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  • 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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    Authors: Leela McKinnon; David R. Samson; Charles L. Nunn; Amanda Rowlands; +2 Authors

    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.

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    Article . 2021
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    Article . 2022
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    Article . 2022
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      Article . 2021
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      Article . 2022
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  • 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

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    Authors: Constance Dubuc; Laura Muniz; Michael Heistermann; Antje Engelhardt; +1 Authors

    In mammals, when females are clumped in space, male access to receptive females is usually determined by a dominance hierarchy based on fighting ability. In polygynandrous primates, as opposed to most mammalian species, the strength of the relationship between male social status and reproductive success varies greatly. It has been proposed that the degree to which paternity is determined by male rank decreases with increasing female reproductive synchrony. The priority-of-access model (PoA) predicts male reproductive success based on female synchrony and male dominance rank. To date, most tests of the PoA using paternity data involved nonseasonally breeding species. Here, we examine whether the PoA explains the relatively low reproductive skew in relation to dominance rank reported in the rhesus macaque, a strictly seasonal species. We collected behavioral, genetic, and hormonal data on one group of the free-ranging population on Cayo Santiago (Puerto Rico) for 2 years. The PoA correctly predicted the steepness of male reproductive skew, but not its relationship to male dominance: the most successful sire, fathering one third of the infants, was high but not top ranking. In contrast, mating success was not significantly skewed, suggesting that other mechanisms than social status contributed to male reproductive success. Dominance may be less important for paternity in rhesus macaques than in other primate species because it is reached through queuing rather than contest, leading to alpha males not necessarily being the strongest or most attractive male. More work is needed to fully elucidate the mechanisms determining paternity in rhesus macaques. peerReviewed

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    Behavioral Ecology and Sociobiology
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      Europe PubMed Central
      Article . 2011
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      Behavioral Ecology and Sociobiology
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  • Authors: 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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  • The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. The following files constitute Wave 1: Core Data, Marital/Cohabiting History Data, Social Networks Data, Medications Data, and Sexual Partners Data. Included in the Core file (Dataset 1) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, medications and alternative therapies, history of sexual and intimate partnerships and patient-physician communication, in addition to bereavement items. In addition data was collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function and a panel of biomeasures including, weight, waist circumference, height, blood pressure, smell, saliva collection, taste, and a self-administered vaginal swab for female respondents. The Core file also contains a count of the total number of drugs taken, and a variable for each observed therapeutic category, indicating whether the respondent reported taking one or more medications in that category. These variables are derived from the information in the medications file, and thus are guaranteed to be consistent with it. The Marital/Cohabiting History file (Dataset 2) contains one record for each marriage or cohabitation identified in Section 3A of the questionnaire. The Social Networks file (Dataset 3) contains one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Medications file (Dataset 4) contains one record for each item listed in the medications log (including alternative medicines and nutritional products). Respondents who did not report taking any medications or who refused to participate in this module are not represented in this file. Lastly, the Sexual Partners file (Dataset 5) contains one record for each sexual partner identified in Section 3A of the questionnaire. A complex, multistage area probability sample was used of community residing adults born between 1920 and 1947, which included an oversampling of African-Americans and Hispanics. The NSHAP sample is built on the foundation of the national household screening carried out by the Health and Retirement Study (HRS) in 2004. Through a collaborative agreement, HRS identified households for the NSHAP eligible population. A sample of 4,400 people was selected from the screened households. NSHAP made one selection per household. Ninety-two percent of the persons selected for the NSHAP interview were eligible. Researchers wishing to compute design-based variance estimates may use the variables STRATUM and CLUSTER. These variables were constructed from the original sampling units for the purpose of variance estimation; the former may be treated as (pseudo) strata and the latter as (pseudo) Primary Sampling Units (PSUs). For more information on sampling, users should refer to the P.I. Documentation in the ICPSR Codebook, as well as visit the NORC at the University of Chicago Web site. Respondents were randomly assigned to one of 6 interview paths, with the path determining which modules were received during the main interview or in the leave-behind questionnaire. Please see the included questionnaire for an in-depth description of the individual paths. In cases where a particular question appeared in the interview for some and in the leave-behind for others, they have been combined into a single variable to facilitate analysis. For more information on study design, users should refer to the P.I. Documentation in the ICPSR Codebook, as well as visit the NORC at the University of Chicago Web site. 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: Standardized missing values.; Checked for undocumented or out-of-range codes.. Response Rates: The weighted sample response rate was 75.5 percent for in-home interviews, including a brief self-administered questionnaire, in-home collection of a broad panel of biomeasures, as well as a leave-behind questionnaire. The response rate for the leave-behind questionnaire was 84 percent. Datasets: DS0: Study-Level Files DS1: Core Data DS2: Marital/Cohabiting History Data DS3: Social Networks Data DS4: Medications Data DS5: Sexual Partners Data DS6: P.I. NSHAP Wave 1 Stata Data Files DS7: P.I. NSHAP Wave 1 Public Use Stata Data Files Community dwelling individuals ages 57-85 in the United States. National Social Life, Health, and Aging Project (NSHAP) computer-assisted telephone interview (CATI) computer-assisted personal interview (CAPI) self-enumerated questionnaire coded on-site observation face-to-face interview telephone interview mixed mode

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  • Authors: Waite, Linda J.; Cagney, Kathleen A.; Dale, William; Huang, Elbert S.; +5 Authors

    The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. Wave 2 interviews were conducted from August 2010 through May 2011, during which Wave 1 Respondents were re-interviewed. An attempt was also made to interview individuals who were sampled in Wave 1 but declined to participate. In addition, spouses or co-resident partners were also interviewed using the same instruments as the main respondents. This process resulted in 3,377 total respondents. The following files constitute Wave 2: Core Data, Disposition of Wave 1 Partner Data, Social Networks Data, Social Networks Update Data, Partner History Data, Partner History Update Data, Medications Data, Proxy Data, and Sleep Statistics Data. Included in the Core files (Datasets 1 and 2) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, history of sexual and intimate partnerships, and patient-physician communication, in addition to bereavement items. Data were also collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function, and a panel of biomeasures, including weight, waist circumference, height, blood pressure, smell, saliva collection, and taste. The Disposition of Wave 1 Partner files (Datasets 3 and 4) detail information derived from Section 6A items regarding the partner from Wave 1 within the questionnaire. This provides a complete history for respondent partners across both waves. The Social Networks files (Datasets 5 and 6) contain one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Social Networks Update files (Datasets 7 and 8) detail respondents' current relationship status with each person identified on the network roster. The Partner History file (Dataset 9) contains one record for each marriage, cohabitation, or romantic relationship identified in Section 6A of the questionnaire, including a current partner in Wave 2 but excluding the partner from Wave 1. The Partner History Update file (Dataset 10) details respondents' current sexual partner information, as well as marital and cohabiting status. The Medications Data file (Dataset 11) contains records for items listed in the medications log. The Proxy Data files (Datasets 12 and 13) contain information from proxy interviews administered for Wave 1 Respondents who were either deceased or whose health was too poor to participate in Wave 2. The Sleep Statistics Data files (Dataset 14 and 15) provide information on actigraphy sleep variables. In Wave 2, NSHAP returned to Wave 1 Respondents and eligible non-interviewed respondents from Wave 1 (Wave 1 Non-Interviewed Respondents). The Wave 2 sample was also extended to include the cohabiting spouses and romantic partners of Wave 1 Respondents and Wave 1 Non-Interviewed Respondents. Partners were considered to be eligible to participate in NSHAP if they resided in the household with Wave 1 Respondent/Wave 1 Non-Interviewed Respondent at the time of the Wave 2 interview and were at least 18 years of age. In order to restrict inferences to the population of Wave 1 age-eligible, analysts may use the variable AGEELIG in the Wave 2 Core Data file. Researchers wishing to compute design-based variance estimates may use the variables STRATUM and CLUSTER. These variables were constructed from the original sampling units for the purpose of variance estimation; the former may be treated as (pseudo) strata and the latter as (pseudo) Primary Sampling Units (PSUs). Users may refer to ICPSR 20541, National Social Life, Health, and Aging Project (NSHAP): Wave 1 for further sampling information, as well as the P.I. Documentation and visit the NORC at the University of Chicago Web site. Datasets: DS0: Study-Level Files DS1: Core Data, Public-Use DS2: Core Data, Restricted-Use DS3: Disposition of Wave 1 Partner Data, Public-Use DS4: Disposition of Wave 1 Partner Data, Restricted-Use DS5: Social Networks Data, Public-Use DS6: Social Networks Data, Restricted-Use DS7: Social Networks Update Data, Public-Use DS8: Social Networks Update Data, Restricted-Use DS9: Partner History Data, Restricted-Use DS10: Partner History Update Data, Restricted-Use DS11: Medications Data, Restricted-Use DS12: Proxy Data, Public-Use DS13: Proxy Data, Restricted-Use DS14: Sleep Statistics Data, Public-Use DS15: Sleep Statistics Data, Restricted-Use DS16: Original NSHAP Wave 2 Stata Public-Use Data Files DS17: Original NSHAP Wave 2 Stata Restricted-Use Data Files In Wave 2, the instrument was simplified to facilitate both administration and analysis. Particularly, all questions were asked entirely in the in-person interview or the leave-behind questionnaire, as was not the case for the previous Wave 1 study design. In addition, partner history items regarding relationship details and sexual experience were combined into the same modules. As in Wave 1, selected biomeasures were administered to a randomized subset of respondents in cases where this provided adequate power for likely analyses. Users may refer to ICPSR 20541, National Social Life, Health, and Aging Project (NSHAP): Wave 1 for further study design information, as well as the P.I. Documentation and visit the NORC at the University of Chicago Web site. 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: Standardized missing values.; Checked for undocumented or out-of-range codes.. Response Rates: The overall unconditional response rate for the Wave 2 panel was 74 percent; the conditional response rate of Wave 1 respondents was 89 percent; the conditional response rate of partners was 84 percent; and the conversion rate for Wave 1 nonrespondents was 26 percent. For additional information on response rates, please refer to the NORC at the University of Chicago web site. Community-dwelling individuals ages 57-85, Wave 1 Respondents, and eligible non-interviewed respondents from Wave 1 (Wave 1 Non-Interviewed Respondents). Cohabiting spouses and romantic partners of Wave 1 Respondents and Wave 1 Non-Interviewed Respondents living within the household, age 18 years or older. National Social Life, Health, and Aging Project (NSHAP) computer-assisted telephone interview (CATI) computer-assisted personal interview (CAPI) self-enumerated questionnaire coded on-site observation face-to-face interview telephone interview mixed mode

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    Authors: Christof Neumann; Julie Duboscq; Constance Dubuc; Andri Ginting; +4 Authors

    Highlights\ud \ud ► Elo-rating generates reliable dominance hierarchies and circumvents drawbacks of established ranking methods. \ud ► It allows visualizing dominance relationships and the detection of rank dynamics. \ud ► An index to objectively assess the stability of a dominance hierarchy is proposed.

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    Animal Behaviour
    Article . 2011
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    Authors: Alyssa Counsell; Robert A. Cribbie; Lisa L. Harlow;

    Quantitative methods (QM) play a central role in psychological research and training. Researchers suggest that courses focusing on statistics and methodology are amongst the most important for fostering critical thinking and reasoning (Lehman & Nisbett, 1990; VanderStoep & Shaughnessy, 1997). The challenge is that these courses are often students' least favourite (e.g., Conners, McCown, & Roskos-Ewoldsen, 1998; Schutz, Drogosz, White, & Distefano, 1998). One of the reasons for this lack of enthusiasm for QM courses is that many students experience "statistics anxiety" (e.g., Dillon, 1982; Zeidner, 1991). In their review, Onwuegbuzie and Wilson (2003) found that approximately two-thirds to threequarters of graduate students experience statistics anxiety. This anxiety often invokes avoidance (e.g., of taking classes, trying newer statistical methods; Onwuegbuzie, 2004), which presents an obvious predicament because knowledge and training in QM is crucial for most researchers in the discipline. With concerns about reporting practices, research quality, and replicability, improving literacy in QM becomes a worthwhile endeavour to reduce some of these discipline-wide issues.In this article, QM refers to any tests, procedures or approaches used to analyse numerical data from psychological studies. This could include statistical tests for relationships (e.g., t tests, correlation coefficients), statistical modelling (factor analysis, structural equation modelling), psychometric analyses (e.g., Cronbach's a), and so forth. It would not include purely qualitative analyses (e.g., grounded theory) because they do not focus on statistics, nor would it include methods exclusive to research design (e.g., experimental design, survey strategies). We acknowledge, however, that a research design heavily informs the statistical methods adopted.The question of interest is how to reconcile the importance of learning and improvement in QM, with the fact that most researchers may not actively seek (and may instead avoid) new training opportunities that would improve their understanding and use of modern QM? The goal of the current article is to discuss some of the quantitative challenges that psychology students and researchers face, and present practical opportunities and recommendations for improving statistical literacy. Specifically, the article will be divided into two major sections: (a) QM challenges and opportunities for students, and (b) QM challenges and opportunities for researchers, clinicians, and faculty. Before this discussion can take place, it is worthwhile to discuss the context for why improving the way psychology approaches QM is important.Innovations in QMThere have been numerous statistical and technological advances in the field of psychology (e.g., new methods for dealing with missing data, robust statistics), yet psychological researchers do not always take advantage of these methods (Sharpe, 2013). Mills, Abdulla, and Cribbie (2010) found that citations for original articles in quantitative journals are minimal, which may suggest that researchers are not aware of these methods at all. Counsell and Harlow (2016) found that of the recently published empirical articles in Canadian psychology journals that used QM, the majority of the studies overwhelmingly used and reported the same univariate procedures that have been used for decades (i.e., traditional t tests, analysis of variances [ANOVAs], and correlations). In many instances researchers can evaluate their hypotheses within larger models (e.g., Counsell & Harlow, 2016; Rodgers, 2010), use an equivalence test when the goal is to demonstrate a lack of relationship (e.g., Cribbie, Gruman, & Arpin-Cribbie, 2004; Maxwell, Lau, & Howard, 2015), or use a robust alternative when faced with assumption violation (e.g., Wilcox, 2012). This is not to say that novel or advanced methods are always necessary. In fact, they are sometimes adopted by researchers who think that running procedures such as t tests or ANOVAs are not advanced enough. …

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  • Authors: Waite, Linda J.; Laumann, Edward O.; Levinson, Wendy S.; Lindau, Stacy Tessler; +1 Authors

    The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. The following files constitute Round 1: Core Data, Marital/Cohabiting History Data, Social Networks Data, Medications Data, and Sexual Partners Data. Included in the Core file (Datasets 1 and 2) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, medications and alternative therapies, history of sexual and intimate partnerships and patient-physician communication, in addition to bereavement items. In addition data was collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function and a panel of biomeasures including, weight, waist circumference, height, blood pressure, smell, saliva collection, taste, and a self-administered vaginal swab for female respondents. The Core file also contains a count of the total number of drugs taken, and a variable for each observed therapeutic category, indicating whether the respondent reported taking one or more medications in that category. These variables are derived from the information in the medications file, and thus are guaranteed to be consistent with it. The Marital/Cohabiting History file (Dataset 3) contains one record for each marriage or cohabitation identified in Section 3A of the questionnaire. The Social Networks file (Datasets 4 and 5) contains one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Medications file (Dataset 6) contains one record for each item listed in the medications log (including alternative medicines and nutritional products). Respondents who did not report taking any medications or who refused to participate in this module are not represented in this file. Lastly, the Sexual Partners file (Dataset 7) contains one record for each sexual partner identified in Section 3A of the questionnaire. NACDA also maintains a Colectica portal with the NSHAP Core data across rounds 1-3, which allows users to interact with variables across rounds and create customized subsets. Registration is required. computer-assisted personal interview (CAPI); computer-assisted telephone interview (CATI); coded on-site observation; face-to-face interview; mixed mode; self-enumerated questionnaire; telephone interviewThis collection is being released in nine parts: Datasets 1 through 7 contain the ICPSR-processed files; Dataset 8 contains the original Public-Use Stata data files with extended and nonextended missing values provided by the P.I. in a zip file package; Dataset 9 contains the original Restricted-Use Stata data files provided by the P.I. in a zip file package.The Core Data (Datasets 1 and 2) and Social Networks Data (Datasets 4 and 5) are available as both public and restricted-use. Datasets 3 (Marital/Cohabiting History Data), 6 (Medications Data), and 7 (Sexual Partners Data) are only available as restricted-use.Please refer to the related data collections ICPSR 34921, National Social Life, Health, and Aging Project (NSHAP): Round 2 and Partner Data Collection and ICPSR 36873, National Social Life, Health, and Aging Project (NSHAP): Round 3 and COVID-19 Study, [United States], 2015-2016, 2020-2021 for further information regarding the NSHAP project.For further information about the National Social Life, Health, and Aging Project (NSHAP), please see the NORC at the University of Chicago Web site.Users can subscribe to the NSHAP data mailing list by emailing nshap-data@lists.uchicago.edu.The grant numbers have been updated as of September 2019; users citing the NSHAP data should ensure their citations are up to date.As of February 2021, the NSHAP Team has agreed upon a new standardized terminology to refer to the timepoints at which NSHAP data are collected. Authors of publications, proposals, and other materials using NSHAP data are asked to adopt this terminology moving forward. Please note that some ICPSR study materials reflect the previous terminology, updates are pending. The term "Round" will refer to each of NSHAP's major periodic data collection efforts: Round 1 [R1], conducted in 2005-06; Round 2 [R2], conducted in 2010-11; Round 3 [R3], conducted in 2015-16; Round 4 [R4], will be conducted in 2021-22. Refer to the NACDA site announcement about the change for more details. The data are not weighted, but contain two weight variables within the Dataset 1 Core data file, which users may wish to apply during analysis. Respondent-level weights representing the inverse probability of selection are contained in the variable WEIGHT_SEL. A second set of weights incorporating a non-response adjustment based on age and urbanicity is contained in the variable WEIGHT_ADJ. Both sets of weights are scaled to sum to the final sample size (3,005). For additional information on weights, please refer to the NORC at the University of Chicago Web site. As noted above, in each round there is a variable called WEIGHT_ADJ which is non-missing for all respondents in that round, and which adjusts for differing probabilities of selection as well as differential non-response. These weight variables should be used for all cross-sectional analyses. Please note that the WEIGHT_ADJ variable differs across rounds (since the selection probabilities and non-response vary across rounds). With respect to longitudinal analyses, NSHAP does not yet have a true panel weight (currently in progress). The Round 2 weight variable (WEIGHT_ADJ) should be used for longitudinal analyses until the panel weight is created. The Round 2 weight is non-missing for all but 38 respondents with data for multiple (i.e., at least two) rounds. Thus, this weight is adequate for longitudinal analyses using the subset of respondents with data from Round 2 and/or at least two rounds (this includes many typical longitudinal analyses). It is not advised to use this weight variable for those cases where someone wishes to include those respondents with data from only one round, except for those with data only from Round 2). A complex, multistage area probability sample was used of community residing adults born between 1920 and 1947, which included an oversampling of African-Americans and Hispanics. The NSHAP sample is built on the foundation of the national household screening carried out by the Health and Retirement Study (HRS) in 2004. Through a collaborative agreement, HRS identified households for the NSHAP eligible population. A sample of 4,400 people was selected from the screened households. NSHAP made one selection per household. Ninety-two percent of the persons selected for the NSHAP interview were eligible. Researchers wishing to compute design-based variance estimates may use the variables STRATUM and CLUSTER. These variables were constructed from the original sampling units for the purpose of variance estimation; the former may be treated as (pseudo) strata and the latter as (pseudo) Primary Sampling Units (PSUs). For more information on sampling, users should refer to the P.I. Documentation in the ICPSR Codebook, as well as visit the NORC at the University of Chicago Web site. Respondents were randomly assigned to one of 6 interview paths, with the path determining which modules were received during the main interview or in the leave-behind questionnaire. Please see the included questionnaire for an in-depth description of the individual paths. In cases where a particular question appeared in the interview for some and in the leave-behind for others, they have been combined into a single variable to facilitate analysis. For more information on study design, users should refer to the P.I. Documentation in the ICPSR Codebook, as well as visit the NORC at the University of Chicago Web site. Datasets: DS0: Study-Level Files DS1: Core Data, Public-Use DS2: Core Data, Restricted-Use DS3: Marital/Cohabiting History Data, Restricted-Use DS4: Social Networks Data, Public-Use DS5: Social Networks Data, Restricted-Use DS6: Medications Data, Restricted-Use DS7: Sexual Partners Data, Restricted-Use DS8: P.I. NSHAP Round 1 Public-Use Stata Data Files DS9: P.I. NSHAP Round 1 Restricted-Use Stata Data Files Response Rates: The weighted sample response rate was 75.5 percent for in-home interviews, including a brief self-administered questionnaire, in-home collection of a broad panel of biomeasures, as well as a leave-behind questionnaire. The response rate for the leave-behind questionnaire was 84 percent. Community dwelling individuals ages 57-85 in the United States.

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  • 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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    Authors: Leela McKinnon; David R. Samson; Charles L. Nunn; Amanda Rowlands; +2 Authors

    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.

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  • 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

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    Authors: Constance Dubuc; Laura Muniz; Michael Heistermann; Antje Engelhardt; +1 Authors

    In mammals, when females are clumped in space, male access to receptive females is usually determined by a dominance hierarchy based on fighting ability. In polygynandrous primates, as opposed to most mammalian species, the strength of the relationship between male social status and reproductive success varies greatly. It has been proposed that the degree to which paternity is determined by male rank decreases with increasing female reproductive synchrony. The priority-of-access model (PoA) predicts male reproductive success based on female synchrony and male dominance rank. To date, most tests of the PoA using paternity data involved nonseasonally breeding species. Here, we examine whether the PoA explains the relatively low reproductive skew in relation to dominance rank reported in the rhesus macaque, a strictly seasonal species. We collected behavioral, genetic, and hormonal data on one group of the free-ranging population on Cayo Santiago (Puerto Rico) for 2 years. The PoA correctly predicted the steepness of male reproductive skew, but not its relationship to male dominance: the most successful sire, fathering one third of the infants, was high but not top ranking. In contrast, mating success was not significantly skewed, suggesting that other mechanisms than social status contributed to male reproductive success. Dominance may be less important for paternity in rhesus macaques than in other primate species because it is reached through queuing rather than contest, leading to alpha males not necessarily being the strongest or most attractive male. More work is needed to fully elucidate the mechanisms determining paternity in rhesus macaques. peerReviewed

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    Behavioral Ecology and Sociobiology
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  • Authors: 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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  • The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. The following files constitute Wave 1: Core Data, Marital/Cohabiting History Data, Social Networks Data, Medications Data, and Sexual Partners Data. Included in the Core file (Dataset 1) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, medications and alternative therapies, history of sexual and intimate partnerships and patient-physician communication, in addition to bereavement items. In addition data was collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function and a panel of biomeasures including, weight, waist circumference, height, blood pressure, smell, saliva collection, taste, and a self-administered vaginal swab for female respondents. The Core file also contains a count of the total number of drugs taken, and a variable for each observed therapeutic category, indicating whether the respondent reported taking one or more medications in that category. These variables are derived from the information in the medications file, and thus are guaranteed to be consistent with it. The Marital/Cohabiting History file (Dataset 2) contains one record for each marriage or cohabitation identified in Section 3A of the questionnaire. The Social Networks file (Dataset 3) contains one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Medications file (Dataset 4) contains one record for each item listed in the medications log (including alternative medicines and nutritional products). Respondents who did not report taking any medications or who refused to participate in this module are not represented in this file. Lastly, the Sexual Partners file (Dataset 5) contains one record for each sexual partner identified in Section 3A of the questionnaire. A complex, multistage area probability sample was used of community residing adults born between 1920 and 1947, which included an oversampling of African-Americans and Hispanics. The NSHAP sample is built on the foundation of the national household screening carried out by the Health and Retirement Study (HRS) in 2004. Through a collaborative agreement, HRS identified households for the NSHAP eligible population. A sample of 4,400 people was selected from the screened households. NSHAP made one selection per household. Ninety-two percent of the persons selected for the NSHAP interview were eligible. Researchers wishing to compute design-based variance estimates may use the variables STRATUM and CLUSTER. These variables were constructed from the original sampling units for the purpose of variance estimation; the former may be treated as (pseudo) strata and the latter as (pseudo) Primary Sampling Units (PSUs). For more information on sampling, users should refer to the P.I. Documentation in the ICPSR Codebook, as well as visit the NORC at the University of Chicago Web site. Respondents were randomly assigned to one of 6 interview paths, with the path determining which modules were received during the main interview or in the leave-behind questionnaire. Please see the included questionnaire for an in-depth description of the individual paths. In cases where a particular question appeared in the interview for some and in the leave-behind for others, they have been combined into a single variable to facilitate analysis. For more information on study design, users should refer to the P.I. Documentation in the ICPSR Codebook, as well as visit the NORC at the University of Chicago Web site. 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: Standardized missing values.; Checked for undocumented or out-of-range codes.. Response Rates: The weighted sample response rate was 75.5 percent for in-home interviews, including a brief self-administered questionnaire, in-home collection of a broad panel of biomeasures, as well as a leave-behind questionnaire. The response rate for the leave-behind questionnaire was 84 percent. Datasets: DS0: Study-Level Files DS1: Core Data DS2: Marital/Cohabiting History Data DS3: Social Networks Data DS4: Medications Data DS5: Sexual Partners Data DS6: P.I. NSHAP Wave 1 Stata Data Files DS7: P.I. NSHAP Wave 1 Public Use Stata Data Files Community dwelling individuals ages 57-85 in the United States. National Social Life, Health, and Aging Project (NSHAP) computer-assisted telephone interview (CATI) computer-assisted personal interview (CAPI) self-enumerated questionnaire coded on-site observation face-to-face interview telephone interview mixed mode

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  • Authors: Waite, Linda J.; Cagney, Kathleen A.; Dale, William; Huang, Elbert S.; +5 Authors

    The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. Wave 2 interviews were conducted from August 2010 through May 2011, during which Wave 1 Respondents were re-interviewed. An attempt was also made to interview individuals who were sampled in Wave 1 but declined to participate. In addition, spouses or co-resident partners were also interviewed using the same instruments as the main respondents. This process resulted in 3,377 total respondents. The following files constitute Wave 2: Core Data, Disposition of Wave 1 Partner Data, Social Networks Data, Social Networks Update Data, Partner History Data, Partner History Update Data, Medications Data, Proxy Data, and Sleep Statistics Data. Included in the Core files (Datasets 1 and 2) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, history of sexual and intimate partnerships, and patient-physician communication, in addition to bereavement items. Data were also collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function, and a panel of biomeasures, including weight, waist circumference, height, blood pressure, smell, saliva collection, and taste. The Disposition of Wave 1 Partner files (Datasets 3 and 4) detail information derived from Section 6A items regarding the partner from Wave 1 within the questionnaire. This provides a complete history for respondent partners across both waves. The Social Networks files (Datasets 5 and 6) contain one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Social Networks Update files (Datasets 7 and 8) detail respondents' current relationship status with each person identified on the network roster. The Partner History file (Dataset 9) contains one record for each marriage, cohabitation, or romantic relationship identified in Section 6A of the questionnaire, including a current partner in Wave 2 but excluding the partner from Wave 1. The Partner History Update file (Dataset 10) details respondents' current sexual partner information, as well as marital and cohabiting status. The Medications Data file (Dataset 11) contains records for items listed in the medications log. The Proxy Data files (Datasets 12 and 13) contain information from proxy interviews administered for Wave 1 Respondents who were either deceased or whose health was too poor to participate in Wave 2. The Sleep Statistics Data files (Dataset 14 and 15) provide information on actigraphy sleep variables. In Wave 2, NSHAP returned to Wave 1 Respondents and eligible non-interviewed respondents from Wave 1 (Wave 1 Non-Interviewed Respondents). The Wave 2 sample was also extended to include the cohabiting spouses and romantic partners of Wave 1 Respondents and Wave 1 Non-Interviewed Respondents. Partners were considered to be eligible to participate in NSHAP if they resided in the household with Wave 1 Respondent/Wave 1 Non-Interviewed Respondent at the time of the Wave 2 interview and were at least 18 years of age. In order to restrict inferences to the population of Wave 1 age-eligible, analysts may use the variable AGEELIG in the Wave 2 Core Data file. Researchers wishing to compute design-based variance estimates may use the variables STRATUM and CLUSTER. These variables were constructed from the original sampling units for the purpose of variance estimation; the former may be treated as (pseudo) strata and the latter as (pseudo) Primary Sampling Units (PSUs). Users may refer to ICPSR 20541, National Social Life, Health, and Aging Project (NSHAP): Wave 1 for further sampling information, as well as the P.I. Documentation and visit the NORC at the University of Chicago Web site. Datasets: DS0: Study-Level Files DS1: Core Data, Public-Use DS2: Core Data, Restricted-Use DS3: Disposition of Wave 1 Partner Data, Public-Use DS4: Disposition of Wave 1 Partner Data, Restricted-Use DS5: Social Networks Data, Public-Use DS6: Social Networks Data, Restricted-Use DS7: Social Networks Update Data, Public-Use DS8: Social Networks Update Data, Restricted-Use DS9: Partner History Data, Restricted-Use DS10: Partner History Update Data, Restricted-Use DS11: Medications Data, Restricted-Use DS12: Proxy Data, Public-Use DS13: Proxy Data, Restricted-Use DS14: Sleep Statistics Data, Public-Use DS15: Sleep Statistics Data, Restricted-Use DS16: Original NSHAP Wave 2 Stata Public-Use Data Files DS17: Original NSHAP Wave 2 Stata Restricted-Use Data Files In Wave 2, the instrument was simplified to facilitate both administration and analysis. Particularly, all questions were asked entirely in the in-person interview or the leave-behind questionnaire, as was not the case for the previous Wave 1 study design. In addition, partner history items regarding relationship details and sexual experience were combined into the same modules. As in Wave 1, selected biomeasures were administered to a randomized subset of respondents in cases where this provided adequate power for likely analyses. Users may refer to ICPSR 20541, National Social Life, Health, and Aging Project (NSHAP): Wave 1 for further study design information, as well as the P.I. Documentation and visit the NORC at the University of Chicago Web site. 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: Standardized missing values.; Checked for undocumented or out-of-range codes.. Response Rates: The overall unconditional response rate for the Wave 2 panel was 74 percent; the conditional response rate of Wave 1 respondents was 89 percent; the conditional response rate of partners was 84 percent; and the conversion rate for Wave 1 nonrespondents was 26 percent. For additional information on response rates, please refer to the NORC at the University of Chicago web site. Community-dwelling individuals ages 57-85, Wave 1 Respondents, and eligible non-interviewed respondents from Wave 1 (Wave 1 Non-Interviewed Respondents). Cohabiting spouses and romantic partners of Wave 1 Respondents and Wave 1 Non-Interviewed Respondents living within the household, age 18 years or older. National Social Life, Health, and Aging Project (NSHAP) computer-assisted telephone interview (CATI) computer-assisted personal interview (CAPI) self-enumerated questionnaire coded on-site observation face-to-face interview telephone interview mixed mode

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    Authors: Christof Neumann; Julie Duboscq; Constance Dubuc; Andri Ginting; +4 Authors

    Highlights\ud \ud ► Elo-rating generates reliable dominance hierarchies and circumvents drawbacks of established ranking methods. \ud ► It allows visualizing dominance relationships and the detection of rank dynamics. \ud ► An index to objectively assess the stability of a dominance hierarchy is proposed.

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    Animal Behaviour
    Article . 2011
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      Animal Behaviour
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    Authors: Alyssa Counsell; Robert A. Cribbie; Lisa L. Harlow;

    Quantitative methods (QM) play a central role in psychological research and training. Researchers suggest that courses focusing on statistics and methodology are amongst the most important for fostering critical thinking and reasoning (Lehman & Nisbett, 1990; VanderStoep & Shaughnessy, 1997). The challenge is that these courses are often students' least favourite (e.g., Conners, McCown, & Roskos-Ewoldsen, 1998; Schutz, Drogosz, White, & Distefano, 1998). One of the reasons for this lack of enthusiasm for QM courses is that many students experience "statistics anxiety" (e.g., Dillon, 1982; Zeidner, 1991). In their review, Onwuegbuzie and Wilson (2003) found that approximately two-thirds to threequarters of graduate students experience statistics anxiety. This anxiety often invokes avoidance (e.g., of taking classes, trying newer statistical methods; Onwuegbuzie, 2004), which presents an obvious predicament because knowledge and training in QM is crucial for most researchers in the discipline. With concerns about reporting practices, research quality, and replicability, improving literacy in QM becomes a worthwhile endeavour to reduce some of these discipline-wide issues.In this article, QM refers to any tests, procedures or approaches used to analyse numerical data from psychological studies. This could include statistical tests for relationships (e.g., t tests, correlation coefficients), statistical modelling (factor analysis, structural equation modelling), psychometric analyses (e.g., Cronbach's a), and so forth. It would not include purely qualitative analyses (e.g., grounded theory) because they do not focus on statistics, nor would it include methods exclusive to research design (e.g., experimental design, survey strategies). We acknowledge, however, that a research design heavily informs the statistical methods adopted.The question of interest is how to reconcile the importance of learning and improvement in QM, with the fact that most researchers may not actively seek (and may instead avoid) new training opportunities that would improve their understanding and use of modern QM? The goal of the current article is to discuss some of the quantitative challenges that psychology students and researchers face, and present practical opportunities and recommendations for improving statistical literacy. Specifically, the article will be divided into two major sections: (a) QM challenges and opportunities for students, and (b) QM challenges and opportunities for researchers, clinicians, and faculty. Before this discussion can take place, it is worthwhile to discuss the context for why improving the way psychology approaches QM is important.Innovations in QMThere have been numerous statistical and technological advances in the field of psychology (e.g., new methods for dealing with missing data, robust statistics), yet psychological researchers do not always take advantage of these methods (Sharpe, 2013). Mills, Abdulla, and Cribbie (2010) found that citations for original articles in quantitative journals are minimal, which may suggest that researchers are not aware of these methods at all. Counsell and Harlow (2016) found that of the recently published empirical articles in Canadian psychology journals that used QM, the majority of the studies overwhelmingly used and reported the same univariate procedures that have been used for decades (i.e., traditional t tests, analysis of variances [ANOVAs], and correlations). In many instances researchers can evaluate their hypotheses within larger models (e.g., Counsell & Harlow, 2016; Rodgers, 2010), use an equivalence test when the goal is to demonstrate a lack of relationship (e.g., Cribbie, Gruman, & Arpin-Cribbie, 2004; Maxwell, Lau, & Howard, 2015), or use a robust alternative when faced with assumption violation (e.g., Wilcox, 2012). This is not to say that novel or advanced methods are always necessary. In fact, they are sometimes adopted by researchers who think that running procedures such as t tests or ANOVAs are not advanced enough. …

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  • Authors: Waite, Linda J.; Laumann, Edward O.; Levinson, Wendy S.; Lindau, Stacy Tessler; +1 Authors

    The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. The following files constitute Round 1: Core Data, Marital/Cohabiting History Data, Social Networks Data, Medications Data, and Sexual Partners Data. Included in the Core file (Datasets 1 and 2) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, medications and alternative therapies, history of sexual and intimate partnerships and patient-physician communication, in addition to bereavement items. In addition data was collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function and a panel of biomeasures including, weight, waist circumference, height, blood pressure, smell, saliva collection, taste, and a self-administered vaginal swab for female respondents. The Core file also contains a count of the total number of drugs taken, and a variable for each observed therapeutic category, indicating whether the respondent reported taking one or more medications in that category. These variables are derived from the information in the medications file, and thus are guaranteed to be consistent with it. The Marital/Cohabiting History file (Dataset 3) contains one record for each marriage or cohabitation identified in Section 3A of the questionnaire. The Social Networks file (Datasets 4 and 5) contains one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Medications file (Dataset 6) contains one record for each item listed in the medications log (including alternative medicines and nutritional products). Respondents who did not report taking any medications or who refused to participate in this module are not represented in this file. Lastly, the Sexual Partners file (Dataset 7) contains one record for each sexual partner identified in Section 3A of the questionnaire. NACDA also maintains a Colectica portal with the NSHAP Core data across rounds 1-3, which allows users to interact with variables across rounds and create customized subsets. Registration is required. computer-assisted personal interview (CAPI); computer-assisted telephone interview (CATI); coded on-site observation; face-to-face interview; mixed mode; self-enumerated questionnaire; telephone interviewThis collection is being released in nine parts: Datasets 1 through 7 contain the ICPSR-processed files; Dataset 8 contains the original Public-Use Stata data files with extended and nonextended missing values provided by the P.I. in a zip file package; Dataset 9 contains the original Restricted-Use Stata data files provided by the P.I. in a zip file package.The Core Data (Datasets 1 and 2) and Social Networks Data (Datasets 4 and 5) are available as both public and restricted-use. Datasets 3 (Marital/Cohabiting History Data), 6 (Medications Data), and 7 (Sexual Partners Data) are only available as restricted-use.Please refer to the related data collections ICPSR 34921, National Social Life, Health, and Aging Project (NSHAP): Round 2 and Partner Data Collection and ICPSR 36873, National Social Life, Health, and Aging Project (NSHAP): Round 3 and COVID-19 Study, [United States], 2015-2016, 2020-2021 for further information regarding the NSHAP project.For further information about the National Social Life, Health, and Aging Project (NSHAP), please see the NORC at the University of Chicago Web site.Users can subscribe to the NSHAP data mailing list by emailing nshap-data@lists.uchicago.edu.The grant numbers have been updated as of September 2019; users citing the NSHAP data should ensure their citations are up to date.As of February 2021, the NSHAP Team has agreed upon a new standardized terminology to refer to the timepoints at which NSHAP data are collected. Authors of publications, proposals, and other materials using NSHAP data are asked to adopt this terminology moving forward. Please note that some ICPSR study materials reflect the previous terminology, updates are pending. The term "Round" will refer to each of NSHAP's major periodic data collection efforts: Round 1 [R1], conducted in 2005-06; Round 2 [R2], conducted in 2010-11; Round 3 [R3], conducted in 2015-16; Round 4 [R4], will be conducted in 2021-22. Refer to the NACDA site announcement about the change for more details. The data are not weighted, but contain two weight variables within the Dataset 1 Core data file, which users may wish to apply during analysis. Respondent-level weights representing the inverse probability of selection are contained in the variable WEIGHT_SEL. A second set of weights incorporating a non-response adjustment based on age and urbanicity is contained in the variable WEIGHT_ADJ. Both sets of weights are scaled to sum to the final sample size (3,005). For additional information on weights, please refer to the NORC at the University of Chicago Web site. As noted above, in each round there is a variable called WEIGHT_ADJ which is non-missing for all respondents in that round, and which adjusts for differing probabilities of selection as well as differential non-response. These weight variables should be used for all cross-sectional analyses. Please note that the WEIGHT_ADJ variable differs across rounds (since the selection probabilities and non-response vary across rounds). With respect to longitudinal analyses, NSHAP does not yet have a true panel weight (currently in progress). The Round 2 weight variable (WEIGHT_ADJ) should be used for longitudinal analyses until the panel weight is created. The Round 2 weight is non-missing for all but 38 respondents with data for multiple (i.e., at least two) rounds. Thus, this weight is adequate for longitudinal analyses using the subset of respondents with data from Round 2 and/or at least two rounds (this includes many typical longitudinal analyses). It is not advised to use this weight variable for those cases where someone wishes to include those respondents with data from only one round, except for those with data only from Round 2). A complex, multistage area probability sample was used of community residing adults born between 1920 and 1947, which included an oversampling of African-Americans and Hispanics. The NSHAP sample is built on the foundation of the national household screening carried out by the Health and Retirement Study (HRS) in 2004. Through a collaborative agreement, HRS identified households for the NSHAP eligible population. A sample of 4,400 people was selected from the screened households. NSHAP made one selection per household. Ninety-two percent of the persons selected for the NSHAP interview were eligible. Researchers wishing to compute design-based variance estimates may use the variables STRATUM and CLUSTER. These variables were constructed from the original sampling units for the purpose of variance estimation; the former may be treated as (pseudo) strata and the latter as (pseudo) Primary Sampling Units (PSUs). For more information on sampling, users should refer to the P.I. Documentation in the ICPSR Codebook, as well as visit the NORC at the University of Chicago Web site. Respondents were randomly assigned to one of 6 interview paths, with the path determining which modules were received during the main interview or in the leave-behind questionnaire. Please see the included questionnaire for an in-depth description of the individual paths. In cases where a particular question appeared in the interview for some and in the leave-behind for others, they have been combined into a single variable to facilitate analysis. For more information on study design, users should refer to the P.I. Documentation in the ICPSR Codebook, as well as visit the NORC at the University of Chicago Web site. Datasets: DS0: Study-Level Files DS1: Core Data, Public-Use DS2: Core Data, Restricted-Use DS3: Marital/Cohabiting History Data, Restricted-Use DS4: Social Networks Data, Public-Use DS5: Social Networks Data, Restricted-Use DS6: Medications Data, Restricted-Use DS7: Sexual Partners Data, Restricted-Use DS8: P.I. NSHAP Round 1 Public-Use Stata Data Files DS9: P.I. NSHAP Round 1 Restricted-Use Stata Data Files Response Rates: The weighted sample response rate was 75.5 percent for in-home interviews, including a brief self-administered questionnaire, in-home collection of a broad panel of biomeasures, as well as a leave-behind questionnaire. The response rate for the leave-behind questionnaire was 84 percent. Community dwelling individuals ages 57-85 in the United States.

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