The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users 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).

About this dataset

Context

The dataset tabulates the Mobile population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Mobile. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 113,805 (61.48% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Mobile population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Mobile is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Mobile is shown in the following column.

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

The College Experience Study represents the most extensive longitudinal mobile sensing study to date, leveraging passive and automatic sensing data from the smartphones of over 200 Dartmouth students across five years (2017 - 2022). This groundbreaking research aimed to assess their mental health (e.g., depression, stress), the impact of COVID-19, and general behavioral trends.

The study's importance has been magnified during the global pandemic, necessitating a better understanding of mental health dynamics among undergraduate students throughout their college years. By tracking two cohorts of first-year students both on and off campus, we have accumulated a rich dataset offering insights into changing behaviors, resilience, and mental health in college life. We hope that this dataset will serve as a cornerstone for researchers, educators, and policymakers alike, seeking to enhance their understanding and interventions related to student mental health and behavior.

This dataset is unique for several reasons. It encompasses deep phone sensing data and self-reports spanning four continuous years for 200 undergraduate students at Dartmouth College, both during term time and breaks. Additionally, it incorporates periodic brain-imaging data for this cohort of students, along with surveys. The College Experience dataset enables researchers to explore numerous issues in behavioral sensing and brain imaging to advance our understanding of college students' mental health.

:rocket: Updates

• Apr 15th 2025: Raw app usage (i.e., list of running apps) is now available.
• Dec 5th 2024: Raw call logs, sms logs, and unlocks are now available.
• Oct 27th 2024: Raw sensing data will be released in batches over the next few weeks!

Content

College Experience Study makes use of the StudentLife app, developed for Android and iOS, autonomously capturing a variety of human behaviors 24/7, including:

• Bed time, wake up time, and sleep duration
• The number of conversations and the duration of each conversation per day (Android only)
• Physical activity (walking, sitting, running, standing)
• Locations visited and duration of stay (e.g., dorm, class, party, gym)
• Stress levels over weeks and throughout college
• App usage (Android only)
• COVID concern
• and more

In addition to passive sensing data, our study also involved gathering responses from detailed surveys and conducting brain scans throughout the research period. These diverse data sources can be used together to uncover insightful correlations and draw meaningful conclusions. An illustrative example of this potential is explored in the study "Predicting Brain Functional Connectivity Using Mobile Sensing", which demonstrates how mobile sensing data can predict brain functional connectivity, offering new avenues for understanding mental health conditions.

Data Availability

Note: Some features are exclusive to Android phones. Each folder includes a data definition file detailing the features and their availability across Android and iOS. Also, note that some features like conversation tracking initially covered both user groups but were later restricted due to iOS policy changes so they might be available for iOS users only during the beginning of the study.

For more details, refer to the College Experience Study paper and the original StudentLife website.

Term Definitions and Academic Calendars

For additional context and understanding of the timeline relevant to the dataset, below are the archived links to Dartmouth College's calendars. These archives provide an overview and detailed breakdown of significant dates for each academic year covered by the study:

Percentage of smartphone users by selected smartphone use habits in a typical day.

Between 2019 and 2021, we collected detailed administrative data of grade 9 students from 54 lower-secondary schools in rural Cambodia. We also collected phone-survey data from these students in July and August 2020, to understand the implications of the nation-wide lockdown, that was implemented to curb the spread of COVID-19 in March 2020. The administrative data contain information on students' grades, school characteristics and teacher characteristics from the school year 2019-2020, as well as information about the students' enrollment in high school after the end of the school year (January 2021). This information is available for 3,258 students. The phone-survey data contains information on students' socio-economic background, parental education and occupation, as well as students' study behavior, time use, educational aspirations and expectations, and perceptions of the COVID-19 crisis. This information is available for 2,197 students.

About this dataset

Context

The dataset tabulates the Mobile City population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Mobile City. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was Under 18 years with a poulation of 157 (49.68% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Mobile City population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Mobile City is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Mobile City is shown in the following column.

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 Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Mobile County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Mobile County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 246,378 (59.63% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Mobile County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Mobile County is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Mobile County is shown in the following column.

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 County Population by Age. You can refer the same here

Alexithymia and mobile phone addiction are common phenomena in daily life. Many studies have explored the internal relationship between them based on different theoretical perspectives, but the extent of the exact correlation is still controversial. To address this controversy and clarify the reasons for the divergence, a meta-analysis of 26 articles comprising 23,387 Chinese students was conducted. The results show that alexithymia was highly positively correlated with mobile phone addiction (r = 0.41, 95% CI = [0.37, 0.45]). Furthermore, the relationship was moderated by mobile phone addiction measurement tool and year of publication, with studies using the Mobile Phone Addiction Tendency Scale (MPATS) having higher correlation coefficients than those using the Mobile Phone Addiction Index (MPAI) or other measurement tools. Studies published in 2020–2021 yielded higher correlations than those published in 2014–2016 and 2017–2019. However, the relationship was not moderated by gender, region, or measures of alexithymia. Therefore, our meta-analysis of available published data indicated that alexithymia and mobile phone addiction in Chinese students are not only highly positively correlated but also affected by mobile phone addiction measurement tools and publication year. Longitudinal studies or experimental studies should be strengthened in the future to further establish the direction(s) of causality for the relation between alexithymia and mobile phone addiction.

Historical Dataset of Mobile Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1987-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1987-2023),American Indian Student Percentage Comparison Over Years (2010-2023),Hispanic Student Percentage Comparison Over Years (1989-2023),Black Student Percentage Comparison Over Years (1989-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2019-2020),Diversity Score Comparison Over Years (1989-2023),Free Lunch Eligibility Comparison Over Years (2007-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2009-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Overall School Rank Trends Over Years (2012-2023)

This domain covers statistics and indicators on key aspects of the education systems across Europe. The data show entrants and enrolments in education levels, education personnel and the cost and type of resources dedicated to education.

For a general technical description of the UOE Data Collection see UNESCO OECD Eurostat (UOE) joint data collection – methodology - Statistics Explained (europa.eu).

The standards on international statistics on education and training systems are set by the three international organisations jointly administering the annual UOE data collection:

• The United Nations Educational, Scientific, and Cultural Organisation Institute for Statistics (UNESCO-UIS),
• The Organisation for Economic Co-operation and Development (OECD) and,
• The Statistical Office of the European Union (EUROSTAT).

The following topics are covered:

• Pupils and students – Enrolments and Entrants,
• Learning mobility,
• Education personnel,
• Education finance,
• Graduates,
• Language learning.

Data on enrolments in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Age or age group,
• NUTS1 and NUTS2 regions,
• Type of educational institution (public or private) – referred to as the ‘sector’ in Eurobase,
• Intensity of participation (full-time, part-time, full-time equivalent) – referred to as ‘working time’ in Eurobase,
• Programme orientation (general/academic or vocational/professional),
• Type of vocational programme (school-based only or combined school and work-based),
• Level of attainment that can be achieved upon programme completion (e.g. insufficient for level completion or partial level completion, sufficient for partial level completion without direct access to tertiary education),
• Field of education (ISCED-F13).

Additionally, the following types of indicators on enrolments are calculated (all indicators using population data use Eurostat’s population database (demo_pjan)):

• Participation rates by age or by age groups as % of corresponding age population.
• Participation rates by age as % of total population.
• Pupils from age 0, 3, 4 and 5 to the starting age of compulsory education at primary level, as % of the population of the corresponding age. In some countries, the start of primary education is not compulsory and in some countries compulsory education starts at pre-primary level. This indicator calculates the participation rates of pupils up until (but not including) the starting age of formal education that is both compulsory and at the primary level. This age varies from 5 years to 7 years across countries and the national starting ages for compulsory primary education used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
• Pupils under the age of 3 as % of corresponding age population. This indicator does not include 3 year olds (includes ages 0, 1 and 2).
• Out-of-school rates at different ages. This indicator is calculated as 100 – (students of a particular age who are enrolled in education at any ISCED level / Total population of that age *100).
  • Out-of-school rates in population of lower secondary school age and in population of upper secondary school age. This indicator is calculated as 100 – (students who are of the official age range for ISCED X who are enrolled in education at any ISCED level / Total population in the official age range for ISCED X *100). The official age range for each ISCED level varies across countries, and national age ranges for lower and upper secondary used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
  • Students in education of post-compulsory school age - as % of the total population of post-compulsory school age. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students participation at the end of compulsory education - as % of the corresponding age population. Indicator is calculated for age (X-1), (X), (X+1), (X+2) where X = the final age at which formal education is compulsory in national education systems. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students in education aged 30 and over - per 1000 of corresponding age population
    • Expected school years of pupils and students at different levels of education
    • Distribution of pupils and students enrolled in general and vocational programmes by education level and NUTS2 regions
    • Distribution of students in different fields of education
    • Ratio of the proportion of the population who are tertiary students in NUTS1 regions to the proportion of the population who are tertiary students in NUTS2 regions

Data on entrants in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Programme orientation (general/academic or vocational/professional),
• Sex,
• Age or age group,
• Field of education (ISCED-F13).

Additionally the following indicator on entrants is calculated:

• Distribution of new entrants in different fields of education.

Data on learning mobility is available for degree mobile students, degree mobile graduates and credit mobile graduates. Degree mobility means that students/graduates are/were enrolled as regular students in any semester/term of a programme taught in the country of destination with the intention of graduating from it in the country of destination. Credit mobility is defined as temporary tertiary education or/and study-related traineeship abroad within the framework of enrolment in a tertiary education programme at a "home institution" (usually) for the purpose of gaining academic credit (i.e. credit that will be recognised in that home institution). Further definitions are in Section 2.8 of the UOE manual.

Degree mobile students are referred to as just ‘mobile students’ in UOE learning mobility tables. Data is disseminated for degree mobile students and degree mobile graduates in absolute numbers with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Field of education (ISCED-F13),
• Country of origin (defined as the country of education prior to entering tertiary although there may be national deviations. These are listed in the Helpsheet of the latest footnotes report available to download in the Annexes section of this page) – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Additionally the following types of indicators on degree mobile students and degree mobile graduates are calculated ((all indicators using population data use Eurostat’s population database (demo_pjan)):

• Share of all students/graduates who are mobile students/degree mobile graduates from abroad,
• Distribution of mobile students/degree mobile graduates from abroad in different fields of education.

For credit mobile graduates, data are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Type of mobility scheme (e.g. Credit mobility under EU programmes i.e. ERASMUS, Credit mobility in other international/national programmes),
• Type of mobility (study period only or study period combined with work placement),
• Country of destination – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Data on personnel in education are available for classroom teachers/academic staff, teacher aides and school-management personnel. Teachers are employed in a professional capacity to guide and direct the learning experiences of students, irrespective of their training, qualifications or delivery mechanism. Teacher aides support teachers in providing instruction to students. Academic staff are personnel employed at the tertiary level of education whose primary assignment is instruction and/or research. School management personnel covers professional personnel who are responsible for school management/administration (ISCED 0-4) or whose primary or major responsibility is the management of the institution, or a recognised department or subdivision of the institution (tertiary levels). Full definitions of these statistical units are in Section 3.5 of the UOE manual.

Data are disseminated on teachers and academic staff in absolute numbers, with breakdowns available for the following dimensions:

• ISCED