Using the Social Network Data From Add Health
James Moody
2000 Add Health Users Workshop
August 1 & 2
Bethesda Maryland
•Introduction: What and Why
•Levels of Network Data
•Composition & Pattern
•Networks on both sides of the equation
•Network Data structures
•Adjacency Matricies
•Adjacency Lists
•Network Analysis Programs
•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 dependant variables
•Networks as independent variables
Levels of Network Data
ego
Best Friends
ego
ego
Local Network
Peer Group
The Social Structure of “Countryside” School District
Points Colored by Grade
7th
8th
9th
10th
11th
12th
The Social Structure of “Countryside” School District
Points Colored by Race
White
Black
Mixed/Other
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 aslo 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 corespond?
•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 strucutre underlying patterns of
relations?
These features (usually) require having nomination data from each person in the
network.
Measuring Network Context
Composition
Compostion 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.
Analysis with Social Network data
Networks as Dependant 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
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 cary weapons to school than
those in dense peer groups? (positional context)
•Tools:
•Depends on dependant variable
•Peer influence models
•Dyad models
•Contextual models, with network level as nested context
(students within peer groups)
Network Data Structures
Adjacency Matrix
Graph
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Node List
Network Analysis Programs
1) UCI-NET
•Genearl Network analysis program, runs in Windows
•Good for computing measures of network topography for single nets
•Input-Output of data is a little clunky, but workable.
•Not optimal for large networks
•Availiable 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 equivalance models
•Manual is a very nice, substantive, introduction to network methods
•Availiable from a link at the INSNA web site:
http://www.heinz.cmu.edu/project/INSNA/soft_inf.html
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.
•Availiable from:
William D. Richards
http://www.sfu.ca/~richards/Pages/negopy.htm
4) PAJEK
•Program for anlayzing 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
•Availiable from:
Network Analysis Programs
5) 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 analysing data
•All of the Add Health data are already in SAS
•Availiable by sending an email to:
Moody.77@osu.edu
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.
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
Network Data Collected in Add Health
In -School Network Data
Tie Accounts Table
Matchable
Sampled
Matchable
Full Information
Sampled
on this cell.
Matchble
Not-Sampled
In school, Not
on Roster
Out of School
Missing data
Valid
nominating data.
Missing data
Matchable NotSampled
Will appear as
nominations to
9999. Nonmatched people
can send, but not
receive ties.
Missing Data
In School,
Not-On Roster
Will appear as
9999 or 8888
nominations
Out of School
(special Codes)
Will appear as
7777 nominations
Missing Data
Missing Data
9999s or 8888s
9999s or 8888s
7777s
Missing Data
Missing Data
Missing Data
Network Data Collected in Add Health
In -School Network Data
Example 1. Ego is a matchable person in the School
Out
Un
Ego
M
M
True Network
Out
Un
Ego
M
M
Out
Un
M
M
M
M
Observed Network
Network Data Collected in Add Health
In -School Network Data
Example 2. Ego is not on the school roster
M
M
Un
Un
M
M
M
M
M
M
M
Un
Un
Un
True Network
M
Observed Network
Network Data Collected in Add Health
In -School Network Data
Characteristics of the Add Health School Sample
Sample Characteristics
Number of schools
Number of students
School Type
Public
Private
Grade Range
Junior High School
High School
7 - 12
Region***
West
Midwest
South
North East
Demographic Characteristics
% of schools >70% single race
Family SES
Behavioral Characteristics
Smoke Regularly
Sexually active
Expect to go to College
Active in school activities
*p<.05, ** p<=.01, ***p<=.001.
All Schools
Schools w. network data
144
90,118
129
75,871
89.6%
10.4
89.9%
10.1
40.6%
43.4
16.1
40.3%
43.4
16.3
19.4%
22.9
40.9
16.7
15.5%
24.0
42.6
17.8
52.7%
6.03
55%
6.02
14.4%
32.3
76.2
14.7%
32.9
76.3
Network Data Collected in Add Health
In -School Network Data
Local - Network Characteristics (Std. Dev. in parentheses)
Total
5.68
(3.45)
Same Sex
Male
Female
3.08
3.57
(1.98)
(1.74)
Out-of-school nominations
1.04
(1.87)
0.42
(0.98)
0.45
(0.93)
0.42
(1.09)
0.78
(1.28)
Local network densitya
0.18
(0.19)
0.22
(0.24)
.26
(.26)
.19
(.25)
.15
(.23)
Reciprocity rateb
0.40
(0.30)
0.40
(0.35)
0.51
(0.34)
0.29
(0.35)
0.27
(0.34)
7th - 8th grade
0.36
(0.29)
0.38
(0.35)
0.46
(0.33)
0.23
(0.33)
0.20
(0.30)
9th - 10th grade
0.38
(0.30)
0.39
(0.35)
0.52
(0.34)
0.25
(0.33)
0.26
(0.34)
0.45
0.43
0.56
(0.31)
(0.36)
(0.34)
a) Includes nominations to people not sampled
b) Proportion of ego's nominations that are reciprocated
0.37
(0.37)
0.32
(0.36)
In-school nominations
11th - 12th grade
a
Cross Sex
Male: Female Female: Male
2.19
2.54
(2.08)
(1.95)
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 alowed to name only one
male and one female friend, while some students who were in the nonsaturated settings were asked to nominate a full slate of 5 and 5.
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.
Network Data Collected in Add Health
In -Home Network Data
Descriptive Statistics for Saturated Settings
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 matricies 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 availiabel in SPAN.
Constructing Network Measures
Adjacency Matricies
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).
Constructing Network Measures
Total Network
Data for 12th grade males in a small school.
Constructing Network Measures
Total Network
Program for creating a network and exporting it to PAJEK
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proc iml;
%include 'c:\moody\sas\programs\modules\adj.mod';
%include 'c:\moody\sas\programs\modules\pajwrite.mod';
%include 'c:\moody\sas\programs\modules\pajpart.mod';
use work.d;
read all var{aidr} into id;
read all var{mf1aid mf2aid mf3aid mf4aid mf5aid} into noms;
adjmat=adj(id,noms); /* adj(*) is a pre-programed module */
adj_id=adjmat[,1];
insamp=j(nrow(adj_id),1,0); /* identify people who are also in the
sub-sample */
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do i=1 to nrow(insamp);
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iloc=loc(id=adj_id[i]);
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if type(iloc)='N' then do;
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insamp[i]=1;
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end;
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free iloc;
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end;
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adjmat=adjmat[,2:ncol(adjmat)];
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file 'c:\moody\conferences\add_health\ptp15_paj.net';
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call pajwrite(adjmat,adj_id,2);
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file 'c:\moody\conferences\add_health\ptp15_paj.clu';
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call pajpart(insamp);
22 quit;
Constructing Network Measures
Total Network
Resulting network as displayed by PAJEK.
Senior Male subsample in Red
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.
Constructing Network Measures
Local Networks.
Constructing Network Measures
Local Networks.
Adjacency Matrix
An example network:
All senior males from a
small (n~350) public HS.
Constructing Network Measures
Local Networks.
Example 2: Suppose you want to identify ego’s friends, calculate what proportion of ego’s
female friends are older than ego, and how many male friends they have (this example came
up in a model of fertility behavior).
You need to:
•Construct a dataset with
(a) ego's id (aid*1 - make it a number instead of a character),
(b) age of each person,
(c) the friendship nominations variables.
•Write a macro that loops over each community/School
•For each community, do
a) Identify ego's friends
b) Identify their age
c) compare it to ego's age
d) count it if it is greater than ego's.
An example SAS program to do this is in the handouts.
Constructing Network Measures
Peer Groups.
Identifying cohesive peer groups requires first specifying what a cohesive peer group is.
Potential defintions 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….
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.
Examples of Peer groups within Add Health High Schools
Crowds Algorithm
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)
Observed Clustering within Adolescent Social Networks
Distribution of Characteristic within groups, relative to school distribution
34%
65%
84%
86%
79%
74%
Grade
Race
College
GPA
Activities
Smoking
Constructing Network Data
School Level
Groups 23 & 24
Group 1
Group 15
Group 18
Constructing Network Data
School Level
Inter-Group Relations
Mostly Seniors
Mostly Juniors
4
1
17
Mostly Sophomores
30
7
27
3
Mostly Freshmen
25
Mixed Grades
12
16
15
Directed Arrow
23
24
19
13
14
31
10
18
21
5
20
2
Analysis Using Network Data
Nets as Dependent Variable: Racial Segregation
Same race friendship preference
by racial heterogeneity
Same Race Friendship Preference1)(b
1.6
Countryside h.s.
1.0
.4
-.2
.1
.3
.6
Racial Heterogeneity
.8
Analysis Using Network Data
Nets as Dependent Variable: Modeling the network
Network Model Coefficients, In school Networks
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
Analysis Using Network Data
Nets as Independant Variable: Suicide
Relational Structures and Forms of Suicide
Regulation
Low
High
High
Anomic
Altruistic
Integration
Low
Egotistic
Fatalistic
Analysis Using Network Data
Nets as Independant Variable: Suicide
Measuring Isolation and Anomie.
Isolation
Peer Anomie
Alter
Ego
School
(
)
Intransitivity
Third
Analysis Using Network Data
Nets as Independant Variable: Suicide
Effect of Friendship Structure on Suicidal Thoughts
Net of demographic, family, school, religion and personal characteristcs.
Males
Females
OR
95% CI
OR
95% CI
Network
Isolation
0.665 (0.307 - 1.445)
2.010 (1.073 - 3.765)
Intransitivity Index
0.747 (0.358 - 1.558)
2.198 (1.221 - 3.956)
Friend Attempted Suicide
2.725 (2.187 - 3.395)
2.374 (2.019 - 2.791)
Trouble with People
0.999 (0.912 - 1.095)
1.027 (0.953 - 1.106)
Analysis Using Network Data
Nets as Independant Variable: Weapons
By Race and Gender
30
Male
27.67
Female
Percent
16.3% of
American
adolescent males
and 5.14% of
adolescent
females report
ever bringing
weapons to
school.
20
19.45
15.71
13.92
10
11.13
10.29
8.51
8.08
3.52
3.23
0
White
Black
Hispanic
Asian
Mix / Other
Analysis Using Network Data
Nets as Independant Variable: Weapons
By position in the school friendship network
20
Member of a group
Bridges groups
Not a group member
18.7
16.2
15
14.13
10
7.43
5
4.53
4.93
0
Males
Females
Analysis Using Network Data
Nets as Independant Variable: Sexual Debut
The Effect of Peer Group Composition on Sexual Debut*
0.40
Estimated Probability of Sexual Debut
0.35
0.30
0.25
0.20
0.15
0.10
0.05
N=380
N=1898
N=2026
N=660
N=88
0.00
0%
1-25 %
26-50%
51-75%
Proportion of High-Risk Adolescents in Peer Group
76-100%
*Probability of experiencing sexual debut during the 18 months following the in-school survey.
Controlling for age, socio-demographic characteristics, family and peer group characteristics (see table
A1, model 6). Bearman and Bruckner, 199
Analysis Using Network Data
Nets as Independant Variable: Pregnancy
The Effect of Close Friends' Risk Status on Pregnancy Risk*
Estimated Probability of Pregnancy
0.20
0.15
0.10
0.05
N=308
N=932
N=100
N=517
N=550
N=427
0.00
no friends
0%
1-25 %
26-50%
51-75%
76-100 %
Proportion of Low-Risk Male and Female Close Friends
*Probability of experiencing a pregnancy during the 18 months following the in-school survey.
Controlling for age, socio-demographic and individual characteristics, family characteristics, and
popularity (see table B1, model 3), Bearman and Brukner 1999.