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description Publicationkeyboard_double_arrow_right Article , Preprint 2023Elsevier BV SSHRC, NSF | Dynamical Approaches for ..., NSF | Stochastic Analysis and N...SSHRC ,NSF| Dynamical Approaches for Some Complex Stochastic Systems ,NSF| Stochastic Analysis and Numerics for Large Scale Dynamical Systems, with ApplicationsAuthors: Lazrak, Ali; Zhang, Jianfeng;Lazrak, Ali; Zhang, Jianfeng;We study pre-vote interactions in a committee that enacts a welfare-improving reform through voting. Committee members use decentralized promises contingent on the reform enactment to influence the vote outcome. Equilibrium promises prevent beneficial coalitional deviations and minimize total promises. We show that multiple equilibria exist, involving promises from high- to low-intensity members to enact the reform. Promises dissuade reform opponents from enticing the least enthusiastic reform supporters to vote against the reform. We explore whether some recipients of the promises can be supporters of the reform and discuss the impact of polarization on the total promises.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2023Springer Science and Business Media LLC SSHRC, NSF | Time-Consistency Theory f..., NSF | Several Problems of Stoch...SSHRC ,NSF| Time-Consistency Theory for Time-Inconsistent Stochastic Optimal Control Problems ,NSF| Several Problems of Stochastic Optimal Controls in Infinite Time HorizonAuthors: Lazrak, Ali; Wang, Hanxiao; Yong, Jiongmin;Lazrak, Ali; Wang, Hanxiao; Yong, Jiongmin;We investigate a linear quadratic stochastic zero-sum game where two players lobby a political representative to invest in a wind turbine farm. Players are time-inconsistent because they discount performance with a non-constant rate. Our objective is to identify a consistent planning equilibrium in which the players are aware of their inconsistency and cannot commit to a lobbying policy. We analyze the equilibrium behavior in both single player and two-player cases, and compare the behavior of the game under constant and non-constant discount rates. The equilibrium behavior is provided in closed-loop form, either analytically or via numerical approximation. Our numerical analysis of the equilibrium reveals that strategic behavior leads to more intense lobbying without resulting in overshooting.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research WT, NIH | Cascades of Network Struc..., NIH | Economic Evaluation of Ad... +207 projectsWT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,NIH| Health Disparities Among a Vulnerable Population: A Longitudinal Analysis ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| The University of Iowa Prevention Research Center ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NSF| Health Lifestyles and the Reproduction of Inequality ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| UIC Program for Interdisciplinary Careers in Womens Health Research ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NHMRC| Experience-dependent cellular plasticity and cognitive deficits in mouse models of schizophrenia ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCER ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NIH| Synthetic Information Systems for Better Informing Public Health Policymakers ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Childhood Family Instability, Adult Stress Reactivity, and Consequences for Health ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,CIHR ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| Transitions to Adulthood and Health Risk Among U.S. Young Adults ,NIH| CUPC Admin Core ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| University of Colorado Population Center ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| Phenotypic refinement of externalizing pathways to alcohol-related behaviors ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,NIH| Innovations in Pediatric Pain ResearchAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview
Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu65 citations 65 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018Public Library of Science (PLoS) NSF | Collaborative Research: A..., NIH | Injection Risk Networks i..., SSHRCNSF| Collaborative Research: Applying Behavioral-Ecological Network Models to Enhance Distributed Spectrum Access in Cognitive Radio ,NIH| Injection Risk Networks in Rural Puerto Rico ,SSHRCAuthors: Elspeth Ready;Elspeth Ready;pmid: 29529040
pmc: PMC5846769
Social institutions that facilitate sharing and redistribution may help mitigate the impact of resource shocks. In the North American Arctic, traditional food sharing may direct food to those who need it and provide a form of natural insurance against temporal variability in hunting returns within households. Here, network properties that facilitate resource flow (network size, quality, and density) are examined in a country food sharing network comprising 109 Inuit households from a village in Nunavik (Canada), using regressions to investigate the relationships between these network measures and household socioeconomic attributes. The results show that although single women and elders have larger networks, the sharing network is not structured to prioritize sharing towards households with low food availability. Rather, much food sharing appears to be driven by reciprocity between high-harvest households, meaning that poor, low-harvest households tend to have less sharing-based social capital than more affluent, high-harvest households. This suggests that poor, low-harvest households may be more vulnerable to disruptions in the availability of country food.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1371/journal.pone.0193759&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu25 citations 25 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2016Elsevier BV NSF | Programs on Critical Prob..., SSHRCNSF| Programs on Critical Problems in Physics, Astrophysics and Biophysics at the Aspen Center for Physics ,SSHRCAuthors: Martin Elvis; Tony Milligan; Alanna Krolikowski;Martin Elvis; Tony Milligan; Alanna Krolikowski;The Outer Space Treaty makes it clear that the Moon is the province of all mankind, with the latter ordinarily understood to exclude state or private appropriation of any portion of its surface. However, there are indeterminacies in the Treaty and in space law generally over the issue of appropriation. These indeterminacies might permit a close approximation to a property claim or some manner of quasi-property. The recently revealed highly inhomogeneous distribution of lunar resources changes the context of these issues. We illustrate this altered situation by considering the Peaks of Eternal Light. They occupy about one square kilometer of the lunar surface. We consider a thought experiment in which a Solar telescope is placed on one of the Peaks of Eternal Light at the lunar South pole for scientific research. Its operation would require nondisturbance, and hence that the Peak remain unvisited by others, effectively establishing a claim of protective exclusion and de facto appropriation. Such a telescope would be relatively easy to emplace with todays technology and so poses a near-term property issue on the Moon. While effective appropriation of a Peak might proceed without raising some of the familiar problems associated with commercial development (especially lunar mining), the possibility of such appropriation nonetheless raises some significant issues concerning justice and the safeguarding of scientific practice on the lunar surface. We consider this issue from scientific, technical, ethical and policy viewpoints. Comment: 20 pages, 3 figures (color). Space Policy in press
arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2016License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu20 citations 20 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2016License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2020Oxford University Press (OUP) SSHRC, NSF | Collaborative Research: E..., NSF | Collaborative Research: E... +1 projectsSSHRC ,NSF| Collaborative Research: Exploring the impact of cosmic ray feedback on galaxy evolution ,NSF| Collaborative Research: Exploring the impact of cosmic ray feedback on galaxy evolution ,NSF| Kavli Institute for Theoretical PhysicsAuthors: Chaoran Wang; Mateusz Ruszkowski; H-Y Karen Yang;Chaoran Wang; Mateusz Ruszkowski; H-Y Karen Yang;Black hole feedback plays a central role in shaping the circumgalactic medium (CGM) of elliptical galaxies. We systematically study the impact of plasma physics on the evolution of ellipticals by performing three-dimensional non-ideal magneto-hydrodynamic simulations of the interactions of active galactic nucleus (AGN) jets with the CGM including magnetic fields, and cosmic rays (CRs) and their transport processes. We find that the physics of feedback operating on large galactic scales depends very sensitively on plasma physics operating on small scales. Specifically, we demonstrate that: (i) in the purely hydrodynamical case, the AGN jets initially maintain the atmospheres in global thermal balance. However, local thermal instability generically leads to the formation of massive cold disks in the vicinity of the central black hole in disagreement with observations; (ii) including weak magnetic fields prevents the formation of the disks because local B-field amplification in the precipitating cold gas leads to strong magnetic breaking, which quickly extracts angular momentum from the accreting clouds. The magnetic fields transform the cold clouds into narrow filaments that do not fall ballistically; (iii) when plasma composition in the AGN jets is dominated by CRs, and CR transport is neglected, the atmospheres exhibit cooling catastrophes due to inefficient heat transfer from the AGN to CGM despite Coulomb/hadronic CR losses being present; (iv) including CR streaming and heating restores agreement with the observations, i.e., cooling catastrophes are prevented and massive cold central disks do not form. The AGN power is reduced as its energy is utilized efficiently. submitted to MNRAS
Monthly Notices of t... arrow_drop_down Monthly Notices of the Royal Astronomical SocietyArticle . 2020License: OUP Standard Publication ReuseData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2019License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1093/mnras/staa550&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Monthly Notices of t... arrow_drop_down Monthly Notices of the Royal Astronomical SocietyArticle . 2020License: OUP Standard Publication ReuseData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2019License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1093/mnras/staa550&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 1984 EnglishICPSR - Interuniversity Consortium for Political and Social Research NSF | Drug Dealing, Incarcerati..., NSF | Mathematical Sciences: Ro..., FCT | D4 +4 projectsNSF| Drug Dealing, Incarceration, and Self-Employment ,NSF| Mathematical Sciences: Robust Estimation and Testing of Econometric Models for Panel Data ,FCT| D4 ,NSF| Research On Semiparametric and Nonparametric Estimation of Econometric Models ,NSF| An Analysis of Job-Seeking Methods Used By Unemployed Youth ,NSF| Identification Problems in the Social Sciences ,SSHRCAuthors: Ohio State University. Center For Human Resource Research.;Ohio State University. Center For Human Resource Research.;Datasets: DS0: Study-Level Files DS1: Mature Men, 1966-1990 DS2: Mature Women, 1967-1986 (Main File) DS3: Young Men, 1966-1981 DS4: Young Women, 1968-1982 (Main File) DS5: Youth, 1979-1992 (Common Demographic Information) DS6: Youth, 1979-1992 (Created Key Variables) DS7: Youth, 1979-1992 (Family Background) DS8: Youth, 1979-1992 (Marital History) DS9: Youth, 1979-1992 (Current Labor Force Status) DS10: Youth, 1979-1992 (Jobs) DS11: Youth, 1979-1992 (Job Information--Employer Supplement) DS12: Youth, 1979-1992 (Periods Not Working--Employer Supplement) DS13: Youth, 1979-1992 (Information Sheet, 1980-1989) DS14: Youth, 1979-1992 (Regular Schooling) DS15: Youth, 1979-1992 (Income and Assets, 1979-1990) DS16: Youth, 1979-1992 (Assets, 1985-1989) DS17: Youth, 1979-1992 (Household Record) DS18: Youth, 1979-1992 (Periods When Respondent Was Not Working or in the Military) DS19: Youth, 1979-1992 (Degrees and Certification, 1979-1984 and 1988- 1989) DS20: Youth, 1979-1992 (Birth Record and Fertility, 1982-1984) DS21: Youth, 1979-1992 (Birth Record and Fertility, 1985) DS22: Youth, 1979-1992 (Birth Record and Fertility, 1986) DS23: Youth, 1979-1992 (Birth Record and Fertility, 1987) DS24: Youth, 1979-1992 (Children Record Form for Biological Children) DS25: Youth, 1979-1992 (Children Record Form for Non-Biological Children) DS26: Youth, 1979-1992 (Fertility, 1979-1981) DS27: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1979) DS28: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1980) DS29: Young Women, 1968-1982 (Attachment 2 File) DS30: Young Women, 1968-1982 (Attachment 3 File) DS31: Young Women, 1968-1982 (KWIC Index) DS32: Young Women, 1968-1982 (Numeric Index) DS33: Young Women, 1983-1991 (Main File) DS34: Young Women, 1983-1991 (Attachment File) DS35: Young Women, 1983-1991 (KWIC Index) DS36: Young Women, 1983-1991 (Numeric Index) DS37: Mature Men, 1966-1990 (Attachment 3) DS38: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1981) DS39: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1982) DS40: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1983) DS41: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1984) DS42: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1985) DS43: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1986) DS44: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1987) DS46: Youth, 1979-1992 (Government Jobs--Employer Supplement, 1979-1987) DS47: Youth, 1979-1992 (Profiles--ASVAB Vocational Test, 1980) DS48: Youth, 1979-1992 (School Survey) DS49: Youth, 1979-1992 (Transcript Survey) DS50: Youth, 1979-1992 (Military Data, 1980-1989) DS51: Child-Mother, 1979-1990 (Merged File) DS52: Merged Child-Mother Data, 1979-1990 (Numeric Index) DS53: Mature Men, 1966-1990 (Numeric Index) DS54: Mature Men, 1966-1990 (KWIC Index) DS55: Mature Women, 1967-1986 (Numeric Index) DS56: Mature Women, 1967-1986 (KWIC Index) DS57: Young Men, 1966-1981 (Numeric Index) DS58: Young Men, 1966-1981 (KWIC Index) DS59: Original Cohort Relationship: Mature Men, Mature Women DS60: Original Cohort Relationship: Mature Men, Young Women DS61: Original Cohort Relationship: Mature Men, Young Men DS62: Original Cohort Relationship: Mature Women, Young Women DS63: Original Cohort Relationship: Mature Women, Young Men DS64: Original Cohort Relationship: Young Men, Young Women DS65: Original Cohort Relationship Documentation DS67: Women's Support Network, 1983-1985: Round 5 Respondent-Relation Distance DS68: Women's Support Network, 1983-1985: Round 5 Respondent-Relation Distance, Record Layout DS69: Women's Support Network, 1983-1985: Round 6 Respondent-Relation Distance DS70: Women's Support Network, 1983-1985: Round 6 Respondent-Relation Distance, Record Layout DS71: Women's Support Network, 1983-1985: Round 7 Respondent-Relation Distance DS72: Women's Support Network, 1983-1985: Round 7 Respondent-Relation Distance, Record Layout DS73: Women's Support Network, 1983-1985: Respondent Mobility File DS74: Women's Support Network, 1983-1985: Respondent Mobility File, Record Layout DS75: Youth, 1979-1992 (Other Training) DS76: Youth, 1979-1992 (Government Training, 1979-1987) DS77: Youth, 1979-1992 (Child Care, 1982-1989) DS78: Youth, 1979-1992 (Health) DS79: Youth, 1979-1992 (Alcohol Use, 1982-1985 and 1988-1989) DS80: Youth, 1979-1992 (Drug Use, 1984 and 1988) DS81: Youth, 1979-1992 (Illegal Activities and Reported Police Contacts, 1980) DS82: Youth, 1979-1992 (Job Search and Job Findings, 1981-1982 and 1986- 1987) DS83: Youth, 1979-1992 (Last Job Lasting 2 Weeks or More, 1979) DS84: Youth, 1979-1992 (Work Experience Prior to 11/1/78, 1979) DS85: Youth, 1979-1992 (Attitudes of Influential Person Toward Respondent's Decisions, 1979) DS86: Youth, 1979-1992 (Attitudes Toward Hypothetical Job Offers, 1979) DS87: Youth, 1979-1992 (Attitudes Toward Work, Self, Traditional Roles, AIDS, 1979-1984 and 1987-1988) DS88: Youth, 1979-1992 (Interviewer Remarks) DS89: Youth, 1979-1992 (Time Spent Working, Going to School, Training, Etc., 1981) DS90: Youth, 1979-1992 (Supplemental Fertility File) DS91: Youth, 1979-1992 (Numeric Index) DS92: Youth, 1979-1992 (KWIC Index) DS93: Youth, 1979-1992 (Codebook) DS94: Youth, 1979-1992 (Workhistory) DS95: Attachment 3 for Mature Women, 1967-1986 DS96: Mature Women, 1987-1989 (Main File) DS97: Mature Women, 1987-1989 (KWIC Index) DS98: Mature Women, 1987-1989 (Numeric Index) DS99: Attachment 3 for Mature Women, 1987-1989 DS100: Youth, 1979-1992 (Birth Record and Fertility, 1988) DS101: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1988) DS102: Young Women, Flowchart for Year 1978 DS103: Young Women, Flowchart for Year 1980 DS104: Young Women, Flowchart for Year 1982 DS105: Young Women, Flowchart for Year 1983 DS106: Young Women, Flowchart for Year 1985 DS107: Young Women, Flowchart for Year 1987 DS108: Mature Women, Flowchart for Year 1979 DS109: Mature Women, Flowchart for Year 1981 DS110: Mature Women, Flowchart for Year 1982 DS111: Mature Women, Flowchart for Year 1984 DS112: Mature Women, Flowchart for Year 1986 DS113: Mature Women, Flowchart for Year 1987 DS116: Young Women, Flowchart for Year 1988 DS117: Young Women, Flowchart for Year 1991 DS118: Youth, 1979-1992 (Birth Record and Fertility, 1989) DS119: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1989) DS120: Youth, 1979-1992 (SAS Data Definition Statements, Common Demographic Information) DS121: Youth, 1979-1992 (SAS Data Definition Statements, Created Key Variables) DS122: Youth, 1979-1992 (SAS Data Definition Statements, Family Background) DS123: Youth, 1979-1992 (SAS Data Definition Statements, Marital History) DS124: Youth, 1979-1992 (SAS Data Definition Statements, Current Labor Force Status) DS125: Youth, 1979-1992 (SAS Data Definition Statements, Jobs) DS126: Youth, 1979-1992 (SAS Data Definition Statements, Job Information--Employer Supplement) DS127: Youth, 1979-1992 (SAS Data Definition Statements, Periods Not Working--Employer Supplement) DS128: Youth, 1979-1992 (SAS Data Definition Statements, Information Sheet, 1980-1989) DS129: Youth, 1979-1992 (SAS Data Definition Statements, Regular Schooling) DS130: Youth, 1979-1992 (SAS Data Definition Statements, Income and Assets, 1979-1990) DS131: Youth, 1979-1992 (SAS Data Definition Statements, Assets, 1985- 1989) DS132: Youth, 1979-1992 (SAS Data Definition Statements, Household Record) DS133: Youth, 1979-1992 (SAS Data Definition Statements, Periods When Respondent Was Not Working or in the Military) DS134: Youth, 1979-1992 (SAS Data Definition Statements, Degrees and Certification, 1979-1984 and 1988-1989) DS135: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1982-1984) DS136: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1985) DS137: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1986) DS138: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1987) DS139: Youth, 1979-1992 (SAS Data Definition Statements, Children Record Form for Biological Children) DS140: Youth, 1979-1992 (SAS Data Definition Statements, Children Record Form for Non-Biological Children) DS141: Youth, 1979-1992 (SAS Data Definition Statements, Fertility, 1979- 1981) DS142: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1979) DS143: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1980) DS144: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1981) DS145: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1982) DS146: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1983) DS147: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1984) DS148: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1985) DS149: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1986) DS150: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1987) DS151: Youth, 1979-1992 (SAS Data Definition Statements, Government Jobs--Employer Supplement, 1979-1987) DS152: Youth, 1979-1992 (SAS Data Definition Statements, Profiles, ASVAB Vocational Test, 1980) DS153: Youth, 1979-1992 (SAS Data Definition Statements, School Survey) DS154: Youth, 1979-1992 (SAS Data Definition Statements, Transcript Survey) DS155: Youth, 1979-1992 (SAS Data Definition Statements, Military Data, 1980-1989) DS156: Youth, 1979-1992 (SAS Data Definition Statements, Other Training) DS157: Youth, 1979-1992 (SAS Data Definition Statements, Governmental Training, 1979-1987) DS158: Youth, 1979-1992 (SAS Data Definition Statements, Child Care, 1982-1989) DS159: Youth, 1979-1992 (SAS Data Definition Statements, Health) DS160: Youth, 1979-1992 (SAS Data Definition Statements, Alcohol Use, 1982-1985 and 1988-1989) DS161: Youth, 1979-1992 (SAS Data Definition Statements, Drug Use, 1984 and 1988) DS162: Youth, 1979-1992 (SAS Data Definition Statements, Illegal Activities and Reported Police Contacts, 1980) DS163: Youth, 1979-1992 (SAS Data Definition Statements, Job Search and Job Findings, 1981-1982 and 1986-1987) DS164: Youth, 1979-1992 (SAS Data Definition Statements, Last Job Lasting 2 Weeks or More, 1979) DS165: Youth, 1979-1992 (SAS Data Definition Statements, Work Experience Prior to 11/1/78, 1979) DS166: Youth, 1979-1992 (SAS Data Definition Statements, Attitudes of Influential Person Toward Respondent's Decisions, 1979) DS167: Youth, 1979-1992 (SAS Data Definition Statements, Attitudes Toward Hypothetical Job Offers, 1979) DS168: Youth, 1979-1992 (SAS Data Definition Statements, Attitudes Toward Work, Self, Traditional Roles, AIDS, 1979-1984 and 1987-1988) DS169: Youth, 1979-1992 (SAS Data Definition Statements, Interviewer Remarks) DS170: Youth, 1979-1992 (SAS Data Definition Statements, Time Spent Working, Going to School, Training, Etc., 1981) DS171: Youth, 1979-1992 (SAS Data Definition Statements, Supplemental Fertility File) DS172: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1988) DS173: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1988) DS174: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1989) DS175: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1989) DS176: Youth, 1979-1992 (Birth Record and Fertility, 1990) DS177: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1990) DS178: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1990) DS179: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1990) DS180: Child Assessment Supplement, 1986 DS181: Child Assessment Supplement, 1988 DS182: Child Assessment Supplement, 1990 DS183: Codebook for All Child Assessment Supplements DS184: Index for All Child Assessment Supplements DS185: Mature Men, 1966-1991 (Flowchart 1978) DS186: Mature Men, 1966-1991 (Flowchart 1980) DS187: Mature Men, 1966-1991 (Flowchart 1981) DS188: Mature Men, 1966-1991 (Flowchart 1983) DS189: Mature Men, 1966-1991 (Flowchart for Widows, 1990) DS190: Mature Men, 1966-1991 (Flowchart 1990) DS191: Youth, 1979-1992 (SAS Data Definition Statements for Birth Record and Fertility, 1991) DS192: Youth, 1979-1992 (SAS Data Definition Statements for Miscellaneous Non-Longitudinal Items, 1991) DS193: Youth, 1979-1992 (Birth Record and Fertility, 1991) DS194: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1991) DS196: Young Women, 1968-1991 (Appendix 33) DS197: Mature Men, 1966-1990 (Appendix 32) DS198: Mature Women, 1989 Pension (ISR Pension Data File) DS199: Mature Women, 1989 Pension (Crosswalk File of NLS Mature Women) DS200: Youth, 1979-1992 (Birth Records and Fertility, 1992) DS201: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1992) DS202: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1992) DS203: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1992) DS204: Handbook 1994 DS205: User Guide 1994 DS206: Child Handbook DS207: Child-Mother and Assessment 1986-1990 Guide DS208: Child-Mother Questionnaire, 1979-1988 DS209: Child-Mother Questionnaire, 1990 DS210: Child-Mother Questionnaire, 1992 DS211: Youth Surveys Questionnaire, 1979 DS212: Youth Surveys Questionnaire, 1980 DS213: Youth Surveys Questionnaire, 1981 DS214: Youth Surveys Questionnaire, 1982 DS222: Youth Surveys Questionnaire, 1992 DS9998: Citation The primary purpose of the five sets of surveys that comprise the National Longitudinal Surveys is the collection of data on the labor force experience of specific age-sex groups of Americans: Older Men aged 45-59 in 1966, Mature Women aged 30-44 in 1967, Young Men aged 14-24 in 1966, Young Women aged 14-24 in 1968, and Youth aged 14-21 in 1979. Each of the 1960s cohorts has been surveyed 12 or more times over the years, and the Youth cohort has been surveyed yearly since 1979. The major topics covered within the surveys of each cohort include: (1) labor market experience variables (including labor force participation, unemployment, job history, and job mobility), (2) socioeconomic and human capital variables (including education, training, health and physical condition, marital and family characteristics, financial characteristics, and job attitudes), and (3) selected environmental variables (size of labor force and unemployment rates for local area). While the surveys of each cohort have collected data on the above core sets of variables, cohort-specific data have been gathered over the years focusing on the particular stage of labor market attachment that each group was experiencing. Thus, the surveys of young people have collected data on their educational goals, high school and college experiences, high school characteristics, and occupational aspirations and expectations, as well as military service. The surveys of women have gathered data on topics such as fertility, child care, responsibility for household tasks, care of parents, volunteer work, attitudes towards women working, and job discrimination. As the older-aged cohorts of men and women approached labor force withdrawal, surveys for these groups collected information on their retirement plans, health status, and pension benefits. Respondents within the 1979 Youth cohort have been the focus of a number of special surveys, including the collection of data on: (1) last secondary school attended, including transcript information and selected aptitude/intelligence scores, (2) test scores from the Armed Services Vocational Aptitude Battery (ASVAB), (3) illegal activities participation including police contacts, and (4) alcohol use and substance abuse. Finally, the 1986 and 1988 surveys of the Youth cohort included the administration of a battery of cognitive-socioemotional assessments to the approximately 7,000 children of the female 1979 Youth respondents. Data for the five cohorts are provided within main file releases, i.e., Mature Women 1967-1989, Young Women 1968-1991, Young Men 1966-1981, Older Men 1966-1990, and NLSY (Youth) 1979-1992. In addition, the following specially constructed data files are available: (1) a file that specifies the relationships among members of the four original cohorts living in the same household at the time of the initial surveys, i.e., husband-wife, mother-daughter, brother-sister, etc., (2) an NLSY workhistory tape detailing the week-by-week labor force attachment of the youth respondents from 1978 through the most current survey date, (3) an NLSY child-mother file linking the child assessment data to other information on children and mothers within the NLSY, (4) a supplemental NLSY file of constructed and edited fertility variables, (5) a women's support network tape detailing the geographic proximity of the relatives, friends, and acquaintances of 6,308 female NLSY respondents who were interviewed during the 1983-1985 surveys, and (6) two 1989 Mature Women's pension file detailing information on pensions and other employer-provided benefits. Each of the first four cohorts is represented by a national probability sample of approximately 5,000 individuals--1,500 Blacks and 3,500 Whites. These four "original cohorts" have been interviewed at least once in every two-year period since the 1960s. Retention rates have remained high, with around two-thirds of the active samples continuing to be interviewed. Three independent probability samples, designed to be representative of the entire population of youth born in the United States between 1957 and 1964, were drawn for the NLSY: (1) a cross-sectional sample of 6,111 respondents designed to be representative of the noninstitutionalized civilian segment of American young people aged 14-21 as of January 1, 1979, (2) a supplemental sample of 5,295 respondents designed to oversample civilian Hispanic, Black, and economically disadvantaged non-Hispanic, non-Black youth, and (3) a military sample of 1,280 respondents designed to represent the population aged 17-21 as of January 1, 1979, and serving in the military as of September 30, 1978. The retention rate for the NLSY, interviewed yearly since 1979, remains at over 90 percent. The military sample was interviewed from 1979-1984. (1) Due to the consolidation of files and removal of obsolete errata files, there are no Parts 45, 66, 114, 115, or 117 in this collection. These data occupy over 30 reels of tape when written at 6,250 bpi, and over 120 reels when written at 1,600 bpi. Due to the magnitude of this collection, interested users should initially request the introductory report that describes the file structure and content prior to submitting their orders. Codebooks are electronic although some supplementary materials are available only on microfiche. Numeric and KWIC indexes and various attachments are supplied as electronic files. Users will need to order Numeric and KWIC indexes along with data files to determine column locations for variables. (2) A change has been made to the structure of the 1979-1992 Youth Workhistory data file. The size of the file necessitated splitting the data into two records per case. The first record contains the data for the A, HOURS and DUALJOBS arrays and the second record contains the remainder of the data pertaining to specific job characteristics, gaps in employment, and summary labor force activity variables. Five cohorts are represented in this collection: Older Men aged 45 to 59 years of age in 1966, Mature Women aged 30 to 44 years in 1967, Young Men aged 14 to 24 years in 1966, Young Women aged 14 to 24 years in 1968, and NLSY (Youth--both males and females) aged 14 to 21 years in 1979.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2015 France, Italy, United KingdomAmerican Association for the Advancement of Science (AAAS) SSHRC, EC | LANGELIN, SNSF | 111-Silver labelling of m... +14 projectsSSHRC ,EC| LANGELIN ,SNSF| 111-Silver labelling of monoclonal antibodies to produce selective cytotoxic agents ,NSF| Statistical Methods for Enabling Medical and Population Genomics of Admixed Human Populations ,SNSF| Islamische Diskurse und die soziale Integration von Muslimen in den USA ,NIH| Mathematical Models and Statistical Methods for Large-Scale Population Genomics ,EC| TGOFA ,UKRI| Detecting signatures of natural selection in the human genome with geographically explicit models ,EC| NEOLITHISATION ,ARC| Molecular Archaeology: Carbon isotope analysis of amino acids as a means to investigate diets, physiology, metabolism and palaeoenvironment. ,NSF| Collaborative Research: Anthropological-Genomic Effects of European Colonization on Native North Americans ,SNSF| Using time serial samples to characterize the timing ad strength of selective sweeps ,WT| Wellcome Trust Sanger Institute - generic account for deposition of all core- funded research papers ,WT ,EC| MALADAPTED ,NIH| Human Population Diversity in Leukocyte Receptors ,SNSF| Characterizing migrations with modern and ancient genomic data: the limits of the Polynesian expansionRaghavan, M.; Steinrücken, M; Harris, M; Schiffels, Stephan; DeGiorgio, Michael; Albrechtsen, M; Valdiosera, M; Ávila-Arcos, M; Malaspinas, M; Eriksson, Anders; Moltke, M; Homburger, M; Wall, Jeff; Cornejo, Omar; Moreno-Mayar, M; Korneliussen, M; Pierre, M; Rasmussen, Rasmus; Campos, Paul; de Barros Damgaard, Peter; Allentoft, M.; Lindo, John; Metspalu, M.; Rodríguez-Varela, Carlos; Mansilla, M; Henrickson, Celeste; Seguin-Orlando, M; Malmström, M; Stafford, M; Shringarpure, M; Moreno-Estrada, M; Karmin, M.; Tambets, Kristiina; Bergström, Anders; Xue, Yali; Vera, Vera; Friend, Andrew; Singarayer, M; Valdes, Paul; Balloux, François; Leboreiro, M; Vera, M; Rangel-Villalobos, M; Pettener, David; Luiselli, Donata; Davis, Loren; Heyer, M; Zollikofer, Chris; Ponce de León, M; Smith, M; Grimes, John; Pike, John; Deal, John; Fuller, M; Arriaza, Bernardo; Standen, Vivien; Luz, M.; Ricaut, M; Guidon, M; Osipova, Ludmila; Voevoda, M.; Posukh, Olga; Balanovsky, M; Lavryashina, M.; Bogunov, M; Khusnutdinova, M; Gubina, M.; Balanovska, M; Fedorova, M; Litvinov, Sergey; Malyarchuk, M; Derenko, M.; Mosher, M.; Archer, David; Cybulski, Jerome; Petzelt, Barbara; Mitchell, Joycelynn; Worl, Rosita; Norman, Paul; Parham, Peter; Kemp, Brian,; Kivisild, Toomas; Smith, Chris; Sandhu, Manjinder,; Crawford, Michael; Villems, Richard; Smith, David; Waters, Michael; Goebel, Ted; Johnson, John; Malhi, Ripan; Jakobsson, Mattias; Meltzer, David; Manica, Andrea; Durbin, Richard; Bustamante, Carlos,; Song, Yun; Nielsen, Rasmus; Willerslev, Eske; Steinrucken, M.; Harris, K.; Rasmussen, S.; Albrechtsen, A.; Valdiosera, C.; Avila-Arcos, M.; Malaspinas, S.; Moltke, I.; Homburger, J.; Moreno-Mayar, J.; Korneliussen, S.; Pierre, T.; Rasmussen, M.; Damgaard, P.; Metspalu, E.; Rodriguez-Varela, R.; Mansilla, J.; Seguin-Orlando, A.; Malmstrom, H.; Stafford, T.; Shringarpure, S.; Moreno-Estrada, A.; Bergstrom, A.; Warmuth, V.; Singarayer, J.; Leboreiro, I.; Vera, J.; Rangel-Villalobos, H.; Heyer, E.; Ponce De Leon, M.; Grimes, V.; Pike, K.; Deal, M.; Fuller, T.; Ricaut, F.; Guidon, N.; Balanovsky, O.; Bogunov, Y.; Khusnutdinova, E.; Balanovska, E.; Fedorova, S.; Malyarchuk, B.; Norman, J.; Kemp, M.; Malhi, S.; Meltzer, J.; Song, S.;How and when the Americas were populated remains contentious. Using ancient and modern genome wide data we found that the ancestors of all present day Native Americans including Athabascans and Amerindians entered the Americas as a single migration wave from Siberia no earlier than 23 thousand years ago (ka) and after no more than an 8000 year isolation period in Beringia. After their arrival to the Americas ancestral Native Americans diversified into two basal genetic branches around 13 ka one that is now dispersed across North and South America and the other restricted to North America. Subsequent gene flow resulted in some Native Americans sharing ancestry with present day East Asians (including Siberians) and more distantly Australo Melanesians. Putative “Paleoamerican” relict populations including the historical Mexican Pericúes and South American Fuego Patagonians are not directly related to modern Australo Melanesians as suggested by the Paleoamerican Model. INTRODUCTION The consensus view on the peopling of the Americas is that ancestors of modern Native Americans entered the Americas from Siberia via the Bering Land Bridge and that this occurred at least {\textasciitilde}14.6 thousand years ago (ka). However the number and timing of migrations into the Americas remain controversial with conflicting interpretations based on anatomical and genetic evidence. RATIONALE In this study we address four major unresolved issues regarding the Pleistocene and recent population history of Native Americans: (i) the timing of their divergence from their ancestral group (ii) the number of migrations into the Americas (iii) whether there was {\textasciitilde}15000 years of isolation of ancestral Native Americans in Beringia (Beringian Incubation Model) and (iv) whether there was post Pleistocene survival of relict populations in the Americas related to Australo Melanesians as suggested by apparent differences in cranial morphologies between some early (“Paleoamerican”) remains and those of more recent Native Americans. We generated 31 high coverage modern genomes from the Americas Siberia and Oceania; 23 ancient genomic sequences from the Americas dating between {\textasciitilde}0.2 and 6 ka; and SNP chip genotype data from 79 present day individuals belonging to 28 populations from the Americas and Siberia. The above data sets were analyzed together with published modern and ancient genomic data from worldwide populations after masking some present day Native Americans for recent European admixture. RESULTS Using three different methods we determined the divergence time for all Native Americans (Athabascans and Amerindians) from their Siberian ancestors to be {\textasciitilde}20 ka and no earlier than {\textasciitilde}23 ka. Furthermore we dated the divergence between Athabascans (northern Native American branch together with northern North American Amerindians) and southern North Americans and South and Central Americans (southern Native American branch) to be {\textasciitilde}13 ka. Similar divergence times from East Asian populations and a divergence time between the two branches that is close in age to the earliest well established archaeological sites in the Americas suggest that the split between the branches occurred within the Americas. We additionally found that several sequenced Holocene individuals from the Americas are related to present day populations from the same geographical regions implying genetic continuity of ancient and modern populations in some parts of the Americas over at least the past 8500 years. Moreover our results suggest that there has been gene flow between some Native Americans from both North and South America and groups related to East Asians and Australo Melanesians the latter possibly through an East Asian route that might have included ancestors of modern Aleutian Islanders. Last using both genomic and morphometric analyses we found that historical Native American groups such as the Pericúes and Fuego Patagonians were not “relicts” of Paleoamericans and hence our results do not support an early migration of populations directly related to Australo Melanesians into the Americas. CONCLUSION Our results provide an upper bound of {\textasciitilde}23 ka on the initial divergence of ancestral Native Americans from their East Asian ancestors followed by a short isolation period of no more than {\textasciitilde}8000 years and subsequent entrance and spread across the Americas. The data presented are consistent with a single migration model for all Native Americans with later gene flow from sources related to East Asians and indirectly Australo Melanesians. The single wave diversified {\textasciitilde}13 ka likely within the Americas giving rise to the northern and southern branches of present day Native Americans. View larger version: In this page In a new window Download PowerPoint Slide for Teaching Population history of present day Native Americans.The ancestors of all Native Americans entered the Americas as a single migration wave from Siberia (purple) no earlier than {\textasciitilde}23 ka separate from the Inuit (green) and diversified into “northern” and “southern” Native American branches {\textasciitilde}13 ka. There is evidence of post divergence gene flow between some Native Americans and groups related to East Asians/Inuit and Australo Melanesians (yellow). Genetic history of Native Americans Several theories have been put forth as to the origin and timing of when Native American ancestors entered the Americas. To clarify this controversy Raghavan et al. examined the genomic variation among ancient and modern individuals from Asia and the Americas. There is no evidence for multiple waves of entry or recurrent gene flow with Asians in northern populations. The earliest migrations occurred no earlier than 23000 years ago from Siberian ancestors. Amerindians and Athabascans originated from a single population splitting approximately 13000 years ago. Science this issue 10.1126/science.aab3884
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For further information contact us at helpdesk@openaire.eu407 citations 407 popularity Top 0.1% influence Top 1% impulse Top 0.1% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research NIH | Center for Family and Dem..., NIH | PROSTATE, LUNG, COLORECTA..., NIH | Computational Methods to ... +195 projectsNIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,WT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,CIHR ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| The University of Iowa Prevention Research Center ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCERAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;Downloads of Add Health require submission of the following information, which is shared with the original producer of Add Health: supervisor name, supervisor email, and reason for download. A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2018 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Wave V data collection took place from 2016 to 2018, when the original Wave I respondents were 33 to 43 years old. For the first time, a mixed mode survey design was used. In addition, several experiments were embedded in early phases of the data collection to test response to various treatments. A similar range of data was collected on social, environmental, economic, behavioral, and health circumstances of respondents, with the addition of retrospective child health and socio-economic status questions. Physical measurements and biospecimens were again collected at Wave V, and included most of the same measures as at Wave IV. Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights DS32: Wave V: Mixed-Mode Survey, Public Use Sample DS33: Wave V: Mixed-Mode Survey, Public Use Sample (Section 16B: Pregnancy, Live Births, Children and Parenting) DS34: Wave V: Biomarkers, Anthropometrics DS35: Wave V: Biomarkers, Cardiovascular Measures DS36: Wave V: Biomarkers, Demographics DS37: Wave V: Biomarkers, Measures of Glucose Homeostasis DS38: Wave V: Biomarkers, Measures of Inflammation and Immune Function DS39: Wave V: Biomarkers, Lipids DS40: Wave V: Biomarkers, Medication Use DS41: Wave V: Biomarkers, Renal Function DS42: Wave V: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample. Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I. Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later. Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. Wave V: All Wave I respondents who were still living were eligible at Wave V, yielding a pool of 19,828 persons. This pool was split into three stratified random samples for the purposes of survey design testing. For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. audio computer-assisted self interview (ACASI); computer-assisted personal interview (CAPI); computer-assisted self interview (CASI); face-to-face interview; mixed mode; paper and pencil interview (PAPI); telephone interviewWave V data files were minimally processed by ICPSR. For value labeling, missing value designation, and question text (where applicable), please see the available P.I. Codebook/Questionnaires. The study-level documentation (Data Guide, User Guide) does not include Wave V datasets.Documentation for Waves prior to Wave V may use an older version of the study title.Users should be aware that version history notes dated prior to 2015-11-09 do not apply to the current organization of the datasets.Please note that dates present in the Summary and Time Period fields are taken from the Add Health Study Design page. The Date of Collection field represents the range of interview dates present in the data files for each wave.Wave I and Wave II field work was conducted by the National Opinion Research Center at the University of Chicago.Wave III, Wave IV, and Wave V field work was conducted by the Research Triangle Institute.For the most updated list of related publications, please see the Add Health Publications Web site.Additional information on the National Longitudinal Study of Adolescent to Adult Health (Add Health) series can be found on the Add Health Web site. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Wave V aimed to track the emergence of chronic disease as the cohort aged into their 30s and early 40s. Add health is a school-based longitudinal study of a nationally-representative sample of adolescents in grates 7-12 in the United States in 1945-45. Over more than 20 years of data collection, data have been collected from adolescents, their fellow students, school administrators, parents, siblings, friends, and romantic partners through multiple data collection components. In addition, existing databases with information about respondents' neighborhoods and communities have been merged with Add Health data, including variables on income poverty, unemployment, availability and utilization of health services, crime, church membership, and social programs and policies. The data files are not weighted. However, the collection features a number of weight variables contained within the following datasets: DS4: Wave I: Public Use Grand Sample Weights DS7: Wave II: Public Use Grand Sample Weights DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS21: Wave III: Public In-Home Weights DS31: Wave IV: Public Use Weights DS42: Wave V: Public Use Weights Please note that these weights files do not apply to the Biomarker data files. For additional information on the application of weights for data analysis, please see the ICPSR User Guide, or the Guidelines for Analyzing Add Health Data. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent Wave II: 88.6 percent Wave III: 77.4 percent Wave IV: 80.3 percent Wave V: 71.8 percent Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States.
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For further information contact us at helpdesk@openaire.eu8 citations 8 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2006 NetherlandsAcoustical Society of America (ASA) NIH | Training for Speech and H..., NIH | VERY-HIGH-FREQUENCY ULTRA..., NIH | DECISION PROCESSES IN DET... +6 projectsNIH| Training for Speech and Hearing Sciences ,NIH| VERY-HIGH-FREQUENCY ULTRASOUND ARRAYS FOR OPHTHALMOLOGY ,NIH| DECISION PROCESSES IN DETECTION AND DISCRIMINATION ,SSHRC ,NSERC ,NIH| Tumor Diagnosis through Enhanced Ultrasound Imaging ,NSF| Physical Modeling of the Piano ,NIH| NORMAL &IMPAIRED TEMPORAL PROCESSING OF COMPLEX SOUNDS ,NSF| Workshop on ToBI for Spontaneous English SpeechAuthors: Broersma, M.;Broersma, M.;Native and nonnative listeners categorized /v/ and /f/ at the end of English nonwords. For each participant, the duration of the previous vowel was kept constant, so that it was not informative and sometimes mismatched other information in the signal. Vowel duration was varied between participants. Previously presented results [M. Broersma, J. Acoust. Soc. Am. 117, 3809–3901 (2005)] showed that native English listeners relied strongly on the misleading vowel duration cue. For Dutch listeners, no effect of vowel duration was found. Due to the redundancy of information in the signal, Dutch listeners categorized the contrast more categorically than English listeners. New analyses investigated whether Dutch listeners did not attempt to use vowel duration at all, or whether they learned to ignore the misleading cue more easily than the English listeners did. The results showed that Dutch listeners did use vowel duration initially, but stopped using this cue after very few trials. By the end of the practice part (33 trials) the effect of vowel duration had fully disappeared. The English listeners used vowel duration as a voicing cue throughout the experiment. This suggests that it may be easier to learn to ignore uninformative perceptual cues in a nonnative language than in one’s native language.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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description Publicationkeyboard_double_arrow_right Article , Preprint 2023Elsevier BV SSHRC, NSF | Dynamical Approaches for ..., NSF | Stochastic Analysis and N...SSHRC ,NSF| Dynamical Approaches for Some Complex Stochastic Systems ,NSF| Stochastic Analysis and Numerics for Large Scale Dynamical Systems, with ApplicationsAuthors: Lazrak, Ali; Zhang, Jianfeng;Lazrak, Ali; Zhang, Jianfeng;We study pre-vote interactions in a committee that enacts a welfare-improving reform through voting. Committee members use decentralized promises contingent on the reform enactment to influence the vote outcome. Equilibrium promises prevent beneficial coalitional deviations and minimize total promises. We show that multiple equilibria exist, involving promises from high- to low-intensity members to enact the reform. Promises dissuade reform opponents from enticing the least enthusiastic reform supporters to vote against the reform. We explore whether some recipients of the promises can be supporters of the reform and discuss the impact of polarization on the total promises.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2023Springer Science and Business Media LLC SSHRC, NSF | Time-Consistency Theory f..., NSF | Several Problems of Stoch...SSHRC ,NSF| Time-Consistency Theory for Time-Inconsistent Stochastic Optimal Control Problems ,NSF| Several Problems of Stochastic Optimal Controls in Infinite Time HorizonAuthors: Lazrak, Ali; Wang, Hanxiao; Yong, Jiongmin;Lazrak, Ali; Wang, Hanxiao; Yong, Jiongmin;We investigate a linear quadratic stochastic zero-sum game where two players lobby a political representative to invest in a wind turbine farm. Players are time-inconsistent because they discount performance with a non-constant rate. Our objective is to identify a consistent planning equilibrium in which the players are aware of their inconsistency and cannot commit to a lobbying policy. We analyze the equilibrium behavior in both single player and two-player cases, and compare the behavior of the game under constant and non-constant discount rates. The equilibrium behavior is provided in closed-loop form, either analytically or via numerical approximation. Our numerical analysis of the equilibrium reveals that strategic behavior leads to more intense lobbying without resulting in overshooting.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research WT, NIH | Cascades of Network Struc..., NIH | Economic Evaluation of Ad... +207 projectsWT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,NIH| Health Disparities Among a Vulnerable Population: A Longitudinal Analysis ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| The University of Iowa Prevention Research Center ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NSF| Health Lifestyles and the Reproduction of Inequality ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| UIC Program for Interdisciplinary Careers in Womens Health Research ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NHMRC| Experience-dependent cellular plasticity and cognitive deficits in mouse models of schizophrenia ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCER ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NIH| Synthetic Information Systems for Better Informing Public Health Policymakers ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Childhood Family Instability, Adult Stress Reactivity, and Consequences for Health ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,CIHR ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| Transitions to Adulthood and Health Risk Among U.S. Young Adults ,NIH| CUPC Admin Core ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| University of Colorado Population Center ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| Phenotypic refinement of externalizing pathways to alcohol-related behaviors ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,NIH| Innovations in Pediatric Pain ResearchAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview
Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu65 citations 65 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018Public Library of Science (PLoS) NSF | Collaborative Research: A..., NIH | Injection Risk Networks i..., SSHRCNSF| Collaborative Research: Applying Behavioral-Ecological Network Models to Enhance Distributed Spectrum Access in Cognitive Radio ,NIH| Injection Risk Networks in Rural Puerto Rico ,SSHRCAuthors: Elspeth Ready;Elspeth Ready;pmid: 29529040
pmc: PMC5846769
Social institutions that facilitate sharing and redistribution may help mitigate the impact of resource shocks. In the North American Arctic, traditional food sharing may direct food to those who need it and provide a form of natural insurance against temporal variability in hunting returns within households. Here, network properties that facilitate resource flow (network size, quality, and density) are examined in a country food sharing network comprising 109 Inuit households from a village in Nunavik (Canada), using regressions to investigate the relationships between these network measures and household socioeconomic attributes. The results show that although single women and elders have larger networks, the sharing network is not structured to prioritize sharing towards households with low food availability. Rather, much food sharing appears to be driven by reciprocity between high-harvest households, meaning that poor, low-harvest households tend to have less sharing-based social capital than more affluent, high-harvest households. This suggests that poor, low-harvest households may be more vulnerable to disruptions in the availability of country food.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu25 citations 25 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2016Elsevier BV NSF | Programs on Critical Prob..., SSHRCNSF| Programs on Critical Problems in Physics, Astrophysics and Biophysics at the Aspen Center for Physics ,SSHRCAuthors: Martin Elvis; Tony Milligan; Alanna Krolikowski;Martin Elvis; Tony Milligan; Alanna Krolikowski;The Outer Space Treaty makes it clear that the Moon is the province of all mankind, with the latter ordinarily understood to exclude state or private appropriation of any portion of its surface. However, there are indeterminacies in the Treaty and in space law generally over the issue of appropriation. These indeterminacies might permit a close approximation to a property claim or some manner of quasi-property. The recently revealed highly inhomogeneous distribution of lunar resources changes the context of these issues. We illustrate this altered situation by considering the Peaks of Eternal Light. They occupy about one square kilometer of the lunar surface. We consider a thought experiment in which a Solar telescope is placed on one of the Peaks of Eternal Light at the lunar South pole for scientific research. Its operation would require nondisturbance, and hence that the Peak remain unvisited by others, effectively establishing a claim of protective exclusion and de facto appropriation. Such a telescope would be relatively easy to emplace with todays technology and so poses a near-term property issue on the Moon. While effective appropriation of a Peak might proceed without raising some of the familiar problems associated with commercial development (especially lunar mining), the possibility of such appropriation nonetheless raises some significant issues concerning justice and the safeguarding of scientific practice on the lunar surface. We consider this issue from scientific, technical, ethical and policy viewpoints. Comment: 20 pages, 3 figures (color). Space Policy in press
arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2016License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu20 citations 20 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2016License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2020Oxford University Press (OUP) SSHRC, NSF | Collaborative Research: E..., NSF | Collaborative Research: E... +1 projectsSSHRC ,NSF| Collaborative Research: Exploring the impact of cosmic ray feedback on galaxy evolution ,NSF| Collaborative Research: Exploring the impact of cosmic ray feedback on galaxy evolution ,NSF| Kavli Institute for Theoretical PhysicsAuthors: Chaoran Wang; Mateusz Ruszkowski; H-Y Karen Yang;Chaoran Wang; Mateusz Ruszkowski; H-Y Karen Yang;Black hole feedback plays a central role in shaping the circumgalactic medium (CGM) of elliptical galaxies. We systematically study the impact of plasma physics on the evolution of ellipticals by performing three-dimensional non-ideal magneto-hydrodynamic simulations of the interactions of active galactic nucleus (AGN) jets with the CGM including magnetic fields, and cosmic rays (CRs) and their transport processes. We find that the physics of feedback operating on large galactic scales depends very sensitively on plasma physics operating on small scales. Specifically, we demonstrate that: (i) in the purely hydrodynamical case, the AGN jets initially maintain the atmospheres in global thermal balance. However, local thermal instability generically leads to the formation of massive cold disks in the vicinity of the central black hole in disagreement with observations; (ii) including weak magnetic fields prevents the formation of the disks because local B-field amplification in the precipitating cold gas leads to strong magnetic breaking, which quickly extracts angular momentum from the accreting clouds. The magnetic fields transform the cold clouds into narrow filaments that do not fall ballistically; (iii) when plasma composition in the AGN jets is dominated by CRs, and CR transport is neglected, the atmospheres exhibit cooling catastrophes due to inefficient heat transfer from the AGN to CGM despite Coulomb/hadronic CR losses being present; (iv) including CR streaming and heating restores agreement with the observations, i.e., cooling catastrophes are prevented and massive cold central disks do not form. The AGN power is reduced as its energy is utilized efficiently. submitted to MNRAS
Monthly Notices of t... arrow_drop_down Monthly Notices of the Royal Astronomical SocietyArticle . 2020License: OUP Standard Publication ReuseData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2019License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Monthly Notices of t... arrow_drop_down Monthly Notices of the Royal Astronomical SocietyArticle . 2020License: OUP Standard Publication ReuseData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2019License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 1984 EnglishICPSR - Interuniversity Consortium for Political and Social Research NSF | Drug Dealing, Incarcerati..., NSF | Mathematical Sciences: Ro..., FCT | D4 +4 projectsNSF| Drug Dealing, Incarceration, and Self-Employment ,NSF| Mathematical Sciences: Robust Estimation and Testing of Econometric Models for Panel Data ,FCT| D4 ,NSF| Research On Semiparametric and Nonparametric Estimation of Econometric Models ,NSF| An Analysis of Job-Seeking Methods Used By Unemployed Youth ,NSF| Identification Problems in the Social Sciences ,SSHRCAuthors: Ohio State University. Center For Human Resource Research.;Ohio State University. Center For Human Resource Research.;Datasets: DS0: Study-Level Files DS1: Mature Men, 1966-1990 DS2: Mature Women, 1967-1986 (Main File) DS3: Young Men, 1966-1981 DS4: Young Women, 1968-1982 (Main File) DS5: Youth, 1979-1992 (Common Demographic Information) DS6: Youth, 1979-1992 (Created Key Variables) DS7: Youth, 1979-1992 (Family Background) DS8: Youth, 1979-1992 (Marital History) DS9: Youth, 1979-1992 (Current Labor Force Status) DS10: Youth, 1979-1992 (Jobs) DS11: Youth, 1979-1992 (Job Information--Employer Supplement) DS12: Youth, 1979-1992 (Periods Not Working--Employer Supplement) DS13: Youth, 1979-1992 (Information Sheet, 1980-1989) DS14: Youth, 1979-1992 (Regular Schooling) DS15: Youth, 1979-1992 (Income and Assets, 1979-1990) DS16: Youth, 1979-1992 (Assets, 1985-1989) DS17: Youth, 1979-1992 (Household Record) DS18: Youth, 1979-1992 (Periods When Respondent Was Not Working or in the Military) DS19: Youth, 1979-1992 (Degrees and Certification, 1979-1984 and 1988- 1989) DS20: Youth, 1979-1992 (Birth Record and Fertility, 1982-1984) DS21: Youth, 1979-1992 (Birth Record and Fertility, 1985) DS22: Youth, 1979-1992 (Birth Record and Fertility, 1986) DS23: Youth, 1979-1992 (Birth Record and Fertility, 1987) DS24: Youth, 1979-1992 (Children Record Form for Biological Children) DS25: Youth, 1979-1992 (Children Record Form for Non-Biological Children) DS26: Youth, 1979-1992 (Fertility, 1979-1981) DS27: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1979) DS28: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1980) DS29: Young Women, 1968-1982 (Attachment 2 File) DS30: Young Women, 1968-1982 (Attachment 3 File) DS31: Young Women, 1968-1982 (KWIC Index) DS32: Young Women, 1968-1982 (Numeric Index) DS33: Young Women, 1983-1991 (Main File) DS34: Young Women, 1983-1991 (Attachment File) DS35: Young Women, 1983-1991 (KWIC Index) DS36: Young Women, 1983-1991 (Numeric Index) DS37: Mature Men, 1966-1990 (Attachment 3) DS38: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1981) DS39: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1982) DS40: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1983) DS41: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1984) DS42: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1985) DS43: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1986) DS44: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1987) DS46: Youth, 1979-1992 (Government Jobs--Employer Supplement, 1979-1987) DS47: Youth, 1979-1992 (Profiles--ASVAB Vocational Test, 1980) DS48: Youth, 1979-1992 (School Survey) DS49: Youth, 1979-1992 (Transcript Survey) DS50: Youth, 1979-1992 (Military Data, 1980-1989) DS51: Child-Mother, 1979-1990 (Merged File) DS52: Merged Child-Mother Data, 1979-1990 (Numeric Index) DS53: Mature Men, 1966-1990 (Numeric Index) DS54: Mature Men, 1966-1990 (KWIC Index) DS55: Mature Women, 1967-1986 (Numeric Index) DS56: Mature Women, 1967-1986 (KWIC Index) DS57: Young Men, 1966-1981 (Numeric Index) DS58: Young Men, 1966-1981 (KWIC Index) DS59: Original Cohort Relationship: Mature Men, Mature Women DS60: Original Cohort Relationship: Mature Men, Young Women DS61: Original Cohort Relationship: Mature Men, Young Men DS62: Original Cohort Relationship: Mature Women, Young Women DS63: Original Cohort Relationship: Mature Women, Young Men DS64: Original Cohort Relationship: Young Men, Young Women DS65: Original Cohort Relationship Documentation DS67: Women's Support Network, 1983-1985: Round 5 Respondent-Relation Distance DS68: Women's Support Network, 1983-1985: Round 5 Respondent-Relation Distance, Record Layout DS69: Women's Support Network, 1983-1985: Round 6 Respondent-Relation Distance DS70: Women's Support Network, 1983-1985: Round 6 Respondent-Relation Distance, Record Layout DS71: Women's Support Network, 1983-1985: Round 7 Respondent-Relation Distance DS72: Women's Support Network, 1983-1985: Round 7 Respondent-Relation Distance, Record Layout DS73: Women's Support Network, 1983-1985: Respondent Mobility File DS74: Women's Support Network, 1983-1985: Respondent Mobility File, Record Layout DS75: Youth, 1979-1992 (Other Training) DS76: Youth, 1979-1992 (Government Training, 1979-1987) DS77: Youth, 1979-1992 (Child Care, 1982-1989) DS78: Youth, 1979-1992 (Health) DS79: Youth, 1979-1992 (Alcohol Use, 1982-1985 and 1988-1989) DS80: Youth, 1979-1992 (Drug Use, 1984 and 1988) DS81: Youth, 1979-1992 (Illegal Activities and Reported Police Contacts, 1980) DS82: Youth, 1979-1992 (Job Search and Job Findings, 1981-1982 and 1986- 1987) DS83: Youth, 1979-1992 (Last Job Lasting 2 Weeks or More, 1979) DS84: Youth, 1979-1992 (Work Experience Prior to 11/1/78, 1979) DS85: Youth, 1979-1992 (Attitudes of Influential Person Toward Respondent's Decisions, 1979) DS86: Youth, 1979-1992 (Attitudes Toward Hypothetical Job Offers, 1979) DS87: Youth, 1979-1992 (Attitudes Toward Work, Self, Traditional Roles, AIDS, 1979-1984 and 1987-1988) DS88: Youth, 1979-1992 (Interviewer Remarks) DS89: Youth, 1979-1992 (Time Spent Working, Going to School, Training, Etc., 1981) DS90: Youth, 1979-1992 (Supplemental Fertility File) DS91: Youth, 1979-1992 (Numeric Index) DS92: Youth, 1979-1992 (KWIC Index) DS93: Youth, 1979-1992 (Codebook) DS94: Youth, 1979-1992 (Workhistory) DS95: Attachment 3 for Mature Women, 1967-1986 DS96: Mature Women, 1987-1989 (Main File) DS97: Mature Women, 1987-1989 (KWIC Index) DS98: Mature Women, 1987-1989 (Numeric Index) DS99: Attachment 3 for Mature Women, 1987-1989 DS100: Youth, 1979-1992 (Birth Record and Fertility, 1988) DS101: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1988) DS102: Young Women, Flowchart for Year 1978 DS103: Young Women, Flowchart for Year 1980 DS104: Young Women, Flowchart for Year 1982 DS105: Young Women, Flowchart for Year 1983 DS106: Young Women, Flowchart for Year 1985 DS107: Young Women, Flowchart for Year 1987 DS108: Mature Women, Flowchart for Year 1979 DS109: Mature Women, Flowchart for Year 1981 DS110: Mature Women, Flowchart for Year 1982 DS111: Mature Women, Flowchart for Year 1984 DS112: Mature Women, Flowchart for Year 1986 DS113: Mature Women, Flowchart for Year 1987 DS116: Young Women, Flowchart for Year 1988 DS117: Young Women, Flowchart for Year 1991 DS118: Youth, 1979-1992 (Birth Record and Fertility, 1989) DS119: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1989) DS120: Youth, 1979-1992 (SAS Data Definition Statements, Common Demographic Information) DS121: Youth, 1979-1992 (SAS Data Definition Statements, Created Key Variables) DS122: Youth, 1979-1992 (SAS Data Definition Statements, Family Background) DS123: Youth, 1979-1992 (SAS Data Definition Statements, Marital History) DS124: Youth, 1979-1992 (SAS Data Definition Statements, Current Labor Force Status) DS125: Youth, 1979-1992 (SAS Data Definition Statements, Jobs) DS126: Youth, 1979-1992 (SAS Data Definition Statements, Job Information--Employer Supplement) DS127: Youth, 1979-1992 (SAS Data Definition Statements, Periods Not Working--Employer Supplement) DS128: Youth, 1979-1992 (SAS Data Definition Statements, Information Sheet, 1980-1989) DS129: Youth, 1979-1992 (SAS Data Definition Statements, Regular Schooling) DS130: Youth, 1979-1992 (SAS Data Definition Statements, Income and Assets, 1979-1990) DS131: Youth, 1979-1992 (SAS Data Definition Statements, Assets, 1985- 1989) DS132: Youth, 1979-1992 (SAS Data Definition Statements, Household Record) DS133: Youth, 1979-1992 (SAS Data Definition Statements, Periods When Respondent Was Not Working or in the Military) DS134: Youth, 1979-1992 (SAS Data Definition Statements, Degrees and Certification, 1979-1984 and 1988-1989) DS135: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1982-1984) DS136: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1985) DS137: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1986) DS138: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1987) DS139: Youth, 1979-1992 (SAS Data Definition Statements, Children Record Form for Biological Children) DS140: Youth, 1979-1992 (SAS Data Definition Statements, Children Record Form for Non-Biological Children) DS141: Youth, 1979-1992 (SAS Data Definition Statements, Fertility, 1979- 1981) DS142: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1979) DS143: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1980) DS144: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1981) DS145: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1982) DS146: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1983) DS147: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1984) DS148: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1985) DS149: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1986) DS150: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1987) DS151: Youth, 1979-1992 (SAS Data Definition Statements, Government Jobs--Employer Supplement, 1979-1987) DS152: Youth, 1979-1992 (SAS Data Definition Statements, Profiles, ASVAB Vocational Test, 1980) DS153: Youth, 1979-1992 (SAS Data Definition Statements, School Survey) DS154: Youth, 1979-1992 (SAS Data Definition Statements, Transcript Survey) DS155: Youth, 1979-1992 (SAS Data Definition Statements, Military Data, 1980-1989) DS156: Youth, 1979-1992 (SAS Data Definition Statements, Other Training) DS157: Youth, 1979-1992 (SAS Data Definition Statements, Governmental Training, 1979-1987) DS158: Youth, 1979-1992 (SAS Data Definition Statements, Child Care, 1982-1989) DS159: Youth, 1979-1992 (SAS Data Definition Statements, Health) DS160: Youth, 1979-1992 (SAS Data Definition Statements, Alcohol Use, 1982-1985 and 1988-1989) DS161: Youth, 1979-1992 (SAS Data Definition Statements, Drug Use, 1984 and 1988) DS162: Youth, 1979-1992 (SAS Data Definition Statements, Illegal Activities and Reported Police Contacts, 1980) DS163: Youth, 1979-1992 (SAS Data Definition Statements, Job Search and Job Findings, 1981-1982 and 1986-1987) DS164: Youth, 1979-1992 (SAS Data Definition Statements, Last Job Lasting 2 Weeks or More, 1979) DS165: Youth, 1979-1992 (SAS Data Definition Statements, Work Experience Prior to 11/1/78, 1979) DS166: Youth, 1979-1992 (SAS Data Definition Statements, Attitudes of Influential Person Toward Respondent's Decisions, 1979) DS167: Youth, 1979-1992 (SAS Data Definition Statements, Attitudes Toward Hypothetical Job Offers, 1979) DS168: Youth, 1979-1992 (SAS Data Definition Statements, Attitudes Toward Work, Self, Traditional Roles, AIDS, 1979-1984 and 1987-1988) DS169: Youth, 1979-1992 (SAS Data Definition Statements, Interviewer Remarks) DS170: Youth, 1979-1992 (SAS Data Definition Statements, Time Spent Working, Going to School, Training, Etc., 1981) DS171: Youth, 1979-1992 (SAS Data Definition Statements, Supplemental Fertility File) DS172: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1988) DS173: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1988) DS174: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1989) DS175: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1989) DS176: Youth, 1979-1992 (Birth Record and Fertility, 1990) DS177: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1990) DS178: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1990) DS179: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1990) DS180: Child Assessment Supplement, 1986 DS181: Child Assessment Supplement, 1988 DS182: Child Assessment Supplement, 1990 DS183: Codebook for All Child Assessment Supplements DS184: Index for All Child Assessment Supplements DS185: Mature Men, 1966-1991 (Flowchart 1978) DS186: Mature Men, 1966-1991 (Flowchart 1980) DS187: Mature Men, 1966-1991 (Flowchart 1981) DS188: Mature Men, 1966-1991 (Flowchart 1983) DS189: Mature Men, 1966-1991 (Flowchart for Widows, 1990) DS190: Mature Men, 1966-1991 (Flowchart 1990) DS191: Youth, 1979-1992 (SAS Data Definition Statements for Birth Record and Fertility, 1991) DS192: Youth, 1979-1992 (SAS Data Definition Statements for Miscellaneous Non-Longitudinal Items, 1991) DS193: Youth, 1979-1992 (Birth Record and Fertility, 1991) DS194: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1991) DS196: Young Women, 1968-1991 (Appendix 33) DS197: Mature Men, 1966-1990 (Appendix 32) DS198: Mature Women, 1989 Pension (ISR Pension Data File) DS199: Mature Women, 1989 Pension (Crosswalk File of NLS Mature Women) DS200: Youth, 1979-1992 (Birth Records and Fertility, 1992) DS201: Youth, 1979-1992 (Miscellaneous Non-Longitudinal Items, 1992) DS202: Youth, 1979-1992 (SAS Data Definition Statements, Birth Record and Fertility, 1992) DS203: Youth, 1979-1992 (SAS Data Definition Statements, Miscellaneous Non-Longitudinal Items, 1992) DS204: Handbook 1994 DS205: User Guide 1994 DS206: Child Handbook DS207: Child-Mother and Assessment 1986-1990 Guide DS208: Child-Mother Questionnaire, 1979-1988 DS209: Child-Mother Questionnaire, 1990 DS210: Child-Mother Questionnaire, 1992 DS211: Youth Surveys Questionnaire, 1979 DS212: Youth Surveys Questionnaire, 1980 DS213: Youth Surveys Questionnaire, 1981 DS214: Youth Surveys Questionnaire, 1982 DS222: Youth Surveys Questionnaire, 1992 DS9998: Citation The primary purpose of the five sets of surveys that comprise the National Longitudinal Surveys is the collection of data on the labor force experience of specific age-sex groups of Americans: Older Men aged 45-59 in 1966, Mature Women aged 30-44 in 1967, Young Men aged 14-24 in 1966, Young Women aged 14-24 in 1968, and Youth aged 14-21 in 1979. Each of the 1960s cohorts has been surveyed 12 or more times over the years, and the Youth cohort has been surveyed yearly since 1979. The major topics covered within the surveys of each cohort include: (1) labor market experience variables (including labor force participation, unemployment, job history, and job mobility), (2) socioeconomic and human capital variables (including education, training, health and physical condition, marital and family characteristics, financial characteristics, and job attitudes), and (3) selected environmental variables (size of labor force and unemployment rates for local area). While the surveys of each cohort have collected data on the above core sets of variables, cohort-specific data have been gathered over the years focusing on the particular stage of labor market attachment that each group was experiencing. Thus, the surveys of young people have collected data on their educational goals, high school and college experiences, high school characteristics, and occupational aspirations and expectations, as well as military service. The surveys of women have gathered data on topics such as fertility, child care, responsibility for household tasks, care of parents, volunteer work, attitudes towards women working, and job discrimination. As the older-aged cohorts of men and women approached labor force withdrawal, surveys for these groups collected information on their retirement plans, health status, and pension benefits. Respondents within the 1979 Youth cohort have been the focus of a number of special surveys, including the collection of data on: (1) last secondary school attended, including transcript information and selected aptitude/intelligence scores, (2) test scores from the Armed Services Vocational Aptitude Battery (ASVAB), (3) illegal activities participation including police contacts, and (4) alcohol use and substance abuse. Finally, the 1986 and 1988 surveys of the Youth cohort included the administration of a battery of cognitive-socioemotional assessments to the approximately 7,000 children of the female 1979 Youth respondents. Data for the five cohorts are provided within main file releases, i.e., Mature Women 1967-1989, Young Women 1968-1991, Young Men 1966-1981, Older Men 1966-1990, and NLSY (Youth) 1979-1992. In addition, the following specially constructed data files are available: (1) a file that specifies the relationships among members of the four original cohorts living in the same household at the time of the initial surveys, i.e., husband-wife, mother-daughter, brother-sister, etc., (2) an NLSY workhistory tape detailing the week-by-week labor force attachment of the youth respondents from 1978 through the most current survey date, (3) an NLSY child-mother file linking the child assessment data to other information on children and mothers within the NLSY, (4) a supplemental NLSY file of constructed and edited fertility variables, (5) a women's support network tape detailing the geographic proximity of the relatives, friends, and acquaintances of 6,308 female NLSY respondents who were interviewed during the 1983-1985 surveys, and (6) two 1989 Mature Women's pension file detailing information on pensions and other employer-provided benefits. Each of the first four cohorts is represented by a national probability sample of approximately 5,000 individuals--1,500 Blacks and 3,500 Whites. These four "original cohorts" have been interviewed at least once in every two-year period since the 1960s. Retention rates have remained high, with around two-thirds of the active samples continuing to be interviewed. Three independent probability samples, designed to be representative of the entire population of youth born in the United States between 1957 and 1964, were drawn for the NLSY: (1) a cross-sectional sample of 6,111 respondents designed to be representative of the noninstitutionalized civilian segment of American young people aged 14-21 as of January 1, 1979, (2) a supplemental sample of 5,295 respondents designed to oversample civilian Hispanic, Black, and economically disadvantaged non-Hispanic, non-Black youth, and (3) a military sample of 1,280 respondents designed to represent the population aged 17-21 as of January 1, 1979, and serving in the military as of September 30, 1978. The retention rate for the NLSY, interviewed yearly since 1979, remains at over 90 percent. The military sample was interviewed from 1979-1984. (1) Due to the consolidation of files and removal of obsolete errata files, there are no Parts 45, 66, 114, 115, or 117 in this collection. These data occupy over 30 reels of tape when written at 6,250 bpi, and over 120 reels when written at 1,600 bpi. Due to the magnitude of this collection, interested users should initially request the introductory report that describes the file structure and content prior to submitting their orders. Codebooks are electronic although some supplementary materials are available only on microfiche. Numeric and KWIC indexes and various attachments are supplied as electronic files. Users will need to order Numeric and KWIC indexes along with data files to determine column locations for variables. (2) A change has been made to the structure of the 1979-1992 Youth Workhistory data file. The size of the file necessitated splitting the data into two records per case. The first record contains the data for the A, HOURS and DUALJOBS arrays and the second record contains the remainder of the data pertaining to specific job characteristics, gaps in employment, and summary labor force activity variables. Five cohorts are represented in this collection: Older Men aged 45 to 59 years of age in 1966, Mature Women aged 30 to 44 years in 1967, Young Men aged 14 to 24 years in 1966, Young Women aged 14 to 24 years in 1968, and NLSY (Youth--both males and females) aged 14 to 21 years in 1979.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2015 France, Italy, United KingdomAmerican Association for the Advancement of Science (AAAS) SSHRC, EC | LANGELIN, SNSF | 111-Silver labelling of m... +14 projectsSSHRC ,EC| LANGELIN ,SNSF| 111-Silver labelling of monoclonal antibodies to produce selective cytotoxic agents ,NSF| Statistical Methods for Enabling Medical and Population Genomics of Admixed Human Populations ,SNSF| Islamische Diskurse und die soziale Integration von Muslimen in den USA ,NIH| Mathematical Models and Statistical Methods for Large-Scale Population Genomics ,EC| TGOFA ,UKRI| Detecting signatures of natural selection in the human genome with geographically explicit models ,EC| NEOLITHISATION ,ARC| Molecular Archaeology: Carbon isotope analysis of amino acids as a means to investigate diets, physiology, metabolism and palaeoenvironment. ,NSF| Collaborative Research: Anthropological-Genomic Effects of European Colonization on Native North Americans ,SNSF| Using time serial samples to characterize the timing ad strength of selective sweeps ,WT| Wellcome Trust Sanger Institute - generic account for deposition of all core- funded research papers ,WT ,EC| MALADAPTED ,NIH| Human Population Diversity in Leukocyte Receptors ,SNSF| Characterizing migrations with modern and ancient genomic data: the limits of the Polynesian expansionRaghavan, M.; Steinrücken, M; Harris, M; Schiffels, Stephan; DeGiorgio, Michael; Albrechtsen, M; Valdiosera, M; Ávila-Arcos, M; Malaspinas, M; Eriksson, Anders; Moltke, M; Homburger, M; Wall, Jeff; Cornejo, Omar; Moreno-Mayar, M; Korneliussen, M; Pierre, M; Rasmussen, Rasmus; Campos, Paul; de Barros Damgaard, Peter; Allentoft, M.; Lindo, John; Metspalu, M.; Rodríguez-Varela, Carlos; Mansilla, M; Henrickson, Celeste; Seguin-Orlando, M; Malmström, M; Stafford, M; Shringarpure, M; Moreno-Estrada, M; Karmin, M.; Tambets, Kristiina; Bergström, Anders; Xue, Yali; Vera, Vera; Friend, Andrew; Singarayer, M; Valdes, Paul; Balloux, François; Leboreiro, M; Vera, M; Rangel-Villalobos, M; Pettener, David; Luiselli, Donata; Davis, Loren; Heyer, M; Zollikofer, Chris; Ponce de León, M; Smith, M; Grimes, John; Pike, John; Deal, John; Fuller, M; Arriaza, Bernardo; Standen, Vivien; Luz, M.; Ricaut, M; Guidon, M; Osipova, Ludmila; Voevoda, M.; Posukh, Olga; Balanovsky, M; Lavryashina, M.; Bogunov, M; Khusnutdinova, M; Gubina, M.; Balanovska, M; Fedorova, M; Litvinov, Sergey; Malyarchuk, M; Derenko, M.; Mosher, M.; Archer, David; Cybulski, Jerome; Petzelt, Barbara; Mitchell, Joycelynn; Worl, Rosita; Norman, Paul; Parham, Peter; Kemp, Brian,; Kivisild, Toomas; Smith, Chris; Sandhu, Manjinder,; Crawford, Michael; Villems, Richard; Smith, David; Waters, Michael; Goebel, Ted; Johnson, John; Malhi, Ripan; Jakobsson, Mattias; Meltzer, David; Manica, Andrea; Durbin, Richard; Bustamante, Carlos,; Song, Yun; Nielsen, Rasmus; Willerslev, Eske; Steinrucken, M.; Harris, K.; Rasmussen, S.; Albrechtsen, A.; Valdiosera, C.; Avila-Arcos, M.; Malaspinas, S.; Moltke, I.; Homburger, J.; Moreno-Mayar, J.; Korneliussen, S.; Pierre, T.; Rasmussen, M.; Damgaard, P.; Metspalu, E.; Rodriguez-Varela, R.; Mansilla, J.; Seguin-Orlando, A.; Malmstrom, H.; Stafford, T.; Shringarpure, S.; Moreno-Estrada, A.; Bergstrom, A.; Warmuth, V.; Singarayer, J.; Leboreiro, I.; Vera, J.; Rangel-Villalobos, H.; Heyer, E.; Ponce De Leon, M.; Grimes, V.; Pike, K.; Deal, M.; Fuller, T.; Ricaut, F.; Guidon, N.; Balanovsky, O.; Bogunov, Y.; Khusnutdinova, E.; Balanovska, E.; Fedorova, S.; Malyarchuk, B.; Norman, J.; Kemp, M.; Malhi, S.; Meltzer, J.; Song, S.;How and when the Americas were populated remains contentious. Using ancient and modern genome wide data we found that the ancestors of all present day Native Americans including Athabascans and Amerindians entered the Americas as a single migration wave from Siberia no earlier than 23 thousand years ago (ka) and after no more than an 8000 year isolation period in Beringia. After their arrival to the Americas ancestral Native Americans diversified into two basal genetic branches around 13 ka one that is now dispersed across North and South America and the other restricted to North America. Subsequent gene flow resulted in some Native Americans sharing ancestry with present day East Asians (including Siberians) and more distantly Australo Melanesians. Putative “Paleoamerican” relict populations including the historical Mexican Pericúes and South American Fuego Patagonians are not directly related to modern Australo Melanesians as suggested by the Paleoamerican Model. INTRODUCTION The consensus view on the peopling of the Americas is that ancestors of modern Native Americans entered the Americas from Siberia via the Bering Land Bridge and that this occurred at least {\textasciitilde}14.6 thousand years ago (ka). However the number and timing of migrations into the Americas remain controversial with conflicting interpretations based on anatomical and genetic evidence. RATIONALE In this study we address four major unresolved issues regarding the Pleistocene and recent population history of Native Americans: (i) the timing of their divergence from their ancestral group (ii) the number of migrations into the Americas (iii) whether there was {\textasciitilde}15000 years of isolation of ancestral Native Americans in Beringia (Beringian Incubation Model) and (iv) whether there was post Pleistocene survival of relict populations in the Americas related to Australo Melanesians as suggested by apparent differences in cranial morphologies between some early (“Paleoamerican”) remains and those of more recent Native Americans. We generated 31 high coverage modern genomes from the Americas Siberia and Oceania; 23 ancient genomic sequences from the Americas dating between {\textasciitilde}0.2 and 6 ka; and SNP chip genotype data from 79 present day individuals belonging to 28 populations from the Americas and Siberia. The above data sets were analyzed together with published modern and ancient genomic data from worldwide populations after masking some present day Native Americans for recent European admixture. RESULTS Using three different methods we determined the divergence time for all Native Americans (Athabascans and Amerindians) from their Siberian ancestors to be {\textasciitilde}20 ka and no earlier than {\textasciitilde}23 ka. Furthermore we dated the divergence between Athabascans (northern Native American branch together with northern North American Amerindians) and southern North Americans and South and Central Americans (southern Native American branch) to be {\textasciitilde}13 ka. Similar divergence times from East Asian populations and a divergence time between the two branches that is close in age to the earliest well established archaeological sites in the Americas suggest that the split between the branches occurred within the Americas. We additionally found that several sequenced Holocene individuals from the Americas are related to present day populations from the same geographical regions implying genetic continuity of ancient and modern populations in some parts of the Americas over at least the past 8500 years. Moreover our results suggest that there has been gene flow between some Native Americans from both North and South America and groups related to East Asians and Australo Melanesians the latter possibly through an East Asian route that might have included ancestors of modern Aleutian Islanders. Last using both genomic and morphometric analyses we found that historical Native American groups such as the Pericúes and Fuego Patagonians were not “relicts” of Paleoamericans and hence our results do not support an early migration of populations directly related to Australo Melanesians into the Americas. CONCLUSION Our results provide an upper bound of {\textasciitilde}23 ka on the initial divergence of ancestral Native Americans from their East Asian ancestors followed by a short isolation period of no more than {\textasciitilde}8000 years and subsequent entrance and spread across the Americas. The data presented are consistent with a single migration model for all Native Americans with later gene flow from sources related to East Asians and indirectly Australo Melanesians. The single wave diversified {\textasciitilde}13 ka likely within the Americas giving rise to the northern and southern branches of present day Native Americans. View larger version: In this page In a new window Download PowerPoint Slide for Teaching Population history of present day Native Americans.The ancestors of all Native Americans entered the Americas as a single migration wave from Siberia (purple) no earlier than {\textasciitilde}23 ka separate from the Inuit (green) and diversified into “northern” and “southern” Native American branches {\textasciitilde}13 ka. There is evidence of post divergence gene flow between some Native Americans and groups related to East Asians/Inuit and Australo Melanesians (yellow). Genetic history of Native Americans Several theories have been put forth as to the origin and timing of when Native American ancestors entered the Americas. To clarify this controversy Raghavan et al. examined the genomic variation among ancient and modern individuals from Asia and the Americas. There is no evidence for multiple waves of entry or recurrent gene flow with Asians in northern populations. The earliest migrations occurred no earlier than 23000 years ago from Siberian ancestors. Amerindians and Athabascans originated from a single population splitting approximately 13000 years ago. Science this issue 10.1126/science.aab3884
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For further information contact us at helpdesk@openaire.eu407 citations 407 popularity Top 0.1% influence Top 1% impulse Top 0.1% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research NIH | Center for Family and Dem..., NIH | PROSTATE, LUNG, COLORECTA..., NIH | Computational Methods to ... +195 projectsNIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,WT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,CIHR ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| The University of Iowa Prevention Research Center ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCERAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;Downloads of Add Health require submission of the following information, which is shared with the original producer of Add Health: supervisor name, supervisor email, and reason for download. A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2018 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Wave V data collection took place from 2016 to 2018, when the original Wave I respondents were 33 to 43 years old. For the first time, a mixed mode survey design was used. In addition, several experiments were embedded in early phases of the data collection to test response to various treatments. A similar range of data was collected on social, environmental, economic, behavioral, and health circumstances of respondents, with the addition of retrospective child health and socio-economic status questions. Physical measurements and biospecimens were again collected at Wave V, and included most of the same measures as at Wave IV. Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights DS32: Wave V: Mixed-Mode Survey, Public Use Sample DS33: Wave V: Mixed-Mode Survey, Public Use Sample (Section 16B: Pregnancy, Live Births, Children and Parenting) DS34: Wave V: Biomarkers, Anthropometrics DS35: Wave V: Biomarkers, Cardiovascular Measures DS36: Wave V: Biomarkers, Demographics DS37: Wave V: Biomarkers, Measures of Glucose Homeostasis DS38: Wave V: Biomarkers, Measures of Inflammation and Immune Function DS39: Wave V: Biomarkers, Lipids DS40: Wave V: Biomarkers, Medication Use DS41: Wave V: Biomarkers, Renal Function DS42: Wave V: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample. Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I. Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later. Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. Wave V: All Wave I respondents who were still living were eligible at Wave V, yielding a pool of 19,828 persons. This pool was split into three stratified random samples for the purposes of survey design testing. For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. audio computer-assisted self interview (ACASI); computer-assisted personal interview (CAPI); computer-assisted self interview (CASI); face-to-face interview; mixed mode; paper and pencil interview (PAPI); telephone interviewWave V data files were minimally processed by ICPSR. For value labeling, missing value designation, and question text (where applicable), please see the available P.I. Codebook/Questionnaires. The study-level documentation (Data Guide, User Guide) does not include Wave V datasets.Documentation for Waves prior to Wave V may use an older version of the study title.Users should be aware that version history notes dated prior to 2015-11-09 do not apply to the current organization of the datasets.Please note that dates present in the Summary and Time Period fields are taken from the Add Health Study Design page. The Date of Collection field represents the range of interview dates present in the data files for each wave.Wave I and Wave II field work was conducted by the National Opinion Research Center at the University of Chicago.Wave III, Wave IV, and Wave V field work was conducted by the Research Triangle Institute.For the most updated list of related publications, please see the Add Health Publications Web site.Additional information on the National Longitudinal Study of Adolescent to Adult Health (Add Health) series can be found on the Add Health Web site. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Wave V aimed to track the emergence of chronic disease as the cohort aged into their 30s and early 40s. Add health is a school-based longitudinal study of a nationally-representative sample of adolescents in grates 7-12 in the United States in 1945-45. Over more than 20 years of data collection, data have been collected from adolescents, their fellow students, school administrators, parents, siblings, friends, and romantic partners through multiple data collection components. In addition, existing databases with information about respondents' neighborhoods and communities have been merged with Add Health data, including variables on income poverty, unemployment, availability and utilization of health services, crime, church membership, and social programs and policies. The data files are not weighted. However, the collection features a number of weight variables contained within the following datasets: DS4: Wave I: Public Use Grand Sample Weights DS7: Wave II: Public Use Grand Sample Weights DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS21: Wave III: Public In-Home Weights DS31: Wave IV: Public Use Weights DS42: Wave V: Public Use Weights Please note that these weights files do not apply to the Biomarker data files. For additional information on the application of weights for data analysis, please see the ICPSR User Guide, or the Guidelines for Analyzing Add Health Data. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent Wave II: 88.6 percent Wave III: 77.4 percent Wave IV: 80.3 percent Wave V: 71.8 percent Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States.
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For further information contact us at helpdesk@openaire.eu8 citations 8 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2006 NetherlandsAcoustical Society of America (ASA) NIH | Training for Speech and H..., NIH | VERY-HIGH-FREQUENCY ULTRA..., NIH | DECISION PROCESSES IN DET... +6 projectsNIH| Training for Speech and Hearing Sciences ,NIH| VERY-HIGH-FREQUENCY ULTRASOUND ARRAYS FOR OPHTHALMOLOGY ,NIH| DECISION PROCESSES IN DETECTION AND DISCRIMINATION ,SSHRC ,NSERC ,NIH| Tumor Diagnosis through Enhanced Ultrasound Imaging ,NSF| Physical Modeling of the Piano ,NIH| NORMAL &IMPAIRED TEMPORAL PROCESSING OF COMPLEX SOUNDS ,NSF| Workshop on ToBI for Spontaneous English SpeechAuthors: Broersma, M.;Broersma, M.;Native and nonnative listeners categorized /v/ and /f/ at the end of English nonwords. For each participant, the duration of the previous vowel was kept constant, so that it was not informative and sometimes mismatched other information in the signal. Vowel duration was varied between participants. Previously presented results [M. Broersma, J. Acoust. Soc. Am. 117, 3809–3901 (2005)] showed that native English listeners relied strongly on the misleading vowel duration cue. For Dutch listeners, no effect of vowel duration was found. Due to the redundancy of information in the signal, Dutch listeners categorized the contrast more categorically than English listeners. New analyses investigated whether Dutch listeners did not attempt to use vowel duration at all, or whether they learned to ignore the misleading cue more easily than the English listeners did. The results showed that Dutch listeners did use vowel duration initially, but stopped using this cue after very few trials. By the end of the practice part (33 trials) the effect of vowel duration had fully disappeared. The English listeners used vowel duration as a voicing cue throughout the experiment. This suggests that it may be easier to learn to ignore uninformative perceptual cues in a nonnative language than in one’s native language.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1121/1.4786141&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1121/1.4786141&type=result"></script>'); --> </script>
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