The main content of this dataset includes the gender ratio of overseas Chinese students graduating from college in various years.

This repository contains datasets and analysis code accompanying the paper "An Empirical Evaluation of Chinese College Admissions Reforms Through A Natural Experiment" by Chen, Jiang, and Kesten. The datasets contain the college admission data for a county in China's Sichuan Province for year 2008 and 2009. These include students' submitted rank-ordered lists of colleges and admission results. All variables are recoded to remove any identifiable information (including college and high school code). The analysis code can be used to replicate the tables and figures in the paper.

This is a dataset for time series analysis. Task list: What is the predicted amount of Chinese college entrance examination admissions in 2038?

Note: The Ministry of Education has already given guidance on the gross enrollment rate, and it will reach 65% in 2035.

Context

More international students are flocking to China than ever before. According to a report, over 540,000 foreigners studied in China in 2018 – marking a 40 percent increase from 2012. China attracts more international students than any other Asian power and ranks third globally, behind the United States and the United Kingdom.

Content

In 2018 there were a total of 492,185 international students from 196 countries/areas pursuing their studies in 1,004 higher education institutions in China’s 31 provinces/autonomous regions/provincial-level municipalities, marking an increase of 3,013 students or 0.62% compared to 2017. International students in Hong Kong, Macau and Taiwan are not included in the datasets. The datasets contain three CSV files (Continent, Country, Province) with different data about international students in China.

Columns Description

@Continent (Number/percent of international students by continent) Continent- The name of continent Number - The number of total international students Deaths- The percentage of total international students

@Country (Number of international students by country of origin) Rank- The rank of the country based on total students in China Country- The name of the country Number- The number of total international students

@Province (The top provinces/cities with the largest number of international students) Province- The name of the city/province Number- The number of total international students

Acknowledgements

This data collected from moe.gov.cn.

Inspiration

Currently, I'm studying at a Chinese university. Every year many international students come to China for their higher study, and the ratio of international students is growing steadily. This data will help us to understand the ratio of international students in China.

This dataset is about universities in China. It has 357 rows. It features 5 columns: city, foundation year, undergraduate students, and graduate students.

BackgroundAbout 1 million people worldwide commit suicide each year, and college students with suicidal ideation are at high risk of suicide. The prevalence of suicidal ideation in college students has been estimated extensively, but quantitative syntheses of overall prevalence are scarce, especially in China. Accurate estimates of prevalence are important for making public policy. In this paper, we aimed to determine the prevalence of suicidal ideation in Chinese college students.Objective and MethodsDatabases including PubMed, Web of Knowledge, Chinese Web of Knowledge, Wangfang (Chinese database) and Weipu (Chinese database) were systematically reviewed to identify articles published between 2004 to July 2013, in either English or Chinese, reporting prevalence estimates of suicidal ideation among Chinese college students. The strategy also included a secondary search of reference lists of records retrieved from databases. Then the prevalence estimates were summarized using a random effects model. The effects of moderator variables on the prevalence estimates were assessed using a meta-regression model.ResultsA total of 41 studies involving 160339 college students were identified, and the prevalence ranged from 1.24% to 26.00%. The overall pooled prevalence of suicidal ideation among Chinese college students was 10.72% (95%CI: 8.41% to 13.28%). We noted substantial heterogeneity in prevalence estimates. Subgroup analyses showed that prevalence of suicidal ideation in females is higher than in males.ConclusionsThe prevalence of suicidal ideation in Chinese college students is relatively high, although the suicide rate is lower compared with the entire society, suggesting the need for local surveys to inform the development of health services for college students.

This study is about the perspectives of stakeholders and students in their activities and their rationales for three case residential colleges in mainland China. It focuses on how the participants have built their perspectives and understanding of residential colleges and the theoretical integration of the traditions. The dataset contains several files within each of which interview materials, original recording documents, and research reports by each case, Each document is named as focus group 1-N or P (principal) 1-N or C (Counselor) 1-N to indicate the category of the data. An Excel form is provided in Chinese to describe the information of my participants. Since the dataset contains some very sensitive information about participants, it is embargoed.

BackgroundAs the pace of economic development slows, college students are facing an increasingly challenging employment landscape. For instance, the expansion of higher education has led to a swell in the number of job seekers, which has in turn intensified competition. Given the limited job opportunities, it’s understandable that many college students are developing a pessimistic employment mindset. Therefore, it’s crucial to explore how objective factors influence their work aspirations. But few studies have explored the role of mediating factors between the two, such as family and resource factors. Thus, this study examines the effects of family support and work resources between the relationship between economic constraints and work volition.MethodsThe study examined 1249 Chinese undergraduate students as participants ((714 men and 535 women; Mage = 19.32, SD = 1.50), using the questionnaire with the Wenjuanxing online survey tool. The questionnaire were collected between August, 2022 and December, 2022. SPSS 21.0 and AMOS 24.0 were used to conducted a comprehensive analysis of the collected data, and investigate the relationships among latent variables and assess the goodness of fit of the observed indicators on their associated latent constructs. Additionally, we evaluated all the hypothesized direct and indirect effects.ResultsThe results showed the direct and indirect relationships among economic constraints, family support, work resources and work volition. Economic constraints can directly predict work volition. Moreover, economic constraints have a significant negative impact on work volition via two mediators: family support and work resources. On the one hand, economic constraints negatively affect work volition through family support and work resource separately. On the other hand, economic constraints negatively predict family support and work resource, thus negatively impact work volition.ContributionThe current study has established the independent mediating and chain-mediated effects of family support and work resources on the relationship between economic constraints and work volition. This deeper understanding of internal mechanisms provides valuable insights that can inform strategies for enhancing individual’s work volition, particularly from the perspectives of economic constraints, family support, and work volition.

Required qualitative dataset for Ph.D. thesis submission: · The dataset consists of forty-one respondents who participated in in-depth semi-structured interviews. · Respondents were recruited from eight educational institutions: three 4-year public universities, two 3-year public technical colleges, two 4-year private colleges (independent second-tier schools), and one 3-year private vocational college. · In detail, twenty-two respondents were female, and four were from minority ethnic groups. · All interviews were audio-recorded and later transcribed. · Interviews were conducted in Chinese, and Word files ( .docx) transcripts were imported into NVivo 11 Plus (Windows) to create nodes (two-layer coding), memos, and thematic maps in NVivo format through reflexive thematic analysis.

This research examines the relationship between physical exercise and subjective well-being via the mediation of body image and self-esteem, thereby providing some suggestions on the improvement of subjective well-being in college students. A total of 671 college students from three universities of science and engineering in Sichuan, China voluntarily participated in the survey. Descriptive statistics, Pearson’s product-moment correlation, and mediation model analysis were conducted using the SPSS statistics 19.0. The results showed that (1) the physical exercise level was positively and significantly correlated with the subjective well-being level in each dimension (R = 0.12–0.64, p < 0.01) (2) college students with the medium and high level of exercise have higher subjective well-being than those with the low level of exercise, and (3) body image and self-esteem played a complete mediation role between physical exercise and subjective well-being. The mediation analysis revealed two paths: first, the single mediating path via self-esteem [indirect effect = 0.087, 95% CI: (0.037, 0.141)] and second, the serial mediating path via body image and self-esteem [indirect effect = 0.038, 95% CI: (0.021, 0.158)]. Some practical implications have been discussed on the physical exercise intervention for promoting the subjective well-being level in college students.

Context

Amidst the COVID-19 outbreak, the world is facing great crisis in every way. The value and things we built as a human race are going through tremendous challenges. It is a very small effort to bring curated data set on Novel Corona Virus to accelerate the forecasting and analytical experiments to cope up with this critical situation. It will help to visualize the country level out break and to keep track on regularly added new incidents.

COVID-19 Country Level Timeseries Dataset

This Dataset contains country wise public domain time series information on COVID-19 outbreak. The Data is sorted alphabetically on Country name and Date of Observation.

Column Descriptions

The data set contains the following columns:
ObservationDate: The date on which the incidents are observed country: Country of the Outbreak Confirmed: Number of confirmed cases till observation date Deaths: Number of death cases till observation date Recovered: Number of recovered cases till observation date New Confirmed: Number of new confirmed cases on observation date New Deaths: Number of New death cases on observation date New Recovered: Number of New recovered cases on observation date latitude: Latitude of the affected country longitude: Longitude of the affected country

Acknowledgements

This data set is a cleaner version of the https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset data set with added geo location information and regularly added incident counts. I would like to thank this great effort by SRK.

Original Data Source

Johns Hopkins University MoBS lab - https://www.mobs-lab.org/2019ncov.html World Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19 Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

Provide the number of students from ASEAN, South Asia, Australia, and New Zealand studying/doing research in Taiwan's universities and colleges for each academic year.

ObjectiveResearch to date has not provided a clear understanding of how different grades and majors affect the physical fitness of college students. It is postulated that there are significant disparities in physical health among college students of different grades and majors. The purpose of this study was to evidence these health disparities and to engage in an extensive analysis and discussion thereof.MethodsA sample of 8,772 (2,404 boys and 6,368 girls) Chinese college students from freshman to junior years, aged 17–22, including 12 different majors in four colleges, were recruited in Jiangxi Province. All seven physical fitness indicators (body mass index (BMI), forced vital capacity, 50-m dash, standing long jump, sit and reach, upper body muscle strength, and endurance runs) were conducted for all participants. One-way ANOVA and LSD tests were conducted to compare the physical fitness scores of different grades in the same major. Independent sample t-tests were utilized to compare the differences in every physical fitness indicator for different majors. Pearson’s correlations among 12 majors for every grade were conducted to study the significant corrections between the two physical fitness indicators. The body mass index (BMI) and physical fitness indicator (PFI) for college students of different grade were investigated using a nonlinear regression model.ResultsThe current state of physical fitness among college students is concerning, as the majority of students were barely passing (with a passing rate of 75.3%). Specifically, junior students exhibited lower scores than freshman and sophomore students across all 12 majors. From freshman to junior year, majors of music (78.01±4.58), English (79.29±5.03), and education (76.26±4.81) had the highest scores, respectively, but major art consistently scored the lowest, which were 73.85±6.02, 74.97±5.53, and 72.59±4.84, respectively. Pairwise comparisons revealed more significant differences in individual physical fitness indicators among the three grades in humanities than in sciences. Pearson’s correlations showed significant correlations among seven physical fitness indicators in all three grades. PFI had a parabolic trend with BMI both for boy and girl college students in Jiangxi province.ConclusionThe physical fitness indicators of college students in Jiangxi province significantly differed in grades and majors, showing undesirable phenomena. The physical fitness of senior and humanities major college students was much weaker and needs sufficient attention. The relationship between BMI and PFI presented an inverted “U”-shaped change characteristic. Continued nationwide interventions are needed to promote physical activity and other healthy lifestyle behaviors in China.

This dataset tracks annual asian student percentage from 2003 to 2023 for Early College At Guilford vs. North Carolina and Guilford County Schools School District

This dataset tracks annual asian student percentage from 2019 to 2023 for College Place High School vs. Washington and College Place School District

This dataset tracks annual asian student percentage from 1991 to 2023 for State College Area High School vs. Pennsylvania and State College Area School District

About this dataset

Context

The dataset presents the median household income across different racial categories in College Station. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of College Station population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 67% of the total residents in College Station. Notably, the median household income for White households is $58,494. Interestingly, despite the White population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $67,348. This reveals that, while Whites may be the most numerous in College Station, Asian households experience greater economic prosperity in terms of median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in College Station.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 College Station median household income by race. You can refer the same here

This dataset tracks annual asian student percentage from 2009 to 2023 for Randolph Early College High School vs. North Carolina and Randolph County School System School District