At A Glance

Noteworthy Characteristics

  • Multiple ethnicities adequately sampled, including oversampling of certain Asian and Hispanic subgroups as well as Black adolescents of parents with a college degree.
  • Provides comprehensive socio-demographic information about adolescents.
  • Samples identical and fraternal twins, siblings, half siblings, and unrelated pairs in the same household to study genetic versus environmental factors.
  • Provides detailed information about diet, physical activity, and sleep.



To collect data about the social, behavioral, and biological linkages in the health trajectories of adolescents in the United States (U.S.).

Target Population

Adolescents in grades 7 through 12 in the U.S.


Begun in 1994. Conducted periodically. Most recent year of data collection was 2008.
• Wave I: Conducted 1994-1995
• Wave II: Conducted 1996
• Wave III: Conducted 2001-2002
• Wave IV: Conducted 2008
• Wave V: Will be conducted 2016-2018


The Eunice Kennedy Shriver National Institute of Child Health and Human Development, a component of the National Institutes of Health, U.S. Department of Health and Human Services. Cooperative funding from 23 other federal agencies and foundations, including several additional NIH Institutes. The University of North Carolina at Chapel Hill conducts the survey.

Special Note(s)

Add Health plans to trace, locate, and re-interview cohort members in a Wave V follow-up during the period 2016-2018 to collect social, environmental, behavioral, and biological data with which to track the emergence of chronic disease as the cohort moves through their fourth decade of life.



Sample Design

Longitudinal study.

The primary sampling frame for Add Health is a database collected by Quality Education Data, Inc. Systematic sampling methods and implicit stratification ensure that the 80 high schools selected are representative of U.S. schools with respect to region of country, urbanicity, size, type, and ethnicity. Learn more about the sampling design. See also a summary slideshow on study design.

Wave I: In-School Component

Information was gathered on schools from school administrators. The In-School Questionnaire, a self-administered instrument formatted for optical scanning, was administered to more than 90,000 students in grades 7 through 12 in a 45- to 60-minute class period between September 1994 and April 1995.

Wave I: In-Home Component

All students who completed the In-School Questionnaire plus those who did not complete a questionnaire but were listed on a school roster were eligible for selection into the core in-home sample. This was a nationally representative sample of adolescents in grades 7 through 12 in the U.S. in the 1994–1995 school year. Students in each school were stratified by grade and sex. About 17 students were randomly chosen from each stratum plus an oversampling of Black students who had at least one parent with a college degree. Chinese, Cuban, and Puerto Rican adolescents also were oversampled. Learn more.

Wave II: Follow-up of in-home sample that was in grades 7 through 11 at Wave I. Learn more.

Wave III: Follow-up of Wave I in-home sample with original adolescents (now young adults) and their partners. Learn more.

Wave IV: Follow-up of Wave I in-home sample of original sample (now adults). Learn more.

Sample Size

Wave I Primary Sample Frame

School Sample: Approximately 90,100 adolescents completed the in-school questionnaire in 1994-1995. School administrators completed questionnaires for 144 schools in 1994-1995.

Wave I In-Home Samples: Approximately 20,700 adolescents from the primary sample completed the home interview in 1994-1995. Approximately 17,700 parents completed the home interview.

Sample included 1,038 Black adolescents with at least one parent with a college degree and 334 Chinese, 450 Cuban, 437 Puerto Rican, and 1,500 Mexican-American adolescents.

Identical twins, fraternal twins, and half siblings in addition to non-related pairs, such as step-siblings, foster children, and adopted (non-related) siblings, also were sampled. The majority of full-sibling pairs entered into the sample by chance. This sample was developed to facilitate analyses that differentiate between parental/social influences and parental/genetic influences and analyses that assess the extent to which environment influences behavior. This special sample of twins and siblings was referred to as the “genetic sample.”

Wave II In-Home Sample: Approximately 14,700 of the adolescents from the original, primary sample completed an in-home interview in 1996. School administrators completed questionnaires for 128 schools.

Wave III In-Home Sample: Approximately 15,200 of the original adolescents participated in an in-home interview as young adults (ages 18-26) in 2001-2002. Approximately 1,500 of their partners also were interviewed in this wave.

Wave IV In Home Sample: Approximately 15,700 of the original adolescents participated in an in-home interview as adults (ages 24-32) in 2008.

Special Note(s)

Schools were selected with probability proportional to size.

Key Variables


NameMethods of Assessment
Disability (ADL/IADL*; cognitive; equipment use; general; movement/physical)Interview/questionnaire
Education and occupation of parents/caregiversInterview/questionnaire (adolescent)
Household income and economic assistanceInterview/questionnaire (parent/caregiver)
Household structureInterview/questionnaire (adolescent)
Race/ethnicityInterview/questionnaire (adolescent)
Responding parent/caregiver’s age, race/ethnicity, and sexInterview/questionnaire (parent/caregiver)
School gradeInterview/questionnaire (adolescent)
SexInterview/questionnaire (adolescent)
Teacher demographics for school (racial/ethnic and sex composition)Interview/questionnaire (school administrator)


NameMethods of Assessment
Length of time that reference adolescent was breastfedInterview/questionnaire (parent/caregiver)
Mandatory school nutrition educationInterview/questionnaire (school administrator)
Number of days ate at specific fast food restaurants during the past weekInterview/questionnaire (adolescent)
Vitamin intakeInterview/questionnaire (adolescent)
Whether adolescent consumed various types of foods yesterdayInterview/questionnaire (adolescent)

Physical Activity-Related

NameMethods of Assessment
Adolescent’s perception of neighborhood safetyInterview/questionnaire (adolescent)
Computer/video/television screen timeInterview/questionnaire (adolescent)
Frequency of performance of household choresInterview/questionnaire (adolescent)
Perceived neighborhood characteristics (e.g., neighborhood crime)Interview/questionnaire (parent/caregiver; adolescent)
Physical activity frequency and intensityInterview/questionnaire (adolescent)
Provision of school physical fitness/recreation center on-siteInterview/questionnaire (school administrator)
Provision of school physicalsInterview/questionnaire (school administrator)
Reported neighborhood characteristics (e.g., income, poverty, crime, social programs)Linked Census and other contextual files
Use of a neighborhood physical fitness or recreation centerInterview/questionnaire (adolescent)


NameMethods of Assessment
Daytime fatigue/sleepiness and/or alertnessInterview/Questionnaire with Adolescent
NapsInterview/Questionnaire with Adolescent
Physical sleep environment: Other electronics in sleep area (might be more ‘specific’ than screen)Interview/Questionnaire with Adolescent
Sleep continuity: Sleep latencyInterview/Questionnaire with Adolescent
Sleep disturbances and quality: Trouble staying asleepInterview/Questionnaire with Adolescent
Sleep disordered breathing: Observed breathing pauses while sleepingInterview/Questionnaire with Adolescent
Sleep disordered breathing: SnoringInterview/Questionnaire with Adolescent
Sleep disorders: Sleep apneaInterview/Questionnaire with Adolescent
Sleep disturbances and quality: Subjective satisfactionInterview/Questionnaire with Adolescent
Sleep duration and quantityInterview/Questionnaire with Adolescent
Sleep timing and regularity: Sleep timing on weekends/holidaysInterview/Questionnaire with Adolescent
Sleep timing and regularity: Sleep timing on workdays/schooldaysInterview/Questionnaire with Adolescent
Sleep-related policies: Extracurricular activities (eg. timing, frequency, etc.)Interview/Questionnaire with Adolescent
Sleep-related policies: Housing policies that impact the sleep environmentInterview/Questionnaire with Adolescent
Sleep-related substance use: CaffeineInterview/Questionnaire with Adolescent
Sleep-related substance use: Daytime sequalae as related to substance use – (e.g., trouble waking, daytime irritability, attentional deficits)Interview/Questionnaire with Adolescent
Sleep-related substance use: Nicotine (e.g., smoking, juuling, vaping)Interview/Questionnaire with Adolescent
Sleep-related substance use: Use of sleep aidsInterview/Questionnaire with Adolescent
Sleep-related substance use: Other (list variable)Interview/Questionnaire with Adolescent
Social determinants of health: Perceived safety of neighborhood and house at nightInterview/Questionnaire with Adolescent
Social sleep environment: Family sleep behaviors (e.g., bedtime routines, bedtime rules, sleep hygiene, sleep schedule)Interview/Questionnaire with Adolescent


NameMethods of Assessment
Adolescent’s birth weightInterview/questionnaire (parent/caregiver)
Height and weightSelf-report at Wave I. Measured at Waves II, III and IV.
Male and female physical/sexual developmentInterview/questionnaire (adolescent)
Weight control/body image counseling offered at schoolInterview/questionnaire (school administrator)
Weight information for biological mother, biological fatherInterview/questionnaire (parent/caregiver)
Whether nutrition/weight loss program offered by school districtInterview/questionnaire (school administrator)

Special Note(s)

*ADL: Activities of Daily Living / IADL: Instrumental Activities of Daily Living

Data Access and Cost

Data Availability

Public-use data are available from three different sources: The Odum Institute at UNC, the Inter-University Consortium for Political and Social Research (ICPSR) and Sociometrics. Users may obtain the data from any source, depending on their needs.

Odum Institute Dataverse

The Odum Institute at UNC distributes the Add Health public-use data at no charge through the Odum Institute Dataverse Network. To download the data, users will be required to sign a guestbook and agree to the Add Health and Dataverse terms of use.

For more information or to obtain the public-use data from Odum, visit the Add Health Dataverse page.


ICPSR distributes the Add Health public-use data at no charge through its Data Sharing for Demographic Research (DSDR) project. Public-use data are available for download from the DSDR website.

For more information or to obtain the public-use data from ICPSR, visit the ICPSR Add Health page.


Sociometrics distributes the public-use data for a fee. The data are distributed in a variety of formats, including SPSS, SAS and ASCII.
For more information or to obtain the public-use data from Sociometrics, visit the Sociometrics Add Health page.

Restricted-use data

Restricted-use data can be obtained from UNC by contractual agreement.

To apply for restricted-use data, please download the contract application and return to the Add Health contract administrator. Please see the Contracts Homepage or FAQ about contracts if you have any questions.


Public-use data are free of charge through the Odum Institute and ICPSR, and for a fee through Sociometrics.

Restricted-use data: New contracts: $850 (includes all restricted-use data through Wave IV).

Special Note(s)

Restricted data are distributed only to certified researchers who commit themselves to maintaining limited access. To be eligible to enter into a contract, researchers must have an Institutional Review Board-approved security plan for handling and storing sensitive data and must sign a data-use contract agreeing to keep the data confidential.


Geocode Variable(s)

Due to disclosure concerns, geocodes are not available in the public or restricted-use data sets. Researchers may add contextual data to Add Health files by using the geocodes at the secure data facility at the University of North Carolina (UNC) or by contracting with UNC to perform a data linkage. Contact for an Ancillary Study application and additional information.

The following restricted-use contextual data are available from UNC by contractual agreement:

• Community contextual variables based on state, county, tract, and block group levels derived from the Wave I, II, and III addresses. Contains information about respondents’ neighborhoods and communities, including variables on income and poverty, unemployment, crime, church membership, social programs and policies, and availability and use of health services.

• Pseudo state, county, tract, and block group variables that allow respondents to be aggregated geographically based on Wave I, II, and III addresses.

Existing Linkages

None noted.

Special Note(s)

Learn more about Add Health geocodes and linkages.

Selected Publications

The Add Health bibliography includes nearly 5,500 publications, presentations, unpublished manuscripts, and dissertations by Add Health researchers. See the complete list.


Chithambo, Taona P.; & Huey, Stanley J. (2013). Black/White differences in perceived weight and attractiveness among overweight women. Journal of Obesity, 2013, 1-4.

Lee, Hedwig; Harris, Kathleen M.; & Lee, Joyce. (2013). Multiple levels of social disadvantage and links to obesity in adolescence and young adulthood. Journal of School Health, 83(3), 139-149.

Physical Activity-Related

Boone-Heinonen J, Casanova K, Richardson AS, Gordon-Larsen P. Where can they play? Outdoor spaces and physical activity among adolescents in U.S. urbanized areas. Preventive Medicine 2010;51(3-4):295-298.

Boone-Heinonen J, Evenson KR, Song Y, Gordon-Larsen P. Built and socioeconomic environments: Patterning and associations with physical activity in U.S. adolescents. The International Journal of Behavioral Nutrition and Physical Activity 2010;7:45.


Gordon-Larsen P, The NS, Adair LS. Longitudinal trends in obesity in the United States from adolescence to the third decade of life. Obesity (Silver Spring) 2010;18(9):1801-1804.

Niemeier HM, Raynor HA, Lloyd-Richardson EE, Rogers ML, Wing RR. Fast food consumption and breakfast skipping: Predictors of weight gain from adolescence to adulthood in a nationally representative sample. Journal of Adolescent Health. 2006;39(6):842-849.

Norton EC, Han E. Genetic information, obesity, and labor market outcomes. Health Economics 2008;17(9):1089-1104.


Harris KM, Halpern CT, Whitsel E, Hussey J, Tabor J, Entzel P, Udry JR. The National Longitudinal Study of Adolescent Health: Research design. Add Health Web site. 2009.



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