Living arrangement by age group for the Department of Development Services (DDS) population. Information is reported by the Regional Centers to the DDS and is extracted from the Client Master File (CMF). The Client Master File (CMF) is the primary source of demographic information on individuals receiving services funded by DDS.

The majority of home buyers across all age groups in the United States purchased a detached single-family home in 2024. The share of home buyers that purchased such a home was at least ** percent across all generations.

According to a survey conducted in May 2021, more than half of consumers in the older age groups (** and over) in the United States preferred big box/department stores and pharmacy/convenience stores for their retail purchases compared to consumers in the younger age groups. Online marketplaces were popular across both younger and older consumers. Over ********* of respondents in the age groups 18-34 and 35-54 stated to have used online marketplaces such as Amazon and Etsy in the past three months. This rate was even higher with those aged over ** (at ** percent).

Annual estimates of the proportion of UK employees in each pension type and contracted-out status (prior to 2016), by age group and gross weekly earnings bands.

Continuous Population Statistics: Resident population by date, sex and age group. Quarterly. Provinces.

The use of Most or Moderately effective contraceptive (M/M) or Long-Acting Reversible Contraceptive (LARC) types by primary language used, contraceptive type, age group, and year of interest, for 2014-2016. This data was compiled for the Measure CCW: Contraceptive Care – All Women Ages 15-44, as part of the Maternal and Infant Health Initiative, Contraceptive Care Quality grant.

Economically Active Population Survey: Employees by type of working day, sex and age group. Absolute values and percentages. Quarterly. National.

The number of graduates by credential type, institution type, age group, program type and gender.

Continuous Population Statistics: Population residing in family dwellings by date, sex, age group and place of birth (Spanish/foreign). Quarterly. National.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Age Group". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.

NWT Type of Disability by Sex and Age Group

Gender-Based Average School Workforce in England

Exploring Differences within Geographic Levels, Grades, Types, and Ages

By Charlie Hutcheson [source]

About this dataset

This dataset contains information about the average school workforce in England for the 2019 reporting year. It includes gender, school type, grade, age category, geographic level and average mean of the school workforce in England. With this data we can gain insight into how different demographics are represented educationally across different locations. By analyzing this data we can start to better understand where educational disparities exist and how to bridge those gaps in order to make education more equitable and accessible for all students across England

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How to use the dataset

Let's take a look at what is included in this dataset. Each row represents an observation reporting year and includes columns for aspects of each observation such as location, geographic level, grade range/school type, age category, gender groupings and average mean (for full-time equivalents or FTEs). There is also a location code column that can be used as an identifier should users wish to separate observations geographically.

Some possible uses for this dataset include studying trends related to gender distribution within specific school populations or across different geographic regions. It can also be used to investigate patterns related to age categories or types of schools within a given location or region. You may even wish to compare overall averages among certain groups such as teachers versus support staff or high schools versus elementary schools across regions or groups within England.

In any case, with all variables considered one can generate some interesting insights about the demographics makeup of staff within an educational system and further research particular questions about its nature The possibilities are practically endless! Thanks for checking out our dataset; we hope that you find something useful during your exploration!

Research Ideas

• Investigating whether gender is a factor in the type of school workforce found in different locations
• Examining the average mean across varying school types, grades, and age categories throughout England
• Visualizing the geographic level of specific school workforces based on their location

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: data-school-workforce-in-england.csv | Column name | Description | |:---------------------|:---------------------------------------------------------------------------------| | location | The geographic location of the school. (String) | | location_code | The code for the geographic location of the school. (String) | | geographic_level | The geographic level of the school (e.g. county, region, etc.). (String) | | time_period | The time period for which the data was collected. (String) | | gender | The gender of the school workforce. (String) | | school_type | The type of school (e.g. maintained nursery, maintained primary, etc.). (String) | | grade | The grade level of the school. (String) | | age_category | The age category of the school workforce. (String) | | average_mean | The average mean salary of the school workforce. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Charlie Hutcheson.

Population by sex and age group. Absolute values and percentages with respects to the total of each sex. National.

Percentage of individuals who shopped online and percentage of online shoppers by type of good and service purchased over the Internet during the past 12 months.

As of February 2024, over a third of online users worldwide were aged between 25 and 34 years. Website visitors in this age bracket constituted the biggest group of online users worldwide. Also, 19 percent of global online users were aged 18 to 24 years. The global digital population aged 65 or older represented approximately 4.2 percent of all internet users worldwide. Social media usage and Meta Social media is a major driver of internet use, with a global penetration rate of 62.2 percent. On average, internet users spend 143 minutes per day on social media, highlighting its significant impact on daily online activities. The usage of social media is mostly dominated by Meta platforms, which own four of the largest social media platforms. Facebook leads the ranking with over three billion active users, followed by Instagram and WhatsApp. Instagram's global popularity Meta’s social video platform, Instagram, had long been one of the most engaging social media platforms worldwide, and it was projected to reach 1.44 billion monthly active users. Instagram was particularly favored by users aged 18 to 34, thanks to its ability to offer a variety of interactive content, from images and carousels. This diverse range of content types was a key factor in its popularity among its young user base.

This statistic shows the results of a survey about the distribution of music consumption in South Korea in 2017, by type and age group. In 2017, about **** percent of respondents in the age group of ** to ** reported to use streaming services when they listened to music.

According to a survey conducted in France, younger generations used AI more than older ones in 2024. While only ** percent of respondents aged 40-59 had already used AI in their private lives, this share reached nearly ** percent for respondents aged 18-24 years old.

This statistic depicts the age distribution of India from 2013 to 2023. In 2023, about 25.06 percent of the Indian population fell into the 0-14 year category, 68.02 percent into the 15-64 age group and 6.92 percent were over 65 years of age. Age distribution in India India is one of the largest countries in the world and its population is constantly increasing. India’s society is categorized into a hierarchically organized caste system, encompassing certain rights and values for each caste. Indians are born into a caste, and those belonging to a lower echelon often face discrimination and hardship. The median age (which means that one half of the population is younger and the other one is older) of India’s population has been increasing constantly after a slump in the 1970s, and is expected to increase further over the next few years. However, in international comparison, it is fairly low; in other countries the average inhabitant is about 20 years older. But India seems to be on the rise, not only is it a member of the BRIC states – an association of emerging economies, the other members being Brazil, Russia and China –, life expectancy of Indians has also increased significantly over the past decade, which is an indicator of access to better health care and nutrition. Gender equality is still non-existant in India, even though most Indians believe that the quality of life is about equal for men and women in their country. India is patriarchal and women still often face forced marriages, domestic violence, dowry killings or rape. As of late, India has come to be considered one of the least safe places for women worldwide. Additionally, infanticide and selective abortion of female fetuses attribute to the inequality of women in India. It is believed that this has led to the fact that the vast majority of Indian children aged 0 to 6 years are male.

This statistic shows the age groups most likely to use hotel amenities in the United States as of April 2014, by type of amenity. During the survey, ** percent of the respondents aged 18 to 34 said they would use massage or spa services in a hotel.