How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Business Context

With the availability of internet services on mobile devices, the way that people work, socialize, organize, and entertain themselves has radically changed. With access to entertainment channels, news, learning and research material, real-time video calling, and more, these multimedia communication devices have become an integral part of our day-to-day lives.

Objective

A reputed research and consultation firm recently conducted a study on the increasing rate of internet usage over the past decade and reported that a typical American spends 144 minutes (2.4 hours) per day, on average, accessing the internet via a mobile device. You wish to test the validity of this statement. So, you reached out to friends and family to understand the time they spend per day accessing the internet via mobile devices. You received responses from 29 people and based on that, you want to check if there is enough evidence to suggest that the mean time spent per day accessing the internet via mobile devices is different from 144 minutes. A 5% significance level has been chosen.

Data Dictionary

The results for the time spent per day accessing the Internet via a mobile device (in minutes) are stored in InternetMobileTime.csv.

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

This dataset simulates anonymized mobile screen time and app usage data collected from Android/iOS users over a 3-month period (Jan–April 2024). It captures daily usage trends across various app categories including:

Productivity: Google Docs, Notion, Slack

Entertainment: YouTube, Netflix, TikTok

Social Media: Instagram, WhatsApp, Facebook

Utilities: Chrome, Gmail, Maps

For YouTube, additional engagement statistics such as views, likes, and comments are included to analyze video popularity and content consumption behavior.

The dataset enables exploration of:

Productivity vs. entertainment screen time patterns

Daily usage fluctuations

App-specific user engagement

Correlation between time spent and user interactions

YouTube content virality metrics

This is a great resource for:

EDA projects

Behavioral clustering

Dashboard development

Time series and anomaly detection

Building recommendation or focus-assistive apps

Time-Wasters on Social Media Dataset Overview The "Time-Wasters on Social Media" dataset offers a detailed look into user behavior and engagement with social media platforms. It captures various attributes that can help analyze the impact of social media on users' time and productivity. This dataset is valuable for researchers, marketers, and social scientists aiming to understand the nuances of social media consumption.

This dataset was generated using synthetic data techniques with the help of NumPy and pandas. The data is artificially created to simulate real-world social media usage patterns for research and analysis purposes.

Columns Description UserID: A unique identifier assigned to each user. Age: The age of the user. Gender: The gender of the user. Location: The geographical location of the user. Income: The annual income of the user. Debt: Tells If the is in Debt or Not. Owns Property: Indicates whether the user owns any property (Yes/No). Profession: The profession or job title of the user. Demographics: Additional demographic information about the user (Rural or Urban Life). Platform: The social media platform used by the user (e.g., Facebook, Instagram, TikTok). Total Time Spent: The total time the user has spent on the platform. Number of Sessions: The number of sessions the user has had on the platform. Video ID: A unique identifier for each video watched. Video Category: The category of the video watched (e.g., Entertainment, Gaming, Pranks, Vlog). Video Length: The length of the video watched. Engagement: The engagement level of the user with the video (e.g., Likes, Comments). Importance Score: A score representing the perceived importance of the video to the user. Time Spent On Video: The amount of time the user spent watching the video. Number of Videos Watched: The total number of videos watched by the user. Scroll Rate: The rate at which the user scrolls through content. Frequency: How frequently the user logs into the platform. Productivity Loss: The amount of productivity lost due to time spent on social media. Satisfaction: The satisfaction level of the user with the content consumed. Watch Reason: The reason why the user watched the video (e.g., Entertainment, Information). DeviceType: The type of device used to access the platform (e.g., Mobile, Desktop). OS: The operating system of the device used. Watch Time: The specific time of day when the user watched the video. Self Control: The user's self-assessed level of self-control while using the platform. Addiction Level: The user's self-assessed level of addiction to social media. Current Activity: The activity the user was engaged in before using the platform. ConnectionType: The type of internet connection used by the user (e.g., Wi-Fi, Mobile Data).

Usage This dataset can be utilized to:

Analyze patterns in social media usage. Understand demographic differences in platform engagement. Examine the impact of social media on productivity. Develop strategies to improve user engagement and satisfaction. Study the correlation between social media usage and various demographic factors.

This dataset explores the relationship between digital device usage (screen time) and various mental health indicators among individuals. The data captures self-reported usage patterns of phones, laptops, tablets, and TVs, as well as daily habits, mood, stress levels, physical activity, and mental well-being scores. It aims to provide insights into how modern digital lifestyles affect mental health.

This dataset can be used for:

Predictive modeling

Behavioral clustering

Time-series simulation

Public health awareness

Wellness recommendation systems

Digital Habits and Employment Status Survey

American Digital Usage and Employment Trends

By Joshua Shepherd [source]

About this dataset

This comprehensive dataset provides a rich and multi-faceted exploration into the intriguing world of digital habits, employment status, and demographics of Americans. Inspired by evolving modern lifestyle trends, this dataset meticulously draws information from varied topics such as gaming habits, job search techniques and broadband usage.

The first part of the dataset delves into the realm of video games and gaming culture. It explores various aspects related to individual's preferences towards different types of games across diverse platforms. It uncovers insights into how much time users spend on these games, their favoured genres and platforms (such as consoles or PC), along with their perspectives on important issues concerning violence in video games.

Next up is an insightful dataset that revolves around job seeking trends through digital channels. In a fast-paced business world where online resources have started playing an integral role in career progression and job hunt processes, this data provides valuable insights about Americans' reliance on internet services for finding potential jobs.

Hard-hitting questions revolving around workforce automation form yet another component of this extensive database. This section throws light upon the use of computers, robots or artificial intelligence to carry out tasks traditionally performed by human workers.

Probing further into modern relationship dynamics comes queries pertaining to online dating landscape. This segment explores Americans' attitudes towards online dating platforms - their usual go-to applications/web portals for seeking new relationships or love interests.

Lastly but importantly is an exhaustive set containing facts and figures regarding home broadband usage among Americans across all age groups & genders including their access to crucial cable TV services & smartphone possession rates & dependency levels over them in daily life activities ranging from shopping to banking & even learning new skills!

Collectively offering a well-rounded snapshot at contemporary American societies –this explorative data aims at providing stepping stones for researchers trying to understand these realms thereby serving larger cause making our society better

How to use the dataset

This dataset provides a rich collection of information about the digital habits, employment status, and secondary demographic data of respondents from the June-July 2015 Gaming, Job Search, and Broadband Usage Among Americans survey. With multiple sections regarding diverse topics such as gaming, online job searches, internet usage patterns and more fundamental demographics details - this dataset can be used for various kinds of exploratory data analysis (EDA), machine learning models or creating informative visualizations.

Here is how you can get started with this dataset:

1. Exploring Digital Habits:

The questions about video games ask if a respondent ever plays video games on a computer or console. This can be used to identify key trends in digital habits among different demographic groups - for instance correlation between age or gender and propensity towards gaming.

2. Analysing Job Searches:

The job seeking portion has information regarding use of internet in search processes and its effectiveness according to respondents’ opinion. You could perform an analysis on how working status (or even age group) affects the way individuals employ technology during their job searches.

3. Studying Broadband Usage:

Data about broadband usage at home would give insights into internet adoption rates among various demographic groups.

4.Predictive Modelling:

Potential predictive modeling could include predicting someone's employment status based on their digital habits or vice versa.

5.Cross-Referencing Data Points:

Using two or more datapoints can yield some interesting results as well - like finding out if gamers are more likely than non-gamers to frequently change jobs or seeing if there is any correlation with high speed broadband usage and employment type etc.

Before conducting any analysis do keep in mind that it would be beneficial to conduct some basic cleaning tasks such as checking for missing values, removing duplicates etc., suitable encoding discrete variables including education level into numerical ones based upon intuition behind categories ordinality could also provide better model performance.

This is just scratching the surface of p...

Experimental results of the pilot Office for National Statistics (ONS) online time-use study (collected 28 March to 26 April 2020 across Great Britain) compared with the 2014 to 2015 UK time-use study.

Annual data on internet usage in Great Britain, including frequency of internet use, internet activities and internet purchasing.

Please cite the following paper when using this dataset: N. Thakur, “Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions,” Preprints, 2022, DOI: 10.20944/preprints202206.0383.v1 Abstract The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and use cases in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a dataset is necessary. The Internet of Everything era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by mining relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. Therefore, this work presents a dataset of about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. Instructions: This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data only for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. The Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets) or any similar application may be used for hydrating this dataset. Data Description This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. Filename: Exoskeleton_TweetIDs_Set1.txt (Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022) Filename: Exoskeleton_TweetIDs_Set2.txt (Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021) Filename: Exoskeleton_TweetIDs_Set3.txt (Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020) Filename: Exoskeleton_TweetIDs_Set4.txt (Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020) Filename: Exoskeleton_TweetIDs_Set5.txt (Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019) Filename: Exoskeleton_TweetIDs_Set6.txt (Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019) Filename: Exoskeleton_TweetIDs_Set7.txt (Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017) Here, the last date for May is May 21 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets.

Average daily time spent by adults on activities including paid work, unpaid household work, unpaid care, travel and entertainment. These are official statistics in development.

Analyzing Customer Spending Habits to Improve Sales Performance

A Cross-Country Study

By Vineet Bahl [source]

About this dataset

This Sales Data dataset offers a unique insight into the spending habits of customers from various countries across the globe. With detailed information on customer age, gender, product category, quantity, unit cost and price, as well as revenue generated through sales of products listed in this dataset, you can explore and discover patterns in consumer behavior. Analyze shifts in consumer trends with qualitative data like customer age and gender to know what drives customers’ decisions when shopping online or offline. Compare different markets to analyze pricing strategies for new product launches or promotional campaigns. Also with this dataset you can gain valuable insights about the changes in consumer demand for specific products over time – find out which Products had better margin or however see how different promotions impacted overall sales performance from different categories and sub-categories! Analyzing consumer behavior is key to success when it comes to commerce business models so this Sales Data offers powerful ways into understanding your customer base better!

More Datasets

For more datasets, click here.

Featured Notebooks

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How to use the dataset

This dataset presents a great opportunity to actively analyze customer spending habits on products and services to improve sales performance. The data contains information about the date of purchase, year, month, customer age, gender, country, state and product category. Further analysis can reveal insights into different customer segments based on their demographic characteristics such as age and gender as well as location (country & state).

The dataset also includes 3 additional columns at the end: quantity purchased in each transaction, unit cost and unit price for each product or service purchased which can be used to determine if customers are purchasing items in bulk or buying more expensive items than usual. Likewise any discrepancies between the unit cost & price can help establish whether discounts were applied during those transactions which could potentially point towards loyalty or reward programs being put in place for returning customers. Lastly the final column shows total revenue generated from those purchases which we can use to identify any patterns whereby certain groups of customers show higher purchasing power than others based on their spends (unit cost & quantity combination) over various periods/months/years of sales interactions with them.

In summary this dataset allows us to explore numerous dimensions related to ascertaining superior sales performance by studying how its various attributes play out together when it comes down to driving profitability through improved customer acquisition strategies as well increasing purchase rates from existing ones minus any discounts available in-between!

Research Ideas

• Analyzing customer demographics by countries and states to better target future marketing campaigns.

• Tracking changes in customers’ spending habits over time for different product categories.

• Identifying which product categories have the highest average revenue per sale to help prioritize resources for those products or services

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: SalesForCourse_quizz_table.csv | Column name | Description | |:---------------------|:--------------------------------------------------| | Date | Date of the sale. (Date) | | Year | Year of the sale. (Integer) | | Month | Month of the sale. (Integer) | | Customer Age | Age of the c...

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

Context

This dataset explores the relationship between digital behavior and mental well-being among 100,000 individuals. It records how much time people spend on screens, use of social media (including TikTok), and how these habits may influence their sleep, stress, and mood levels.

It includes six numerical features, all clean and ready for analysis, making it ideal for machine learning tasks like regression or classification. The data enables researchers and analysts to investigate how modern digital lifestyles may impact mental health indicators in measurable ways.

Dataset Applications

• Quantify how screen‑time, TikTok use, or multi‑platform engagement statistically relate to stress, sleep loss, and mood.
• Train regression or classification models that forecast stress level or mood score from real‑time digital‑usage metrics.
• Feed user‑specific data into recommender systems that suggest screen‑time caps or bedtime routines to improve mental health.
• Provide evidence for guidelines on youth screen‑time limits and platform moderation based on observed stress‑sleep trade‑offs.
• Serve as a teaching dataset for EDA, feature engineering, and model evaluation in data‑science or psychology curricula.
• Evaluate app interventions (e.g., screen‑time nudges) by comparing predicted versus actual post‑intervention stress or mood shifts.
• Cluster individuals into digital‑behavior personas (e.g., “heavy late‑night scrollers”) to tailor mental‑health resources.
• Generate synthetic time‑series scenarios (what‑if reductions in TikTok hours) to estimate downstream impacts on sleep and stress.
• Use engineered features (ratio of TikTok hours to total screen‑time, etc.) in broader wellbeing models that include diet or exercise data.
• Assess whether mental‑health prediction models remain accurate and unbiased across different screen‑time or platform‑use segments. # Column Descriptions
• screen_time_hours – Daily total screen usage in hours across all devices.
• social_media_platforms_used – Number of different social media platforms used per day.
• hours_on_TikTok – Time spent on TikTok daily, in hours.
• sleep_hours – Average number of sleep hours per night.
• stress_level – Stress intensity reported on a scale from 1 (low) to 10 (high).
• mood_score – Self-rated mood on a scale from 2 (poor) to 10 (excell # Inspiration This dataset was inspired by growing concerns about how screen time and social media affect mental health. It enables analysis of the links between digital habits, stress, sleep, and mood—encouraging data-driven solutions for healthier online behavior and emotional well-being. # Ethically Mined Data: This dataset has been ethically mined and synthetically generated without collecting any personally identifiable information. All values are artificial but statistically realistic, allowing safe use in academic, research, and public health projects while fully respecting user privacy and data ethics.

Pandemic has influenced all spheres of the humanity. COVID-19 impacted the education vertical in larger manner. Traditional classroom environment plays a very vital role in molding the life of an individual. Bond nurtured in the early ages of the life acts as the great moral support in the latter stages of the journey. As the pandemic has forced us into online education, this data collection aims to analyze the impact of online education. To check out the satisfactory level of the learners, review was conducted.

Gender – Male, Female Home Location – Rural, Urban Level of Education – Post Graduate, School, Under Graduate Age – Years Number of Subjects – 1- 20 Device type used to attend classes – Desktop, Laptop, Mobile Economic status – Middle Class, Poor, Rich Family size – 1 -10 Internet facility in your locality – Number scale (Very Bad to Very Good) Are you involved in any sports? – Yes, No Do elderly people monitor you? – Yes, No Study time – Hours Sleep time – Hours Time spent on social media – Hours Interested in Gaming? – Yes, No Have separate room for studying? – Yes, No Engaged in group studies? – Yes, No Average marks scored before pandemic in traditional classroom – range Your interaction in online mode - Number scale (Very Bad to Very Good) Clearing doubts with faculties in online mode - Number scale (Very Bad to Very Good) Interested in? – Practical, Theory, Both Performance in online - Number scale (Very Bad to Very Good) Your level of satisfaction in Online Education – Average, Bad, Good

The online overview offers comprehensive metadata on the EVS datasets and variables.

The variable overview of the four EVS waves 1981, 1990, 1999/2000, and 2008 allows for identifying country specific deviations in the question wording within and across the EVS waves.

This overview can be found at: Online Variable Overview.

Moral, religious, societal, political, work, and family values of Europeans.

Themes: Feeling of happiness; state of health; ever felt: very excited or interested, restless, proud, lonely, pleased, bored, depressed, upset because of criticism; when at home: feeling relaxed, anxious, happy, aggressive, secure; respect and love for parents; important child qualities: good manners, politeness and neatness, independance, hard work, honesty, felling of responsibility, patience, imaginantion, tolerance, leadership, self-control, saving money, determination perseverance, religious faith, unselfishness, obedience, loyalty; attitude towards abortion; way of spending leisure time: alone, with family, with friends, in a lively place; frequency of political discussions; opinion leader; volentary engagement in: welfare service for elderly, education, labour unions, polititcal parties, human rights, environment, professional associations, youth work, consumer groups; dislike being with people with different ideas; will to help; characterisation of neighbourhood: people with a ciminal record, of a different race, heavy drinkers, emotionally unstable people, immigrants or foreign workers, left-wing or right-wing extremists, people with large families, students, unmarried mothers, members of minority religious sects or cults; general confidence; young people trust in older people and vice versa; satisfaction with life; freedom of choice and control; satisfaction with financial situation of the household; financial situation in 12 months; important values at work: good pay, not too much pressure, job security, a respected job, good hours, opportunity to use initiative, generous holidays, responsibility, interesting job, a job that meets one´s abilities, pleasant people, chances for promotion, useful job for society, meeting people; look forward to work after weekend; pride in one´s work; exploitation at work; job satisfaction; freedom of decision taking in job; behaviour at paid free days: find extra work, use spare time to study, spend time with family and friends, find additional work to avoid boredom, use spare time for voluntary work, spend time on hobbies, run own business, relaxing; fair payment; preferred management type; attitude towards following instructions at work; satisfaction with home life; sharing attitudes with partner and parents: towards religion, moral standards, social attitudes, polititcal views, sexual attitudes; ideal number of children; child needs a home with father and mother; a woman has to have children to be fulfilled; sex cannot entirely be left to individual choice; marriage as an out-dated institution; woman as a single parent; enjoy sexual freedom; important values for a successful marriage: faithfulness, adequate income, same social background, respect and appreciation, religious beliefs, good housing, agreement on politics, understanding and tolerance, apart from in-laws, happy sexual relationship, sharing household chores, children, taste and interests in common; accepted reasons for divorce; main aim of imprisonment; willingness to fight for the own country; fear of war; expected future changes of values; opinion about scientific advances; interest in politics; political action: signing a petition, joining in boycotts, attending lawful demonstrations, joining unofficial strikes, occupying buildings or factories, damaging things and personal violence; prefence for freedom or equality; self-positioning on a left-right scale; basic kinds of attitudes concerning society; confidence in institutions: churches, armed forces, education system, the press, labour unions, the police, parliament, the civil services, major companies and the justice system; living day to day because of uncertain future; party preference and identification; regularly reading of a daily newspaper; frequency of TV watching; opinion on terrorism; thinking about meaning and purpose of life; feeling that life is meaningless; thoughts about dead; good and evil in everyone; regret having done something; worth risking life for: country, anoth...

The dataset contains demographic and case characteristics of children in foster care each month. The dataset includes the children’s sex, age, race, goal and average time spent in foster care in Norfolk. The data is from Virginia’s Online Automated Services Information System (OASIS). OASIS is a comprehensive system that tracks the day-to-day activities performed by social workers statewide and is the official case record system for foster care and adoption cases in Virginia.

This dataset details the work accomplished by staff at the Norfolk Department of Human Services with the goal of finding safe, permanent homes for children in Norfolk’s foster care system. This dataset is updated monthly.