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Research 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
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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 2016Ovid Technologies (Wolters Kluwer Health) NIH | Phase 2 Study of Mexileti..., NIH | Novel Molecular Mechanism..., CIHR +24 projectsNIH| Phase 2 Study of Mexiletine for the Treatment of Myotonic Dystrophy ,NIH| Novel Molecular Mechanisms of Neuromuscular Disease: Implications for Therapy ,CIHR ,NIH| BIOEQUIVALENCE AND CLINICAL IMPLICATIONS OF GENERIC BUPROPION ,NIH| IN VIVO NMR METABOLIC STUDY OF REGIONAL CARDIAC ISCHEMIA ,NIH| Evaluation of Treatments and Services to People with Duchenne Muscular Dystrophy ,NIH| BIOLOGY OF THE BONE MARROW DERIVED 3A1 STEM CELL ,NIH| Novel Strategy for Perioperative Beta-Blocker Therapy ,NIH| GENETICS-InFORMATICS TRIAL (GIFT) OF WARFARIN TO PREVENT DVT ,NIH| 9th International Myotonic Dystrophy Consortium Meeting ,NIH| Biiostatistics ,NIH| Disease Progression in Myotonic Dystrophy ,NIH| EXPERIMENTAL THERAPEUTICS IN NEUROLOGICAL DISEASE ,NIH| Comp A-NY State Surveillance and Research of MD and Neuromuscular Disorders ,NIH| BIOENERGETIC MECHANISMS ,NIH| POSITRON EMISSION TOMOGRAPHIC IMAGING OF LUNG TRANSPLANT ,SSHRC ,NIH| Phase 2 Study of 4-Aminopyridine for the Treatment of Episodic Ataxia Type 2 ,NIH| Remediating Age Related Cognitive Decline: Mindfulness-Based Stress Reduction and Exercise ,NIH| Genetically Informed Smoking Cessation Trial ,NIH| Ocular Hypertension Treatment Study 20-Year Follow-up: Clinical Center Grant ,NIH| RNA-mediated mechanisms in the myotonic dystrophies ,NIH| 7th International Myotonic Dystrophy Consortium Meeting ,NIH| FOR-DMD: Double-blind randomized trial to optimize steroid regimen in Duchenne MD ,NIH| Washington University Institute of Clinical and Translational Sciences ,NIH| Tenth International Myotonic Dystrophy Consortium Meeting ,NIH| ANALYSIS OF THE E COLI STB HEAT STABLE ENTEROTOXINAuthors: Griggs, Robert C.; Miller, J. Phillip; Greenberg, Cheryl R.; Fehlings, Darcy L.; +11 AuthorsGriggs, Robert C.; Miller, J. Phillip; Greenberg, Cheryl R.; Fehlings, Darcy L.; Pestronk, Alan; Mendell, Jerry R.; Moxley, Richard T.; King, Wendy; Kissel, John T.; Cwik, Valerie; Vanasse, Michel; Florence, Julaine M.; Pandya, Shree; Dubow, Jordan S.; Meyer, James M.;Objective: To assess safety and efficacy of deflazacort (DFZ) and prednisone (PRED) vs placebo in Duchenne muscular dystrophy (DMD). Methods: This phase III, double-blind, randomized, placebo-controlled, multicenter study evaluated muscle strength among 196 boys aged 5–15 years with DMD during a 52-week period. In phase 1, participants were randomly assigned to receive treatment with DFZ 0.9 mg/kg/d, DFZ 1.2 mg/kg/d, PRED 0.75 mg/kg/d, or placebo for 12 weeks. In phase 2, placebo participants were randomly assigned to 1 of the 3 active treatment groups. Participants originally assigned to an active treatment continued that treatment for an additional 40 weeks. The primary efficacy endpoint was average change in muscle strength from baseline to week 12 compared with placebo. The study was completed in 1995. Results: All treatment groups (DFZ 0.9 mg/kg/d, DFZ 1.2 mg/kg/d, and PRED 0.75 mg/kg/d) demonstrated significant improvement in muscle strength compared with placebo at 12 weeks. Participants taking PRED had significantly more weight gain than placebo or both doses of DFZ at 12 weeks; at 52 weeks, participants taking PRED had significantly more weight gain than both DFZ doses. The most frequent adverse events in all 3 active treatment arms were Cushingoid appearance, erythema, hirsutism, increased weight, headache, and nasopharyngitis. Conclusions: After 12 weeks of treatment, PRED and both doses of DFZ improved muscle strength compared with placebo. Deflazacort was associated with less weight gain than PRED. Classification of evidence: This study provides Class I evidence that for boys with DMD, daily use of either DFZ and PRED is effective in preserving muscle strength over a 12-week period.
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For further information contact us at helpdesk@openaire.eu115 citations 115 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!visibility 2visibility views 2 download downloads 0 Powered bymore_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 2022Public Library of Science (PLoS) NIH | UCLA Clinical Translation..., SSHRC, NSERC +4 projectsNIH| UCLA Clinical Translational Science Institute ,SSHRC ,NSERC ,NIH| CTSA INFRASTRUCTURE FOR CLINICAL TRIALS ,NIH| Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) ,NIH| Integrative Pathways to Health and Illness ,NIH| Integrative Pathways to Health and IllnessLeela McKinnon; David R. Samson; Charles L. Nunn; Amanda Rowlands; Katrina G. Salvante; Pablo A. Nepomnaschy;Sleep duration, quality, and rest-activity pattern—a measure for inferring circadian rhythm—are influenced by multiple factors including access to electricity. Recent findings suggest that the safety and comfort afforded by technology may improve sleep but negatively impact rest-activity stability. According to the circadian entrainment hypothesis, increased access to electric lighting should lead to weaker and less uniform circadian rhythms, measured by stability of rest-activity patterns. Here, we investigate sleep in a Maya community in Guatemala who are in a transitional stage of industrialization. We predicted that (i) sleep will be shorter and less efficient in this population than in industrial settings, and that (ii) rest-activity patterns will be weaker and less stable than in contexts with greater exposure to the natural environment and stronger and more stable than in settings more buffered by technologic infrastructure. Our results were mixed. Compared to more industrialized settings, in our study population sleep was 4.87% less efficient (78.39% vs 83.26%). We found no significant difference in sleep duration. Rest-activity patterns were more uniform and less variable than in industrial settings (interdaily stability = 0.58 vs 0.43; intradaily variability = 0.53 vs 0.60). Our results suggest that industrialization does not inherently reduce characteristics of sleep quality; instead, the safety and comfort afforded by technological development may improve sleep, and an intermediate degree of environmental exposure and technological buffering may support circadian rhythm strength and stability.
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.0277416&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=10.1371/journal.pone.0277416&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018 United StatesPublic Library of Science (PLoS) SSHRC, NIH | The National Person-Cente..., NIH | University of Pittsburgh ... +2 projectsSSHRC ,NIH| The National Person-Centered Assessment Resource (PCAR) ,NIH| University of Pittsburgh Clinical and Translational Science Institute ,NIH| PROMIS Statistical Center ,NIH| Institutional Career DevelopmentHanmer, Janel; Dewitt, Barry; Yu, Lan; Tsevat, Joel; Roberts, Mark; Revicki, Dennis; Pilkonis, Paul A.; Hess, Rachel; Hays, Ron D.; Fischhoff, Baruch; Feeny, David; Condon, David; Cella, David;Author(s): Hanmer, Janel; Dewitt, Barry; Yu, Lan; Tsevat, Joel; Roberts, Mark; Revicki, Dennis; Pilkonis, Paul A; Hess, Rachel; Hays, Ron D; Fischhoff, Baruch; Feeny, David; Condon, David; Cella, David | Abstract: ObjectivesThe PROMIS-Preference (PROPr) score is a recently developed summary score for the Patient-Reported Outcomes Measurement Information System (PROMIS). PROPr is a preference-based scoring system for seven PROMIS domains created using multiplicative multi-attribute utility theory. It serves as a generic, societal, preference-based summary scoring system of health-related quality of life. This manuscript evaluates construct validity of PROPr in two large samples from the US general population.MethodsWe utilized 2 online panel surveys, the PROPr Estimation Survey and the Profiles-Health Utilities Index (HUI) Survey. Both included the PROPr measure, patient demographic information, self-reported chronic conditions, and other preference-based summary scores: the EuroQol-5D (EQ-5D-5L) and HUI in the PROPr Estimation Survey and the HUI in the Profiles-HUI Survey. The HUI was scored as both the Mark 2 and the Mark 3. Known-groups validity was evaluated using age- and gender-stratified mean scores and health condition impact estimates. Condition impact estimates were created using ordinary least squares regression in which a summary score was regressed on age, gender, and a single health condition. The coefficient for the health condition is the estimated effect on the preference score of having a condition vs. not having it. Convergent validity was evaluated using Pearson correlations between PROPr and other summary scores.ResultsThe sample consisted of 983 respondents from the PROPr Estimation Survey and 3,000 from the Profiles-HUI survey. Age- and gender-stratified mean PROPr scores were lower than EQ-5D and HUI scores, with fewer subjects having scores corresponding to perfect health on the PROPr. In the PROPr Estimation survey, all 11 condition impact estimates were statistically significant using PROPr, 8 were statistically significant by the EQ-5D, 7 were statistically significant by HUI Mark 2, and 9 were statistically significant by HUI Mark 3. In the Profiles-HUI survey, all 21 condition impact estimates were statistically significant using summary scores from all three scoring systems. In these samples, the correlations between PROPr and the other summary measures ranged from 0.67 to 0.70.ConclusionsThese results provide evidence of construct validity for PROPr using samples from the US general population.
Europe PubMed Centra... arrow_drop_down eScholarship - University of CaliforniaArticle . 2018Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2018Data sources: eScholarship - University of Californiaadd 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.0201093&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu54 citations 54 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down eScholarship - University of CaliforniaArticle . 2018Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2018Data sources: eScholarship - University of Californiaadd 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 2010 EnglishInter-university Consortium for Political and Social Research (ICPSR) NIH | University of Michigan's ..., AKA | Work stress and risk of c..., NIH | Vulnerability to Drug Use... +33 projectsNIH| University of Michigan's TMJD and Orofacial Pain Interdisciplinary Consortium ,AKA| Work stress and risk of coronary heart disease: Does a healthy life style eliminate the adverse effect? Pooled analysis of 6 major prospective cohort studies from Europe ,NIH| Vulnerability to Drug Use & HIV: Advancing Prevention for Rural African Americans ,NIH| The Brain as a Target for Pre and Essential Hypertension ,SSHRC ,NIH| UCLA OLDER AMERICANS INDEPENDENCE CENTER ,NIH| Examining the Bi-directional Relationship between Sleep and Stress: A Vicious Cycle ,WT ,NIH| MEASUREMENT OF ESTRADIOL AND OTHER RELATED HORMONES BY TANDEM MASS SPECTROSCOPY ,CIHR ,NIH| Neurobiological pathways linking stress and emotion to atherosclerosis ,NIH| Integrative Pathways to Health and Illness ,NIH| Integrative Pathways to Health and Illness ,NIH| Health behaviors over the adult lifecourse and cognitive aging ,NIH| GCRC ,AKA| Determinants of Early Exit from Work Force: An International Multicohort Study. ,NIH| Biological Embedding of Early-Life SES ,NIH| Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) ,UKRI| Assessment of biomedical risk factors and disease outcomes in the British 1958 cohort ,NSF| Sleep Disruption as an Amplifier of Aggressive Behavior ,NIH| UCLA Clinical Translational Science Institute ,AKA| Work, well-being and health - a life course perspective: British arm of Academy of Finland consortia 10190 and 10187 / Consortium: ELRFWCDD ,NIH| Self-regulation as a Health-Protective Factor in Adverse Socioeconomic Conditions ,NIH| Health Disparities Research Scholars T32 ,NIH| Personality and Well-Being Trajectories in Adulthood ,NIH| CTSA INFRASTRUCTURE FOR CLINICAL TRIALS ,NIH| Institutional Clinical and Translational Science Award ,NIH| SOCIAL AND OCCUPATIONAL INFLUENCES ON HEALTH AND ILLNESS ,UKRI| RootDetect: Remote Detection and Precision Management of Root Health ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Biopsychosocial Pathways to Type 2 Diabetes ,NIH| Cardiovascular Behavioral Medicine Research Training ,NIH| Midlife Health in Japan (MIDJA) and the U.S. (MIDUS) ,NIH| Training in Behavioral &Preventive Medicine ,NIH| Integrative Pathways to Health and Illness ,NIH| Biobehavioral Influences and the Ovarian Tumor MicroenvironmentAuthors: Ryff, Carol D.; Seeman, Teresa; Weinstein, Maxine;Ryff, Carol D.; Seeman, Teresa; Weinstein, Maxine;These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues.The Biomarker study is Project 4 of the MIDUS longitudinal study, a national survey of more than 7,000 Americans (aged 25 to 74) begun in 1994. The purpose of the larger study was to investigate the role of behavioral, psychological, and social factors in understanding age-related differences in physical and mental health. With support from the National Institute on Aging, a longitudinal follow-up of the original MIDUS samples [core sample (N = 3,487), metropolitan over-samples (N = 757), twins (N = 957 pairs), and siblings (N = 950)] was conducted in 2004-2006. Guiding hypotheses, at the most general level, were that behavioral and psychosocial factors are consequential for health (physical and mental). A description of the study and findings from it are available on the MIDUS Web site. The Biomarker Project (Project 4) of MIDUS II contains data from 1,255 respondents. These respondents include two distinct subsamples, all of whom completed the Project 1 Survey: (1) longitudinal survey sample (n = 1,054) and (2) Milwaukee sample (n = 201). The Milwaukee group contained individuals who participated in the baseline MIDUS Milwaukee study, initiated in 2005. The purpose of the Biomarker Project (Project 4) was to add comprehensive biological assessments on a subsample of MIDUS respondents, thus facilitating analyses that integrate behavioral and psychosocial factors with biology. The broad aim is to identify biopsychosocial pathways that contribute to diverse health outcomes. A further theme is to investigate protective roles that behavioral and psychosocial factors have in delaying morbidity and mortality, or in fostering resilience and recovery from health challenges once they occur. The research was not disease-specific, given that psychosocial factors have relevance across multiple health endpoints. Biomarker data collection was carried out at three General Clinical Research Centers (at UCLA, University of Wisconsin, and Georgetown University). The biomarkers reflect functioning of the hypothalamic-pituitary-adrenal axis, the autonomic nervous system, the immune system, cardiovascular system, musculoskeletal system, antioxidants, and metabolic processes. Our specimens (fasting blood draw, 12-hour urine, saliva) allow for assessment of multiple indicators within these major systems. The protocol also included assessments by clinicians or trained staff, including vital signs, morphology, functional capacities, bone densitometry, medication usage, and a physical exam. Project staff obtained indicators of heart-rate variability, beat to beat blood pressure, respiration, and salivary cortisol assessments during an experimental protocol that included both a cognitive and orthostatic challenge. Finally, to augment the self-reported data collected in Project 1, participants completed a medical history, self-administered questionnaire, and self-reported sleep assessments. For respondents at one site (UW-Madison), objective sleep assessments were also obtained with an Actiwatch(R) activity monitor. The MIDUS and MIDJA Biomarker Clinic Visits include collection of comprehensive information about medications of all types, as well as basic information about allergic reactions to any type of medication. Respondents were instructed to bring all their medications, or information about their medications, to the clinic visit to ensure the information about those medications was recorded accurately. Information regarding Prescription Medications (FDA approved medications prescribed by someone authorized/licensed under the Western medical tradition, or medications prescribed by individuals authorized under Japanese law to prescribe Western and/or Eastern/Chinese traditional medicine), Quasi Medications (including Over the Counter Medications i.e. vitamins, minerals, non-prescription pain relief, antacids, etc. that can be purchased without a prescription) and Alternative Medications (i.e. herbs, herbal blends (excluding herbal teas), homeopathic remedies, and other alternative remedies that may be purchased over the counter or "prescribed" by a health care practitioner trained in a non-western tradition)was collected at this time.The following information was collected for each medication type Medication name, dosage, and route of administration; How often the medication is taken(frequency); How long the participant has been taking a given medication; Why they think they are taking the medication; After basic cleaning protocols were completed, standardized protocols were applied to both MIDUS and MIDJA medication data to link medications first to Generic Names and associated DrugIDs and then to therapeutic and pharmacologic class information from the Lexicomp Lexi-Data database, and also to code text data describing why participants think they are taking a given medication. The scope of this collected medication data lends itself to within person analysis of medication use, thus the medication data are also released in a standalone stacked format. The stacked file only contains data about medications used where each case represents an individual medication, thus it does not include any data about medication allergies. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. All respondents participating in MIDUS II (ICPSR 4652) or the Milwaukee study (ICPSR 22840) who completed Project 1 were eligible to participate in the Biomarker assessments. Presence of Common Scales: Data users interested in the scales used for this study should refer to the scaling documentation provided on both the ICPSR and NACDA Web site. Adult non-institutionalized population of the United States. Smallest Geographic Unit: No geographic information is included other than for the Milwaukee cases. Response Rates: The response rate was 39.3 percent for each of the 2 samples (longitudinal survey sample, and Milwaukee). Datasets: DS0: Study-Level Files DS1: Aggregated Data DS2: Stacked Medication Data Midlife in the United States (MIDUS) Series face-to-face interview on-site questionnaire mixed mode
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.
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.eu28 citations 28 popularity Top 10% influence Average impulse Average 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.
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 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 2018Springer Science and Business Media LLC NSERC, SSHRC, NIH | The Vanderbilt Institute ... +3 projectsNSERC ,SSHRC ,NIH| The Vanderbilt Institute for Clinical and Translational Research (VICTR) ,NIH| Training in Fundamental Neuroscience ,NIH| Individual differences in cochlear implant users' audiovisual integration and links to speech proficiency ,NIH| Research Project: Sensory and Multisensory Contributions to AutismIliza M. Butera; Ryan A. Stevenson; Brannon D Mangus; Tiffany G. Woynaroski; René H. Gifford; Mark T. Wallace;AbstractFor many cochlear implant (CI) users, visual cues are vitally important for interpreting the impoverished auditory speech information that an implant conveys. Although the temporal relationship between auditory and visual stimuli is crucial for how this information is integrated, audiovisual temporal processing in CI users is poorly understood. In this study, we tested unisensory (auditory alone, visual alone) and multisensory (audiovisual) temporal processing in postlingually deafened CI users (n = 48) and normal-hearing controls (n = 54) using simultaneity judgment (SJ) and temporal order judgment (TOJ) tasks. We varied the timing onsets between the auditory and visual components of either a syllable/viseme or a simple flash/beep pairing, and participants indicated either which stimulus appeared first (TOJ) or if the pair occurred simultaneously (SJ). Results indicate that temporal binding windows—the interval within which stimuli are likely to be perceptually ‘bound’—are not significantly different between groups for either speech or non-speech stimuli. However, the point of subjective simultaneity for speech was less visually leading in CI users, who interestingly, also had improved visual-only TOJ thresholds. Further signal detection analysis suggests that this SJ shift may be due to greater visual bias within the CI group, perhaps reflecting heightened attentional allocation to visual cues.
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.eu13 citations 13 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 , Other literature type 2020American Association for Cancer Research (AACR) NIH | Institutional Career Deve..., NIH | Project 1: Modeling tumor..., NIH | Clinical and Translationa... +2 projectsNIH| Institutional Career Development Core ,NIH| Project 1: Modeling tumor evolution in mouse and organoid models ,NIH| Clinical and Translational Science Award ,SSHRC ,NIH| MEDICAL SCIENTIST TRAINING PROGRAMAuthors: Robyn D. Gartrell-Corrado; Andrew X. Chen; Emanuelle M. Rizk; Douglas K. Marks; +13 AuthorsRobyn D. Gartrell-Corrado; Andrew X. Chen; Emanuelle M. Rizk; Douglas K. Marks; Margaret Bogardus; Thomas D. Hart; Andrew M. Silverman; Claire-Audrey Y. Bayan; Grace G. Finkel; Luke W. Barker; Kimberly M. Komatsubara; Richard D. Carvajal; Basil A. Horst; Rui Chang; Anthea Monod; Raul Rabadan; Yvonne M. Saenger;Abstract Patients with resected stage II-III melanoma have approximately a 35% chance of death from their disease. A deeper understanding of the tumor immune microenvironment (TIME) is required to stratify patients and identify factors leading to therapy resistance. We previously identified that the melanoma immune profile (MIP), an IFN-based gene signature, and the ratio of CD8+ cytotoxic T lymphocytes (CTL) to CD68+ macrophages both predict disease-specific survival (DSS). Here, we compared primary with metastatic tumors and found that the nuclei of tumor cells were significantly larger in metastases. The CTL/macrophage ratio was significantly different between primary tumors without distant metastatic recurrence (DMR) and metastases. Patients without DMR had higher degrees of clustering between tumor cells and CTLs, and between tumor cells and HLA-DR+ macrophages, but not HLA-DR− macrophages. The HLA-DR− subset coexpressed CD163+CSF1R+ at higher levels than CD68+HLA-DR+ macrophages, consistent with an M2 phenotype. Finally, combined transcriptomic and multiplex data revealed that densities of CD8 and M1 macrophages correlated with their respective cell phenotype signatures. Combination of the MIP signature with the CTL/macrophage ratio stratified patients into three risk groups that were predictive of DSS, highlighting the potential use of combination biomarkers for adjuvant therapy. Significance: These findings provide a deeper understanding of the tumor immune microenvironment by combining multiple modalities to stratify patients into risk groups, a critical step to improving the management of patients with melanoma.
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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.eu11 citations 11 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.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 1999 EnglishInter-university Consortium for Political and Social Research (ICPSR) SSHRC, NHMRC | Australian Centre of Exce..., UKRI | Centre for Cognitive Agei... +91 projectsSSHRC ,NHMRC| Australian Centre of Excellence in Twin Research ,UKRI| Centre for Cognitive Ageing & Cognitive Epidemiology ,NIH| Daily Stress and Well-Being during Adulthood ,NIH| CORE--PROGRAM DEVELOPMENT ,NIH| CONTROL BELIEFS, MEMORY, AND AGING ,NHMRC| Gene-environment interaction in healthy brain ageing and age related neurodegeneration ,AKA| Indicators of marginalization - Role of cognition, substance use and mental health disorders: Longitudinal studies from childhood to end of adolescence ,NIH| A Longitudinal Twin Study of Cognition and Personality ,NIH| A 55-Year Follow-up Study of Project TALENT Twins and Siblings ,NHMRC| The genetic and environmental determinants of amyloid deposition in older individuals: an amyloid imaging study using the twin design ,CIHR ,NIH| Integrative Analysis of Change in Cognition and Health ,NIH| CHANGES IN HEALTH--SOCIOECONOMIC STATUS AND PATHWAYS ,NIH| SOCIAL AND OCCUPATIONAL INFLUENCES ON HEALTH AND ILLNESS ,NIH| TWIN STUDY OF NORMAL AGING ,NIH| Sex differences in the relationship between APOE and AD: Role of sexual differentiation ,NIH| MEASUREMENT OF ESTRADIOL AND OTHER RELATED HORMONES BY TANDEM MASS SPECTROSCOPY ,NIH| SES health gradients in late life: testing models of gene-environment interplay in an international twin consortium ,NIH| Sexuality, Aging and Heart Disease: Translating from Population to Patient ,NIH| The Population Research Institute ,NIH| Genetic Moderators of Divorce Adjustment: A Pilot Investigation ,NIH| Health behaviors over the adult lifecourse and cognitive aging ,NIH| GCRC ,NIH| WISCONSIN LONGITUDINAL STUDY ,NIH| A Longitudinal Twin Study of Cognition and Aging ,NIH| Daily stressor reactivity and profiles of physical health across adulthood ,AKA| Oxygenology of soil ,NIH| Social Regulation of Gene Expression ,AKA| Determinants of Early Exit from Work Force: An International Multicohort Study. ,NIH| Biological Embedding of Early-Life SES ,NIH| Bio-social Determinants of Fertility &Related Behaviors ,NIH| Sleep and Divorce: Identifying Bidirectional Vulnerability and Resilience ,NIH| Causal Effects of Schooling on Adult and Child Health ,NIH| Integrative Pathways to Health and Illness ,NIH| TRAINING IN RESEARCH ON MENTAL HEALTH AND AGING ,AKA| Predictors, neuropsychological correlates, and consequences of cannabis and alcohol use among Finnish young adults. A twin and population approach ,NHMRC| The Older Australian Twins Study (OATS) of healthy brain ageing and age-related neurocognitive disorders ,NIH| Informing anti-tobacco communications with affective and decision science: Application of the Appraisal Tendency Framework ,NIH| Wisconsin Longitudinal Study: Tracking the Life Course ,NIH| Aging, Emotional Well-being, and Physical Health ,NIH| Clarifying risk and protective factors for dementia with the Interplay of Genes and Environment in Multiple Studies (IGEMS) consortium ,AKA| Work stress and risk of coronary heart disease: Does a healthy life style eliminate the adverse effect? Pooled analysis of 6 major prospective cohort studies from Europe ,NSF| IBSS: Understanding Long-Term Effects on Children in Economic Distress ,NIH| ORIGINS OF VARIANCE IN THE OLD-OLD: OCTOGENARIAN TWINS ,NIH| OLDEST-OLD MORTALITY AND DISABILITY AMONG DANISH TWINS ,NIH| Vulnerability to Drug Use & HIV: Advancing Prevention for Rural African Americans ,NWO| A Twin-sibling Study of Adolescent Wellness ,NIH| The VETSA Longitudinal Twin Study of Cortisol and Aging ,AKA| Genomic epidemiology of addictions and their consequences - national, Nordic and international dimensions. ,AKA| Work, well-being and health - a life course perspective: British arm of Academy of Finland consortia 10190 and 10187 / Consortium: ELRFWCDD ,NIH| HEALTH AND PSYCHOSOCIAL FACTORS IN OLDER BLACK TWINS ,NIH| Self-regulation as a Health-Protective Factor in Adverse Socioeconomic Conditions ,AKA| Determinants of labour market participation and prognosis of common chronic diseases in working populations: a study of cohorts in Finland, United Kingdom and France ,NIH| Midlife Health in Japan (MIDJA) and the U.S. (MIDUS) ,NIH| GENETIC AND ENVIRONMENTAL INFLUENCES IN BEHAVIORAL AGING ,NIH| Stress, Aging and Working Memory ,EC| ENGAGE ,NIH| GENETIC &ENVIRONMENTAL INFLUENCES--BIOBEHAVIORAL AGING ,NIH| Implementing World Health Assembly Resolution 60.26 ???Workers' Health: Global Pl ,NIH| Optimizing Couple-Oriented Interventions for Chronic Illness ,NIH| Institutional Clinical and Translational Science Award ,NIH| Integrative Pathways to Health and Illness ,AKA| Genomic epidemiology of addictions and their consequences - national, Nordic and international dimensions ,AKA| Determinants of labour market participation and prognosis of common chronic diseases in working populations: a study of cohorts in Finland, United Kingdom and France ,NIH| IMSD Program at Wayne State University ,NIH| 1995 SUMMER INSTITUTE IN GERIATRIC MEDICINE ,NIH| AGING AND INTRAINDIVIDUAL COGNITIVE VARIABILITY ,AKA| Midlife predictors of dementia, frailty and disability at older ages ,NIH| AGING AND HEALTH TRAJECTORIES AMONG BLACK &WHITE ADULTS ,ARC| Discovery Projects - Grant ID: DP200100876 ,NIH| Genes, Enivronment and the Adjustment of Family Members ,UKRI| Offshore Platform for Energy Competitiveness (OPEC) ,NIH| UCLA OLDER AMERICANS INDEPENDENCE CENTER ,NWO| Genetische en omgevingsinvloeden op psychopathologie en geluk tijdens de adolescentie ,NIH| The VETSA Longitudinal MRI Twin Study of Aging ,NIH| DEMOGRAPHY ,NIH| Integrative Pathways to Health and Illness ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| The Greatest Generation: The NAS-NRC WWII Twin Registry as a Scientific Resource ,NIH| Personality and Well-Being Trajectories in Adulthood ,NIH| CTSA INFRASTRUCTURE FOR CLINICAL TRIALS ,NIH| Social and Economic Analysis of Demographic Change ,NIH| Gene-Environment Interplay of Social Contexts and Aging-Related Outcomes ,NIH| Risk for Alzheimer's Disease and Cognitive Decline in Project TALENT ,AKA| Heterogeneity of depression at symptom level: Specific versus general patterns in etiology, development, and disability ,NIH| Histories of Social Engagement and Cognitive Functioning ,AKA| Genetic and environmental predictors of tobacco, drug and alcohol addiction in adolescence and young adulthood ¿ a lifecourse twin and population approach / Consortium: addictgene ,AKA| CoE in Complex Disease Genetics ,UKRI| RootDetect: Remote Detection and Precision Management of Root Health ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Biopsychosocial Pathways to Type 2 Diabetes ,NIH| Infrastructure for the Office of Population Research ,AKA| Center of Excellence in Complex Disease Genetics-from Discovery to Precision Medicine / Consortium: CoECDGBrim, Orville Gilbert; Baltes, Paul B.; Bumpass, Larry L.; Cleary, Paul D.; Featherman, David L.; Hazzard, William R.; Kessler, Ronald C.; Lachman, Margie E.; Markus, Hazel Rose; Marmot, Michael G.; Rossi, Alice S.; Ryff, Carol D.; Shweder, Richard A.;doi: 10.3886/icpsr02760.v11 , 10.3886/icpsr02760.v12 , 10.3886/icpsr02760.v6 , 10.3886/icpsr02760.v15 , 10.3886/icpsr02760 , 10.3886/icpsr02760.v10 , 10.3886/icpsr02760.v17 , 10.3886/icpsr02760.v5 , 10.3886/icpsr02760.v8 , 10.3886/icpsr02760.v4 , 10.3886/icpsr02760.v14 , 10.3886/icpsr02760.v19 , 10.3886/icpsr02760.v2 , 10.3886/icpsr02760.v7 , 10.3886/icpsr02760.v9 , 10.3886/icpsr02760.v18 , 10.3886/icpsr02760.v16 , 10.3886/icpsr02760.v1 , 10.3886/icpsr02760.v13 , 10.3886/icpsr02760.v3
doi: 10.3886/icpsr02760.v11 , 10.3886/icpsr02760.v12 , 10.3886/icpsr02760.v6 , 10.3886/icpsr02760.v15 , 10.3886/icpsr02760 , 10.3886/icpsr02760.v10 , 10.3886/icpsr02760.v17 , 10.3886/icpsr02760.v5 , 10.3886/icpsr02760.v8 , 10.3886/icpsr02760.v4 , 10.3886/icpsr02760.v14 , 10.3886/icpsr02760.v19 , 10.3886/icpsr02760.v2 , 10.3886/icpsr02760.v7 , 10.3886/icpsr02760.v9 , 10.3886/icpsr02760.v18 , 10.3886/icpsr02760.v16 , 10.3886/icpsr02760.v1 , 10.3886/icpsr02760.v13 , 10.3886/icpsr02760.v3
The Midlife in the United States (MIDUS) is a collaborative, interdisciplinary investigation of patterns, predictors, and consequences of midlife development in the areas of physical health, psychological well-being, and social responsibility. A description of the study and findings from it are available at http://www.midus.wisc.edu. The first wave of the MIDUS study (MIDUS 1 or M1) collected survey data from a total of 7,108 participants. The baseline sample was comprised of individuals from four subsamples: (1) a national RDD (random digit dialing) sample (n=3,487); (2) oversamples from five metropolitan areas in the U.S. (n=757); (3) siblings of individuals from the RDD sample (n=950); and (4) a national RDD sample of twin pairs (n=1,914). All eligible participants were non-institutionalized, English-speaking adults in the coterminous United States, aged 25 to 74. Data from the samples were collected primarily in 1995/96. The survey (Project 1) dataset contains responses from a 30-minute Phone interview and two 50-page Self-Administered Questionnaire (SAQ) instruments. Of the 7,108 respondents who completed the Phone interview, 6,325 also completed the SAQ. This updated version of the study is comprised of three primary datasets: Dataset 1, Main, Siblings, and Twin Data, contains responses from the main survey of 7,108 respondents. Respondents were asked to provide extensive information on their physical and mental health throughout their adult lives, and to assess the ways in which their lifestyles, including relationships and work-related demands, contributed to the conditions experienced. Those queried were asked to describe their histories of physical ailments, including heart-related conditions and cancer, as well as the treatment and/or lifestyle changes they went through as a result. A series of questions addressed alcohol, tobacco, and illegal drug use, and focused on history of use, regularity of use, attempts to quit, and how the use of those substances affected respondents' physical and mental well-being. Additional questions addressed respondents' sense of control over their health, their awareness of changes in their medical conditions, commitment to regular exercise and a healthy diet, experience with menopause, the decision-making process used to deal with health concerns, experiences with nontraditional remedies or therapies, and history of attending support groups. Respondents were asked to compare their overall well-being with that of their peers and to describe social, physical, and emotional characteristics typical of adults in their 20's, 40's, and 60's. Information on the work histories of respondents and their significant others was also elicited, with items covering the nature of their occupations, work-related physical and emotional demands, and how their personal health had correlated to their jobs. An additional series of questions focusing on childhood queried respondents regarding the presence/absence of their parents, religion, rules/punishments, love/affection, physical/verbal abuse, and the quality of their relationships with their parents and siblings. Respondents were also asked to consider their personal feelings of accomplishment, desire to learn, sense of control over their lives, interests, and hopes for the future. The Datasets previously numbered 2 and 3 have been removed to avoid redundancies, and all datasets have been renumbered. Please refer to the readme file. Dataset 2, Twin Screener Data, provides the first national sample of twin pairs ascertained randomly via the telephone. Dataset 3, Coded Text Responses, describes how open-ended textual responses in the MIDUS 1 Computer-Assisted Telephone Interview (CATI) and Self-Administered Questionnaire (SAQ) were transformed into categorical numeric codes. These codes are included in a stand-alone dataset containing only those cases (N=3,950) that contained text data in their responses. Online Analysis Only: Datasets 1, 2, and 3 were merged together by the SU_ID variable to form "Merged Data with Weights (Online Analysis Only)" (Dataset 4) for online analysis capabilities. MIDUS also maintains a Colectica portal, which allows users to interact with variables across waves and create customized subsets. Registration is required. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Presence of Common Scales: Generalized Anxiety Disorder (GAD) Scale; Somatic Amplification Scale; The Alcohol Screening Test; The Conflict Tactics (CT) Scales; The Revised Conflict Tactics Scales (CTS2); Loyola Generativity Scale (LGS); Many scales were constructed for use in the Midlife in the United States (MIDUS 1), 1995-1996 Study. For additional information on scale construction and sources, please refer to the scale documentation included with the data collection. Respondents were drawn from a nationally representative random-digit-dial sample of non-institutionalized, English-speaking adults, aged 25-74, selected from working telephone banks in the coterminous United States. Those queried participated in an initial telephone interview and responded to a mail questionnaire. Please see the Descriptions of Midlife in the United Sates (MIDUS) Samples documentation provided by ICPSR for more detailed information. Respondents were drawn from a nationally representative random-digit-dial sample of non-institutionalized, English-speaking adults, aged 25-74, selected from working telephone banks in the coterminous United States. Those queried participated in an initial telephone interview and responded to a mail questionnaire. Smallest Geographic Unit: None Datasets: DS0: Study-Level Files DS1: Main, Siblings and Twin Data DS2: Twin Screener Data DS3: Coded Text Data DS4: Merged Data with Weights (Online Analysis Only) DS6: Midlife in the United States (MIDUS 1), 1995-1996, Merged Data with Weights (Online Analysis Only) Response Rates: The response rate for the national Random-Digit Dialing (RDD) sample was 70 percent. The Self-Administered Questionnaire (SAQ) follow-up response rate was 89 percent. computer-assisted telephone interview (CATI) Midlife in the United States (MIDUS) Series self-enumerated questionnaire mail questionnaire
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For further information contact us at helpdesk@openaire.eu33 citations 33 popularity Top 10% influence Top 10% 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 , Other literature type , Preprint 2023 Switzerland, United KingdomCenter for Open Science SSHRC, SNSF | Personality and Well-Bein..., NIH | PSID, CDS, and TA: Data E... +1 projectsSSHRC ,SNSF| Personality and Well-Being: An Individual, Couple, and Family Perspective ,NIH| PSID, CDS, and TA: Data Enhancements and User Training, Support, and Outreach ,NIH| University of Pittsburgh Clinical and Translational Science InstituteWeidmann, Rebekka; Chopik, William J; Ackerman, Robert A; Allroggen, Marc; Bianchi, Emily C; Brecheen, Courtney; Campbell, W Keith; Gerlach, Tanja M; Geukes, Katharina; Grijalva, Emily; Grossmann, Igor; Hopwood, Christopher J; Hutteman, Roos; Konrath, Sara; Küfner, Albrecht C P; Leckelt, Marius; Miller, Joshua D; Penke, Lars; Pincus, Aaron L; Renner, Karl-Heinz; Richter, David; Roberts, Brent W; Sibley, Chris G; Simms, Leonard J; Wetzel, Eunike; Wright, Aidan G C; Back, Mitja D;pmid: 37184962
pmc: PMC10188200
Age and gender differences in narcissism have been studied often. However, considering the rich history of narcissism research accompanied by its diverging conceptualizations, little is known about age and gender differences across various narcissism measures. The present study investigated age and gender differences and their interactions across eight widely used narcissism instruments (i.e., Narcissistic Personality Inventory, Hypersensitive Narcissism Scale, Dirty Dozen, Psychological Entitlement Scale, Narcissistic Personality Disorder Symptoms from the , Version IV, Narcissistic Admiration and Rivalry Questionnaire-Short Form, Single-Item Narcissism Scale, and brief version of the Pathological Narcissism Inventory). The findings of Study 1 (N = 5,736) revealed heterogeneity in how strongly the measures are correlated. Some instruments loaded clearly on one of the three factors proposed by previous research (i.e., Neuroticism, Extraversion, Antagonism), while others cross-loaded across factors and in distinct ways. Cross-sectional analyses using each measure and meta-analytic results across all measures (Study 2) with a total sample of 270,029 participants suggest consistent linear age effects (random effects meta-analytic effect of r = -.104), with narcissism being highest in young adulthood. Consistent gender differences also emerged (random effects meta-analytic effect was -.079), such that men scored higher in narcissism than women. Quadratic age effects and Age × Gender effects were generally very small and inconsistent. We conclude that despite the various conceptualizations of narcissism, age and gender differences are generalizable across the eight measures used in the present study. However, their size varied based on the instrument used. We discuss the sources of this heterogeneity and the potential mechanisms for age and gender differences.
Journal of Personali... arrow_drop_down Zurich Open Repository and ArchiveOther literature type . 2023add 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.eu7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Journal of Personali... arrow_drop_down Zurich Open Repository and ArchiveOther literature type . 2023add 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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Research 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 2016Ovid Technologies (Wolters Kluwer Health) NIH | Phase 2 Study of Mexileti..., NIH | Novel Molecular Mechanism..., CIHR +24 projectsNIH| Phase 2 Study of Mexiletine for the Treatment of Myotonic Dystrophy ,NIH| Novel Molecular Mechanisms of Neuromuscular Disease: Implications for Therapy ,CIHR ,NIH| BIOEQUIVALENCE AND CLINICAL IMPLICATIONS OF GENERIC BUPROPION ,NIH| IN VIVO NMR METABOLIC STUDY OF REGIONAL CARDIAC ISCHEMIA ,NIH| Evaluation of Treatments and Services to People with Duchenne Muscular Dystrophy ,NIH| BIOLOGY OF THE BONE MARROW DERIVED 3A1 STEM CELL ,NIH| Novel Strategy for Perioperative Beta-Blocker Therapy ,NIH| GENETICS-InFORMATICS TRIAL (GIFT) OF WARFARIN TO PREVENT DVT ,NIH| 9th International Myotonic Dystrophy Consortium Meeting ,NIH| Biiostatistics ,NIH| Disease Progression in Myotonic Dystrophy ,NIH| EXPERIMENTAL THERAPEUTICS IN NEUROLOGICAL DISEASE ,NIH| Comp A-NY State Surveillance and Research of MD and Neuromuscular Disorders ,NIH| BIOENERGETIC MECHANISMS ,NIH| POSITRON EMISSION TOMOGRAPHIC IMAGING OF LUNG TRANSPLANT ,SSHRC ,NIH| Phase 2 Study of 4-Aminopyridine for the Treatment of Episodic Ataxia Type 2 ,NIH| Remediating Age Related Cognitive Decline: Mindfulness-Based Stress Reduction and Exercise ,NIH| Genetically Informed Smoking Cessation Trial ,NIH| Ocular Hypertension Treatment Study 20-Year Follow-up: Clinical Center Grant ,NIH| RNA-mediated mechanisms in the myotonic dystrophies ,NIH| 7th International Myotonic Dystrophy Consortium Meeting ,NIH| FOR-DMD: Double-blind randomized trial to optimize steroid regimen in Duchenne MD ,NIH| Washington University Institute of Clinical and Translational Sciences ,NIH| Tenth International Myotonic Dystrophy Consortium Meeting ,NIH| ANALYSIS OF THE E COLI STB HEAT STABLE ENTEROTOXINAuthors: Griggs, Robert C.; Miller, J. Phillip; Greenberg, Cheryl R.; Fehlings, Darcy L.; +11 AuthorsGriggs, Robert C.; Miller, J. Phillip; Greenberg, Cheryl R.; Fehlings, Darcy L.; Pestronk, Alan; Mendell, Jerry R.; Moxley, Richard T.; King, Wendy; Kissel, John T.; Cwik, Valerie; Vanasse, Michel; Florence, Julaine M.; Pandya, Shree; Dubow, Jordan S.; Meyer, James M.;Objective: To assess safety and efficacy of deflazacort (DFZ) and prednisone (PRED) vs placebo in Duchenne muscular dystrophy (DMD). Methods: This phase III, double-blind, randomized, placebo-controlled, multicenter study evaluated muscle strength among 196 boys aged 5–15 years with DMD during a 52-week period. In phase 1, participants were randomly assigned to receive treatment with DFZ 0.9 mg/kg/d, DFZ 1.2 mg/kg/d, PRED 0.75 mg/kg/d, or placebo for 12 weeks. In phase 2, placebo participants were randomly assigned to 1 of the 3 active treatment groups. Participants originally assigned to an active treatment continued that treatment for an additional 40 weeks. The primary efficacy endpoint was average change in muscle strength from baseline to week 12 compared with placebo. The study was completed in 1995. Results: All treatment groups (DFZ 0.9 mg/kg/d, DFZ 1.2 mg/kg/d, and PRED 0.75 mg/kg/d) demonstrated significant improvement in muscle strength compared with placebo at 12 weeks. Participants taking PRED had significantly more weight gain than placebo or both doses of DFZ at 12 weeks; at 52 weeks, participants taking PRED had significantly more weight gain than both DFZ doses. The most frequent adverse events in all 3 active treatment arms were Cushingoid appearance, erythema, hirsutism, increased weight, headache, and nasopharyngitis. Conclusions: After 12 weeks of treatment, PRED and both doses of DFZ improved muscle strength compared with placebo. Deflazacort was associated with less weight gain than PRED. Classification of evidence: This study provides Class I evidence that for boys with DMD, daily use of either DFZ and PRED is effective in preserving muscle strength over a 12-week period.
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.eu115 citations 115 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!visibility 2visibility views 2 download downloads 0 Powered bymore_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 2022Public Library of Science (PLoS) NIH | UCLA Clinical Translation..., SSHRC, NSERC +4 projectsNIH| UCLA Clinical Translational Science Institute ,SSHRC ,NSERC ,NIH| CTSA INFRASTRUCTURE FOR CLINICAL TRIALS ,NIH| Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) ,NIH| Integrative Pathways to Health and Illness ,NIH| Integrative Pathways to Health and IllnessLeela McKinnon; David R. Samson; Charles L. Nunn; Amanda Rowlands; Katrina G. Salvante; Pablo A. Nepomnaschy;Sleep duration, quality, and rest-activity pattern—a measure for inferring circadian rhythm—are influenced by multiple factors including access to electricity. Recent findings suggest that the safety and comfort afforded by technology may improve sleep but negatively impact rest-activity stability. According to the circadian entrainment hypothesis, increased access to electric lighting should lead to weaker and less uniform circadian rhythms, measured by stability of rest-activity patterns. Here, we investigate sleep in a Maya community in Guatemala who are in a transitional stage of industrialization. We predicted that (i) sleep will be shorter and less efficient in this population than in industrial settings, and that (ii) rest-activity patterns will be weaker and less stable than in contexts with greater exposure to the natural environment and stronger and more stable than in settings more buffered by technologic infrastructure. Our results were mixed. Compared to more industrialized settings, in our study population sleep was 4.87% less efficient (78.39% vs 83.26%). We found no significant difference in sleep duration. Rest-activity patterns were more uniform and less variable than in industrial settings (interdaily stability = 0.58 vs 0.43; intradaily variability = 0.53 vs 0.60). Our results suggest that industrialization does not inherently reduce characteristics of sleep quality; instead, the safety and comfort afforded by technological development may improve sleep, and an intermediate degree of environmental exposure and technological buffering may support circadian rhythm strength and stability.
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.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.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018 United StatesPublic Library of Science (PLoS) SSHRC, NIH | The National Person-Cente..., NIH | University of Pittsburgh ... +2 projectsSSHRC ,NIH| The National Person-Centered Assessment Resource (PCAR) ,NIH| University of Pittsburgh Clinical and Translational Science Institute ,NIH| PROMIS Statistical Center ,NIH| Institutional Career DevelopmentHanmer, Janel; Dewitt, Barry; Yu, Lan; Tsevat, Joel; Roberts, Mark; Revicki, Dennis; Pilkonis, Paul A.; Hess, Rachel; Hays, Ron D.; Fischhoff, Baruch; Feeny, David; Condon, David; Cella, David;Author(s): Hanmer, Janel; Dewitt, Barry; Yu, Lan; Tsevat, Joel; Roberts, Mark; Revicki, Dennis; Pilkonis, Paul A; Hess, Rachel; Hays, Ron D; Fischhoff, Baruch; Feeny, David; Condon, David; Cella, David | Abstract: ObjectivesThe PROMIS-Preference (PROPr) score is a recently developed summary score for the Patient-Reported Outcomes Measurement Information System (PROMIS). PROPr is a preference-based scoring system for seven PROMIS domains created using multiplicative multi-attribute utility theory. It serves as a generic, societal, preference-based summary scoring system of health-related quality of life. This manuscript evaluates construct validity of PROPr in two large samples from the US general population.MethodsWe utilized 2 online panel surveys, the PROPr Estimation Survey and the Profiles-Health Utilities Index (HUI) Survey. Both included the PROPr measure, patient demographic information, self-reported chronic conditions, and other preference-based summary scores: the EuroQol-5D (EQ-5D-5L) and HUI in the PROPr Estimation Survey and the HUI in the Profiles-HUI Survey. The HUI was scored as both the Mark 2 and the Mark 3. Known-groups validity was evaluated using age- and gender-stratified mean scores and health condition impact estimates. Condition impact estimates were created using ordinary least squares regression in which a summary score was regressed on age, gender, and a single health condition. The coefficient for the health condition is the estimated effect on the preference score of having a condition vs. not having it. Convergent validity was evaluated using Pearson correlations between PROPr and other summary scores.ResultsThe sample consisted of 983 respondents from the PROPr Estimation Survey and 3,000 from the Profiles-HUI survey. Age- and gender-stratified mean PROPr scores were lower than EQ-5D and HUI scores, with fewer subjects having scores corresponding to perfect health on the PROPr. In the PROPr Estimation survey, all 11 condition impact estimates were statistically significant using PROPr, 8 were statistically significant by the EQ-5D, 7 were statistically significant by HUI Mark 2, and 9 were statistically significant by HUI Mark 3. In the Profiles-HUI survey, all 21 condition impact estimates were statistically significant using summary scores from all three scoring systems. In these samples, the correlations between PROPr and the other summary measures ranged from 0.67 to 0.70.ConclusionsThese results provide evidence of construct validity for PROPr using samples from the US general population.
Europe PubMed Centra... arrow_drop_down eScholarship - University of CaliforniaArticle . 2018Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2018Data sources: eScholarship - University of Californiaadd 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.eu54 citations 54 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down eScholarship - University of CaliforniaArticle . 2018Data sources: eScholarship - University of CaliforniaeScholarship - University of CaliforniaArticle . 2018Data sources: eScholarship - University of Californiaadd 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 2010 EnglishInter-university Consortium for Political and Social Research (ICPSR) NIH | University of Michigan's ..., AKA | Work stress and risk of c..., NIH | Vulnerability to Drug Use... +33 projectsNIH| University of Michigan's TMJD and Orofacial Pain Interdisciplinary Consortium ,AKA| Work stress and risk of coronary heart disease: Does a healthy life style eliminate the adverse effect? Pooled analysis of 6 major prospective cohort studies from Europe ,NIH| Vulnerability to Drug Use & HIV: Advancing Prevention for Rural African Americans ,NIH| The Brain as a Target for Pre and Essential Hypertension ,SSHRC ,NIH| UCLA OLDER AMERICANS INDEPENDENCE CENTER ,NIH| Examining the Bi-directional Relationship between Sleep and Stress: A Vicious Cycle ,WT ,NIH| MEASUREMENT OF ESTRADIOL AND OTHER RELATED HORMONES BY TANDEM MASS SPECTROSCOPY ,CIHR ,NIH| Neurobiological pathways linking stress and emotion to atherosclerosis ,NIH| Integrative Pathways to Health and Illness ,NIH| Integrative Pathways to Health and Illness ,NIH| Health behaviors over the adult lifecourse and cognitive aging ,NIH| GCRC ,AKA| Determinants of Early Exit from Work Force: An International Multicohort Study. ,NIH| Biological Embedding of Early-Life SES ,NIH| Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) ,UKRI| Assessment of biomedical risk factors and disease outcomes in the British 1958 cohort ,NSF| Sleep Disruption as an Amplifier of Aggressive Behavior ,NIH| UCLA Clinical Translational Science Institute ,AKA| Work, well-being and health - a life course perspective: British arm of Academy of Finland consortia 10190 and 10187 / Consortium: ELRFWCDD ,NIH| Self-regulation as a Health-Protective Factor in Adverse Socioeconomic Conditions ,NIH| Health Disparities Research Scholars T32 ,NIH| Personality and Well-Being Trajectories in Adulthood ,NIH| CTSA INFRASTRUCTURE FOR CLINICAL TRIALS ,NIH| Institutional Clinical and Translational Science Award ,NIH| SOCIAL AND OCCUPATIONAL INFLUENCES ON HEALTH AND ILLNESS ,UKRI| RootDetect: Remote Detection and Precision Management of Root Health ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Biopsychosocial Pathways to Type 2 Diabetes ,NIH| Cardiovascular Behavioral Medicine Research Training ,NIH| Midlife Health in Japan (MIDJA) and the U.S. (MIDUS) ,NIH| Training in Behavioral &Preventive Medicine ,NIH| Integrative Pathways to Health and Illness ,NIH| Biobehavioral Influences and the Ovarian Tumor MicroenvironmentAuthors: Ryff, Carol D.; Seeman, Teresa; Weinstein, Maxine;Ryff, Carol D.; Seeman, Teresa; Weinstein, Maxine;These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues.The Biomarker study is Project 4 of the MIDUS longitudinal study, a national survey of more than 7,000 Americans (aged 25 to 74) begun in 1994. The purpose of the larger study was to investigate the role of behavioral, psychological, and social factors in understanding age-related differences in physical and mental health. With support from the National Institute on Aging, a longitudinal follow-up of the original MIDUS samples [core sample (N = 3,487), metropolitan over-samples (N = 757), twins (N = 957 pairs), and siblings (N = 950)] was conducted in 2004-2006. Guiding hypotheses, at the most general level, were that behavioral and psychosocial factors are consequential for health (physical and mental). A description of the study and findings from it are available on the MIDUS Web site. The Biomarker Project (Project 4) of MIDUS II contains data from 1,255 respondents. These respondents include two distinct subsamples, all of whom completed the Project 1 Survey: (1) longitudinal survey sample (n = 1,054) and (2) Milwaukee sample (n = 201). The Milwaukee group contained individuals who participated in the baseline MIDUS Milwaukee study, initiated in 2005. The purpose of the Biomarker Project (Project 4) was to add comprehensive biological assessments on a subsample of MIDUS respondents, thus facilitating analyses that integrate behavioral and psychosocial factors with biology. The broad aim is to identify biopsychosocial pathways that contribute to diverse health outcomes. A further theme is to investigate protective roles that behavioral and psychosocial factors have in delaying morbidity and mortality, or in fostering resilience and recovery from health challenges once they occur. The research was not disease-specific, given that psychosocial factors have relevance across multiple health endpoints. Biomarker data collection was carried out at three General Clinical Research Centers (at UCLA, University of Wisconsin, and Georgetown University). The biomarkers reflect functioning of the hypothalamic-pituitary-adrenal axis, the autonomic nervous system, the immune system, cardiovascular system, musculoskeletal system, antioxidants, and metabolic processes. Our specimens (fasting blood draw, 12-hour urine, saliva) allow for assessment of multiple indicators within these major systems. The protocol also included assessments by clinicians or trained staff, including vital signs, morphology, functional capacities, bone densitometry, medication usage, and a physical exam. Project staff obtained indicators of heart-rate variability, beat to beat blood pressure, respiration, and salivary cortisol assessments during an experimental protocol that included both a cognitive and orthostatic challenge. Finally, to augment the self-reported data collected in Project 1, participants completed a medical history, self-administered questionnaire, and self-reported sleep assessments. For respondents at one site (UW-Madison), objective sleep assessments were also obtained with an Actiwatch(R) activity monitor. The MIDUS and MIDJA Biomarker Clinic Visits include collection of comprehensive information about medications of all types, as well as basic information about allergic reactions to any type of medication. Respondents were instructed to bring all their medications, or information about their medications, to the clinic visit to ensure the information about those medications was recorded accurately. Information regarding Prescription Medications (FDA approved medications prescribed by someone authorized/licensed under the Western medical tradition, or medications prescribed by individuals authorized under Japanese law to prescribe Western and/or Eastern/Chinese traditional medicine), Quasi Medications (including Over the Counter Medications i.e. vitamins, minerals, non-prescription pain relief, antacids, etc. that can be purchased without a prescription) and Alternative Medications (i.e. herbs, herbal blends (excluding herbal teas), homeopathic remedies, and other alternative remedies that may be purchased over the counter or "prescribed" by a health care practitioner trained in a non-western tradition)was collected at this time.The following information was collected for each medication type Medication name, dosage, and route of administration; How often the medication is taken(frequency); How long the participant has been taking a given medication; Why they think they are taking the medication; After basic cleaning protocols were completed, standardized protocols were applied to both MIDUS and MIDJA medication data to link medications first to Generic Names and associated DrugIDs and then to therapeutic and pharmacologic class information from the Lexicomp Lexi-Data database, and also to code text data describing why participants think they are taking a given medication. The scope of this collected medication data lends itself to within person analysis of medication use, thus the medication data are also released in a standalone stacked format. The stacked file only contains data about medications used where each case represents an individual medication, thus it does not include any data about medication allergies. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. All respondents participating in MIDUS II (ICPSR 4652) or the Milwaukee study (ICPSR 22840) who completed Project 1 were eligible to participate in the Biomarker assessments. Presence of Common Scales: Data users interested in the scales used for this study should refer to the scaling documentation provided on both the ICPSR and NACDA Web site. Adult non-institutionalized population of the United States. Smallest Geographic Unit: No geographic information is included other than for the Milwaukee cases. Response Rates: The response rate was 39.3 percent for each of the 2 samples (longitudinal survey sample, and Milwaukee). Datasets: DS0: Study-Level Files DS1: Aggregated Data DS2: Stacked Medication Data Midlife in the United States (MIDUS) Series face-to-face interview on-site questionnaire mixed mode
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.eu28 citations 28 popularity Top 10% influence Average impulse Average 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.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 2018Springer Science and Business Media LLC NSERC, SSHRC, NIH | The Vanderbilt Institute ... +3 projectsNSERC ,SSHRC ,NIH| The Vanderbilt Institute for Clinical and Translational Research (VICTR) ,NIH| Training in Fundamental Neuroscience ,NIH| Individual differences in cochlear implant users' audiovisual integration and links to speech proficiency ,NIH| Research Project: Sensory and Multisensory Contributions to AutismIliza M. Butera; Ryan A. Stevenson; Brannon D Mangus; Tiffany G. Woynaroski; René H. Gifford; Mark T. Wallace;AbstractFor many cochlear implant (CI) users, visual cues are vitally important for interpreting the impoverished auditory speech information that an implant conveys. Although the temporal relationship between auditory and visual stimuli is crucial for how this information is integrated, audiovisual temporal processing in CI users is poorly understood. In this study, we tested unisensory (auditory alone, visual alone) and multisensory (audiovisual) temporal processing in postlingually deafened CI users (n = 48) and normal-hearing controls (n = 54) using simultaneity judgment (SJ) and temporal order judgment (TOJ) tasks. We varied the timing onsets between the auditory and visual components of either a syllable/viseme or a simple flash/beep pairing, and participants indicated either which stimulus appeared first (TOJ) or if the pair occurred simultaneously (SJ). Results indicate that temporal binding windows—the interval within which stimuli are likely to be perceptually ‘bound’—are not significantly different between groups for either speech or non-speech stimuli. However, the point of subjective simultaneity for speech was less visually leading in CI users, who interestingly, also had improved visual-only TOJ thresholds. Further signal detection analysis suggests that this SJ shift may be due to greater visual bias within the CI group, perhaps reflecting heightened attentional allocation to visual cues.
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For further information contact us at helpdesk@openaire.eu13 citations 13 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 , Other literature type 2020American Association for Cancer Research (AACR) NIH | Institutional Career Deve..., NIH | Project 1: Modeling tumor..., NIH | Clinical and Translationa... +2 projectsNIH| Institutional Career Development Core ,NIH| Project 1: Modeling tumor evolution in mouse and organoid models ,NIH| Clinical and Translational Science Award ,SSHRC ,NIH| MEDICAL SCIENTIST TRAINING PROGRAMAuthors: Robyn D. Gartrell-Corrado; Andrew X. Chen; Emanuelle M. Rizk; Douglas K. Marks; +13 AuthorsRobyn D. Gartrell-Corrado; Andrew X. Chen; Emanuelle M. Rizk; Douglas K. Marks; Margaret Bogardus; Thomas D. Hart; Andrew M. Silverman; Claire-Audrey Y. Bayan; Grace G. Finkel; Luke W. Barker; Kimberly M. Komatsubara; Richard D. Carvajal; Basil A. Horst; Rui Chang; Anthea Monod; Raul Rabadan; Yvonne M. Saenger;Abstract Patients with resected stage II-III melanoma have approximately a 35% chance of death from their disease. A deeper understanding of the tumor immune microenvironment (TIME) is required to stratify patients and identify factors leading to therapy resistance. We previously identified that the melanoma immune profile (MIP), an IFN-based gene signature, and the ratio of CD8+ cytotoxic T lymphocytes (CTL) to CD68+ macrophages both predict disease-specific survival (DSS). Here, we compared primary with metastatic tumors and found that the nuclei of tumor cells were significantly larger in metastases. The CTL/macrophage ratio was significantly different between primary tumors without distant metastatic recurrence (DMR) and metastases. Patients without DMR had higher degrees of clustering between tumor cells and CTLs, and between tumor cells and HLA-DR+ macrophages, but not HLA-DR− macrophages. The HLA-DR− subset coexpressed CD163+CSF1R+ at higher levels than CD68+HLA-DR+ macrophages, consistent with an M2 phenotype. Finally, combined transcriptomic and multiplex data revealed that densities of CD8 and M1 macrophages correlated with their respective cell phenotype signatures. Combination of the MIP signature with the CTL/macrophage ratio stratified patients into three risk groups that were predictive of DSS, highlighting the potential use of combination biomarkers for adjuvant therapy. Significance: These findings provide a deeper understanding of the tumor immune microenvironment by combining multiple modalities to stratify patients into risk groups, a critical step to improving the management of patients with melanoma.
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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.eu11 citations 11 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.euResearch data keyboard_double_arrow_right Dataset 1999 EnglishInter-university Consortium for Political and Social Research (ICPSR) SSHRC, NHMRC | Australian Centre of Exce..., UKRI | Centre for Cognitive Agei... +91 projectsSSHRC ,NHMRC| Australian Centre of Excellence in Twin Research ,UKRI| Centre for Cognitive Ageing & Cognitive Epidemiology ,NIH| Daily Stress and Well-Being during Adulthood ,NIH| CORE--PROGRAM DEVELOPMENT ,NIH| CONTROL BELIEFS, MEMORY, AND AGING ,NHMRC| Gene-environment interaction in healthy brain ageing and age related neurodegeneration ,AKA| Indicators of marginalization - Role of cognition, substance use and mental health disorders: Longitudinal studies from childhood to end of adolescence ,NIH| A Longitudinal Twin Study of Cognition and Personality ,NIH| A 55-Year Follow-up Study of Project TALENT Twins and Siblings ,NHMRC| The genetic and environmental determinants of amyloid deposition in older individuals: an amyloid imaging study using the twin design ,CIHR ,NIH| Integrative Analysis of Change in Cognition and Health ,NIH| CHANGES IN HEALTH--SOCIOECONOMIC STATUS AND PATHWAYS ,NIH| SOCIAL AND OCCUPATIONAL INFLUENCES ON HEALTH AND ILLNESS ,NIH| TWIN STUDY OF NORMAL AGING ,NIH| Sex differences in the relationship between APOE and AD: Role of sexual differentiation ,NIH| MEASUREMENT OF ESTRADIOL AND OTHER RELATED HORMONES BY TANDEM MASS SPECTROSCOPY ,NIH| SES health gradients in late life: testing models of gene-environment interplay in an international twin consortium ,NIH| Sexuality, Aging and Heart Disease: Translating from Population to Patient ,NIH| The Population Research Institute ,NIH| Genetic Moderators of Divorce Adjustment: A Pilot Investigation ,NIH| Health behaviors over the adult lifecourse and cognitive aging ,NIH| GCRC ,NIH| WISCONSIN LONGITUDINAL STUDY ,NIH| A Longitudinal Twin Study of Cognition and Aging ,NIH| Daily stressor reactivity and profiles of physical health across adulthood ,AKA| Oxygenology of soil ,NIH| Social Regulation of Gene Expression ,AKA| Determinants of Early Exit from Work Force: An International Multicohort Study. ,NIH| Biological Embedding of Early-Life SES ,NIH| Bio-social Determinants of Fertility &Related Behaviors ,NIH| Sleep and Divorce: Identifying Bidirectional Vulnerability and Resilience ,NIH| Causal Effects of Schooling on Adult and Child Health ,NIH| Integrative Pathways to Health and Illness ,NIH| TRAINING IN RESEARCH ON MENTAL HEALTH AND AGING ,AKA| Predictors, neuropsychological correlates, and consequences of cannabis and alcohol use among Finnish young adults. A twin and population approach ,NHMRC| The Older Australian Twins Study (OATS) of healthy brain ageing and age-related neurocognitive disorders ,NIH| Informing anti-tobacco communications with affective and decision science: Application of the Appraisal Tendency Framework ,NIH| Wisconsin Longitudinal Study: Tracking the Life Course ,NIH| Aging, Emotional Well-being, and Physical Health ,NIH| Clarifying risk and protective factors for dementia with the Interplay of Genes and Environment in Multiple Studies (IGEMS) consortium ,AKA| Work stress and risk of coronary heart disease: Does a healthy life style eliminate the adverse effect? Pooled analysis of 6 major prospective cohort studies from Europe ,NSF| IBSS: Understanding Long-Term Effects on Children in Economic Distress ,NIH| ORIGINS OF VARIANCE IN THE OLD-OLD: OCTOGENARIAN TWINS ,NIH| OLDEST-OLD MORTALITY AND DISABILITY AMONG DANISH TWINS ,NIH| Vulnerability to Drug Use & HIV: Advancing Prevention for Rural African Americans ,NWO| A Twin-sibling Study of Adolescent Wellness ,NIH| The VETSA Longitudinal Twin Study of Cortisol and Aging ,AKA| Genomic epidemiology of addictions and their consequences - national, Nordic and international dimensions. ,AKA| Work, well-being and health - a life course perspective: British arm of Academy of Finland consortia 10190 and 10187 / Consortium: ELRFWCDD ,NIH| HEALTH AND PSYCHOSOCIAL FACTORS IN OLDER BLACK TWINS ,NIH| Self-regulation as a Health-Protective Factor in Adverse Socioeconomic Conditions ,AKA| Determinants of labour market participation and prognosis of common chronic diseases in working populations: a study of cohorts in Finland, United Kingdom and France ,NIH| Midlife Health in Japan (MIDJA) and the U.S. (MIDUS) ,NIH| GENETIC AND ENVIRONMENTAL INFLUENCES IN BEHAVIORAL AGING ,NIH| Stress, Aging and Working Memory ,EC| ENGAGE ,NIH| GENETIC &ENVIRONMENTAL INFLUENCES--BIOBEHAVIORAL AGING ,NIH| Implementing World Health Assembly Resolution 60.26 ???Workers' Health: Global Pl ,NIH| Optimizing Couple-Oriented Interventions for Chronic Illness ,NIH| Institutional Clinical and Translational Science Award ,NIH| Integrative Pathways to Health and Illness ,AKA| Genomic epidemiology of addictions and their consequences - national, Nordic and international dimensions ,AKA| Determinants of labour market participation and prognosis of common chronic diseases in working populations: a study of cohorts in Finland, United Kingdom and France ,NIH| IMSD Program at Wayne State University ,NIH| 1995 SUMMER INSTITUTE IN GERIATRIC MEDICINE ,NIH| AGING AND INTRAINDIVIDUAL COGNITIVE VARIABILITY ,AKA| Midlife predictors of dementia, frailty and disability at older ages ,NIH| AGING AND HEALTH TRAJECTORIES AMONG BLACK &WHITE ADULTS ,ARC| Discovery Projects - Grant ID: DP200100876 ,NIH| Genes, Enivronment and the Adjustment of Family Members ,UKRI| Offshore Platform for Energy Competitiveness (OPEC) ,NIH| UCLA OLDER AMERICANS INDEPENDENCE CENTER ,NWO| Genetische en omgevingsinvloeden op psychopathologie en geluk tijdens de adolescentie ,NIH| The VETSA Longitudinal MRI Twin Study of Aging ,NIH| DEMOGRAPHY ,NIH| Integrative Pathways to Health and Illness ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| The Greatest Generation: The NAS-NRC WWII Twin Registry as a Scientific Resource ,NIH| Personality and Well-Being Trajectories in Adulthood ,NIH| CTSA INFRASTRUCTURE FOR CLINICAL TRIALS ,NIH| Social and Economic Analysis of Demographic Change ,NIH| Gene-Environment Interplay of Social Contexts and Aging-Related Outcomes ,NIH| Risk for Alzheimer's Disease and Cognitive Decline in Project TALENT ,AKA| Heterogeneity of depression at symptom level: Specific versus general patterns in etiology, development, and disability ,NIH| Histories of Social Engagement and Cognitive Functioning ,AKA| Genetic and environmental predictors of tobacco, drug and alcohol addiction in adolescence and young adulthood ¿ a lifecourse twin and population approach / Consortium: addictgene ,AKA| CoE in Complex Disease Genetics ,UKRI| RootDetect: Remote Detection and Precision Management of Root Health ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Biopsychosocial Pathways to Type 2 Diabetes ,NIH| Infrastructure for the Office of Population Research ,AKA| Center of Excellence in Complex Disease Genetics-from Discovery to Precision Medicine / Consortium: CoECDGBrim, Orville Gilbert; Baltes, Paul B.; Bumpass, Larry L.; Cleary, Paul D.; Featherman, David L.; Hazzard, William R.; Kessler, Ronald C.; Lachman, Margie E.; Markus, Hazel Rose; Marmot, Michael G.; Rossi, Alice S.; Ryff, Carol D.; Shweder, Richard A.;doi: 10.3886/icpsr02760.v11 , 10.3886/icpsr02760.v12 , 10.3886/icpsr02760.v6 , 10.3886/icpsr02760.v15 , 10.3886/icpsr02760 , 10.3886/icpsr02760.v10 , 10.3886/icpsr02760.v17 , 10.3886/icpsr02760.v5 , 10.3886/icpsr02760.v8 , 10.3886/icpsr02760.v4 , 10.3886/icpsr02760.v14 , 10.3886/icpsr02760.v19 , 10.3886/icpsr02760.v2 , 10.3886/icpsr02760.v7 , 10.3886/icpsr02760.v9 , 10.3886/icpsr02760.v18 , 10.3886/icpsr02760.v16 , 10.3886/icpsr02760.v1 , 10.3886/icpsr02760.v13 , 10.3886/icpsr02760.v3
doi: 10.3886/icpsr02760.v11 , 10.3886/icpsr02760.v12 , 10.3886/icpsr02760.v6 , 10.3886/icpsr02760.v15 , 10.3886/icpsr02760 , 10.3886/icpsr02760.v10 , 10.3886/icpsr02760.v17 , 10.3886/icpsr02760.v5 , 10.3886/icpsr02760.v8 , 10.3886/icpsr02760.v4 , 10.3886/icpsr02760.v14 , 10.3886/icpsr02760.v19 , 10.3886/icpsr02760.v2 , 10.3886/icpsr02760.v7 , 10.3886/icpsr02760.v9 , 10.3886/icpsr02760.v18 , 10.3886/icpsr02760.v16 , 10.3886/icpsr02760.v1 , 10.3886/icpsr02760.v13 , 10.3886/icpsr02760.v3
The Midlife in the United States (MIDUS) is a collaborative, interdisciplinary investigation of patterns, predictors, and consequences of midlife development in the areas of physical health, psychological well-being, and social responsibility. A description of the study and findings from it are available at http://www.midus.wisc.edu. The first wave of the MIDUS study (MIDUS 1 or M1) collected survey data from a total of 7,108 participants. The baseline sample was comprised of individuals from four subsamples: (1) a national RDD (random digit dialing) sample (n=3,487); (2) oversamples from five metropolitan areas in the U.S. (n=757); (3) siblings of individuals from the RDD sample (n=950); and (4) a national RDD sample of twin pairs (n=1,914). All eligible participants were non-institutionalized, English-speaking adults in the coterminous United States, aged 25 to 74. Data from the samples were collected primarily in 1995/96. The survey (Project 1) dataset contains responses from a 30-minute Phone interview and two 50-page Self-Administered Questionnaire (SAQ) instruments. Of the 7,108 respondents who completed the Phone interview, 6,325 also completed the SAQ. This updated version of the study is comprised of three primary datasets: Dataset 1, Main, Siblings, and Twin Data, contains responses from the main survey of 7,108 respondents. Respondents were asked to provide extensive information on their physical and mental health throughout their adult lives, and to assess the ways in which their lifestyles, including relationships and work-related demands, contributed to the conditions experienced. Those queried were asked to describe their histories of physical ailments, including heart-related conditions and cancer, as well as the treatment and/or lifestyle changes they went through as a result. A series of questions addressed alcohol, tobacco, and illegal drug use, and focused on history of use, regularity of use, attempts to quit, and how the use of those substances affected respondents' physical and mental well-being. Additional questions addressed respondents' sense of control over their health, their awareness of changes in their medical conditions, commitment to regular exercise and a healthy diet, experience with menopause, the decision-making process used to deal with health concerns, experiences with nontraditional remedies or therapies, and history of attending support groups. Respondents were asked to compare their overall well-being with that of their peers and to describe social, physical, and emotional characteristics typical of adults in their 20's, 40's, and 60's. Information on the work histories of respondents and their significant others was also elicited, with items covering the nature of their occupations, work-related physical and emotional demands, and how their personal health had correlated to their jobs. An additional series of questions focusing on childhood queried respondents regarding the presence/absence of their parents, religion, rules/punishments, love/affection, physical/verbal abuse, and the quality of their relationships with their parents and siblings. Respondents were also asked to consider their personal feelings of accomplishment, desire to learn, sense of control over their lives, interests, and hopes for the future. The Datasets previously numbered 2 and 3 have been removed to avoid redundancies, and all datasets have been renumbered. Please refer to the readme file. Dataset 2, Twin Screener Data, provides the first national sample of twin pairs ascertained randomly via the telephone. Dataset 3, Coded Text Responses, describes how open-ended textual responses in the MIDUS 1 Computer-Assisted Telephone Interview (CATI) and Self-Administered Questionnaire (SAQ) were transformed into categorical numeric codes. These codes are included in a stand-alone dataset containing only those cases (N=3,950) that contained text data in their responses. Online Analysis Only: Datasets 1, 2, and 3 were merged together by the SU_ID variable to form "Merged Data with Weights (Online Analysis Only)" (Dataset 4) for online analysis capabilities. MIDUS also maintains a Colectica portal, which allows users to interact with variables across waves and create customized subsets. Registration is required. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Presence of Common Scales: Generalized Anxiety Disorder (GAD) Scale; Somatic Amplification Scale; The Alcohol Screening Test; The Conflict Tactics (CT) Scales; The Revised Conflict Tactics Scales (CTS2); Loyola Generativity Scale (LGS); Many scales were constructed for use in the Midlife in the United States (MIDUS 1), 1995-1996 Study. For additional information on scale construction and sources, please refer to the scale documentation included with the data collection. Respondents were drawn from a nationally representative random-digit-dial sample of non-institutionalized, English-speaking adults, aged 25-74, selected from working telephone banks in the coterminous United States. Those queried participated in an initial telephone interview and responded to a mail questionnaire. Please see the Descriptions of Midlife in the United Sates (MIDUS) Samples documentation provided by ICPSR for more detailed information. Respondents were drawn from a nationally representative random-digit-dial sample of non-institutionalized, English-speaking adults, aged 25-74, selected from working telephone banks in the coterminous United States. Those queried participated in an initial telephone interview and responded to a mail questionnaire. Smallest Geographic Unit: None Datasets: DS0: Study-Level Files DS1: Main, Siblings and Twin Data DS2: Twin Screener Data DS3: Coded Text Data DS4: Merged Data with Weights (Online Analysis Only) DS6: Midlife in the United States (MIDUS 1), 1995-1996, Merged Data with Weights (Online Analysis Only) Response Rates: The response rate for the national Random-Digit Dialing (RDD) sample was 70 percent. The Self-Administered Questionnaire (SAQ) follow-up response rate was 89 percent. computer-assisted telephone interview (CATI) Midlife in the United States (MIDUS) Series self-enumerated questionnaire mail questionnaire
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For further information contact us at helpdesk@openaire.eu33 citations 33 popularity Top 10% influence Top 10% 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 , Other literature type , Preprint 2023 Switzerland, United KingdomCenter for Open Science SSHRC, SNSF | Personality and Well-Bein..., NIH | PSID, CDS, and TA: Data E... +1 projectsSSHRC ,SNSF| Personality and Well-Being: An Individual, Couple, and Family Perspective ,NIH| PSID, CDS, and TA: Data Enhancements and User Training, Support, and Outreach ,NIH| University of Pittsburgh Clinical and Translational Science InstituteWeidmann, Rebekka; Chopik, William J; Ackerman, Robert A; Allroggen, Marc; Bianchi, Emily C; Brecheen, Courtney; Campbell, W Keith; Gerlach, Tanja M; Geukes, Katharina; Grijalva, Emily; Grossmann, Igor; Hopwood, Christopher J; Hutteman, Roos; Konrath, Sara; Küfner, Albrecht C P; Leckelt, Marius; Miller, Joshua D; Penke, Lars; Pincus, Aaron L; Renner, Karl-Heinz; Richter, David; Roberts, Brent W; Sibley, Chris G; Simms, Leonard J; Wetzel, Eunike; Wright, Aidan G C; Back, Mitja D;pmid: 37184962
pmc: PMC10188200
Age and gender differences in narcissism have been studied often. However, considering the rich history of narcissism research accompanied by its diverging conceptualizations, little is known about age and gender differences across various narcissism measures. The present study investigated age and gender differences and their interactions across eight widely used narcissism instruments (i.e., Narcissistic Personality Inventory, Hypersensitive Narcissism Scale, Dirty Dozen, Psychological Entitlement Scale, Narcissistic Personality Disorder Symptoms from the , Version IV, Narcissistic Admiration and Rivalry Questionnaire-Short Form, Single-Item Narcissism Scale, and brief version of the Pathological Narcissism Inventory). The findings of Study 1 (N = 5,736) revealed heterogeneity in how strongly the measures are correlated. Some instruments loaded clearly on one of the three factors proposed by previous research (i.e., Neuroticism, Extraversion, Antagonism), while others cross-loaded across factors and in distinct ways. Cross-sectional analyses using each measure and meta-analytic results across all measures (Study 2) with a total sample of 270,029 participants suggest consistent linear age effects (random effects meta-analytic effect of r = -.104), with narcissism being highest in young adulthood. Consistent gender differences also emerged (random effects meta-analytic effect was -.079), such that men scored higher in narcissism than women. Quadratic age effects and Age × Gender effects were generally very small and inconsistent. We conclude that despite the various conceptualizations of narcissism, age and gender differences are generalizable across the eight measures used in the present study. However, their size varied based on the instrument used. We discuss the sources of this heterogeneity and the potential mechanisms for age and gender differences.
Journal of Personali... arrow_drop_down Zurich Open Repository and ArchiveOther literature type . 2023add 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.eu7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Journal of Personali... arrow_drop_down Zurich Open Repository and ArchiveOther literature type . 2023add 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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