Design Features of Add Health
Kathleen Mullan Harris*
Carolina Population Center
University of North Carolina at Chapel Hill
September 2005
Abstract: This document was prepared to be used by those interested in a ready reference for the
design features of the National Longitudinal Study of Adolescent Health (Add Health). It
provides a summary of features incorporated in the first ten years of completed work on the
project. It also provides a description of the field work planned for a fourth wave of home
interviews to be conducted in the years 2007-08 on the same sample. The reader is referred to
our web site (www.cpc.unc.edu/addhealth) for additional information, and for availability of
data. Parts of this document may be incorporated into other documents for grant applications,
papers for publication, or public presentations, without further permission. This document will
be useful for those planning to use existing Add Health data. It should also be useful for those
who want to submit independent grant proposals that require Wave IV data. Those data will be
available early in 2009.
*Kathleen Mullan Harris, PhD, is Principal Investigator and Director of Add Health
([email protected]). Address correspondence to Add Health, Carolina Population Center,
CB# 8120, University Square, 123 W. Franklin St., Chapel Hill, NC 27516.
INTRODUCTION AND OVERVIEW
The National Longitudinal Study of Adolescent Health (Add Health) is a longitudinal study of a
nationally representative sample of adolescents in grades 7-12 in the United States in 1994-95
who have been followed through adolescence and the transition to adulthood with three in-home
interviews. Add Health was developed in response to a mandate from the U.S. Congress to fund
a study of adolescent health and was designed by a nation-wide team of multidisciplinary
investigators from the social, behavioral, and health sciences. The original purpose of the
research program was to help explain the causes of adolescent health and health behavior with
special emphasis on the effects of multiple contexts of adolescent life. The study to date has
been funded by two program projects over the period 1994-2004. During this period Add Health
has become a national data resource for over 2,500 Add Health researchers who have obtained
more than 200 independently funded research grants and have produced hundreds of research
articles published in multiple disciplinary journals and research outlets.
Add Health research has exploited its unique design to explore the role of social contexts in the
lives of adolescents. Early research identified the importance of family and school connections
as protective factors in adolescence (Resnick et al. 1997). New research on how peers matter,
which peers matter, and in what contexts and for which behaviors peer influences are most
important have been conducted with the extraordinary network data in Add Health (e.g.,
Bearman and Brückner 1999; Harris and Cavanagh 2005; Haynie 2001; Moody 2001a, 2001b;
South and Haynie 2004; Quillian and Campbell 2003). The primal role of the family context for
adolescent development has been confirmed with Add Health data, highlighting the importance
of family structure, family income, family social capital, parental education, parental
involvement, and parent-child relations for adolescent behavior and well-being (e.g., Crosnoe
2004; Dittus and Jaccard 2000; Evenhouse and Reilly 2004; Harris and Ryan 2004; Rowe and
Jacobson 1999; Tillman 2003; Videon 2002).
Neighborhood and school effects have been identified with respect to such outcomes as academic
achievement, school attachment, future expectations, aggression, sexual behavior, emotional
well-being, delinquency, violence, physical activity, and obesity (Cleveland 2003; Crosnoe and
Muller 2005; Duncan, Harris, and Boisjoly 2001; Gordon-Larsen, Adair, and Popkin 2003;
Harris, Duncan, and Boisjoly 2002; Rowe, Almeida and Jacobson 1999). The hallmark of Add
Health is innovative analysis of the ways in which contexts, measured at multiple levels of
observation, interact (Bearman and Brückner 2001; Regnerus and Elder 2003). New
collaborations, focusing on the identification of social contexts that facilitate genetic expression
on health behaviors, such as regular tobacco and alcohol use as well as delinquent and violent
behavior are ground-breaking (Duncan, Harris, and Boisjoly 2001; Guo and Stearns 2002; Rowe,
Almeida, and Jacobson 1999).
In addition to the findings on social context effects, Add Health research has contributed more
broadly to the literature on the health status and successful adjustment of adolescents. Critical
health concerns of adolescents have received substantial research attention, including the
correlates of overweight status and obesity, physical exercise, diet, and body image (Harris, King,
and Gordon-Larsen 2005; Gordon-Larsen, Adair, and Popkin 2002; Gordon-Larsen, Popkin, and
McMurray 2000), pubertal timing and development (Adair and Gordon-Larsen 2001; Cavanagh
2
2004), health care (Billy et al. 1998; Ford, Bearman, and Moody 1999), use of contraception and
sexually transmitted infections (Halpern et al. 2004; Kelley, Borawski, and Keen 2003; Jaccard,
Dodge, and Dittus 2001; Upchurch and Kusunoki 2004; Upchurch et al. 2005), mental health
(Bearman and Moody 2004; Borowsky, Ireland, and Resnick 2001; Hallfors et al. 2004), and risk
behavior (Hagan and Foster 2001; Halpern et al. 2000; Haynie 2001; Harris, Duncan, and
Boisjoly 2002; Kandel et al. 2004; Pearce and Haynie 2004).
Add Health has also made possible important new research on the health status and health
behaviors of special populations such as disabled youth (Cheng and Udry 2002; 2003; 2005),
adopted youth (Feigelman and Finley 2004; Miller et al. 2000a, 2000b), youth living with
surrogate parents or relatives (Harris and Ryan 2004; Heard 2002), multiracial youth (Harris and
Sim 2002; Udry, Li, and Hendrickson-Smith 2003), youth with same-sex romantic attractions or
relationships (Halpern et al. 2004; Russell 2003; Udry and Chantala 2002, 2005), and adolescents
in immigrant families (Bankston and Zhou 2002; Harris 1999; Tillman, Guo, and Harris 2005).
The remainder of this document describes the design features of Add Health, for data already
collected in Waves I, II, and III, and to be collected in a planned Wave IV follow up. We
encourage researchers and students of public health, human development, biomedical sciences,
and related fields to explore the possibilities in this rich dataset.
THE ADD HEALTH SAMPLE AND DESIGN
Schools as Primary Sampling Units
Add Health used a school-based design. The primary sampling frame was derived from the
Quality Education Database (QED). From this frame we selected a stratified sample of 80 high
schools (defined as schools with an 11th grade and more than 30 students) with probability
proportional to size. Schools were stratified by region, urbanicity, school type (public, private,
parochial), ethnic mix, and size. For each high school selected, we identified and recruited one
of its feeder schools (typically a middle school) with probability proportional to its student
contribution to the high school, yielding one school pair in each of 80 different communities.
More than 70 percent of the originally selected schools agreed to participate in the study.
Replacement schools were selected within each stratum until an eligible school or school-pair
was found. Overall, 79 percent of the schools that we contacted agreed to participate in the
study. Because some schools spanned grades 7 to 12, we have 132 schools in our sample, each
associated with one of 80 communities. School size varied from fewer than 100 students to more
than 3,000 students. Our communities were located in urban, suburban, and rural areas of the
country.
From September 1994 until April 1995, in-school questionnaires were administered to students
in these schools. Each school administration occurred on a single day within one 45- to 60minute class period. Add Health completed in-school questionnaires from over 90,000 students.
The in-school questionnaire provided measurement on the school context, friendship networks,
school activities, future expectations, and a variety of health conditions. An additional purpose
of the school questionnaire was to identify and select special supplementary samples of
individuals in rare but theoretically crucial categories. School administrators also completed a
3
30-minute questionnaire in the first and second waves of the study.
Core and Special Supplemental Samples
Add Health obtained rosters of all enrolled students in each school. From the union of students
on school rosters and students not on rosters who completed in-school questionnaires, we chose a
sample of adolescents for a 90-minute in-home interview constituting the Wave I in-home
sample. To form a core sample, we stratified students in each school by grade and sex and
randomly chose about 17 students from each strata to yield a total of approximately 200
adolescents from each pair of schools. (Students who did not participate in the in-school survey
were eligible to be selected for participation in this sample.) The core in-home sample is
essentially self-weighting, and provides a nationally representative sample of 12,105 American
adolescents in grades 7 to 12. From answers provided on the in-school survey, we drew
supplemental samples based on ethnicity (Cuban, Puerto Rican, and Chinese), genetic relatedness
to siblings (twins, full sibs, half sibs, and unrelated adolescents living in the same household),
adoption status, and disability. We also oversampled black adolescents with highly educated
parents. The number of adolescents in each of these special samples who were interviewed at
Wave I are shown in Table 1 below. Note that individuals can be assigned to more than one
group. For example, a Cuban twin pair would be counted as two of the 538 Cubans and one of
the 767 twin pairs in Wave I.
Table 1. Case Counts in Add Health Wave I Data
(Notes: overlaps not removed; * numbers represent pairs of adolescents).
Core Sample
Cuban
Puerto Rican
Chinese
High-Education Black
Disabled
Full Sibling*
Half Sibling*
Non-related*
Adopted
Twin*
Saturated Sample
12,105
538
633
406
1,547
957
1,251
442
662
560
784
3,702
For two large schools and 14 small schools, interviews with all enrolled students were also
attempted in Wave I. The two large schools were selected purposefully. One is predominantly
white and is located in a small town; the other is characterized by marked ethnic heterogeneity
and is located in a major metropolitan area. The 14 smaller schools are located in rural and urban
areas, and both public and private schools are represented. We collected complete social network
data in the saturated field-settings, providing unbiased and complete coverage of the social
networks and romantic partnerships in which adolescents are embedded by generating a large
number of romantic and friendship pairs for which both members of the pair have in-home
interviews. The core sample plus the special samples produced a sample size of 20,745
adolescents in Wave I. Below we show the Add Health study design for selecting the in-school
4
5
and in-home Wave I samples. The Wave I in-home sample is the basis for all subsequent
longitudinal follow-up interviews, and thus this innovative design remains a major strength of the
longitudinal data as well.
Seventy-nine percent of all sampled students in all of the groups participated in Wave I of the inhome phase of the survey (20,745). A parent, usually the resident mother, also completed a 30minute op-scan interviewer-assisted interview. Over 85 percent of the parents of participating
adolescents completed the parental interview in the first wave. The parent questionnaire
gathered data on such topics as heritable health conditions, marriage and marriage-like
relationships, involvement in volunteer, civic, or school activities, health-related behaviors,
education, employment, household income and economic assistance, parent-adolescent
communication and interaction, the parent’s familiarity with the adolescent’s friends and friends’
parents, and neighborhood characteristics.
In 1996, all adolescents in grades 7 through 11 in Wave I (plus 12th graders who were part of the
genetic sample and the adopted sample) were followed up one year later for the Wave II in-home
interview (N=14,738). We conducted the adolescent in-home interviews using audio-CASI
technology (audio-computer assisted self interview) on laptop computers for sensitive health
status and health-risk behavior questions. Add Health was the first national study to use ACASI
technology in an adolescent population. The use of ACASI and CASI techniques has been found
to enhance the quality of self-reporting of sensitive and illegal information (Turner et al. 1998).
The school and Waves I and II in-home interviews constitute the adolescent period in Add Health
and contain unique data about family context, school context, peer networks, spatial networks,
and genetic pairs. The social context data, in particular, are unusual because we did not rely on
self-reports to generate an image of an adolescent’s world. Family context data come from
parent questionnaires, from adolescent in-school and in-home questionnaires, and from
interviews with additional adolescents living in the same household. For certain measures, such
as parenting behaviors and parent-child relations, we have reports from both the child’s and the
parent’s perspective (and in some cases from a sibling as well).
Contextual Data
School context data come from the administrator questionnaires (usually principals) who
reported on school policies, the provision of health services, and other school characteristics. In
addition, school context variables can be constructed by aggregating student responses from the
in-school and in-home questionnaires, enabling researchers to describe schools with respect to
their social demographic composition, the behaviors of their students, the health status of their
students, and the attitudes of their students towards school.
Peer network data were obtained in the in-school questionnaire. Adolescents nominated their
five best male and five best female friends from the school roster (using a unique id). Because
nominated school friends also took the in-school interview, characteristics of respondents peer
networks can be constructed by linking friends’ data from the in-school questionnaire and
constructing variables based on friends’ actual responses. In the in-home Wave I and Wave II
interviews, respondents nominated their best friend, as well as their romantic and sexual partners.
If their friend or partner is also a member of the in-home sample, their data can be linked to
6
construct friendship and partner contexts. In the 16 schools that were part of the “saturated”
sample, all students in the school were also interviewed in the home. Complete friendship and
sexual networks can therefore be constructed with these data.
Spatial data indicating the exact location of all households in the survey were collected using
hand-held Global Positioning System (GPS) devices or recording actual addresses. These data
make possible the interweaving of spatial and social networks, and the construction of
community contexts. More than 2,500 attributes for community and neighborhood contexts at
multiple spatial units of observation have been obtained and merged with the Wave I and Wave
II survey data to describe the neighborhood and community contexts in which adolescents are
embedded. Neighborhood and community data were gathered from a variety of sources, such as
the U.S. Census, the Centers for Disease Control and Prevention, the National Center for Health
Statistics, the Federal Bureau of Investigation, and the National Council of Churches.
Finally, the “genetic pairs data,” based on more than 3,000 pairs of adolescents who have varying
degrees of genetic relatedness (see Table 1), represent a fully articulated behavioral genetic
design, and are unprecedented for a national study of this magnitude. These data represent pairs
of adolescents who took the exact same questionnaires, share the same home environment, and
share, in most cases, the same school and neighborhood environment. Thus, from the outset,
Add Health was designed to address biological contributions to health by permitting researchers
to explore gene-environment interactions in relation to health and behavioral outcomes. In
addition, the embedded genetic design created new opportunities for research on adolescents
living in relatively rare family structures, such as blended step-families and surrogate-parent
families, and research on adopted children with a remarkable sample of 560 adopted children in
Wave I of Add Health. In all followup interviews, high priority has been placed on locating and
reinterviewing pairs in the genetic sample to maintain the integrity of this sample for longitudinal
research purposes.
The Transition to Adulthood: Wave III
With NICHD funding for a continuation of the program project, Add Health conducted a Wave
III follow-up interview with original Wave I respondents as they entered the transition to
adulthood. When adolescents finish high school, they enjoy greater independence and begin to
explore new lifestyles. As a result, their social contexts change and their experiences broaden.
Wave III data capture these expanding experiences by focusing on the multiple domains of young
adult life that individuals enter during the transition to adulthood, and their well-being in these
domains: labor market, higher education, relationships, parenting, civic participation, and
community involvement. With the longitudinal data from adolescence, this third wave of inhome interviews allows researchers to map early trajectories out of adolescence in health,
achievement, social relationships, and economic status and to document how adolescent
experiences and behaviors are related to decisions, behavior, and health outcomes in the
transition to adulthood. The fundamental purpose of this third follow-up was to understand how
what happens in adolescence is linked to what happens in the transition to adulthood when
adolescents begin to negotiate the social world on their own and develop their expectations and
goals for their future adult roles.
Wave III data collection was conducted nationwide (including Hawaii and Alaska) between
7
August 2001 and April 2002. Respondents were now aged 18-26 and in the midst of the
transition to adulthood. Add Health completed interviews on 15,170 respondents at Wave III,
resulting in a 76% response rate. (For more details, see Wave III nonresponse report at
http://www.cpc.unc.edu/projects/addhealth/files/W3nonres.pdf). In the interest of
confidentiality, no paper questionnaires were used. As in earlier waves, data were recorded on
laptop computers. For less sensitive material, the interviewer read the questions and entered the
respondent's answers. For more sensitive material, the respondent entered his or her own answers
in privacy. The average length of a complete interview was 134 minutes. The laptop interview
took approximately 90 minutes and was immediately followed by the collection of biological
specimens. Most interviews were conducted in respondents' homes.
In Wave III we continued to collect data on health and health related behavior that were
measured at earlier waves, including repeated measures of diet, physical activity, access and use
of health services, sexual behavior, contraception, sexually transmitted infections, pregnancy and
childbearing, suicidal intentions and thoughts, mental health and depression, substance use and
abuse, injury, delinquency, and violence. We again obtained physical measurements of height
and weight, and collected data on pubertal development, chronic and disabling conditions, and
other forms of morbidity. Wave III contains new data specific to the late adolescent, young
adulthood life stage on parent-child and sibling relations, contact with friends from high school,
the role of mentors and mentoring relationships, personal income, wealth and debt, civic and
political participation, children and parenting, involvement with the criminal justice system, and
religion and spirituality. Extensive data were collected on relationships, including a complete
history since Wave I and measures of relationship intimacy, quality, commitment, shared
activities, length, exclusivity, and sexual, union, and fertility behaviors. Codebooks for all three
waves of Add Health instruments can be downloaded from the Add Health website at
http://www.cpc.unc.edu/projects/addhealth/codebooks.
Wave III Design Features
We incorporated several new design features into Wave III that tapped research topics
particularly salient in the late adolescent-early adulthood life stage.
•
Binge Sample: A special sub-sample of freshman and sophomores in 2- and 4-year
colleges was chosen, along with a control group of non-college same-age peers, and
administered additional questions about binge drinking for an independently funded R01
grant on that topic.
•
College Context: College names were recorded so that college context data can be linked
and merged with respondent data for those in college.
•
Biological Specimens: New data collection of biological specimens was included in
Wave III. At the end of the Wave III interview, urine and saliva samples were collected
for tests of HIV and curable STDs. Additional saliva was also collected from a
subsample of the genetic sample (full sibs and twins) for DNA extraction. (For more
information, see the report on Wave III Biomarker Data Collection at
http://www.cpc.unc.edu/projects/addhealth/files/biomark.pdf.) Several publications using
these data have reported STD and HIV prevalence rates found in our national sample at
8
Wave III (Miller et al. 2004a, 2004b, forthcoming; Morris et al. forthcoming).
•
Couples Sample: Wave III contains a new “couples sample” in which Add Health
respondents recruited their romantic partners to take the same Wave III interview as their
Add Health partner. The couples sample is made up of slightly over 1,500 pairs of
partners, with roughly 500 married couples, 500 cohabiting couples, and 500 dating
couples.
For more details on the Add Health research design, design documents, available data sets,
codebooks, and publications, please see Harris et al., 2003.
Additional Add Health Wave III Data
There are two independently funded projects that will contribute additional data to Add Health.
Researchers at the University of Texas have collected the high school transcripts of Add Health
Wave III sample members and their partners in the couples sample in coordination with the
Wave III fieldwork. This study, Adolescent Health and Academic Achievement (AHAA)
collected high school transcripts and other data from all Add Health high schools except two
special education schools that did not maintain students’ academic transcripts, and from
approximately 1,400 additional schools where Add Health respondents last attended high school.
Approximately 91% of Wave III respondents signed a valid transcript release form and high
school transcripts were collected for most respondents (N= approximately 12,000). AHAA also
collected detailed school information, course offerings, and school contextual measures from the
schools last attended by the AHAA Wave III sample. The transcripts were coded using
procedures designed for the National Educational Longitudinal Study (NELS) and the National
Assessment of Educational Progress (NAEP). The transcript data provide detailed information
on students’ grades, courses taken, the overarching academic structure of each students school,
and information on the last school each student attended. The AHAA data provides indicators of
(1) educational achievement, (2) course taking patterns, (3) curricular exposure, and (4)
educational contexts within and between schools. Some of these constructed measures (e.g.,
course sequences, academic intensity, grade trajectories) have been merged with Add Health data
and have been released to all Add Health users; remaining educational data from this study will
be released in the coming year. (See http://www.prc.utexas.edu/ahaa/ for more information on
AHAA).
Add Health researchers Barry Popkin and Penny Gordon-Larsen at UNC have received funding
to develop a database of time-varying modifiable physical activity-related environmental factors
for Waves I, II, and III. Using a multidisciplinary approach that blends spatial analysis
methodologies with traditional epidemiological methods, they are linking area-level data to the
individual data in Add Health that will add community-level measures such as recreation
facilities (public, private), transportation options, crime, land use, air pollution, walkability,
climate, and cost of living. The environmental data come from US Geologic Survey, US Census,
US Department of Labor Statistics, and others extant sources. These additional environmental
data for Waves I, II, and III will be made publicly available in the coming year.
9
PLANS FOR WAVE IV
Rationale
In a second continuation of the Add Health program project, a fourth in-home interview is
planned with original Wave I respondents who will be aged 24-32 at the time of data collection.
The goal is to trace, locate, and reinterview respondents who participated in Wave I of the
National Longitudinal Study of Adolescent Health (Add Health) with a 2-hour personal interview
in 2007-08 representing a Wave IV follow-up of a nationally representative sample of over
20,000 adolescents in 1994-95. In Wave IV we will expand the scientific frontiers of research
and knowledge by taking Add Health in new directions to increase our understanding of biology
in relation to social, behavioral, psychological, and environmental processes of human
development. We plan to collect longitudinal survey data on the social, economic,
psychological, and health circumstances of our respondents, longitudinal geographic data, and
new biological data to capture the prevailing health concerns of our Add Health cohort as well as
biological markers of future chronic health conditions and disease. When Wave IV data are
combined with existing longitudinal Add Health data over 10 years of our respondents’ lives
beginning in adolescence and extending through their transition to adulthood, Add Health will
provide unique opportunities to study linkages in social, behavioral, environmental, and
biological processes that lead to health and achievement outcomes in young adulthood.
The research purpose of Wave IV is to study developmental and health trajectories across the life
course of adolescence into young adulthood using an integrative approach that combines social,
behavioral, and biomedical sciences in its research objectives, design, data collection, and
analysis. Add Health participants will be ages 24-32 and settling into young adulthood. At the
same time that the Add Health cohort assumes adult roles and responsibilities, they develop
crucial health habits and lifestyle choices that set pathways for their future adult health and wellbeing. We have designed Add Health Wave IV in the vision of the NIH Roadmap by integrating
biological information into our models of health and human development and by stimulating
interdisciplinary research teams that aim to reshape biomedical research to improve people's
health (Zerhouni 2003).
Several features of the proposed data collection represent new directions in Add Health,
including methods to obtain more objective measures of health status and strategies to explore
alternative modes of reinterviewing our sample in the future. Wave IV will employ innovations
in the collection of biological measures in a field setting on a large national sample that are both
practical and ground-breaking. For example, we plan to collect DNA on everyone in our national
sample and obtain indicators of metabolic syndrome and immune functioning using noninvasive
procedures. The combination of longitudinal social, behavioral, and environmental data with
new biological data will expand the breadth of research questions that can be addressed in Add
Health regarding predisease pathways, gene-environment interactions, the relationship between
personal ties and health, factors that contribute to resilience and wellness, and environmental
sources of health disparities.
Data Collection
Data collection for the National Longitudinal Study of Adolescent Health: Wave IV will be
carried out by the Research Triangle Institute (RTI) under sub-contract to the University of North
10
Carolina at Chapel Hill. The basic plan for data collection is to locate Wave I respondents and
complete a two-hour interview. We will collect survey data using a 90-minute CAPI/CASI
instrument, followed by biospecimen data collection. We will collect saliva samples for buccal
cell DNA and blood spots for a lipids profile and to dry for assays of C-reactive protein (hsCRP),
glycosolated hemoglobin (HbA1c), and Epstein-Barr virus (EBV) antibodies. Interviewers will
instruct respondents on self-collection of three saliva samples for assays of cortisol on the day
following the interview, which they will mail in prepared packages to Salimetrics Laboratories in
State College, PA. DNA samples will be mailed by the interviewers to the Institute for
Behavioral Genetics in Boulder, Colorado, the DNA subcontracter, where DNA will be
extracted, quantified, genotyped, and stored. Dried blood spots will be mailed to Craft
Technologies in Wilson, NC the contractor who will conduct the assays on blood spots.
Locating Respondents
The challenging part of the data-collection project is tracing respondents. RTI has a special
operational unit that is organized for respondent tracing. Our tracing and locating activities start
from a solid foundation. We have accurate addresses for all respondents interviewed in Waves II
and III of Add Health, and parent addresses for all respondents interviewed in Wave I. In
addition, at Wave III we gained consent from 67% of Add Health respondents to use their social
security numbers for purposes of following them up for subsequent interviews. RTI expects to
be successful in tracing and interviewing 85 percent of the Wave I respondents (about 17,000
cases). Because all of the respondents have been interviewed at least once before, and most have
been interviewed at least three times, we expect refusals to be minimal once respondents are
contacted. (In Wave III, only 6 percent of respondents refused to be interviewed.) The Add
Health sample is dispersed across the nation; at Wave III respondents lived in all 50 states.
Recruitment for Wave IV
Using respondent and parent locating information, current addresses for respondents will be
solicited. More intensive techniques will be sequentially employed to maximize the number of
original respondents who can be located. Every effort will be made to locate women and
minorities, including computerized searches of electronic databases and vital statistics (e.g.,
marriage records, DMV records). In 2006, original Wave I respondents will be sent advance
letters notifying them of the upcoming survey. Interviewers will then schedule with respondents,
either by telephone or in person, a mutually acceptable time to meet. Interviewers will explain
Add Health Wave IV and obtain written, informed consent from respondents.
Interview Setting
Most interviews will be conducted at the respondent’s home, but this is not a requirement. Add
Health interviewers have always been flexible to accommodate respondents’ schedule, lifestyle,
or living arrangements by conducting a handful of interviews in restaurants, college lounges, or
places of work. The Wave IV interview will be administered on a laptop computer with a
CAPI/CASI survey instrument. The Wave IV questionnaire administration will be
approximately 90 minutes long. Less-sensitive questionnaire sections and questions will be
administered with the assistance of an interviewer (computer-assisted personal interview). We
discovered at Wave III that when the interviewer and the respondent sit side by side during the
CAPI sections so that the respondent can read along while the interviewer reads out loud the
questions on the computer screen, this setting improves respondents understanding and reduces
11
need for clarification of questions, and considerably shortens the length of the interview. More
sensitive questionnaire sections will be self-adminis;tered using CASI technology (computerassisted self interview). Interviewers will obtain residential latitude and longitude readings using
a Global Positioning System (GPS) device which will be directly transferred into the survey
instrument on the laptop. Immediately following the 90-minute interview, interviewers will take
physical measurements and collect biological specimens, which should take no more than 30
minutes.
Survey Content
In determining questionnaire content, Add Health consulted widely with experts on each specific
health outcome and with representatives of NIH institutes who had contributed financial support
to the project. For example, in conjunction with the National Institute of Alcohol Abuse and
Alcoholism (NIAAA), we developed a short inventory of alcohol use and abuse items. Likewise,
in conjunction with the National Cancer Institute (NCI), we introduced new questions on sun
exposure and diet in the Wave II questionnaire. In Wave IV we are following a similar strategy,
drawing on the expert advice of NIH representatives to ensure that our questions capture the
necessary elements of each health domain.
The Wave IV survey will maintain longitudinal elements from earlier waves of data collection,
but will add new questions and sections to the survey that are more pertinent to the lives of young
adults. In Wave IV we plan to continue to collect longitudinal data on household composition
and residence history; contact with and emotional/financial support from parents, siblings, and
friends; educational achievement; occupational and military service history; romantic
relationship/cohabitation/marriage history; pregnancy history; childbearing and health conditions
of children; general sexual experiences and STDs; access to health services and insurance; self
esteem, depression, and suicidality; religiosity; violence and involvement with the criminal
justice system; substance use; and civic participation and citizenship. Questions in these
domains update sociodemographic transitions that are central to the movement into adulthood,
and provide information about continuity and change in multiple indicators of physical and
mental health status and health care; in social, emotional, and physical contexts; and in risk
taking, prosocial, and antisocial behavior.
New Survey Data
New data will be collected in a number of domains, reflecting funded research needs and salient
aspects of the young adult years of the 20s. For example, we plan to expand survey questions on
educational transitions, economic status and financial resources and strains, sleep patterns and
quality, eating habits and nutrition, illnesses and medications, physical activities, emotional
content and quality of current and recent romantic/cohabiting/marriage relationships, and
maltreatment during childhood by caregivers and other adults. We plan to add new questions to
tap the “Big 5" personality dimensions (the NEO-FFI; McCrae and Costa 1989), interpersonal
and occupational stressors, loneliness, substance addiction and treatment (Composite
International Diagnostic Interview - Substance Abuse Module; Cottler and Keating 1990;
Robins, Cottler, and Babor 1993; Young et al. 2002), obstacles to educational goals, and
intersections and balance between work and family responsibilities. We plan to collect the name
of the college from which any degrees were obtained and the names of all colleges attended to
facilitate a project that will collect the college transcripts and college contexts of our sample.
12
Moreover, particular survey data will be added to the instrument for purposes of interpreting
biological data, such as currently breast-feeding, medication use, recent infections, and recent
participation in intense forms of exercise.
Event History
The survey will continue to use a CAPI/CASI event history calendar to collect the dates and
circumstances of key life events that occur to young adults, including a complete relationship
history since Wave III, pregnancy and fertility histories from both men and women, an
educational history of dates of degrees and school attendance, and an employment history of jobs,
with respective information on occupation, industry, wages, hours, and benefits. We will also
collect a residential history of state locations since Wave III, and recent mobility (e.g., previous
address, length of time at previous residence). We can repeat our questions on relationship and
childbearing events and activities because Wave III had rather in-depth sections on these
domains. However, we plan to expand our data collection of education and work events because
these activities are particularly intense and quite consequential in this stage of life. Our event
history survey data will also include major health events (e.g., hospitalizations, the occurrences
of severe or chronic illnesses, accidents causing disability), involvement with the criminal justice
system (e.g., arrests, imprisonment), and other forms of institutionalization.
Geographic Data
At Wave IV we will collect geographic data in two forms: home and work addresses and latitude
longitude coordinates from GPS devices. Geocodes will be obtained from all reported addresses
using state of the art methods as well as from the latitude and longitude coordinates of the home
residence. Residential geocodes from multiple sources (e.g., from reported home addresses as
well as from GPS readings at the time of the interview) enable us to cross-check the validity and
clean the geographic data to produce a valid residential geocode. In addition, if the interview
does not take place at the respondent’s home, residential geocodes can still be obtained from the
home address reported in the survey.
A set of core community variables will then be assembled and made available to Add Health
users. Working under strict confidentiality protocols defined by Add Health, community-level
environmental data will be collected, compiled, or derived from public domain and commercially
available spatial/environmental data for each of the Add Health respondents for Wave IV.
Contextual geographic data available to Add Health investigators will include a large array of
socioeconomic and demographic census block, tract, and county-level characteristics based on
matching earlier census data created for Add Health in previous waves. For example, we will
add community data on race and ethnic composition, linguistic isolation, household income,
poverty status, educational attainment, unemployment, household tenure and migration,
household value, and a wealth of other contextual data. We will also add quarterly time-varying
price data for each area in the US for food (at home, fast food), housing, cigarette and other cost
of living elements from ACCRA (American Chamber of Commerce Researchers Association).
Biological Data
Given the size and geographic spread of our sample, and the non-clinical setting of our
interviews, we have chosen methods to collect biological data that are noninvasive, innovative,
cost-efficient, and practical for population-level research.
13
Anthropometric Measures. As in earlier waves, interviewers will measure the weight and height
of Wave IV participants who are dressed, have removed their shoes and are standing on an
uncarpeted floor (if possible). Weight will be measured to the nearest 0.5 lb (0.2 kg) using a UC321 Precision Personal Health Scale (max: 330 lb). Height will be measured to the nearest 0.5
cm using a Seca 214 Road Rod stadiometer (max: 200 cm). In addition, arm and waist
circumferences will be measured to the nearest 0.5 cm using a QM2000 Myo-Tape (max: 152
cm).
Cardiovascular Measures. Interviewers will measure the SBP, DBP and pulse of Wave IV
participants according to a standardized protocol. After a five-minute quiet rest in the sitting
position, resting, seated values of SBP, DBP (mmHg) and pulse (beats/min) will be automatically
recorded to the nearest whole unit using an appropriately sized blood pressure cuff and the
Omron HEM-747-IC BPM—a computer-controlled, digital, oscillometric blood pressure
monitor. Pulse pressure and mean arterial pressure will be estimated indirectly—the former as a
simple difference (PP = SBP – DBP) and the latter as a weighted sum of its components: [SBP +
2 * DBP] ¸ 3. Three serial determinations will be performed automatically from the right arm
(when possible) at two-minute intervals. Factors affecting blood pressure, such as current
medications, temperature, and time of day will be recorded.
Finger Prick Whole Blood. Trained and certified interviewers will use finger prick to obtain
whole blood for automated analyses in the field and subsequent assays of dried blood spots in a
centrally located facility, Craft Laboratories. Compared to venipuncture, this procedure provides
a relatively convenient, inexpensive, and non-invasive means for collecting blood samples.
Interviewers will obtain whole blood spots by [1] cleaning each participants’ middle or ring
finger with isopropyl alcohol; [2] pricking it with a sterile, disposable micro-lancet; [3] placing
the first drop of whole blood (~50 L per drop) on a test strip in a portable analyzer to obtain a
lipid panel in the field; and [4] placing five more drops onto standardized filter paper for
absorption.
Metabolic Measures. A Professional CardioChek PA will be used to obtain the lipid panel from a
blood spot placed on a PTS PANELS test strip. Whole blood spot assays for glycosylated
hemoglobin (HbA1c [%]) have been in use for several years in home and commercial settings and
have been shown to provide the accuracy, precision, and reproducibility of clinical tests (Parkes
et al. 1999). Craft Technologies will use a modification of these methods based on previously
validated whole blood spot assays with established performance characteristics.
Inflammatory Measures. High sensitivity CRP (hsCRP [mg/L]) will be measured using an
enzyme-linked immunosorbent assay (ELISA) previously validated for use with whole blood
spots (McDade, Burhop, and Dohnal 2004). The assay yields measures that are reliable and
precise (average coefficient of variation = 7.6% and 5.6%, respectively), and that have a lower
detection limit of 0.028 mg/L.
Immune Measures. There is persuasive evidence that Epstein-Barr Virus (EBV) is among the
strongest and most consistent immunological correlates of chronic stress (Herbert and Cohen
1993). We will measure EBV antibody titers using an ELISA protocol previously validated for
use with blood spots (McDade et al. 2000). Higher levels of circulating antibodies against EBV
14
indicate poorer cell-mediated immune performance.
HPA Axis Measures. In Wave IV, Add Health participants will be asked to self-collect three
saliva samples (on awakening, thirty minutes later, before bed) and complete a collection
checklist the day after their interview. The field interviewer will give saliva collection kits
(Salivettes), instructions, and a pre-addressed, postage paid envelope to each participant. Before
leaving the participant’s home, the interviewer will 1) record medical conditions or use of
medications known to alter plasma cortisol or cortisol binding globulin concentrations (e.g.,
Cushing’s disease; pregnancy; birth control pills; corticosteroids), 2) review collection
procedures, and 3) demonstrate how to obtain saliva and complete the collection checklist. The
practice saliva and checklist will be destroyed by the interviewer. The participant will use the
envelope to send samples and the collection checklist to Salimetrics Laboratories.
Saliva samples will be assayed for cortisol in duplicate by Salimetrics Laboratories. The
Salimetrics HS-Cortisol kit is a competitive enzyme immunoassay specifically designed for the
quantitative measurement of salivary cortisol. It addresses issues of matrix calibration specific to
salivary cortisol, is designed to capture the full range of salivary cortisol levels while using only
25 L of saliva per singlet determination, and has a built-in indicator regarding sample pH
(immunoassay performance is compromised when pH<4). The assay has a range of sensitivity
from .007 to 1.8 g/dl, and average intra- and inter-assay coefficients of variation less than 5%
and 9%, respectively. Method accuracy, determined by spike recovery, and linearity, determined
by serial dilution, are 105% and 95%.
Genetic Measures. In collaboration with the Institute for Behavioral Genetics (IBG) in Boulder,
Colorado, Add Health will collect, extract, quantify, and store DNA samples from all 17,000
respondents in Wave IV. Genomic DNA will be isolated from buccal cells in saliva. To collect
the DNA, the cheeks and gums will be rubbed for 20 sec with three swabs: one at the beginning
of the interview, a second about half-way through, and a final one near the end of the interview.
The swabs will be placed immediately in a 50 ml capped polypropylene tube containing lysis
buffer. After the third swabbing, the respondents will rinse out the mouth with 10 ml of bottled
water (supplied) for 20 sec, and expel it into the tube containing the three swabs. The tubes will
be stored at 4oC and shipped to the IBG laboratory by FedEx for DNA extraction. The buffer
lyses the cells and stabilizes the DNA.
IBG will assay at least ten candidates. Depending on the locus, the genotyping will consist of a
single VNTR, STR, SNP, or a panel of SNPs across the gene. To account for possible variation
contributed by additional SNPs contained within a given gene we will also genotype an average
of eight SNPs per gene, depending on the size and the number of variants residing within each
gene. Larger or more variable genes would likely necessitate genotyping more than eight
variants, while smaller or less variable genes will require genotyping fewer variants. Although
we cannot empirically test every variant in each gene, a limited number of genetic variants within
the promoter, coding, and non-coding regions should be sufficient to capture the majority of
variation accounted for by these genes. Thus, we will type an average of eight genetic variants
within the promoter, coding, and non-coding regions in at least 5 of the genes identified for
study. The list of candidate loci, along with all proposed biomarkers for Wave IV are shown in
the following table.
15
Proposed Wave IV Biomarkers
Biological Measure
Anthropometric
Weight [kg]
Height [cm]
BMI [kg/m2]
Waist Circumference [cm]
Arm Circumference [cm]
Biological Specimen
Method of Measurement
NA
"
"
"
"
UC-321 Precision Personal Health Scale
Seca 214 Road Rod Stadiometer
Weight ÷ Height2
QM2000 Myo-Tape
"
NA
"
"
"
"
HEM-747-IC Automatic BPM
"
"
SBP – DBP
(SBP + 2 × DBP) ÷ 3
Finger Prick Whole
Blood
"
"
"
"
"
"
CardioChek PA & PTS PANELS
Test Strips
"
"
TC – HDL-C – (TG ÷ 5)
TC ÷ HDL-C
TC – HDL-C
Blood Spot Assay
"
"
"
"
Saliva
"
"
"
"
Saliva Assay
Mean of 3 Sample Concentrations
Regress 3 Cortisol Concentrations on Time
Trapezoid Method Using 3 Samples
Sample #1 – Sample #2
Buccal Cell DNA
"
"
"
"
"
"
"
"
"
VNTR and SNP Panel
VNTR and SNP Panel
VNTR and SNP Panel
VNTR and SNP Panel
VNTR and SNP Panel
VNTR
STR
STR
STR
STR
Cardiovascular
SBP [mmHg]
DBP [mmHg]
Pulse [beats/min]
PP [mmHg]
MAP [mmHg]
Metabolic
TC [mg/dL]
HDL-C [mg/dL]
TG [mg/dL]
LDL-C [mg/dL]
TC:HDL-C
Non-HDL-C [mg/dL]
HbA1c [%]
Inflammatory
hs
CRP [mg/L]
Immune
EBV [ELISA units]
HPA Axis
Cortisol [ìg/dl]
Mean Cortisol [ìg/dl]
Diurnal Slope
Area Under Curve (AUC)
Awakening Response
Genetic
DAT1
DRD4
DRD2
5HTT
5-HT2A
MAOA-uVNTR
MAOA[GT]n
DRD5
COMT
IGF1
16
Procedures for Obtaining Informed Consent
Signed, informed consent will be obtained from respondents at two data-collection points—first,
before conducting the interview, second, at the conclusion of the interview and before collecting
biospecimens. All original respondents will be 18 years old or older. Before the start of the
interview, the interviewer will state that in addition to standard demographic, attitude, and
health-status questions, we will ask questions about the respondent’s friends, romantic and
sexual partners, and acquaintances, including activities that they have engaged in with these
individuals and the characteristics of the individuals with whom they associate. In addition,
respondents will be informed that they will be asked to provide saliva and blood spot specimens
at the end of the interview and, at the time they are asked to provide these specimens, they will
be asked to sign an additional consent form.
Post-Interview Data Handling
After an interview has been completed and biospecimens collected, identifying information will
be separated from the data and specimens. The laptop computer will create two files—one
containing an SID (a temporary identification number used for survey data) and the data (data
file), the other an SID, a set of BIDs, and identifying information (ID file). The biospecimens
will be labeled only with the BID. The data file will be sent to RTI for processing and then sent
to CPC; the ID file will be sent to RTI, who will send it on to the Add Health security manager in
Canada within three days. Upon confirmation that the ID file has been successfully received at
York, RTI will destroy their copy, thereby breaking the link between identifying information and
(1) the questionnaire data and (2) the biospecimens. This system also has the advantage of
separating the questionnaire data from the biospecimen results, reducing the risk of linking assay
results to an individual through deductive disclosure. At the end of the data-collection period,
the Security Manager will provide CPC with a file containing one record for each respondent,
with an AID, an SID, and a set of BIDs. CPC will replace all SIDs and BIDs with the
appropriate AIDs. This allows researchers to link Wave IV data with data collected in Waves I,
II and III.
This complex security system was designed and implemented to eliminate the possibility of
either direct or deductive disclosure of individuals, and has guaranteed a level of confidentiality
and security unprecedented in the social sciences. It is described in more detail in Confidentiality
of the Data, below. The system was necessary because many of the adolescent participants in our
sample provided detailed information about, and identified by unique identification numbers,
their friends, romantic partners, and/or sexual partners. Respondents, as young adults, will be
asked to provide this same information in Wave IV, and therefore the crucial element of the
security system developed Add Health (i.e., the separation of data from identifying information)
will be continued.
Compensation and Costs
Respondents will be given $30 for completing a laptop administered interview and up to an
additional $70 if they provide saliva and blood spot specimens. Respondents are free to
withdraw from the study at any time and to refuse to answer any question or to provide
biospecimens. The monetary inducements are paid to respondents at (1) the completion of the
17
interview and (2) the provision of the biospecimens. There are no costs to participants.
Risks to Subjects
In Add Health, respondents disclose information about themselves. This is usual. Respondents
also identify others (friends, siblings, romantic and sexual partners) and disclose information
about them. This is unusual. Further, analysis requires linking records to form pairs of
respondents so that information the respondent supplies about others is linked to what others
supply (as respondents) about themselves. This is also unusual.
Two principal risks to individuals participating in the study—breach of confidentiality and
deductive disclosure—were addressed in earlier waves of Add Health, and a number of
precautions and protocols were implemented to reduce the risk that either would occur. We will
continue these unusually thorough precautions to make breach of confidentiality and deductive
disclosure impossible.
Research material obtained from individual respondents will be in the form of questionnaire
responses and biospecimens. Respondents might experience social or legal consequences from
the disclosure of some of the information we plan to request from them. We intend to ask about
illegal drug use, illegal forms of sexual behavior, delinquent and violent behavior, criminal acts,
and names of romantic or sexual partners, and to collect saliva and blood spots to be assayed for
health conditions, as well as DNA. Respondents may be subject to arrest, embarrassment,
lawsuits, ridicule, loss of employment or insurance coverage, loss of access to student or other
types of loans, and other problems if the confidentiality of data is breached. Therefore, it is
imperative to protect the identity and identifying information of respondents. Precautions that
have been implemented in Add Health, and will be continued include:
1. Everyone associated with the project is required to sign a security pledge agreeing to
maintain confidentiality.
2. Identifiers and identifying information are kept separate from data given by respondents.
3. We have protection from subpoena of data by any agency of government by application to
the Department of Health and Human Services for a Confidentiality Certificate in
accordance with the provisions of section 301(d) of the Public Health Service Act (42
U.S.C. § 241(d)).
4. Identification numbers used during the Wave IV data-collection period will be different
from ID numbers used in previous waves of data collection and in Add Health data files.
A respondent’s biospecimen identification number will be different from his or her
interview identification number.
5. All users of contractual data are required to sign a security pledge agreeing to maintain
confidentiality.
6. All applicants for contractual data are required to submit a letter from their institutional
IRB verifying and approving the researcher’s plan for keeping the data secure and
minimizing the deductive disclosure risk.
7. Project staff monitor the use of contractual data at outside institutions through arranged
visits to investigators who have entered into contractual agreement with UNC.
8. Researchers who use third-party nomination data and HIV data must comply with
18
additional high security provisions for analysis of these data.
By this combination of procedures, we have been able to provide access to the data by the
research community and virtually to eliminate the risk that any respondent is identified. The
collection of information about the long-term consequences of adolescent health behaviors
justifies the collection of sensitive information that can be used to develop strategies promoting
long-term health and positive health behaviors.
Our respondents are familiar with and have participated previously in similar nomination
strategies. All respondents in previous romantic relationships have nominated their romantic
partners by name, all of our respondents have nominated their sexual partners (if any) by name,
and all of our respondents have nominated by name their five best male and female friends. The
task of nominating individuals is thus not foreign to them. In over 35,000 interviews conducted
between 1995 and 1996, yielding hundreds of thousands of friendship and romantic and sexual
partnership nominations, we have received no indication that this task is burdensome or
unpleasant for our respondents.
Confidentiality of the Data
In most studies it is important to protect the identity of respondents. Given the sensitive nature
of much of the data gathered in Add Health, as well as the request that respondents identify
others with whom they may have engaged in illegal behaviors, extraordinary measures are taken
to ensure complete confidentiality.
Detailed procedures for maintaining the confidentiality of the data are discussed in detail in
“Risks to Subjects,” above. Below are the guidelines used to develop the security and
confidentiality protocols. Two general principles of security apply to all waves of data. First, the
link between a respondent’s name and his or her data is kept secret. Second, the functions of
data collection, processing, and storage are separated from the function of maintaining
identification numbers, names, and locating information links.
A central measure in implementing these principles is the employment of a Security Manager
located outside the United States. Only the Security Manager can link the name/address/etc. of a
respondent, or someone nominated by a respondent, to information provided on a questionnaire
or in an interview. (Because the study has needed to return to respondents for follow-up
interviews, it has been necessary for the Security Manager to retain identifying and locating
information.)
The system is fairly complicated. To deal with the in-school surveys, for example, these steps
were followed: (1) the school provided a roster of students’ names and addresses; (2) the study
assigned short identification numbers (RIDs) to students on the roster; (3) printed versions of the
roster were prepared containing only names and RIDs, for use by students in identifying their
friends and themselves during the survey; (4) a roster file including names, RIDs, and addresses
was sent to the Security Manager; (5) in-school questionnaires were designed for optical
scanning, and each page of each copy was printed with that copy’s unique scannable
19
identification number (QID); (6) when a student filled out the in-school questionnaire, he or she
wrote his or her name, address, and telephone number on the first page, then looked himself or
herself up in the roster and provided his or her RID in both a handwritten and a scannable form;
(7) to identify friends, the student provided their RIDs; (8) when questionnaires were collected,
the first pages were separated, labeled with the school’s identification number, and sent
immediately to the Security Manager; (9) the interior pages of questionnaires were scanned and
the paper questionnaires shredded; (10) the Security Manager scanned the first pages of the
questionnaires, used the handwritten address and telephone number information to correct the
roster provided by the school, and then destroys the first pages; (11) using a combination of the
unique school identification number and the RID, according to an algorithm known only to him,
the Security Manager created for each respondent an altered identification number (AID); (12)
the file containing scanned questionnaire data was sent to the Security Manager (a copy was not
kept by the study); (13) the Security Manager used QIDs to add respondents’ AIDs to the file,
and friends’ RIDs to add friends’ AIDs; (14) the Security Manager removed friends’ RIDs from
the in-school data file and returned it to the study. Once the first pages of the in-school
questionnaires were sent to the Security Manager immediately after the survey session, the
study’s link between respondents’ names and their questionnaire data was irrevocably broken.
Measures similar in concept were followed for the three in-home interviews. Identification
numbers unique to the interview (like QID for the in-school survey) were assigned, respondents
identified themselves and their friends/partners using RIDs from an electronic version of the
school-based roster, and files recorded on laptop computers were split in two, with the file
containing the ID link between the respondent and his or her data being sent at the close of the
interview to the Security Manager, who substituted AIDs for RIDs.
At Wave III, an added element to the security system was necessary to address the biological
specimens. The containers of biological specimens were labeled with the respondent’s UID, an
identification number different from that which links the respondent to his or her direct survey
data (SID). As before, the Security Manager will replace SID with AID for survey data. When
biological specimens were analyzed, the analysis results were provided to the Security Manager,
attached to the UID. At the end of the field-work period, the Security Manager will again replace
the UID with the respondent’s AID. Security for Wave IV will again follow this procedures.
Public Release of Data
Data from Wave IV will contain an AID that will enable researchers to link Wave IV data with
that collected in Waves I, II, and III. A public-use dataset will be created containing survey data
for the same people who are in the current Add Health public-use dataset. The contractual
dataset will contain survey data for all respondents and will be available under contractual
agreement to qualified researchers. The Add Health safeguards—(1) providing only half the core
cases for public use to limit the risk of deductive disclosure, and (2) requiring researchers and
their institutions to enter into a contract with CPC—will be continued for the Wave IV data.
Availability of Data
Please see http://www.cpc.unc.edu/addhealth, the Add Health web site, for details of
20
currently available data. Requests for information about Add Health should be sent to
[email protected].
21
LITERATURE CITED
Adair, L. and P. Gordon-Larsen. 2001. “Maturational Timing and Overweight Prevalence in US
Adolescent Girls.” American Journal of Public Health 91(4):642-644.
Bankston III, C. L. and M. Zhou. 2002. “Being Well vs. Doing Well: Self-Esteem and School
Performance Among Immigrant and Non-Immigrant Racial and Ethnic Groups.”
International Migration Review 36(2):389-415.
Bearman, P. S. and H. Brückner. 1999. Peer Effects on Adolescent Girls’ Sexual Debut and
Pregnancy. Washington, DC: National Campaign to Prevent Teen Pregnancy: Research
Monographs.
Bearman, P. S. and H. Brückner. 2001. “Promising the Future: Abstinence Pledges and the
Transition to First Intercourse.” American Journal of Sociology 106(4):859-912.
Bearman, P. S. and J. Moody. 2004. “Adolescent Suicidality.” American Journal of Public
Health 94:89-95.
Billy, J. O. G., W. R. Grady, A. T. Wenzlow, N. D. Brener, J. L. Collins, and L. Kann. 1998.
“Contextual Influences on School Provision of Health Services.” Journal of Adolescent
Health 27(1):12-24.
Borowsky, I. W., M. Ireland, and M. D. Resnick. 2001. “Adolescent Suicide Attempts: Risks
and Protectors.” Pediatrics 107(3):485-493.
Cavanagh, S. 2004. “The Sexual Debut of Girls in Early Adolescence: The Intersection of Race,
Pubertal Timing, and Friendship Group Dynamics.” Journal of Research on Adolescence
14(3):285-312.
Cheng, M. M. and J. R. Udry. 2002. “Sexual Behaviors of Physically Disabled Adolescents in
the US.” Journal of Adolescent Health 31(1):48-58.
Cheng, M. M. and J. R. Udry. 2003. “How Much Do Mentally Disabled Adolescents Know
About Sex and Birth Control?” Adolescent & Family Health 3(1):28-38.
Cheng, M. M. and J. R. Udry. 2005. “Sexual Experiences of Adolescents with Low Cognitive
Abilities in the US.” Journal of Developmental and Physical Disabilities 17(2):155-172.
Cleveland, H. H. 2003. “Disadvantaged Neighborhoods and Adolescent Aggression: A
Behavioral Genetic Examination.” Journal of Research on Adolescence 13(2):211-238.
Cottler, L. B. and S. K. Keating. 1990. “Operationalization of Alcohol and Drug Dependence
Criteria by Means of a Structured Interview.” Recent Developments in Alcoholism
8:69-83.
22
Crosnoe, R. 2004. “Social Capital and the Interplay of Families and Schools.” Journal of
Marriage and the Family 66:267-280.
Crosnoe, R. and C. Muller. 2005. “Body Mass Index, Academic Achievement, and School
Context: Examining the Educational Experiences of Adolescents at Risk of Obesity.”
Journal of Health and Social Behavior 45:393-407.
Dittus, P. J. and J. Jaccard. 2000. “Adolescents’ Perceptions of Maternal Disapproval of Sex:
Relationship to Sexual Outcomes.” Journal of Adolescent Health 26(4):268-278.
Duncan, G. J., K. M. Harris, and J. Boisjoly. 2001. “Sibling, Peer, Neighbor and Schoolmate
Correlations as Indicators of the Importance of Context for Adolescent Development.”
Demography 38(3):437-447.
Evenhouse, E. and S. Reilly. 2004. “A Sibling Study of Stepchild Well-Being.” Journal of
Human Resources 30(1):248-276.
Feigelman, W., and Finley, G. E. (2004). “Youth Problems Among Adoptees Living in
One-Parent Homes: A Comparison with Others from One-Parent Biologic Families.”
American Journal of Orthopsychiatry 74(3): 305-315.
Ford, C. A., P. S. Bearman, and J. Moody. 1999. “Foregone Health Care Among Adolescents.”
JAMA 282(23):2227-2234.
Gordon-Larsen, P., B. M. Popkin, and R. G. McMurray. 2000. “Determinants of Adolescent
Physical Activity and Inactivity Patterns.” Pediatrics 105(6)e83.
Gordon-Larsen, P., L. S. Adair, and B. M. Popkin. 2002. “Ethnic Differences in Physical
Activity and Inactivity Patterns and Overweight Status.” Obesity Research 10(3):141149.
Gordon-Larsen, P., L. S. Adair, and B. M. Popkin. 2003. “The Relationship of Ethnicity,
Socioeconomic Factors, and Overweight in US Adolescents.” Obesity Research
11(1):121-129.
Guo, G. and E. Stearns. 2002. “The Social Influences on the Realization of Genetic Potential for
Intellectual Development.” Social Forces 80(3):881-910.
Hagan, J. and H. Foster. 2001. “Youth Violence and the End of Adolescence.” American
Sociological Review 66:874-899.
Hallfors, D. D., M. W. Waller, C. A. Ford, C. T. Halpern, P. H. Brodish, and B. Iritani. 2004.
“Adolescent Depression and Suicide Risk: Association with Sex and Drug Behavior.”
American Journal of Preventive Medicine 27(3):224-231.
23
Halpern, C. T., K. Joyner, J. R. Udry, and C. Suchindran. 2000. “Smart Teens Don’t Have Sex
(or Kiss Much Either).” Journal of Adolescent Health 26(3):213-225.
Halpern, C.T., M. L. Young, M. Waller, S. L. Martin, and L. Kupper. 2004. “Prevalence of
Partner Violence in Same-sex Romantic and Sexual Relationships in a National Sample
of Adolescents.” Journal of Adolescent Health 35(2):124-131.
Harris, K. M. 1999. “The Health Status and Risk Behavior of Adolescents in Immigrant
Families,” pp. 286-347 in D. J. Hernandez (Ed.), Children of Immigrants: Health,
Adjustment, and Public Assistance. Washington, DC: National Academy Press.
Harris, K. M., G. J. Duncan, and J. Boisjoly. 2002. “Evaluating the Role of ‘Nothing to Lose’
Attitudes on Risky Behavior in Adolescence.” Social Forces 80(3):1005-1039.
Harris, D. R. and J. J. Sim. 2002. “Who is Multiracial? Assessing the Complexity of Lived
Race.” American Sociological Review 67(4):614-627.
Harris, K. M., F. Florey, J. W. Tabor, P. S. Bearman, J. Jones, and J. R. Udry. 2003. The
National Longitudinal Study of Adolescent Health: Research Design [WWW document].
URL: http://www.cpc.unc.edu/projects/addhealth/design.
Harris, K. M. and S. Ryan. 2004. “Father Involvement and the Diversity of Family Context,”
pp. 293-319 in R. D. Day and M. Lamb (Eds.), Conceptualizing and Measuring Father
Involvement. Mahwah, NJ: Lawrence Erlbaum Associates.
Harris, K.M. and S.E. Cavanagh. 2005. “Indicators of the Peer Environment in Adolescence,”
Chapter in Brett B. Brown (Ed.), Key Indicators of Child and Youth Well-Being:
Completing the Picture. Hillsdale, NJ: Lawrence Erlbaum Associates.
Harris, K.M., R.B. King, and P. Gordon-Larsen. 2005. “Healthy Habits Among Adolescents:
Sleep, Exercise, Diet, and Body Image,” pp.111-132 in Kristin Anderson Moore and
Laura H. Lippman (Eds.), What Do Children Need to Flourish? Conceptualizing and
Measuring Indicators of Positive Development. New York: Springer.
Haynie, D. L. 2001. “Delinquent Peers Revisited: Does Network Structure Matter?” American
Journal of Sociology 106(4):1013-1057.
Heard, H. 2002. “The Life Course of Family Structure and School Engagement Among
Adolescents.” Unpublished doctoral dissertation, University of North Carolina at Chapel
Hill.
Jaccard, J., T. Dodge, and P. Dittus. 2001. “Do Adolescents Want to Avoid Pregnancy?
Attitudes Toward Pregnancy as Predictors of Pregnancy.” Journal of Adolescent Health
33:79-83.
24
Kandel, D. B., G.-E. Kiros, C. Schaffran, and M.-C. Hu. 2004. “Racial/Ethnic Differences in
Cigarette Smoking Initiation and Progression to Daily Smoking: A Multilevel Analysis”
American Journal of Public Health 94:128-135.
Kelley, S. S., E. A. Borawski, and K. J. Keen. 2003. “The Role of Sequential and Concurrent
Sexual Relationships in the Risk of Sexually Transmitted Diseases Among Adolescents.”
Journal of Adolescent Health 32:296-305.
McCrae, R. R. and P. T. Costa. 1989. “Reinterpreting the Myers-Briggs Type Indicator from the
Perspective of the Five-Factor Model of Personality.” Journal of Personality 57(1):1740.
McDade, T. W., J. Stallings, A. Angold, E. Costello, M. Burleson, J. Cacioppo, R. Glaser, and C.
Worthman. 2000. “Epstein-Barr Virus Antibodies in Whole Blood Spots: A MinimallyInvasive Method for Assessing an Aspect of Cell-Mediated Immunity.” Psychosomatic
Medicine 62:560-567.
McDade, T. W., J. Burhop, and J. Dohnal. 2004. “High Sensitivity Enzyme Immunoassay for
C-Reactive Protein in Dried Blood Spots.” Clinical Chemistry 50(3):652-654.
Miller, B. C., X. Fan, M. Christensen, H. D. Grotevant, and M. van Dulmen. 2000a.
“Comparisons of Adopted and Non-adopted Adolescents in a Large, Nationally
Representative Sample.” Child Development 71(5):1458-1473.
Miller, B. C., X. Fan, M. Christensen, D. Coyl, H. D. Grotevant, and M. van Dulmen. 2000b.
“Adopted Adolescents’ Overrepresentation in Mental Health Counseling: Adoptee’s
Problems or Parent’s Lower Threshold for Referral?” Journal of the American Academy
of Child and Adolescent Psychiatry 39(12):1504-1511.
Miller, W.C., C.A. Ford, M. Morris, M.S. Handcock, J.L. Schmitz, M.M. Hobbs, M.S. Cohen,
K.M. Harris, and J.R. Udry. 2004a. “The Prevalence of Chlamydial and Gonococcal
Infection Among Young Adults in the United States.” Journal of the American Medical
Association 291(18):2229-2236.
Miller, W.C., C.A. Ford, M.M. Hobbs, M.S. Cohen, J.L. Schmitz,, K.M. Harris, M.S.
Handcock, and M. Morris. 2004b. “Letters: Prevalence of Chlamydial and Gonococcal
Infection Among Young Adults.” Journal of the American Medical Association
292(7):801-802.
Miller, W.C., H.Swygard, M.M. Hobbs, C.A. Ford, M. Morris, M.S. Handcock, J.L. Schmitz,
M.S. Cohen, K.M. Harris, and J.R. Udry. “The Prevalence of Trichomoniasis in Young
Adults in the United States.” Sexually Transmitted Diseases, forthcoming.
Moody, J. 2001a. “Race, School Integration, and Friendship Segregation in America.”
American Journal of Sociology 107(3):679-716.
25
Moody, J. 2001b. “Peer Influence Groups: Identifying Dense Clusters in Large Networks.”
Social Networks 23:261-283.
Morris, M., M.S. Handcock, W.C. Miller, C.A. Ford, J.L. Schmitz, M.M. Hobbs, M.S. Cohen,
K.M. Harris, and J.R. Udry. “Prevalence of HIV Infection Among Young Adults in the
U.S.: Results from the Add Health Study.” The American Journal of Public Health,
forthcoming.
Parkes, J., R. Ray, S. Kerestan, H. Davis, and B. Ginsberg. 1999. “Prospective Evaluation of
Accuracy, Precision, and Reproducibility of an At-Home Hemoglobin A1c Sampling Kit.”
Diabetes Technology and Therapeutics 1(4):411-419.
Pearce, L. D. and D. L. Haynie. 2004. “Intergenerational Religious Dynamics and Adolescent
Delinquency.” Social Forces 82(4):1553-1572.
Quillian, L. and M. E. Campbell. 2003. “Beyond Black and White: The Present and Future of
Multiracial Friendship Segregation.” American Sociological Review 68:540-566.
Regnerus, M. D. and G. H. Elder, Jr. 2003. “Staying on Track in School: Religious Influence in
High and Low Risk Settings.” Journal for the Scientific Study of Religion 42(4):633-649.
Resnick, M.D., P.S. Bearman, R.W. Blum, K.E. Bauman, K.M. Harris, J. Jones, J. Tabor, T.
Beuhring, R. Sieving, M. Shew, M. Ireland, L. Bearinger, and J.R. Udry. 1997. “Protecting
Adolescents from Harm: Findings from the National Longitudinal Study of Adolescent
Health.” Journal of the American Medical Association 278(10):823-832.
Robins, L. N., L. B. Cottler, and T. F. Babor. 1993. WHO-NIH Composite International Diagnostic
Interview—Substance Abuse Module. St. Louis, MO: Washington University School of
Medicine.
Rowe, D. C., D. Almeida, and K. Jacobson. 1999. “School Context and Genetic Influences on
Aggression in Adolescence.” Psychological Science 10(3):277-280.
Rowe, D. C. and K. C. Jacobson. 1999. “Genetic and Environmental Influences on Vocabulary IQ:
Parental Education Level as Moderator.” Child Development 70(5):1151-1162.
Russell, S. T. 2003. “Sexual Minority Youth and Suicide Risk.” American Behavioral Scientist
46(9):1241-1257.
South, J. S. and D. L. Haynie. 2004. “Friendship Networks of Mobile Adolescents.” Social Forces
83(1):315-350.
Tillman, K. H. 2003. “Academic Outcomes Among Adolescent in Stepfamilies: The Importance
of Family Structure Pathways, Family Composition, and Family Relationships.”
Unpublished doctoral dissertation, University of North Carolina at Chapel Hill.
26
Tillman, K. H., G. Guo, and K. M. Harris. 2005. “Grade Retention Among Generations of
Immigrant Children.” Social Science Research, in press.
Turner, C. F., L. Ku, S. Lindberg, J. H. Pleck, and F. L. Sonenstein. 1998. “Adolescent Sexual
Behavior, Drug Use, and Violence: New Survey Technology Detects Elevated Prevalence
Among US Males.” Science 280:867-873.
Upchurch, D. M. and U. Kusunoki. 2004. “Associations Between Forced Sex, Sexual and
Protective Practices, and STDs Among a National Sample of Adolescent Girls.” Women’s
Health Issues 14(3):75-84.
Upchurch, D. M., W. M. Mason, Y. Kusunoki, and M. J. Kriechbaum. 2005. “Social and
Behavioral Determinants of Self-Reported STDs Among Adolescents.” Perspectives on
Sexual and Reproductive Health 36(6): 276-287.
Udry, J. R. and K. Chantala. 2002. “Risk Assessment of Adolescents with Same-Sex
Relationships.” Journal of Adolescent Health 31(1):84-92.
Udry, J. R. and K. Chantala. 2005. “Risk Factors Differ According to Same-Sex and Opposite-Sex
Interest.” Journal of Biosocial Science 37:481-497.
Udry, J. R., R. M. Li, and J. Hendrickson-Smith. 2003. “Health and Behavior Risks of Adolescents
with Mixed-Race Identity.” American Journal of Public Health 93(11):1865-1870.
Videon, T. M. 2002. “Who Plays and Who Benefits? Gender, Interscholastic Athletics, and
Academic Outcomes.” Sociological Perspectives 45(4):415-444.
Young, S. E., R. P. Corley, M. C. Stallings, S. H. Rhee, T. J. Crowley, and J. K. Hewitt. 2002.
“Substance Use, Abuse and Dependence in Adolescence: Prevalence, Symptom Profiles and
Correlates.” Drug and Alcohol Dependence 68(3):309-322.
Zerhouni, E. 2003. “The NIH Roadmap.” Science 302:63, 64, 72.
27