About this dataset

Context

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

Key observations

The largest age group in College Park, GA was for the group of age 10 to 14 years years with a population of 1,309 (9.37%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in College Park, GA was the 85 years and over years with a population of 56 (0.40%). 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 College Park is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of College Park 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 College Park Population by Age. You can refer the same here

The study assessed the knowledge, attitude and practice of undergraduate students regarding family planning methods. A descriptive quantitative design was used. The population was undergraduate students, and the sample size was four hundred (400) students. The study was conducted in a selected institution of higher learning in the Tshwane district of the Gauteng Province. A questionnaire was used to collect data, and descriptive statistics analysis was used to analyze the data. The data collected were entered into Microsoft Office 2019. The IBM SPSS Statistics version 28 was used to perform the analysis. Test for associations the Pearson Chi-square test was performed.

About this dataset

Context

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

Key observations

The largest age group in State College, PA was for the group of age 20 to 24 years years with a population of 15,146 (37.24%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in State College, PA was the 80 to 84 years years with a population of 284 (0.70%). 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 State College is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of State College 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 State College Population by Age. You can refer the same here

The 2012 Beginning Postsecondary Students Longitudinal Study, 2012 Base Year (BPS:12) is part of the Beginning Postsecondary Students Longitudinal Study (BPS) program. BPS:12 is the base year of a longitudinal study that followed a cohort of students who enrolled in postsecondary education for the first time during the 2011-12 academic year, irrespective of date of high school completion. The 2012-13 National Postsecondary Student Aid Study (NPSAS:12) data provided the base-year sample for BPS:12. BPS:12 data are representative of all first-time beginning (FTB) students enrolled in postsecondary institutions in the 50 United States and the District of Columbia that were eligible to participate in the federal financial aid programs in Title IV of the Higher Education Act. The cohort includes a subset of students initially sampled for participation in NPSAS:12 and classified by their NPSAS institution as FTBs. Key statistics produced from BPS:12 are data on student persistence in, and completion of, postsecondary education programs; their transition to employment; demographic characteristics; and changes over time in their goals, marital status, income, and debt, among other measures.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

The National Postsecondary Student Aid Study, 1992-93 (NPSAS:93), is a study that is part of the National Postsecondary Student Aid Study (NPSAS) program; program data is available since 1989-90 at https://nces.ed.gov/pubsearch/getpubcats.asp?sid=013. NPSAS:93 (https://nces.ed.gov/surveys/npsas/about.asp) is a cross-sectional survey that is designed to compile a comprehensive research dataset, based on student-level records, on financial aid provided by the federal government, the states, postsecondary institutions, employers, and private agencies along with student demographic and enrollment data. The study was conducted using multiple sources, including institutional records, government databases, and student interviews. NPSAS:93 contains the data on a sample of about 66,000 eligible postsecondary students who were enrolled at any time between July 1, 1992 and June 30, 1993 in about 1,100 postsecondary institutions. The data are representative of all undergraduate and graduate students enrolled in postsecondary institutions in the 50 United States, the District of Columbia, and Puerto Rico that were eligible to participate in the federal financial aid programs in Title IV of the Higher Education Act. After adjusting for institutional nonresponse and for attendance at more than one institution, the overall weighted study response rate was 85 percent. Statistics produced from the NPSAS:93 provide reliable national estimates of characteristics related to financial aid for postsecondary students.

The dataset includes data from the survey on the Gdańsk University of Technology foreign graduates socio-demographic characteristics. The research was conducted over a four-month period, from December 2019 to March 2020, using the Computer-Assisted Web Interview (CAWI). The research sample included 142 respondents. The study concerned such variables such as i.a. nationality, gender, and the faculty graduated. Summarizing, the most of the graduates came from India, Eastern Europe (Ukraine and Belarus) and China.

Abstract

The 2013 Turkey Demographic and Health Survey (TDHS-2013) is a nationally representative sample survey. The primary objective of the TDHS-2013 is to provide data on socioeconomic characteristics of households and women between ages 15-49, fertility, childhood mortality, marriage patterns, family planning, maternal and child health, nutritional status of women and children, and reproductive health. The survey obtained detailed information on these issues from a sample of women of reproductive age (15-49). The TDHS-2013 was designed to produce information in the field of demography and health that to a large extent cannot be obtained from other sources.

Specifically, the objectives of the TDHS-2013 included: - Collecting data at the national level that allows the calculation of some demographic and health indicators, particularly fertility rates and childhood mortality rates, - Obtaining information on direct and indirect factors that determine levels and trends in fertility and childhood mortality, - Measuring the level of contraceptive knowledge and practice by contraceptive method and some background characteristics, i.e., region and urban-rural residence, - Collecting data relative to maternal and child health, including immunizations, antenatal care, and postnatal care, assistance at delivery, and breastfeeding, - Measuring the nutritional status of children under five and women in the reproductive ages, - Collecting data on reproductive-age women about marriage, employment status, and social status

The TDHS-2013 information is intended to provide data to assist policy makers and administrators to evaluate existing programs and to design new strategies for improving demographic, social and health policies in Turkey. Another important purpose of the TDHS-2013 is to sustain the flow of information for the interested organizations in Turkey and abroad on the Turkish population structure in the absence of a reliable and sufficient vital registration system. Additionally, like the TDHS-2008, TDHS-2013 is accepted as a part of the Official Statistic Program.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Children under age of five

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years and women age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design and sample size for the TDHS-2013 makes it possible to perform analyses for Turkey as a whole, for urban and rural areas, and for the five demographic regions of the country (West, South, Central, North, and East). The TDHS-2013 sample is of sufficient size to allow for analysis on some of the survey topics at the level of the 12 geographical regions (NUTS 1) which were adopted at the second half of the year 2002 within the context of Turkey’s move to join the European Union.

In the selection of the TDHS-2013 sample, a weighted, multi-stage, stratified cluster sampling approach was used. Sample selection for the TDHS-2013 was undertaken in two stages. The first stage of selection included the selection of blocks as primary sampling units from each strata and this task was requested from the TURKSTAT. The frame for the block selection was prepared using information on the population sizes of settlements obtained from the 2012 Address Based Population Registration System. Settlements with a population of 10,000 and more were defined as “urban”, while settlements with populations less than 10,000 were considered “rural” for purposes of the TDHS-2013 sample design. Systematic selection was used for selecting the blocks; thus settlements were given selection probabilities proportional to their sizes. Therefore more blocks were sampled from larger settlements.

The second stage of sample selection involved the systematic selection of a fixed number of households from each block, after block lists were obtained from TURKSTAT and were updated through a field operation; namely the listing and mapping fieldwork. Twentyfive households were selected as a cluster from urban blocks, and 18 were selected as a cluster from rural blocks. The total number of households selected in TDHS-2013 is 14,490.

The total number of clusters in the TDHS-2013 was set at 642. Block level household lists, each including approximately 100 households, were provided by TURKSTAT, using the National Address Database prepared for municipalities. The block lists provided by TURKSTAT were updated during the listing and mapping activities.

All women at ages 15-49 who usually live in the selected households and/or were present in the household the night before the interview were regarded as eligible for the Women’s Questionnaire and were interviewed. All analysis in this report is based on de facto women.

Note: A more technical and detailed description of the TDHS-2013 sample design, selection and implementation is presented in Appendix B of the final report of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two main types of questionnaires were used to collect the TDHS-2013 data: the Household Questionnaire and the Individual Questionnaire for all women of reproductive age. The contents of these questionnaires were based on the DHS core questionnaire. Additions, deletions and modifications were made to the DHS model questionnaire in order to collect information particularly relevant to Turkey. Attention also was paid to ensuring the comparability of the TDHS-2013 findings with previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In the process of designing the TDHS-2013 questionnaires, national and international population and health agencies were consulted for their comments.

The questionnaires were developed in Turkish and translated into English.

Cleaning operations

TDHS-2013 questionnaires were returned to the Hacettepe University Institute of Population Studies by the fieldwork teams for data processing as soon as interviews were completed in a province. The office editing staff checked that the questionnaires for all selected households and eligible respondents were returned from the field. A total of 29 data entry staff were trained for data entry activities of the TDHS-2013. The data entry of the TDHS-2013 began in late September 2013 and was completed at the end of January 2014.

The data were entered and edited on microcomputers using the Census and Survey Processing System (CSPro) software. CSPro is designed to fulfill the census and survey data processing needs of data-producing organizations worldwide. CSPro is developed by MEASURE partners, the U.S. Bureau of the Census, ICF International’s DHS Program, and SerPro S.A. CSPro allows range, skip, and consistency errors to be detected and corrected at the data entry stage. During the data entry process, 100% verification was performed by entering each questionnaire twice using different data entry operators and comparing the entered data.

Response rate

In all, 14,490 households were selected for the TDHS-2013. At the time of the listing phase of the survey, 12,640 households were considered occupied and, thus, eligible for interview. Of the eligible households, 93 percent (11,794) households were successfully interviewed. The main reasons the field teams were unable to interview some households were because some dwelling units that had been listed were found to be vacant at the time of the interview or the household was away for an extended period.

In the interviewed 11,794 households, 10,840 women were identified as eligible for the individual interview, aged 15-49 and were present in the household on the night before the interview. Interviews were successfully completed with 9,746 of these women (90 percent). Among the eligible women not interviewed in the survey, the principal reason for nonresponse was the failure to find the women at home after repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TDHS-2013 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TDHS-2013 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall

The Pittsburgh Youth Study (PYS) is part of the larger "Program of Research on the Causes and Correlates of Delinquency" initiated by the Office of Juvenile Justice and Delinquency Prevention in 1986. PYS aims to document the development of antisocial and delinquent behavior from childhood to early adulthood, the risk factors that impinge on that development, and help seeking and service provision of boys' behavior problems. The study also focuses on boys' development of alcohol and drug use, and internalizing problems. PYS consists of three cohorts of boys who were in the first, fourth, and seventh grades in Pittsburgh, Pennsylvania public schools during the 1987-1988 academic year (called the youngest, middle, and oldest cohorts, respectively). Using a screening risk score that measured each boy's antisocial behavior, boys identified at the top 30 percent within each grade cohort on the screening risk measure (n=~250), as well as an equal number of boys randomly selected from the remainder (n=~250), were selected for follow-up. Consequently, the final sample for the study consisted of 1,517 total students selected for follow-up. 506 of these students were in the oldest sample, 508 were in the middle sample, and 503 were in the youngest sample. Assessments were conducted semiannually and then annually using multiple informants (i.e., boys, parents, and teachers) between 1987 and 2010. The youngest cohort was assessed from ages 6-19 and again at ages 25 and 28. The middle cohort was assessed from ages 9-13 and again at age 23. The oldest cohort was assessed from ages 13-25, with an additional assessment at age 35. Information has been collected on a broad range of risk and protective factors across multiple domains (e.g., individual, family, peer, school, and neighborhood). Measures of conduct problems, substance use/abuse, criminal behavior, mental health problems have been collected. This collection contains data and syntax files for demographic constructs. The datasets include constructs on repeated grade status, demographic information of participants, participants' biological mother, biological father, female caretaker, and male caretaker, change of caretaker since last phase, number of family members and other adults or children in the home, family structure, followup participation by youth, caretaker, and teacher, and housing characteristics. The demographic constructs were created by using the PYS raw data. The raw data are available at ICPSR in the following studies: Pittsburgh Youth Study Youngest Sample (1987 - 2001) [Pittsburgh, Pennsylvania], Pittsburgh Youth Study Middle Sample (1987 - 1991) [Pittsburgh, Pennsylvania], and Pittsburgh Youth Study Oldest Sample (1987 - 2000) [Pittsburgh, Pennsylvania].

The Project Information Literacy (PIL) lifelong learning survey dataset was produced as part of a two-year federally funded study on relatively recent US college graduates and their information-seeking behavior for continued learning. The goal of the survey was to collect quantitative data about the information-seeking behavior of a sample of recent graduates—the strategies, techniques, information support systems, and best practices—used to support lifelong learning in post-college life. The dataset contains responses from 1,651 respondents to a 21-item questionnaire administered between October 9, 2014 and December 15, 2014. The voluntary sample of respondents consisted of relatively recent graduates, who had completed their degrees between 2007 and 2012, from one of 10 US colleges and universities in the institutional sample. Quantitative data are included in the dataset about the learning needs of relatively recent graduates as well as the information sources they used in three arenas of their post-college lives (i.e., personal life, workplace, and the communities in which they resided). Demographic information—including age, gender, major, GPA, employment status, graduate school attendance, and geographic proximity of current residence to their alma mater—is also included in the dataset for the respondents. "Staying Smart: How Today's Graduates Continue to Learn Once They Complete College," Alison J. Head, Project Information Literacy Research Report, Seattle: University of Washington Information School (January 5, 2016), 112 pages, 6.9 MB.

This dataset presents a cluster analysis of UK universities based on four synthetic environments: social, cultural, physical and economic. These were developed based on variables that represented an educational ecosystem of well-being. The cluster analysis was initially linked to the LSYPE-Secure dataset using the UKPRNs (i.e. higher education institutional number) and hence the cluster analysis used data from around 2009-2012 to represent Wave 6 and Wave 7 of the LSYPE-Secure dataset. The cluster analysis was based on using a variety of variables available from HESA and the Office for Students (OfS) to represent these environments, for example: Social: had demographics of students and staff including ethnicity and sex Cultural: had data on research and teaching scores Economic: had data on student: staff ratio and expenditure Physical: had data related to the built and natural environment including residential sites, blue and green spacesEarlier last year (April 2018), the UK Office for Students (OfS) noted that students from underrepresented groups such as black and minority ethnic (BME) students and those from disadvantaged backgrounds were less likely to succeed at university. Coupled with this, research has shown that students from these groups are also more likely to have poorer mental health and wellbeing. However, there is substantial social and political pressure on universities to act to improve student mental health. For example, the Telegraph ran the headline "Do British universities have a suicide problem?" Thus, in June 2018, the Hon. Sam Gyimah, the then UK universities minister, informed university vice-chancellors that student mental health and wellbeing has to be one of their top priorities. Universities are investing substantive sums in activities to tackle student mental health but doing so with no evidence base to guide strategic policy and practice. These activities may potentially be ineffective, financially wasteful, and possibly, counter-productive. Therefore, we need a better evidence base which this project intends to fulfil. Currently, there is a lack of evidence and understanding about which groups of young people going to universities may have poorer life outcomes (such as education, employment, and mental health and well-being) as a result of their mental health and wellbeing during their adolescent years. These life outcomes and their mental health and wellbeing, however, are important for understanding the context of the complex social identities of the young people, such as the intersections between their gender, ethnicity, sexuality, religion and socio-economic status. Otherwise, these young people may feel misunderstood or judged. Most of the large body of quantitative research on life outcomes tend to focus on one social characteristic/identity of the student, such as the young person's gender or ethnicity or socio-economic status, but not the combination of all of these, i.e. the intersectionalities. Primarily, the reason for this has been the lack of sufficient data. This research draws on data from the Longitudinal Study of Young People in England (LSYPE), which tracked over 15,000 adolescents' education and health over 7 years between 2004-2010 (from when they were 13-19 years old), and the Next Steps Survey, which collected data from the same individuals in 2015 when they were 25 years and in the job market. This dataset also had an ethnic boost, which thus allows for the exploratory analysis of intersectionalities. Currently, there are a number of interventions being implemented to improve the university environment. However, there is a lack of evidence on how the university environment (such as their its size, amount of academic support available, availability of sports activities, students' sense of belonging, etc.) can affect the young person'students' mental health and wellbeing life outcomes. This evidence can be determined through by using the LSYPE data supplemented and by university environment data supplemented from the National Student Survey (NSS) and the Higher Education Statistics Agency (HESA). Thus this research uses an intersectional approach to investigate the extent to which the life outcomes of young persons who go to university are affected by their social inequality groupings and mental health and well-being during adolescence. Additionally, this research also aims to determine the characteristics of university environments that can improve the life outcomes of these young people depending on their social and mental health/wellbeing background. We use secondary data analysis of mainly HESA and OfS variables and created derived variables.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and zip code levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsTargeted Interventions: Facilitates the development of targeted interventions to address the needs of vulnerable populations within specific zip codes.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability at the zip code level.Research: Provides a rich dataset for academic and applied research in socio-economic and demographic studies at a granular zip code level.Community Planning: Supports the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities within specific zip code areas.Note: Due to limitations in the data environment, variable names may be truncated. Refer to the provided table for a clear understanding of the variables. CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computerThis table provides a mapping between the CSV variable names and the shapefile variable names, along with a brief description of each variable.

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

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Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

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The Baccalaureate and Beyond Longitudinal Study 2008, Base Year (B&B:08) is part of the Baccalaureate and Beyond Longitudinal Study (B&B) program. B&B:08 (https://nces.ed.gov/surveys/b&b/) is a base year of a longitudinal study that followed a cohort of graduating college seniors who participated in the 2008 National Postsecondary Student Aid Study (NPSAS:08). The 2008 National Postsecondary Student Aid Study (NPSAS:08) data provided the base-year sample for B&B:08. NPSAS:08 data are representative of all undergraduate and graduate students enrolled in postsecondary institutions in the 50 United States, the District of Columbia, and Puerto Rico that were eligible to participate in the federal financial aid programs in Title IV of the Higher Education Act, and the B&B cohort is a representative sample of graduating seniors in all majors. Key statistics produced from B&B:08 are information on bachelor's degree recipients' undergraduate experience, demographic backgrounds, expectations regarding graduate study and work, and participation in community service.

This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper.

Dataset

The artifact contains the resources described below.

Experiment resources

The resources needed for replicating the experiment, namely in directory experiment:

alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment.

alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English.

docker-compose.yml: a Docker Compose configuration file to launch Alloy4Fun populated with the tasks in directory data/experiment for the 2 sessions of the experiment.

api and meteor: directories with source files for building and launching the Alloy4Fun platform for the study.

Experiment data

The task database used in our application of the experiment, namely in directory data/experiment:

Model.json, Instance.json, and Link.json: JSON files with to populate Alloy4Fun with the tasks for the 2 sessions of the experiment.

identifiers.txt: the list of all (104) available participant identifiers that can participate in the experiment.

Collected data

Data collected in the application of the experiment as a simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Ethics Committee for Research in Social and Human Sciences of the Ethics Council of the University of Minho, where the experiment took place. Data is shared the shape of JSON and CSV files with a header row, namely in directory data/results:

data_sessions.json: data collected from task-solving in the 2 sessions of the experiment, used to calculate variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1).

data_socio.csv: data collected from socio-demographic questionnaire in the 1st session of the experiment, namely:

participant identification: participant's unique identifier (ID);

socio-demographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure).

data_emo.csv: detailed data collected from the emotional questionnaire in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D);

detailed emotional response data: the differential in the 5-point Likert scale for each of the 14 measured emotions in the 2 sessions, ranging from -5 to -1 if decreased, 0 if maintained, from 1 to 5 if increased, or NA denoting failure to submit the questionnaire. Half of the emotions are positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2), and half are negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2). This detailed data was used to compute the aggregate data in data_emo_aggregate.csv and in the detailed discussion in Section 6 of the paper.

data_umux.csv: data collected from the user experience questionnaires in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID);

user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100).

participants.txt: the list of participant identifiers that have registered for the experiment.

Analysis scripts

The analysis scripts required to replicate the analysis of the results of the experiment as reported in the paper, namely in directory analysis:

analysis.r: An R script to analyse the data in the provided CSV files; each performed analysis is documented within the file itself.

requirements.r: An R script to install the required libraries for the analysis script.

normalize_task.r: A Python script to normalize the task JSON data from file data_sessions.json into the CSV format required by the analysis script.

normalize_emo.r: A Python script to compute the aggregate emotional response in the CSV format required by the analysis script from the detailed emotional response data in the CSV format of data_emo.csv.

Dockerfile: Docker script to automate the analysis script from the collected data.

Setup

To replicate the experiment and the analysis of the results, only Docker is required.

If you wish to manually replicate the experiment and collect your own data, you'll need to install:

A modified version of the Alloy4Fun platform, which is built in the Meteor web framework. This version of Alloy4Fun is publicly available in branch study of its repository at https://github.com/haslab/Alloy4Fun/tree/study.

If you wish to manually replicate the analysis of the data collected in our experiment, you'll need to install:

Python to manipulate the JSON data collected in the experiment. Python is freely available for download at https://www.python.org/downloads/, with distributions for most platforms.

R software for the analysis scripts. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac.

Usage

Experiment replication

This section describes how to replicate our user study experiment, and collect data about how different hints impact the performance of participants.

To launch the Alloy4Fun platform populated with tasks for each session, just run the following commands from the root directory of the artifact. The Meteor server may take a few minutes to launch, wait for the "Started your app" message to show.

cd experimentdocker-compose up

This will launch Alloy4Fun at http://localhost:3000. The tasks are accessed through permalinks assigned to each participant. The experiment allows for up to 104 participants, and the list of available identifiers is given in file identifiers.txt. The group of each participant is determined by the last character of the identifier, either N, L, E or D. The task database can be consulted in directory data/experiment, in Alloy4Fun JSON files.

In the 1st session, each participant was given one permalink that gives access to 12 sequential tasks. The permalink is simply the participant's identifier, so participant 0CAN would just access http://localhost:3000/0CAN. The next task is available after a correct submission to the current task or when a time-out occurs (5mins). Each participant was assigned to a different treatment group, so depending on the permalink different kinds of hints are provided. Below are 4 permalinks, each for each hint group:

Group N (no hints): http://localhost:3000/0CAN

Group L (error locations): http://localhost:3000/CA0L

Group E (counter-example): http://localhost:3000/350E

Group D (error description): http://localhost:3000/27AD

In the 2nd session, likewise the 1st session, each permalink gave access to 12 sequential tasks, and the next task is available after a correct submission or a time-out (5mins). The permalink is constructed by prepending the participant's identifier with P-. So participant 0CAN would just access http://localhost:3000/P-0CAN. In the 2nd sessions all participants were expected to solve the tasks without any hints provided, so the permalinks from different groups are undifferentiated.

Before the 1st session the participants should answer the socio-demographic questionnaire, that should ask the following information: unique identifier, age, sex, familiarity with the Alloy language, and average academic grade.

Before and after both sessions the participants should answer the standard PrEmo 2 questionnaire. PrEmo 2 is published under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons licence (CC BY-NC-ND 4.0). This means that you are free to use the tool for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and do not modify the original material. The original material, namely the depictions of the diferent emotions, can be downloaded from https://diopd.org/premo/. The questionnaire should ask for the unique user identifier, and for the attachment with each of the depicted 14 emotions, expressed in a 5-point Likert scale.

After both sessions the participants should also answer the standard UMUX questionnaire. This questionnaire can be used freely, and should ask for the user unique identifier and answers for the standard 4 questions in a 7-point Likert scale. For information about the questions, how to implement the questionnaire, and how to compute the usability metric ranging from 0 to 100 score from the answers, please see the original paper:

Kraig Finstad. 2010. The usability metric for user experience. Interacting with computers 22, 5 (2010), 323–327.

Analysis of other applications of the experiment

This section describes how to replicate the analysis of the data collected in an application of the experiment described in Experiment replication.

The analysis script expects data in 4 CSV files,

Demographic characteristics of the study samples.

Abstract copyright UK Data Service and data collection copyright owner. The Young People's STEM Aspirations and Trajectories, Age 15-19, 2013-2017 data were gathered as part of the ASPIRES 2 project. This was a five-year (2014-2019) mixed-methods project which investigated the science and career aspirations of young people in England from age 14 to 19 years. The study extended previous research (ASPIRES - see SN 9222) conducted with the same cohort of young people who had participated in the first project at ages 10-14. It comprised a quantitative online survey of the cohort and repeat (longitudinal) interviews with a selected sub-sample of students and their parents. Please note that this dataset comprises the quantitative (survey) data only, not the qualitative (interview) data.Survey data were collected at two time points: at the end of Key Stage 4/National GCSE exams (age 15/16, Year 11) and the end of Key Stage 5/College (age 17/18/19, Year 13 or equivalent). The surveys collected a range of demographic data (including gender, ethnicity and measures of cultural capital) and attitudinal data. Data gathered under the first ASPIRES project, when participants were aged 10-14 years, are held under SN 9222. Data gathered under the subsequent ASPIRES 3 project, when participants were aged 20-22 years, are held under SN 9224. Main Topics: Topics included aspirations in science, attitudes towards school and science, self-concept in science, images of scientists, participation in science-related activities outside of school, parental attitudes towards science, career education, work experience, and post-16 choices. Most questions used a five-point Likert-type scale to elicit attitudinal responses. Response options were on a five-point scale from ‘strongly agree’ to ‘strongly disagree’ with ‘neither agree nor disagree’ as a midpoint. The dataset is a study of the cohort and is not tracked or longitudinal. Sampling done at school level. See documentation for details.

The National Postsecondary Student Aid Study, 2007-08 (NPSAS:08), is a study that is part of the National Postsecondary Student Aid Study (NPSAS) program. NPSAS:08 [https://nces.ed.gov/surveys/npsas/about.asp]is a cross-sectional survey that is designed to compile a comprehensive research dataset, based on student-level records, on financial aid provided by the federal government, the states, postsecondary institutions, employers, and private agencies, along with student demographic and enrollment data. The study was conducted using multiple sources, including institutional records, government databases, and student interviews. NPSAS:08 contains the data on a sample of 114,000 undergraduate students and 14,000 graduate students. These students were enrolled between July 1, 2007 and June 30, 2008 in about 1,730 postsecondary institutions. The data are representative of all undergraduate and graduate students enrolled in postsecondary institutions in the 50 United States, the District of Columbia, and Puerto Rico that were eligible to participate in the federal financial aid programs in Title IV of the Higher Education Act. Statistics produced from the NPSAS:08 provide reliable national estimates of characteristics related to financial aid for postsecondary students.

The Canada Millennium Scholarship Foundation (the Foundation) undertook a survey of secondary school students across five provinces focusing on their post-secondary school expectations. This research involved the in-class administration of a survey to Grade 6 to 12 students in British Columbia, Alberta, Manitoba, New Brunswick, and Newfoundland and Labrador. This study was conducted by two independent research companies under contract to the Foundation: Prairie Research Associates (PRA) Inc. and R.A. Malatest and Associates Ltd. The in-class survey instrument was designed to collect information about how secondary school students view education, what they know about the various forms of funding available for post-secondary education and how students envision paying for their future education. The survey administered to Grade 6 to 8 students gathered approximately 65 pieces of information, while the one given to Grade 9 to 12 students collected about 110 pieces of information. To ensure consistent administration of the in class survey for each student sample, a Survey Administration Guide was developed to recommend procedures to be followed. This dataset was received from the Canada Millennium Scholarship Foundation as is. Issues with value labels and missing values were discovered and corrected as best as possible with the documentation received. The variable -gasst: "Do you receive any government assistance?"- was not corrected due to lack of documentation about this variable. Some caution should be used with this dataset.

Tuberculosis (TB) stigma remains a significant barrier to TB control efforts globally, especially in countries with a high TB burden. Studies about TB stigma done in Uganda so far have been limited in scope and focused on data collected health facilities. In this study we report TB related stigma at community level for the period 2021/2022. We used the 2021/22 Lot Quality Assurance Sampling (LQAS) data from a sample of 33,349 participants across 77 districts, to measure TB stigma determine factors associated. We included demographic characteristics, knowledge and participant perspectives as our study variables. Univariable and multivariate logistic regression analyses were performed to identify factors associated with TB stigma. TB stigma was assessed as a categorical variable (below or above the median) due to the skewness of the data when fitting the scores. The data set had equal proportions of males and females. The largest age group was 20–29 years old (38.47%). Most participants were married (62.94%) and had primary level education (65.80%). The TB stigma scores were assigned on a scale from 0 to 30, with an average score of 21.67 (±8.22) and a median score of 24 (19–28). Overall, 45.48% of participants had TB stigma scores above the median. Variations in TB stigma levels were observed across different districts. Factors associated with higher TB stigma included older age, higher education levels, urban residence, and TB knowledge. To reduce TB stigma and misinformation that can make an impact on TB response, community interventions should balance increasing awareness with minimizing fear. These interventions should be well-rounded and context-specific to address disparities within communities and bolster TB control efforts in the country.