Time series data for the statistic Immunization, Pol3 (% of one-year-old children) and country United States. Indicator Definition:Child immunization rate, polio, is the percentage of children ages 12-23 months who received polio vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized after three doses.The indicator "Immunization, Pol3 (% of one-year-old children)" stands at 93.00 as of 12/31/2023. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is 1.09.The 5 year change in percent is 0.0.The 10 year change in percent is 0.0.The Serie's long term average value is 91.55. It's latest available value, on 12/31/2023, is 1.59 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1992, to it's latest available value, on 12/31/2023, is +29.17%.The Serie's change in percent from it's maximum value, on 12/31/1982, to it's latest available value, on 12/31/2023, is -4.12%.

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest.

6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals.

7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives.

8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest.

9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group.

10. scChldHome:

This dataset includes data on weight status for children aged 3 months to 4 years old from Women, Infant, and Children Participant and Program Characteristics (WIC-PC). This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about WIC-PC visit https://www.fns.usda.gov/wic/national-survey-wic-participants.

Vaccination Coverage among Young Children (0-35 Months)

  Description

Vaccination Coverage among Young Children (0-35 Months) • National, regional, state, and selected local area vaccination coverage estimates for 2-year-old children by birth year and birth year cohorts from the National Immunization Survey-Child. • Additional information available at https://www.cdc.gov/vaccines/imz-managers/coverage/childvaxview/index.html

  Dataset Details

Publisher:… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/vaccination-coverage-among-young-children-0-35-mon.

All NYC children are required to be tested for lead poisoning at around age 1 and age 2, and to be screened for risk of lead poisoning, and tested if at risk, up until age 6. These data are an indicator of the number and percentage of children turning 3 years old in a given year who were tested for lead poisoning.

About the Data All NYC children are required to be tested for lead poisoning at around age 1 and age 2, and to be screened for risk of lead poisoning, and tested if at risk, up until age 6. These data are an indicator of the number and percentage of children turning 3 years old in a given year who were tested for lead poisoning. How calculated: To identify children tested for lead poisoning, birth records for all children born in New York City to New York City resident mothers, and turning 3 years old in a given year were matched to children tested for lead poisoning before age 3.

The Colorado Immunization Information System (CIIS) can estimate immunization uptake and track vaccination coverage levels over time. This data includes routine immunization rates calculated for 19-35 month old children and 13-17 year old adolescents in Colorado. To correct for active records in CIIS with out of date address information who likely no longer live in Colorado, 19-35 month olds are included in rate calculations if they have at least two non-COVID-19 and non-flu visits on record in CIIS before nine months of age. A visit is defined as a distinct date of immunization services. Adolescents are included in rate calculations if they have received at least one non-COVID-19 vaccine in the past five years. Routine immunization coverage rates are based solely on information available in CIIS. CIIS is a mandatory reporting system; vaccine providers are required by law to submit both immunization and exemption information to CIIS. Rate estimates are highly dependent on the completeness and accuracy of the data in CIIS. The widest variation in rates is typically seen in smaller counties, where fewer children and adolescents are included in rate calculations. Routine immunization rates calculated for 19-35 month old children include 1+ doses of MMR, 1+ Varicella, 3+ Hepatitis B, 3+ Hib, 3+ Polio, 4+ DTaP, 4+ PCV, and the seven-antigen series (4:3:1:3:3:1:4). Routine immunization rates calculated for 13-17 year old adolescents include 1+ doses of MenACWY, 1+ Tdap, up-to-date status for HPV overall, up-to-date status for HPV for males, and up-to-date status for HPV for females. Rates are provided at the state and county level. Rates are updated twice annually, and are available starting from the six month period spanning from July 1, 2015 through December 31, 2015. For each six month period, child or adolescent age is defined as of the beginning of the six month period. Immunizations administered and reported to CIIS as of the end of the six month period are included in rate calculations. Here is a brief description of the individual data fields (columns) included in this dataset: county: This field contains the geographic level of the corresponding rate. For state-level rates, ‘county’ is set to “COLORADO”. For county-level rates, ‘county’ corresponds to the Colorado county name (character). vaccine: This field contains the vaccine of the corresponding rate (character). percent: This field contains the percentage of children or adolescents who have received the corresponding number of doses of the vaccine in CIIS (numeric, three digits). starting_date: This field contains the beginning date of the six month time period (date, yyyy-mm-dd). time_period: This field contains a description of the six month time period (character). group: This field describes the age group of the corresponding rate (“19-35 month olds” or “13-17 year olds”) (character).publish_date: Data that this dataset was published to the CDPHE Open Data Portal.

"Enrollment counts are based on the October 31 Audited Register for the 2017-18 to 2019-20 school years. To account for the delay in the start of the school year, enrollment counts are based on the November 13 Audited Register for 2020-21 and the November 12 Audited Register for 2021-22. * Please note that October 31 (and November 12-13) enrollment is not audited for charter schools or Pre-K Early Education Centers (NYCEECs). Charter schools are required to submit enrollment as of BEDS Day, the first Wednesday in October, to the New York State Department of Education." Enrollment counts in the Demographic Snapshot will likely exceed operational enrollment counts due to the fact that long-term absence (LTA) students are excluded for funding purposes. Data on students with disabilities, English Language Learners, students' povery status, and students' Economic Need Value are as of the June 30 for each school year except in 2021-22. Data on SWDs, ELLs, Poverty, and ENI in the 2021-22 school year are as of March 7, 2022. 3-K and Pre-K enrollment totals include students in both full-day and half-day programs. Four-year-old students enrolled in Family Childcare Centers are categorized as 3K students for the purposes of this report. All schools listed are as of the 2021-22 school year. Schools closed before 2021-22 are not included in the school level tab but are included in the data for citywide, borough, and district. Programs and Pre-K NYC Early Education Centers (NYCEECs) are not included on the school-level tab. Due to missing demographic information in rare cases at the time of the enrollment snapshot, demographic categories do not always add up to citywide totals. Students with disabilities are defined as any child receiving an Individualized Education Program (IEP) as of the end of the school year (or March 7 for 2021-22). NYC DOE "Poverty" counts are based on the number of students with families who have qualified for free or reduced price lunch, or are eligible for Human Resources Administration (HRA) benefits. In previous years, the poverty indicator also included students enrolled in a Universal Meal School (USM), where all students automatically qualified, with the exception of middle schools, D75 schools and Pre-K centers. In 2017-18, all students in NYC schools became eligible for free lunch. In order to better reflect free and reduced price lunch status, the poverty indicator does not include student USM status, and retroactively applies this rule to previous years. "The school’s Economic Need Index is the average of its students’ Economic Need Values. The Economic Need Index (ENI) estimates the percentage of students facing economic hardship. The 2014-15 school year is the first year we provide ENI estimates. The metric is calculated as follows: * The student’s Economic Need Value is 1.0 if: o The student is eligible for public assistance from the NYC Human Resources Administration (HRA); o The student lived in temporary housing in the past four years; or o The student is in high school, has a home language other than English, and entered the NYC DOE for the first time within the last four years. * Otherwise, the student’s Economic Need Value is based on the percentage of families (with school-age children) in the student’s census tract whose income is below the poverty level, as estimated by the American Community Survey 5-Year estimate (2020 ACS estimates were used in calculations for 2021-22 ENI). The student’s Economic Need Value equals this percentage divided by 100. Due to differences in the timing of when student demographic, address and census data were pulled, ENI values may vary, slightly, from the ENI values reported in the School Quality Reports. In previous years, student census tract data was based on students’ addresses at the time of ENI calculation. Beginning in 2018-19, census tract data is based on students’ addresses as of the Audited Register date of the g

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled primary sampling units (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This "second replenishment sample" was combined for estimation and analysis purposes with the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Wave 4.5 was a special data collection for youth only who were aged 12 to 17 at the time of the Wave 4.5 interview. Wave 4.5 was the fourth annual follow-up wave for those who were members of the Wave 1 Cohort. For those who were sampled at Wave 4, Wave 4.5 was the first annual follow-up wave. Wave 5.5, conducted in 2020, was a special data collection for Wave 4 Cohort youth and young adults ages 13 to 19 at the time of the Wave 5.5 interview. Also in 2020, a subsample of Wave 4 Cohort adults ages 20 and older were interviewed via the PATH Study Adult Telephone Survey (PATH-ATS). Wave 7.5 was a special collection for Wave 4 and Wave 7 Cohort youth and young adults ages 12 to 22 at the time of the Wave 7.5 interview. For those who were sampled at Wave 7, Wave 7.5 was the first annual follow-up wave. Dataset 1002 (DS1002) contains the data from the Wave 4.5 Youth and Parent Questionnaire. This file contains 1,617 variables and 13,131 cases. Of these cases, 11,378 are continuing youth having completed a prior Youth Interview. The other 1,753 cases are "aged-up youth" having previously been sampled as "shadow youth" Datasets 1112, 1212, and 1222, (DS1112, DS1212, and DS1222) are data files comprising the weight variables for Wave 4.5. The "all-waves" weight file contains weights for participants in the Wave 1 Cohort who completed a Wave 4.5 Youth Interview and completed interviews (if old enough to do so) or verified their information with the study (if not old enough to be interviewed) in Waves 1, 2, 3, and 4. There are two separate files with "single wave" weights: one for the Wave 1 Cohort and one for the Wave 4 Cohort. The "single-wave" weight file for the Wave 1 Cohort contains weights for youth who c

Effective June 28, 2023, this dataset will no longer be updated. Similar data are accessible from CDC WONDER (https://wonder.cdc.gov/mcd-icd10-provisional.html).

Deaths involving coronavirus disease 2019 (COVID-19) with a focus on ages 0-18 years in the United States.

Background: Early empirical data shows that school-aged children, adolescents and adults are experiencing elevated levels of anxiety and depression during the COVID-19 pandemic. Currently, there is very little research on mental health outcomes for young children. Objectives: To describe the formation of a global collaboration entitled, ‘COVID-19 Unmasked’. The collaborating researchers aim to (1) describe and compare the COVID-19 related experiences within and across countries; (2) examine mental health outcomes for young children (1 to 5 years) and caregivers over a 12-month period during the COVID-19 pandemic; (3) explore the trajectories/time course of psychological outcomes of the children and parents over this period and (4) identify the risk and protective factors for different mental health trajectories. Data will be combined from all participating countries into one large open access cross-cultural dataset to facilitate further international collaborations and joint publications. Methods: COVID-19 Unmasked is an online prospective longitudinal cohort study. An international steering committee was formed with the aim of starting a global collaboration. Currently, partnerships have been formed with 9 countries (Australia, Cyprus, Greece, the Netherlands, Poland, Spain, Turkey, the UK, and the United States of America). Research partners have started to start data collection with caregivers of young children aged 1–5 years old at baseline, 3-months, 6-months, and 12-months. Caregivers are invited to complete an online survey about COVID-19 related exposure and experiences, child’s wellbeing, their own mental health, and parenting. Data analysis: Primary study outcomes will be child mental health as assessed by scales from the Patient-Reported Outcomes Measurement Information System – Early Childhood (PROMIS-EC) and caregiver mental health as assessed by the Depression Anxiety Stress Scale (DASS-21). The trajectories/time course of mental health difficulties and the impact of risk and protective factors will be analysed using hierarchical linear models, accounting for nested effects (e.g. country) and repeated measures. This article describes the formation of a global collaboration between 9 countries that are collecting data to examine mental health outcomes for young children (1 to 5 years) and caregivers over a 12-month period during the COVID-19 pandemic. This article describes the formation of a global collaboration between 9 countries that are collecting data to examine mental health outcomes for young children (1 to 5 years) and caregivers over a 12-month period during the COVID-19 pandemic.

Context

The Programme for International Student Assessment (PISA) is a test given every three years to 15-year-old students from around the world to evaluate their performance in mathematics, reading, and science. This test provides a quantitative way to compare the performance of students from different parts of the world. In this homework assignment, we will predict the reading scores of students from the United States of America on the 2009 PISA exam.

The datasets pisa2009train.csv and pisa2009test.csv contain information about the demographics and schools for American students taking the exam, derived from 2009 PISA Public-Use Data Files distributed by the United States National Center for Education Statistics (NCES). While the datasets are not supposed to contain identifying information about students taking the test, by using the data you are bound by the NCES data use agreement, which prohibits any attempt to determine the identity of any student in the datasets.

Each row in the datasets pisa2009train.csv and pisa2009test.csv represents one student taking the exam. The datasets have the following variables:

Content

grade: The grade in school of the student (most 15-year-olds in America are in 10th grade)

male: Whether the student is male (1/0)

raceeth: The race/ethnicity composite of the student

preschool: Whether the student attended preschool (1/0)

expectBachelors: Whether the student expects to obtain a bachelor's degree (1/0)

motherHS: Whether the student's mother completed high school (1/0)

motherBachelors: Whether the student's mother obtained a bachelor's degree (1/0)

motherWork: Whether the student's mother has part-time or full-time work (1/0)

fatherHS: Whether the student's father completed high school (1/0)

fatherBachelors: Whether the student's father obtained a bachelor's degree (1/0)

fatherWork: Whether the student's father has part-time or full-time work (1/0)

selfBornUS: Whether the student was born in the United States of America (1/0)

motherBornUS: Whether the student's mother was born in the United States of America (1/0)

fatherBornUS: Whether the student's father was born in the United States of America (1/0)

englishAtHome: Whether the student speaks English at home (1/0)

computerForSchoolwork: Whether the student has access to a computer for schoolwork (1/0)

read30MinsADay: Whether the student reads for pleasure for 30 minutes/day (1/0)

minutesPerWeekEnglish: The number of minutes per week the student spend in English class

studentsInEnglish: The number of students in this student's English class at school

schoolHasLibrary: Whether this student's school has a library (1/0)

publicSchool: Whether this student attends a public school (1/0)

urban: Whether this student's school is in an urban area (1/0)

schoolSize: The number of students in this student's school

readingScore: The student's reading score, on a 1000-point scale

Acknowledgements

MITx ANALYTIX

The second National Survey of Child and Adolescent Well-Being (NSCAW II) is a longitudinal study intended to answer a range of fundamental questions about the well-being, functioning, service needs, and service use of children who come in contact with the child welfare system. The study is sponsored by the Administration for Children and Families (ACF), U.S. Department of Health and Human Services (DHHS). It examines the well-being of children involved with child welfare agencies; captures information about their families; provides information about child welfare interventions and other services; and describes key characteristics of child development. Of particular interest to the study are children's health, mental health, and developmental risks, especially for those children who experienced the most severe abuse and exposure to violence.

The NSCAW II study design essentially mirrors that of NSCAW I. The NSCAW II cohort includes 5,872 children, aged birth to 17.5 years old, who had contact with the child welfare system within a 15-month period that began in February 2008. Children were sampled from investigations closed during the reference period. The cohort of 5,872 children was selected from 81 of the original NSCAW 92 Primary Sampling Units (PSUs) in 83 counties in 30 states that agreed to participate in NSCAW II. Retaining most of the NSCAW I PSUs will allow researchers to assess the change in context from the late 1990s, and enable longitudinal analysis of organizational measures such as staff turnover, climate, and work environment.

The sample of investigated/assessed cases includes both cases that receive ongoing services and cases that are not receiving services, either because they were not substantiated or because it was determined that services were not required. The sample design-with oversampling of infants and children in out-of-home placement, and undersampling of cases not receiving services to ensure appropriate representation among subgroups-allows in-depth analysis of subgroups of special interest (e.g., young children, adolescents in foster care) while providing national estimates for the full population of children and families entering the system.

Like NSCAW I, NSCAW II is a longitudinal study with multiple informants associated with each sampled child, to get the fullest possible depiction of that child. Face-to-face interviews or assessments were conducted with children, parents, and nonparent adult caregivers (e.g., foster parents, kin caregivers, group home caregivers), and investigative caseworkers. Baseline data collection began in March 2008 and was completed in September 2009. The second wave of the study, 18 months after the close of the NSCAW II index investigation, began in October 2009 and was completed in January 2011. At Wave 3, children and families were reinterviewed approximately 36 months after the close of the NSCAW II index investigation. The NSCAW II cohort of children who were approximately 2 months to 17.5 years old at baseline ranged in age from 34 months to 20 years old at Wave 3. Data collection for the third wave of the study began in June 2011 and was completed in December 2012.

NDACAN's dedicated NSCAW User Support page contains a video and several documents to assist researchers with these data.

Investigators: Research Triangle Institute

This report offers updated estimates of the number of people eligible for WIC benefits in 2011, including (1) estimates by participant category (including children by single year of age) and coverage rates; (2) updated estimates in U.S. territories; and (3) confidence intervals. The national estimates presented in this report are based on a methodology developed in 2003 by the Committee on National Statistics of the National Research Council (CNSTAT). The report’s State-level estimates use a methodology developed by the Urban Institute that apportions the national figures using data from the American Community Survey

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of tobacco users and non-users. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled PSUs and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1 and Wave 4 Cohorts. Dataset 0001 (DS0001) contains the data from the Master Linkage file. This file contains 42 variables and 67,276 cases. The file provides a master list of every person's unique identification number and what type of respondent they were for each wave. Dataset 1011 (DS1011) contains the data from the Wave 1 Adult Questionnaire. This data file contains 2,021 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1012 (DS1012) contains the data from the Wave 1 Youth (and Parent) Questionnaire. This file contains 1,431 variables and 13,651 cases. Dataset 1411 (DS1411) contains the Wave 1 State Identifier data for Adults and has 5 variables and 32,320 cases. Dataset 1412 (DS1412) contains the Wave 1 State Identifier data for Youth (and Parents) and has 5 variables and 13,651 cases. The same 5 variables are in each State Identifier dataset, including PERSONID for linking the State Identifier to the questionnaire and biomarker data and 3 variables designating the state (state FIPS, state abbreviation, and full name of the state). Dataset 2011 (DS2011) contains the data from the Wave 2 Adult Questionnaire. This data file contains 2,421 variables and 28,362 cases. Of these cases, 26,447 also completed a Wave 1 Adult Questionnaire. The other 1,915 cases are "aged-up adults" having previously completed a Wave 1 Youth Questionnaire.Dataset 2012 (DS2012) contains the data from the Wave 2 Youth (and Parent) Questionnaire. This data file contains 1,596 variables and 12,172 cases. Of these cases, 10,081 also completed a Wave 1 Youth Questionnaire. The other 2,091 cases are "aged-up youth" having previously been sampled as "shadow youth." Dataset 3011 (DS3011) contains the data from the Wave 3 Adult Questionnaire. This data file contains 2,359 variables and 28,148 cases. Of these cases, 26,241 are continuing adults having completed a prior Adult Questionnaire. The other 1,907 cases are "aged-up adults" having previously completed a Youth Questionnaire. Dataset 3012 (DS3012) contains the data from the Wave 3 Youth (and Parent) Questionnaire. This data file contains 1,492 variables and 11,814 cases. Of these cases, 9,769 are continuing youth having completed a prior Youth Interview. The other 2,045 cases are "aged-up youth" having previously been sampled as "shadow youth." Datasets 3111, 3211, 3112, and 3212 (DS3111, DS3211, DS3112, and DS3212) are data files comprising the weight variables for Wave 3. The weight variables for Wave 1 and Wave 2 are included in the main data files. However, starting with Wave 3, the weight variables have been separated into individual data files. The "all-waves" weight files contain weights for respondents who completed an interview for all waves in which they were old enough to do so or verified their information with the study for waves in which they were not old enough to be interviewed. The "single-wave" weight files contain weights for all respondents in Wave 3 regardless of their

This dataset contains replication files for "The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment" by Raj Chetty, Nathaniel Hendren, and Lawrence Katz. For more information, see https://opportunityinsights.org/paper/newmto/. A summary of the related publication follows. There are large differences in individuals’ economic, health, and educational outcomes across neighborhoods in the United States. Motivated by these disparities, the U.S. Department of Housing and Urban Development designed the Moving to Opportunity (MTO) experiment to determine whether providing low-income families assistance in moving to better neighborhoods could improve their economic and health outcomes. The MTO experiment was conducted between 1994 and 1998 in five large U.S. cities. Approximately 4,600 families living in high-poverty public housing projects were randomly assigned to one of three groups: an experimental voucher group that was offered a subsidized housing voucher that came with a requirement to move to a census tract with a poverty rate below 10%, a Section 8 voucher group that was offered a standard housing voucher with no additional contingencies, and a control group that was not offered a voucher (but retained access to public housing). Previous research on the MTO experiment has found that moving to lower-poverty areas greatly improved the mental and physical health of adults. However, prior work found no impacts of the MTO treatments on the earnings of adults and older youth, leading some to conclude that neighborhood environments are not an important component of economic success. In this study, we present a new analysis of the effect of the MTO experiment on children’s long-term outcomes. Our re-analysis is motivated by new research showing that a neighborhood’s effect on children’s outcomes may depend critically on the duration of exposure to that environment. In particular, Chetty and Hendren (2015) use quasi-experimental methods to show that every year spent in a better area during childhood increases a child’s earnings in adulthood, implying that the gains from moving to a better area are larger for children who are younger at the time of the move. In light of this new evidence on childhood exposure effects, we study the long-term impacts of MTO on children who were young when their families moved to better neighborhoods. Prior work has not been able to examine these issues because the younger children in the MTO experiment are only now old enough to be entering the adult labor market. For older children (those between ages 13-18), we find that moving to a lower-poverty neighborhood has a statistically insignificant or slightly negative effect. More generally, the gains from moving to lower-poverty areas decline steadily with the age of the child at the time of the move. We do not find any clear evidence of a “critical age” below which children must move to benefit from a better neighborhood. Rather, every extra year of childhood spent in a low-poverty environment appears to be beneficial, consistent with the findings of Chetty and Hendren (2015). The MTO treatments also had little or no impact on adults’ economic outcomes, consistent with previous results. Together, these studies show that childhood exposure plays a critical role in neighborhoods’ effects on economic outcomes. The experimental voucher increased the earnings of children who moved at young ages in all five experimental sites, for Whites, Blacks, and Hispanics, and for boys and girls. Perhaps most notably, we find robust evidence that the experimental voucher improved long-term outcomes for young boys, a subgroup where prior studies have found little evidence of gains. Our estimates imply that moving a child out of public housing to a low-poverty area when young (at age 8 on average) using a subsidized voucher like the MTO experimental voucher will increase the child’s total lifetime earnings by about $302,000. This is equivalent to a gain of $99,000 per child moved in present value at age 8, discounting future earnings at a 3% interest rate. The additional tax revenue generated from these earnings increases would itself offset the incremental cost of the subsidized voucher relative to providing public housing. We conclude that offering low-income families housing vouchers and assistance in moving to lowerpoverty neighborhoods has substantial benefits for the families themselves and for taxpayers. It appears important to target such housing vouchers to families with young children – perhaps even at birth – to maximize the benefits. Our results provide less support for policies that seek to improve the economic outcomes of adults through residential relocation. More broadly, our findings suggest that efforts to integrate disadvant... Visit https://dataone.org/datasets/sha256%3Aa12b8c1f14eeabc92c1d91bd0311bc4aa3ddf6d7fb69ca798ca6926e7fa292c7 for complete metadata about this dataset.

The Population Assessment of Tobacco and Health (PATH) Study began originally surveying 45,971 adult and youth respondents. The PATH Study was launched in 2011 to inform Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Unit (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort.Dataset 0001 (DS0001) contains the data from the Master Linkage file. This file contains 14 variables and 67,276 cases. The file provides a master list of every person's unique identification number and what type of respondent they were for each wave. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This second replenishment sample was combined for estimation and analysis purposes with Wave 7 adult and youth respondents from the Wave 4 Cohort who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Public-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts.Dataset 1001 (DS1001) contains the data from the Wave 1 Adult Questionnaire. This data file contains 1,732 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1002 (DS1002) contains the data from the Youth and Parent Questionnaire. This file contains 1,228 variables and 13,651 cases.Dataset 2001 (DS2001) contains the data from the Wave 2 Adult Questionnaire. This data file contains 2,197 variables and 28,362 cases. Of these cases, 26,447 also completed a Wave 1 Adult Questionnaire. The other 1,915 cases are "aged-up adults" having previously completed a Wave 1 Youth Questionnaire. Dataset 2002 (DS2002) contains the data from the Wave 2 Youth and Parent Questionnaire. This data file contains 1,389 variables and 12,172 cases. Of these cases, 10,081 also completed a Wave 1 Youth Questionnaire. The other 2,091 cases are "aged-up youth" having previously been sampled as "shadow youth." Dataset 3001 (DS3001) contains the data from the Wave 3 Adult Questionnaire. This data file contains 2,139 variables and 28,148 cases. Of these cases, 26,241 are continuing adults having completed a prior Adult Questionnaire. The other 1,907 cases are "aged-up adults" having previously completed a Youth Questionnaire. Dataset 3002 (DS3002) contains the data from t

The second National Survey of Child and Adolescent Well-Being (NSCAW II) is a longitudinal study intended to answer a range of fundamental questions about the well-being, functioning, service needs, and service use of children who come in contact with the child welfare system. The study is sponsored by the Administration for Children and Families (ACF), U.S. Department of Health and Human Services (DHHS). It examines the well-being of children involved with child welfare agencies; captures information about their families; provides information about child welfare interventions and other services; and describes key characteristics of child development. Of particular interest to the study are children's health, mental health, and developmental risks, especially for those children who experienced the most severe abuse and exposure to violence.

The NSCAW II study design essentially mirrors that of NSCAW I. The NSCAW II cohort includes 5,872 children, aged birth to 17.5 years old, who had contact with the child welfare system within a 15-month period that began in February 2008. Children were sampled from investigations closed during the reference period. The cohort of 5,872 children was selected from 81 of the original NSCAW 92 Primary Sampling Units (PSUs) in 83 counties in 30 states that agreed to participate in NSCAW II. Retaining most of the NSCAW I PSUs will allow researchers to assess the change in context from the late 1990s, and enable longitudinal analysis of organizational measures such as staff turnover, climate, and work environment.

The sample of investigated/assessed cases includes both cases that receive ongoing services and cases that are not receiving services, either because they were not substantiated or because it was determined that services were not required. The sample design-with oversampling of infants and children in out-of-home placement, and undersampling of cases not receiving services to ensure appropriate representation among subgroups-allows in-depth analysis of subgroups of special interest (e.g., young children, adolescents in foster care) while providing national estimates for the full population of children and families entering the system.

Like NSCAW I, NSCAW II is a longitudinal study with multiple informants associated with each sampled child, to get the fullest possible depiction of that child. Face-to-face interviews or assessments were conducted with children, parents, and nonparent adult caregivers (e.g., foster parents, kin caregivers, group home caregivers), and investigative caseworkers. Baseline data collection began in March 2008 and was completed in September 2009. The second wave of the study, 18 months after the close of the NSCAW II index investigation, began in October 2009 and was completed in January 2011. At Wave 3, children and families were reinterviewed approximately 36 months after the close of the NSCAW II index investigation. The NSCAW II cohort of children who were approximately 2 months to 17.5 years old at baseline ranged in age from 34 months to 20 years old at Wave 3. Data collection for the third wave of the study began in June 2011 and was completed in December 2012.

NDACAN's dedicated NSCAW User Support page contains a video and several documents to assist researchers with these data.

Investigators: Research Triangle Institute

The purpose of this study was to examine two sets of children -- those alleged dependent and neglected and those alleged delinquent -- in order to better understand the influence of maltreatment on delinquent conduct. Data were collected from official court records. The first group of children was selected from the dependency and neglected cases filed with the Juvenile Court in 1984 and 1985. The 1984-1985 Non-Pooled Dependency and Neglect Cohort Data (Part 1) contains a total of 1,062 cases, representing 1,062 alleged dependent and neglected children and their siblings. The 1984-1985 Pooled Dependency and Neglect Cohort Data (Part 3) includes 4,474 cases which correspond to up to 20 complaints for each of the 1,062 alleged dependent and neglected children. The second group was selected from delinquency petitions of children 16 and 17 years old filed in the years 2000 and 2001. The 2000-2001 Non-Pooled Delinquency Cohort Data (Part 2) contains a total of 549 cases, representing 549 delinquent children. The 2000-2001 Pooled Delinquency Cohort Data (Part 4) includes 2,076 cases which correspond to up to 20 complaints for each of the 549 delinquent children. Part 1 contains a total of 11 and Part 2 contains a total of 10 demographics and summary count information variables. Part 3 and Part 4 contain a total of 68 and 58 variables, respectively, including demographics and information on delinquent charges, complaints of maltreatment, placements, and dispositions for each child.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

The National Survey of Children's Exposure to Violence (NatSCEV) series involved three rounds of data collection, NatSCEV I (baseline), NatSCEV II, and this study, NatSCEV III. For more information on other parts to the series, please use the following links: NatSCEV I (ICPSR 35203) - http://doi.org/10.3886/ICPSR35203.v1 NatSCEV II (ICPSR 36177) - http://doi.org/10.3886/ICPSR36177.v1 The National Survey of Children's Exposure to Violence III was designed to obtain lifetime and one-year incidence estimates of a comprehensive range of childhood victimizations across gender, race, and developmental stage. Conducted between August 2013 and April 2014, it assessed the experiences of a nationally representative sample of 4,000 children less than 18 years of age living in the contiguous United States (excluding New Hampshire). A short interview was conducted with an adult caregiver (usually a parent) to obtain family demographic information. One child was randomly selected from all eligible children in a household by selecting the child with the most recent birthday. If the selected child was 1 month to 9 years old, the main interview was conducted with the caregiver. If the selected child was 10-17 years old, the main interview was conducted with the child.The NatSCEV III questionnaire was very similar to the previous wave minus the extended family exposure to violence follow-up section that was included in NatSCEV II. The questionnaire asked for household demographics and questions about the focal child's health. A series of 52 juvenile victimization screening questions (JVQ) were asked, and for every screener the respondent endorsed, a series of follow-up questions about that victimization was asked. In addition, the survey included sections on lifetime and past year adversity, internet victimization, community disorder, bullying, delinquency, and the child/parent relationship.