution.

Analysis of ‘Youth and old age quotient by district-free cities and districts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/a4c1412b-14b3-5319-80d8-c14d462414b6 on 18 January 2022.

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

Definition: In order to illustrate the long-term demographic change and the associated changes in the age structure of the population, the ratio of certain age groups is shown. The youth ratio compares the generation of children and adolescents, which are mostly in the education and training phase, with the middle generation, which is predominantly in employment. For the generation of children and adolescents the age limit “under 20 years” is chosen and for the middle generation the age limit “20 to under 65 years” is chosen. The old-age dependency ratio compares the older generation, who have left the labour force, to the middle generation. For the older generation, the age limit “from 65 years” is chosen.

Data source:
It.NRW, update of population

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

Population Figures: Resident population by date, sex, generation (age on 31 December), nationality (groups of countries) and place of birth (groups of countries). Semi-annual. National.

Curious about age demographics of your clientele in France? Wondering about which generation can be most often seen flocking to your store? Dive deep into customer insights using our population by age group data of France. Whether your customers are down your street or across the globe, we empower you to pinpoint the ideal demographic for your marketing campaigns or projects. Our dataset offers intricate details on this country's age distribution.

Definition: The long-term demographic change and the associated changes in the age structure of the population are represented by the numerical ratio of certain age groups. The youth ratio compares the child and youth generation, which is predominantly in the education and training phase, with the middle generation, which is predominantly in the labour market. The age limit for children and adolescents is ‘less than 20 years’ and the age limit for the middle generation is ‘20 to less than 65 years’. The old-age dependency ratio contrasts the older generation, which has largely left the labour force, with the middle generation. For the older generation, the age limit “from 65 years” is chosen. In order to be able to assess the financial burdens of the social system, an overall quotient is formed that places the “young” and “old” in relation to the population in middle age. Data source: IT.NRW, Population update

According to data collected during the first quarter of 2020, adults aged 18 to 34 spent more time browsing the web via smartphone than any other age group in the United States. Overall media consumption was highest among adults aged 50 to 64 during that period.

Traditional media

Traditional media is gradually losing its appeal to younger, more tech-savvy generations. While television consumption is highest among adults who have not grown up with the internet or other digital channels, young Millennials and Gen Z tend to prefer non-linear forms of news and entertainment. Data on the median age of media users in the U.S. showed that the average age of TV viewers and print magazine readers was higher than that of internet users in 2020, and similar generational trends can be observed in many digitally developed markets globally.

Impact of COVID-19 on media usage

The onset of the coronavirus (COVID-19) pandemic boosted media consumption across the United States and worldwide in 2020. While the average time spent with traditional media increased for the first time in over a decade, digital media consumption saw a particularly impressive spike that year due to remote working and schooling setups. In the following years, the gap between traditional and digital media consumption is expected to widen even further.

This table contains figures on the highest educational level and direction of birth generations. The figures in this table are determined by age and gender. This makes it possible to compare the highest attained level of education among individuals of the same age group in the different birth generations. Only percentages are presented in this table. The table provides information for age groups from 30 to 35 years because people under the age of 30 have often not yet completed their education, thus underestimating the level of education for this group. Only percentages are presented in this table. The figures come from the Occupational Population Survey (EBB).

Data available from: birth generation 1925 to 1930. This concerns the EBB since 1990.

Status of the figures: The figures in this table are final.

Changes as of 28 February 2019 The underlying codings of the classifications used in this table (birth generation, education direction, gender, age) have been adjusted. These are now in line with the standard coding established by CBS. The structure and data of the table have not been modified.

When are new figures coming? The new figures will be released in June 2020.

This dataset contains 300 samples representing individuals from Generation Y (Gen Y) and Generation Z (Gen Z). The data captures insights into their UPI (Unified Payments Interface) usage, financial literacy levels, spending habits, and budgeting behaviors. The primary goal is to analyze the impact of UPI usage on financial literacy and money management skills across different age groups. The sample consists of 60% Gen Z and 40% Gen Y respondents to maintain a balance in generational insights.

The S3 dataset contains the behavior (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones.
All attributes of the different kinds of data are writed in a vector. The dataset contains the fellow vectors:
Sensors:
This type of vector contains data belonging to smartphone sensors (accelerometer and gyroscope) that has been acquired in a given windows of time. Each vector is obtained every 20 seconds, and the monitored features are:- Average of accelerometer and gyroscope values.- Maximum and minimum of accelerometer and gyroscope values.- Variance of accelerometer and gyroscope values.- Peak-to-peak (max-min) of X, Y, Z coordinates.- Magnitude for gyroscope and accelerometer.

Statistics:
These vectors contain data about the different applications used by the user recently. Each vector of statistics is calculated every 60 seconds and contains : - Foreground application counters (number of different and total apps) for the last minute and the last day.- Most common app ID and the number of usages in the last minute and the last day. - ID of the currently active app. - ID of the last active app prior to the current one.- ID of the application most frequently utilized prior to the current application. - Bytes transmitted and received through the network interfaces.

Voice:
This kind of vector is generated when the microphone is active in a call o voice note. The speaker vector is an embedding, extracted from the audio, and it contains information about the user's identity. This vector, is usually named "x-vector" in the Speaker Recognition field, and it is calculated following the steps detailed in "egs/sitw/v2" for the Kaldi library, with the models available for the extraction of the embedding.


A summary of the details of the collected database.
- Users: 21 - Sensors vectors: 417.128 - Statistics app's usage vectors: 151.034 - Speaker vectors: 2.720 - Call recordings: 629 - Voice messages: 2.091

Data on the daily time spent on music streaming worldwide showed that Baby Boomers spent the least time streaming music each day, with an average of just 31 minutes. By contrast, Gen Z internet users spent one hour and 45 minutes streaming music on a daily basis, more than users in any other age group.