The number of smartphone users in France was forecast to continuously increase between 2024 and 2029 by in total 3.2 million users (+5.96 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 56.89 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Belgium and Luxembourg.

About this dataset

Context

The dataset tabulates the Mobile 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 Mobile. The dataset can be utilized to understand the population distribution of Mobile by age. For example, using this dataset, we can identify the largest age group in Mobile.

Key observations

The largest age group in Mobile, AL was for the group of age 20 to 24 years years with a population of 14,449 (7.81%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Mobile, AL was the 80 to 84 years years with a population of 3,589 (1.94%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Mobile is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Mobile 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 Mobile Population by Age. You can refer the same here

Series Name: Proportion of individuals who own a mobile telephone by sex (percent)Series Code: IT_MOB_OWNRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.b.1: Proportion of individuals who own a mobile telephone, by sexTarget 5.b: Enhance the use of enabling technology, in particular information and communications technology, to promote the empowerment of womenGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

The number of smartphone users in Sweden was forecast to continuously increase between 2024 and 2029 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach **** million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find further information concerning Denmark and Norway.

About this dataset

Context

The dataset tabulates the Mobile 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 Mobile 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 Mobile was 182,595, a 0.38% decrease year-by-year from 2022. Previously, in 2022, Mobile population was 183,290, a decline of 1.02% compared to a population of 185,176 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Mobile decreased by 20,735. In this period, the peak population was 203,330 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 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 Mobile is shown in this column.
• Year on Year Change: This column displays the change in Mobile 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 Mobile Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Mobile City 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 Mobile City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Mobile City was 154, a 2.67% increase year-by-year from 2021. Previously, in 2021, Mobile City population was 150, an increase of 5.63% compared to a population of 142 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Mobile City decreased by 42. In this period, the peak population was 233 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Mobile City is shown in this column.
• Year on Year Change: This column displays the change in Mobile City 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 Mobile City Population by Year. You can refer the same here

Facebook is the leading social network worldwide, and its accessibility through multiple mobile apps as well as its mobile website. In January 2021, over 98 percent of active user accounts worldwide accessed the social network via any kind of mobile phone.

Facebook in mobile-first markets India is thecountry with the largest Facebook audience by far, with 340 million users on the platform, followed the United States, Indonesia, and Brazil all of which have more than 100 million Facebook users each. With the exception of the United States, all of these are digital markets with mobile-first audiences. In many emerging markets, mobile is often the first online experience, providing online users with their first internet experience through inexpensive smartphones and mobile data contracts. In India and Indonesia, mobile by far surpasses desktop in terms of audiences and time spent.

Mobile Facebook access Due to the social network’s wide reach on mobile, it is unsurprising that Facebook consistently ranks as one of the most-downloaded app publishers worldwide. Some of the apps published by Facebook include the eponymous social networking app (and its low-bandwidth version, Facebook Lite), Facebook Messenger (also available as Messenger Lite), Facebook Pages Manager and Facebook Local. In the Google Play Store, Facebook Messenger, Messenger Lite and Facebook frequently rank among the top downloaded apps every month.

📨 Direct Mail Marketing Response Dataset

This dataset focuses on predicting which customers are most likely to respond to a direct mail marketing promotion.
It is based on real data from a clothing store chain in New England.

• Rows: 28,799 customers
• Features: 51 fields (customer demographics, purchase history, and marketing interactions)
• Target: RESP (whether a customer responded to a promotion)

📊 Dataset Details

Each row corresponds to a unique customer, with information about spending behavior, product preferences, and marketing exposure.

Key Fields

• HHKEY → Unique encrypted customer ID
• ZIP_CODE → Customer’s ZIP code
• FRE → Number of purchase visits
• MON → Total net sales
• AVRG → Average spend per visit
• AMSPEND, PSSPEND, CSSPEND, AXSPEND → Spend across four different franchise brands
• OMONSPEND, TMONSPEND, SMOSPEND → Spend over past 1, 3, and 6 months
• PREVPD → Spend in the same period last year
• GMP → Gross margin percentage
• PROMOS → Number of marketing promotions on file
• DAYS → Number of days customer has been on file
• FREDAYS, LTFREDAY → Time between purchases (recent & lifetime average)
• CLASSES → Number of different product classes purchased
• STYLES → Number of individual items purchased
• STORES → Number of stores shopped at
• MARKDOWN, COUPONS, MAILED, RESPONDE, RESPONSERATE → Promotion and discount engagement
• HI → Product uniformity (lower = more diverse purchases)
• CLUSTYPE → Lifestyle cluster type (encrypted)
• PERCRET → Percent of returns
• CC_CARD, VALPHON, WEB → Flags for credit card, valid phone number, and web shopper status

🛍 Clothing Category Spend Variables

Variables: PSWEATERS, PKNIT_TOPS, PKNIT_DRES, PBLOUSES,PJACKETS, PCAR_PNTS, PCAS_PNTS, PSHIRTS, PDRESSES, PSUITS, POUTERWEAR, PJEWELRY, PFASHION, PLEGWEAR, PCOLLSPND; AC_CALC20

Percentages of spend across 15 clothing/product categories:

sweaters, knit tops, knit dresses, blouses, jackets, career pants, casual pants, shirts, dresses, suits, outerwear, jewelry, fashion, legwear, collectibles

🎯 Target Variable

• RESP → Binary variable indicating whether the customer responded to a mailed promotion.

Credits: https://iscap.pt/~aazevedo/variaveis.html

About this dataset

Context

The dataset tabulates the population of Mobile by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Mobile across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.78% of total population being female. 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.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Mobile is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Mobile 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 Mobile Population by Gender. You can refer the same here

The Impact of COVID-19 on Veterans in America

County-Level Data on Veteran Cases and Proportion of Population

About this dataset

Welcome to the Kaggle dataset on The Impact of COVID-19 on Veterans in the United States! This dataset contains data on confirmed cases of COVID-19 in counties across the United States, as well as information on the percentage of each county's population that are veterans. With this dataset, you can investigate how the pandemic has impacted veterans specifically, and compare veteran case rates to the general population. How do veteran cases differ across age groups? Are there any geographical patterns? What can we learn about risk factors for COVID-19 among veterans? Download the dataset and explore for yourself today!

How to use the dataset

This dataset includes information on the number of confirmed cases of COVID-19 by county, as well as the percentage of the population in each county that are veterans. This data can be used to examine the relationship between veteran cases and the proportion of population who are veterans.

To do this, simply look at the 'CASES' and 'VET_CASES' columns for each county. The 'CASES' column represents the total number of confirmed cases of COVID-19 in that county, while the 'VET_CASES' column represents the number of confirmed cases among veterans. To compare these two values, simply divide 'VET_CASES' by 'CASES'. This will give you a ratio of veteran cases to total cases for each county.

You can then use this ratio to compare counties and see which ones have a higher proportion of veteran cases. This data can be used to help understand where more outreach may be needed to support veterans during this pandemic

Research Ideas

• Find the correlation between the number of veterans in a county and the number of confirmed cases of COVID-19.
• Find the counties with the highest percentage of veterans and the lowest number of confirmed cases of COVID-19.
• Predict how many veterans in a county will contract COVID-19 based on the percentage of veterans in the population

Columns

File: CountyVACOVID.csv | Column name | Description | |:---------------------------|:-----------------------------------------------------------------------------------------------------------------------| | FIPS | Federal Information Processing Standards code that uniquely identifies counties within the USA. (String) | | COUNTY | County name. (String) | | STATE | State name. (String) | | POP | County population. (Integer) | | VETS | Number of veterans in the county. (Integer) | | VET_PERCENT | Percentage of the population that are veterans. (Float) | | CASES | Number of confirmed cases of COVID-19 in the county. (Integer) | | YESTER_CASES | Number of confirmed cases of COVID-19 in the county from the previous day. (Integer) | | VET_CASES | Number of confirmed cases of COVID-19 in veterans in the county. (Integer) | | VET_YESTER | Number of confirmed cases of COVID-19 in veterans in the county from the previous day. (Integer) | | LOWER_Hospitalizations | Lower bound of the 95% confidence interval for the number of hospitalizations due to COVID-19 in the county. (Integer) | | UPPER_Hospitalizations | Upper bound of the 95% confidence interval for the number of hospitalizations due to COVID-19 in the county. (Integer) | | DATE | Date of data. (Date) |

File: VAChart.csv | Column name | Description | |:------------------------|:----------------------------------------------------------------------------------| | DATE | Date of data. (Date) | | US Cases | The number of confirmed cases of COVID-19 in the United States. (Integer) | | **New US ...

In 2020, the Philippines’ mobile internet user penetration was nearly 72.1 percent. It was forecasted that by 2025, 77.1 percent of mobile phone users would access the internet via their mobile phones resulting in increasing mobile subscribers.

Mobile phone penetration in the Philippines
As of 2019, there were nearly 169 million mobile subscribers in the Philippines. Mobile phones have been a significant device for many Filipinos. Telecommunication operators provide mobile data plans for many mobile phone subscribers and for which mobile phone vendors have gained profit. Of the mobile phones available in the market, Samsung was the leading mobile vendor, accounting for almost 24 percent of the market share.
Slow adaption to mobile phones with latest cellular network technology in the Philippines Throughout the years, the market saw an influx of mobile devices providing more sophisticated applications with the latest cellular network technology. While many Asian countries were quick to adapt to these developments, like the fourth generation (4G) broadband cellular network technology, the Philippines was slow to adapt. This was mainly due to the slow implementation of the necessary infrastructure needed by internet service providers. As of 2018, the Philippines had the lowest share of 4G mobile connections among other countries in the Asia Pacific region.

International Panel Data Analysis of the Effect of Digitalization on Economic Growth

Description

The effect of digitalization on economic growth is examined with reference to a cobb-Douglas production function. So, the dependent variable is the economic growth measured by the Gross Domestic Product per capita measured at 2015 constant US dollars. To reproduce the digitalization, we consider four indicators which are : 1. Number of fixe subscriptions (per 100 people) 2. Number of mobile cellular subscriptions (per 100 people) 3. Number of broadband subscriptions (per 100 people) 4. Number of individuals using the internet (%of population) 5. Digitalization level as obtained by applying a PCA Moreover, we include several macro-economic variables as control variables which affect the relationship between Digitalization and economic growth: 6. Investment measured by gross fixed capital formation (as percentage of GDP). 7. Trade openness which is a country’s trade volume used as a proxy for the degree of openness of a country’s economy (as percentage of GDP) and which is measured as the sum of imports and exports. 8. Labor force which is the total of labor force participation rate. 9. Inflation is measured by the consumer price index (%). 10. Population 11. Consumption is the government consumption expenditure for goods and services (as a percentage of GDP).

Categories

Finance, Economic Growth, Information and Communication Technologies, Emerging Country, Developing Countries

Acknowledgements & Source

Abderrazek ELKHALDI,Nadia Sghaier,Monia Chikhaoui

Data Source: https://data.mendeley.com/datasets/ctm7vvpp7n/1

Coverage of Social Insurance Programs in Richest Quintile

Percent of Population Eligible

By data.world's Admin [source]

About this dataset

This dataset offers a unique insight into the coverage of social insurance programs for the wealthiest quintile of populations around the world. It reveals how many individuals in each country are receiving support from old age contributory pensions, disability benefits, and social security and health insurance benefits such as occupational injury benefits, paid sick leave, maternity leave, and more. This data provides an invaluable resource to understand the health and well-being of those most financially privileged in society – often having greater impact on decision making than other groups. With up-to-date figures from 2019-05-11 this dataset is invaluable in uncovering where there is work to be done for improved healthcare provision in each country across the world

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Understand the context: Before you begin analyzing this dataset, it is important to understand the information that it provides. Take some time to read the description of what is included in the dataset, including a clear understanding of the definitions and scope of coverage provided with each data point.

• Examine the data: Once you have a general understanding of this dataset's contents, take some time to explore its contents in more depth. What specific questions does this dataset help answer? What kind of insights does it provide? Are there any missing pieces?

• Clean & Prepare Data: After you've preliminarily examined its content, start preparing your data for further analysis and visualization. Clean up any formatting issues or irregularities present in your data set by correcting typos and eliminating unnecessary rows or columns before working with your chosen programming language (I prefer R for data manipulation tasks). Additionally, consider performing necessary transformations such as sorting or averaging values if appropriate for the findings you wish to draw from your analysis.

• Visualize Results: Once you've cleaned and prepared your data, use visualizations such as charts, graphs or tables to reveal patterns within it that support specific conclusions about how insurance coverage under social programs vary among different groups within society's quintiles - based on age groups etc.. This type of visualization allows those who aren't familiar with programming to process complex information quickly and accurately than when displayed numerically in tabular form only!

5 Final Analysis & Export Results: Finally export your visuals into presentation-ready formats (e.g., PDFs) which can be shared with colleagues! Additionally use these results as part of a narrative conclusion report providing an accurate assessment and meaningful interpretation about how social insurance programs vary between different members within society's quintiles (i..e., accordingest vs poorest), along with potential policy implications relevant for implementing effective strategies that improve access accordingly!

Research Ideas

• Analyzing the effectiveness of social insurance programs by comparing the coverage levels across different geographic areas or socio-economic groups;
• Estimating the economic impact of social insurance programs on local and national economies by tracking spending levels and revenues generated;
• Identifying potential problems with access to social insurance benefits, such as racial or gender disparities in benefit coverage

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: coverage-of-social-insurance-programs-in-richest-quintile-of-population-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

This dataset represents the counts of those individuals who have been determined to have a share of cost (SOC) obligation, which is the monthly amount of medical expenses they must incur before they are eligible to receive Medi-Cal benefits. The dataset includes individuals who have a met or unmet monthly SOC obligation. Individuals who have not met their monthly SOC obligation are not eligible for Medi-Cal. SOC obligations are calculated during the eligibility determination process based on household income.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for France is 1000.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

This measure represents the percentage of identified as having visual & communicative impairments who were competitively employed after receiving services from Iowa Vocational Rehabilitation Services.

Mortality Rates for Lake County, Illinois. Explanation of field attributes: Average Age of Death – The average age at which a people in the given zip code die. Cancer Deaths – Cancer deaths refers to individuals who have died of cancer as the underlying cause. This is a rate per 100,000. Heart Disease Related Deaths – Heart Disease Related Deaths refers to individuals who have died of heart disease as the underlying cause. This is a rate per 100,000. COPD Related Deaths – COPD Related Deaths refers to individuals who have died of chronic obstructive pulmonary disease (COPD) as the underlying cause. This is a rate per 100,000.

This dataset captures simulated student interaction data within a virtual learning environment (VLE), focusing on behavioral indicators related to academic engagement. It includes a variety of features that reflect how students participate in online courses, such as time spent on the platform, quiz scores, forum activity, and content completion.

Each entry is labeled with an engagement level—Low, Medium, or High—based on aggregated interaction metrics. The dataset supports exploratory analysis and the development of data-driven strategies to understand and improve student engagement in virtual settings.

🔑 Key Features: Time Spent Weekly: Average number of minutes a student spends on the platform.

Quiz Score Average: Mean score across online assessments.

Forum Posts: Number of contributions to discussion forums.

Video Watched Percent: Percentage of course video content completed.

Assignments Submitted: Count of assignments submitted on time.

Login Frequency: Number of logins per week.

Session Duration Average: Average duration per platform session.

Device Type: Platform used to access the content (e.g., Desktop, Mobile).

Course Difficulty: Self-reported or platform-defined difficulty of enrolled courses.

Region: Geographic classification (Urban, Suburban, Rural).

Engagement Level: Categorical label indicating Low, Medium, or High engagement.

This dataset can assist educators, researchers, and learning platform designers in understanding key behavioral patterns that influence student participation and success in online learning environments.

CDC COVID-19 Vaccine Tracker

Cumulative and Daily Counts of COVID-19 Vaccine Doses in the United States

By Nicky Forster [source]

About this dataset

The dataset contains data points such as the cumulative count of people who have received at least one dose of the vaccine, new doses administered on a specific date, cumulative count of doses distributed in the country, percentage of population that has completed the full vaccine series, cumulative count of Pfizer and Moderna vaccine doses administered in each state, seven-day rolling averages for new doses administered and distributed, among others.

It also provides insights into the vaccination status at both national and state levels. The dataset includes information on the percentage of population that has received at least one dose of the vaccine, percentage of population that has completed the full vaccine series, cumulative counts per 100k population for both distributed and administered doses.

Additionally, it presents data specific to each state, including their abbreviation and name. It outlines details such as cumulative counts per 100k population for both distributed and administered doses in each state. Furthermore, it indicates if there were instances where corrections resulted in single-day negative counts.

The dataset is compiled from daily snapshots obtained from CDC's COVID Data Tracker. Please note that there may be reporting delays by healthcare providers up to 72 hours after administering a dose.

This comprehensive dataset serves various purposes including tracking vaccination progress over time across different locations within the United States. It can be used by researchers, policymakers or anyone interested in analyzing trends related to COVID-19 vaccination efforts at both national and state levels

How to use the dataset

• Familiarize Yourself with the Columns: Take a look at the available columns in this dataset to understand what information is included. These columns provide details such as state abbreviations, state names, dates of data snapshots, cumulative counts of doses distributed and administered, people who have received at least one dose or completed the vaccine series, percentages of population coverage, manufacturer-specific data, and seven-day rolling averages.

• Explore Cumulative Counts: The dataset includes cumulative counts that show the total number of doses distributed or administered over time. You can analyze these numbers to track trends in vaccination progress in different states or regions.

• Analyze Daily Counts: The dataset also provides daily counts of new vaccine doses distributed and administered on specific dates. By examining these numbers, you can gain insights into vaccination rates on a day-to-day basis.

• Study Population Coverage Metrics: Metrics such as pct_population_received_at_least_one_dose and pct_population_series_complete give you an understanding of how much of each state's population has received at least one dose or completed their vaccine series respectively.

• Utilize Manufacturer Data: The columns related to Pfizer and Moderna provide information about the number of doses administered for each manufacturer separately. By analyzing this data, you can compare vaccination rates between different vaccines.

• Consider Rolling Averages: The seven-day rolling average columns allow you to smooth out fluctuations in daily counts by calculating an average over a week's time window. This can help identify long-term trends more accurately.

• Compare States: You can compare vaccination progress between different states by filtering the dataset based on state names or abbreviations. This way, you can observe variations in distribution and administration rates among different regions.

• Visualize the Data: Creating charts and graphs will help you visualize the data more effectively. Plotting trends over time or comparing different metrics for various states can provide powerful visual representations of vaccination progress.

• Stay Informed: Keep in mind that this dataset is continuously updated as new data becomes available. Make sure to check for any updates or refreshed datasets to obtain the most recent information on COVID-19 vaccine distributions and administrations

Research Ideas

• Vaccination Analysis: This dataset can be used to analyze the progress of COVID-19 vaccinations in the United States. By examining the cumulative counts of doses distributed and administered, as well as the number of people who have received at least one dose or completed the vaccine series, researchers and policymakers can assess how effectively vaccines are being rolled out and monitor...

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