Using the Social Network Data From Add Health

1. Using the Social Network Data From Add Health James Moody Sunbelt Social Networks Conference February 13, 2001 New Orleans

2. Introduction: What and Why • Background to Add Health • Levels of Network Data • Composition & Pattern • Networks on both sides of the equation • Network Data structures • Adjacency Matrices • Adjacency Lists • Network Data in Add Health • In School Friendship Nominations • In Home Friendship Nominations • Constructing Networks • Total Networks • Local Networks • Peer Groups • Analyses Using Networks • Networks as dependent variables • Networks as independent variables

3. History of the National Longitudinal Survey of Adolescent Health* better known as Add Health. * a program project designed by J. Richard Udry and Peter S. Bearman, and funded by a grant HD31921 from the National Institute of Child Health and Human Development to the Carolina Population Center, University of North Carolina at Chapel Hill, with cooperative funding participation by the following agencies: The National Cancer Institute; The National Institute of Alcohol Abuse and Alcoholism; the National Institute on Deafness and other Communication Disorders; the National Institute on Drug Abuse; the National Institute of General Medical Sciences; the National Institute of Mental Health; the Office of AIDS Research, NIH; the Office of Director, NIH; The National Center for Health Statistics, Centers for Disease Control and Prevention, HHS; Office of Minority Health, Centers for Disease Control and Prevention, HHS, Office of the Assistant Secretary for Planning and Evaluation, HHS; and the National Science Foundation.

4. Initially proposed as an adolescent version of the National Health and Social Life Study (Laumann et al) known as the “Teen Sex” study. • Jesse Helms’ crew decided that asking teens about sexual behavior was inappropriate, and the study had the dubious distinction of being the only study ever explicitly outlawed. • Fortunately, the same legislation stipulated that NIH fund a national health survey, and from the ashes of Teen Sex, Add Health was born. • Funded at $24M for the first 4 years, Add Health was designed to provide a comprehensive image of the state of adolescent health and the behaviors that affect adolescent health.

5. Social Networks Peer Groups Sample Information Genetic High SES Black Contextual Variables Partners Relational Data Behavior Characteristics of Peers and Peer Group The Add Health Design:Adolescents in Social Context In-School Contextual Community/Neighborhood Contextual Data Base School Neighborhood Community Characteristics Health Service Peers and Networks School Context Dyadic Relations Family • Individual • Attributes • Attitudes • Behavior • Capacities Health Status Parent In-Home In-Home Parent Saturation Saturated Sample Parenting Family Data Relations Between Family and Adolescent Ideal Sequence In-School In-School Contextual Database

6. Substantive Domains covered in the Add Health Design Relations, Peers & Nets: Community Individual: Family: • Detailed Household Roster • Family Structure • Parental Interview • Sibling relations • Parental behaviors • Multiple observations in the same family • Parent’s knowledge of adol: • Activities • Friends • Adolescent Assessment of parents expectations and rule behavior • Twin Design • Population sample in schools provides complete network images • Constructed network data • Friendship nomination files • Romantic relation characteristics • Real and Ideal • Relationship timing and duration • Information from both sides of the relation in many cases • Peer assessment of peer activity: not just respondent assessment • GIS links for spatial analysis • Contextual data at the Block Group, City, County and State level • Topics include: • Population • Vital Statistics • Group Quarters • Households • Income • Poverty • Education • Labor Force • Housing • At Risk Children • Health Care • STD Levels • Crime • Religion • Elections • Social Welfare • Gov’t Expenditure • Abortion Access • Tobacco • Health Policy • Demographics • Detailed, multiple race /ethnic categories • Immigrant status • Socio-Economic Status • Health Status • Nutrition • STD & Sexual Behavior • Exposure • Emotional • Physical • Insurance/ Access • Daily Activities • Exercise • TV/Hobbies • Academic Exposure • Subjects taught • Sexual knowledge • Future Expectations • Risk taking activity • Delinquency • Drugs • Fighting and Violence • Motivation • Personality • Religion • Neighborhood assessments

7. HS HS HS HS HS Feeder Feeder Feeder Feeder Feeder Puerto Rican Chinese Main Sample 200/Community Cuban Unrelated Pairs in Same HH Fraternal Twins Full Sibs Half Sibs Identical Twins Sampling Structure for Add Health School Sampling Frame = QED SamplingFrame of Adolescents and Parents N = 100,000+ (100 to 4,000 per pair of schools) Ethnic Samples High Educ Black Disabled Sample Saturation Samples from 16 Schools Genetic Samples Cuban

9. The National Longitudinal Study of Adolescent Health: Demographic Sub-Sample Sizes Core Sample 12,104 White: 8,467 Black: 2,384 Hispanic: 1,456 Male: 4,075 Female: 4,392 Male: 1,092 Female: 1,292 Male: 708 Female: 748 One Parent 760 Two Parents 3325 One Parent 842 Two Parents 496 One Parent 466 Two Parents 569 One Parent 593 Two Parents 477 One Parent 184 Two Parents 505 One Parent 189 Two Parents: 3150 7th: 484 108 552 126 92 75 88 98 62 28 76 26 8th: 522 115 526 137 93 93 93 102 82 18 78 33 9th: 543 135 574 156 73 84 95 97 79 34 78 38 10th: 536 141 540 151 80 80 99 99 94 37 92 23 11th: 581 132 551 125 79 64 91 96 87 29 89 31 12th: 445 108 524 117 67 50 87 84 58 30 80 30

10. Deductive Disclosure Risks: Start with: 536 White, Male, 10th Graders in Two parent Households: Who are Jewish: 10 And Have No Siblings: 1 Start with: 484 White, Male, 7th Graders in Two parent Households: Who Have Ever Been Held Back A Grade in School: 87 And Play Basketball: 5 And Smoke: 1

11. Deductive Disclosure Risks: Start with: 87 Black, Female, 12th Graders in Two parent Households: Who have Never been Held Back: 77 And Smoke Regularly: 5 And Have 2 siblings 1 And are Catholic 1

12. Deductive Disclosure Risks: Start with: 98 Black, Female, 7th Graders in One parent Households: Who Are Baptist: 41 And have no Siblings: 9 And Play Baskettball: 1 And have one Sibling: 13 And Smoke: 1 And have > one Sibling: 19 And are Born in April: 1

13. ego Levels of Network Data Best Friends ego ego Local Network Peer Group

16. Measuring Network Context Patterns • Pattern measures capture some feature of the distribution of relations across nodes in the network. These include: • Density: % of all possible ties actually made • Reciprocity: likelihood that given a tie from i to j there will also be a tie from j to i. • Transitivity: extent to which friends of friends are also friends • Hierarchy: Is there a status order to nominations? How is it patterned? • Clustering: Are there significant groups? How so? • Segregation: Do attributes (such as race) and nominations correspond? • Distance: How many steps separate the average pair of persons in the school? Is this larger or smaller than expected? • Block models: What is the implied role structure underlying patterns of relations? • These features (usually) require having nomination data from each person in the network.

17. Measuring Network Context Composition • Composition measures capture characteristics of the population of people within a given network level. These include: • Heterogeneity: How dispersed are actors with respect to a given attribute? • Means: What is the mean GPA of ego’s friends? How likely is it that most of ego’s friends will go to college? • Dispersion: What is the age-range of people ego hangs out with? • These features can often be measured from the simple ego network.

18. Analysis with Social Network data • Networks as Dependent Variables • Interest is in explaining the observed patterns of relations. • Examples: • Why are some schools segregated and others not? • What accounts for differences in hierarchy across schools? • What accounts for homophily in friendship choice? • Tools: • Descriptive tools to capture properties • Standard analysis tools at the level of networks to explain the measures • p* and other specialized network statistical and simulation models

19. Analysis with Social Network data • Networks as independent Variables • Interest is in explaining behavior with network context (Peer influence/ context models) • Examples: • Is ego’s probability of smoking related to the smoking levels of those he/she hangs out with? (compositional context) • Is the transition to first intercourse affected by the peer context? • Are isolated students more likely to carry weapons to school than those in dense peer groups? (positional context) • Tools: • Depends on dependent variable • Peer influence models • Dyad models • Contextual models, with network level as nested context (students within peer groups)

20. Network Data Structures 1 2 3 Send 1 1 2 3 4 4 4 4 5 5 5 Recv 2 3 4 2 1 2 3 5 1 3 4 5 4 Adjacency Matrix Graph Arc List Node List

21. Network Analysis Programs • 1) UCI-NET • General Network analysis program, runs in Windows • Good for computing measures of network topography for single nets • Input-Output of data is a little chunky, but workable. • Not optimal for large networks • Available from: • Analytic Technologies • Borgatti@mediaone.net • 2) STRUCTURE • “A General Purpose Network Analysis Program providing Sociometric Indices, Cliques, Structural and Role Equivalence, Density Tables, Contagion, Autonomy, Power and Equilibria In Multiple Network Systems.” • DOS Interface w. somewhat awkward syntax • Great for role and structural equivalence models • Manual is a very nice, substantive, introduction to network methods • Available from a link at the INSNA web site: • http://www.heinz.cmu.edu/project/INSNA/soft_inf.html

22. Network Analysis Programs • 3) NEGOPY • Program designed to identify cohesive sub-groups in a network, based on the relative density of ties. • DOS based program, need to have data in arc-list format • Moving the results back into an analysis program is difficult. • Available from: • William D. Richards • http://www.sfu.ca/~richards/Pages/negopy.htm • 4) PAJEK • Program for analyzing and plotting very large networks • Intuitive windows interface • Used for all of the real data plots in this presentation • Mainly a graphics program, but is expanding the analytic capabilities • Free • Available from:

23. Network Analysis Programs • 5) Cyram Netminer for Windows: A new exploratory tool for networks • 6) SPAN - Sas Programs for Analyzing Networks (Moody, ongoing) • is a collection of IML and Macro programs that allow one to: • a) create network data structures from the Add Health nominations • b) import/export data to/from the other network programs • c) calculate measures of network pattern and composition • d) analyze network models • Allows one to work with multiple, large networks • Easy to move from creating measures to analyzing data • All of the Add Health data are already in SAS • Available by sending an email to: • Moody.77@osu.edu

24. Network Data Collected in Add Health In -School Network Data • Complete Network Data collected in every school • Each student was asked to name up to 5 male and 5 female friends • These data provide the basic information needed to construct network context measures. • Due to response rates, we computed data on 129 of the 144 total schools. • Variable is named MF<#>AID form male friend, FF<#>AID for female friends.

25. Slide here of the survey instrument

26. Network Data Collected in Add Health In -School Network Data • Nomination Categories: • Matchable people inside ego’s school or sister school • People who were present that day • ID starting with 9 and are in the sample • People who were absent that day • ID starting with 9, but not in the school sample • People in ego’s school, but not on the directory • Nomination appears as 99999999 • People in ego’s sister school, but not on the director • Nomination appears as 88888888 • People not in ego’s school or the sister school • Nomination appears as 77777777 • Other special codes • Nominations appears as 99959995 • Nominator Categories • Matchable nominator • Person who was on the roster, ID starts is 9. • Unmatchable nominator • Person who was NOT on the roster, ID starts with 5 or 8

27. Network Data Collected in Add Health In -School Network Data Example 1. Ego is a matchable person in the School Out Un Out Out Un Un M Ego M Ego M M M M M M True Network Observed Network

28. Network Data Collected in Add Health In -School Network Data Example 2. Ego is not on the school roster M M M Un M Un M M M M M M Un Un Un True Network Observed Network

29. Network Data Collected in Add Health In -School Network Data

30. Network Data Collected in Add Health In -School Network Data

31. Network Data Collected in Add Health In -Home Network Data • Network Data were collected in both Wave1 and Wave 2 Surveys • There were two procedures: • Saturated Settings • Attempted to survey every student from the In-School sample. • 2 large schools, and 10 small schools. • Was supposed to replicate the in-school design exactly. • Unsaturated Settings • Each person was only asked to name one other person • In both cases, the design was not always carried out. As such, some of the students in the saturated settings were allowed to name only one male and one female friend, while some students who were in the non-saturated settings were asked to nominate a full slate of 5 and 5.

32. Network Data Collected in Add Health In -Home Network Data • Data Usage Notes: • Romantic Relation Overlap • For the W1 and W2 friendship data, any friendship that was also a romantic relation was recoded to 55555555, to protect the romantic relation nominations. • Bad Machine on Wave 2 Data • Data on from one school in wave 2 seems to be corrupted. We have no way to show this for certain, but it seems to be the case that data from machines 200065 or 200106 gave incorrect data. We suspect this is so, because almost everyone who used these two machines “nominated” the same person multiple times. This results in one person having an abnormally large in-degree. • All nomination #s are now valid • Unlike the in-school data, Ids starting with something other than ‘9’ can be nominated. • Same out-of-sample special codes • All other special codes for these data are the same as in the in-school data.

33. Network Data Collected in Add Health In -Home Network Data Descriptive Statistics for Saturated Settings

34. Constructing Network Measures Total Network To construct the social network from the nomination data, we need to integrate each person’s nominations with every other nomination. Methods: 1) Export the Nomination data to construct network in other program MOST of the other programs require you to pre-process the data a great deal before they can read them. As such, it is usually easier to create the files in SAS first, then bring them into UCINET or some such program. 2) Construct the network in SAS The best way to do this is to combine IML and the MACRO language. SAS IML lets you work with matrices in a (fairly) strait forward language, the SAS MACRO language makes it easy to work with all of the schools at once. Programs already set up to do this are available in SPAN.

35. Constructing Network Measures Adjacency Matrices The key to analyzing / measuring the total network is constructing either an adjacency matrix or an adjacency list. These data structures allow you to directly identify both the people ego nominates and the people that nominate ego. Thus, the first step in any network analysis will be to construct the adjacency matrix. To do this you need to: 1) Identify the universe of possible people in the network. This is usually the same as the set of people that you have sampled. However, if you want to include ties to non-sampled people you may make the universe include all people named by anyone. 2) create a blank matrix with n rows and n columns. 3) loop over all respondents, placing a value in the column that corresponds to the persons they nominate. This can be binary (named or not) or valued (number of activities they do with alter).

36. Constructing Network Measures Local Networks. • To create and calculate measures based only on the people ego nominates, you can work directly from the nomination list (don’t need to construct the adjacency matrix). • To create and calculate measures based on the received or reciprocated ties, you need to have a list of people who nominate ego, which is easiest to get given the adjacency matrix. • To calculate positional measures (density, reciprocity, etc.) all you need is the nomination data. • To calculate compositional data, you need both the nomination data and matching attribute data.

37. Constructing Network Measures Peer Groups. Identifying cohesive peer groups requires first specifying what a cohesive peer group is. Potential definitions could be: a) all people within k steps of ego (extended ego-network) b) a set of people who interact with each other often (relative density) c) a set of people with a particular pattern of ties (a closed loop, for example) UCINET, STRUCTURE, NEGOPY and SPAN all provide methods for identifying cohesive groups. They all differ on the underlying definition of what constitutes a group. The FACTIONS algorithm in UCINET and NEGOPY’s algorithm use relative density. The CROWD algorithm is SPAN uses a combination of relative density and pattern. Once you have constructed the adjacency matrix, you can export to these other programs fairly easily. However, most of them are QUITE time consuming (FACTIONS, for example, is a bear) and take a good deal of time to run, so be sure you have identified exactly what you want before you start processing….

38. Constructing Network Measures Peer Groups Characteristics. Identifying Cohesive Sub-Groups • Cohesion: The group is difficult to separate; the connection of the group does not depend on one relation or person. • Groupness: Relative to the rest of the network, a cohesive sub - group has high relational volume. • Inclusion: Some people are not in groups while others bridge groups.

39. Examples of Peer groups within Add Health High Schools Crowds Algorithm

40. Observed Clustering within Adolescent Social Networks Network Characteristics of Sub Groups • On average, 65% of a school’s adolescents are in cohesive sub-groups. • 87% of all relations are within sub-groups. • The average sub-group has 22 members. • The average diameter for a sub-group is 3 steps. • The mean segregation index is .96 (1=Complete, 0=Random)

41. 34% 65% 84% 86% 79% 74% Grade College Activities Race GPA Smoking Observed Clustering within Adolescent Social Networks Distribution of Characteristic within groups, relative to school distribution

42. Constructing Network Data School Level

43. Constructing Network Data School Level Inter-Group Relations

44. Analysis Using Network Data Nets as Dependent Variable: Racial Segregation Same race friendship preference by racial heterogeneity 1.6 Countryside h.s. 1.0 Same Race Friendship Preference (b1) .4 -.2 .1 .8 .3 .6 Racial Heterogeneity

45. 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 GPA SES Fight College Drinking Same Sex Transitivity Same Race Both Smoke Same Clubs Intransitivity Same Grade Reciprocity Analysis Using Network Data Nets as Dependent Variable: Modeling the network Network Model Coefficients, In school Networks

46. Analysis Using Network Data Nets as Independent Variable: Suicide Relational Structures and Forms of Suicide Regulation Low High High Anomic Altruistic Integration Low Egotistic Fatalistic

47. Isolation Peer Anomie Alter School Ego Third ( ) Intransitivity Analysis Using Network Data Nets as Independent Variable: Suicide Measuring Isolation and Anomie.

48. Analysis Using Network Data Nets as Independent Variable: Suicide

49. Analysis Using Network Data Nets as Independent Variable: Weapons Probability of Carrying a Weapon by Race and Gender 0.14 0.12 0.1 Probability of carrying a weapon 0.08 Males Females 0.06 0.04 0.02 0 White Black Hispanic Asian Native American Other Race/Ethnicity a) Figure represents predicted probabilities model 6 of table 5, holding all other variables at the full sample mean.

50. Analysis Using Network Data Nets as Independent Variable: Weapons Network Effects on Weapon Carrying 0.18 0.16 Peer Group Deviance 0.14 0.12 0.1 Probability of carrying a weapon to school Social Outsiders 0.08 0.06 School Oriented Peer Group 0.04 0.02 0 Positive: 0.08 0.19 0.3 0.41 0.52 0.63 0.74 0.85 Negative: 0 1 2 3 4 5 6 7 character of peer context