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Local Networks
Overview
Personal Relations:
•Core Discussion Networks
•To Dwell Among Friends
•Questions to answer with local network data
•Mixing
•Local Context
•Social Support
•Strategies for Analysis
•Content
•Structure
•Software:
•UCINET & PAJEK
Local Networks
Core Discussion networks
Question asked:
“From time to time, most people discuss important matters with other
people. Looking back over the last six months -- who are the people with whom you
discussed matters important to you? Just tell me their first names or initials.”
Why this question?
•Only time for one question
•Normative pressure and influence likely travels through strong ties
•Similar to ‘best friend’ or other strong tie generators
Local Networks
Types of measures:
Network Range: the extent to which a person’s ties connects them to a diverse set of
other actors.
Includes:
Size, density, homogeneity
Network Composition: The types of alters in ego’s networks.
Can include many things, here it is about kin.
Local Networks
Distribution of total network size, GSS 1985
25
Percent
20
15
10
5
0
0
1
2
3
4
5
6+
Local Networks
Network size by:
Age:
Drops with age at an increasing rate. Elderly have few close ties.
Education:
Increases with education. College degree ~ 1.8 times larger
Sex (Female):
No gender differences on network size.
Race:
African Americans networks are smaller (2.25) than White Networks (3.1).
Local Networks
Proportion Kin, GSS 1985
35
30
25
20
15
10
5
0
0
0.15
0.45
0.75
1
Local Networks
Proportion Kin by:
Age:
Proportion Kin
0.65
0.6
0.55
0.5
0.45
10
20
30
40
Age
50
60
70
Local Networks
Proportion Kin by:
Education:
Proportion decreases with education, but they nominate more of both kin
and non-kin in absolute numbers.
Sex (Female):
Females name slightly more kin than males do.
Race:
African American cite fewer kin (absolute and proportion) than do Whites.
Local Networks
Network Density
Recall that density is the average value of the relation among all pairs
of ties. Here, density is only calculated over the alters in the network.
2
1
R
3
1
3
4
5
1 2 3 4 5
2
4
5
D=0.5
1
2
3
4
5
1
1
1
1
1
Local Networks
40
35
30
25
20
15
10
5
0
<.25
.25-.49
.50-.74
Density
>.74
Local Networks
Network Density
Age:
Increases as we age.
Education:
Decreases among the most educated.
Race:
No differences by race.
Size of Place:
People from large cities have lower density than do those in small cities.
Local Networks
Network Heterogeneity
Heterogeneity is the variance in type of people in your network.
Networks tend to be more homogeneous than the population. Marsden
reports differences by Age, Education, Race and Gender. He finds that:
•Age distribution is fairly wide, almost evenly distributed,
though lower than the population at large
•Homogenous by education (30% differ by less than a year, on
average)
•Very homogeneous with respect to race (96% are single race)
•Heterogeneous with respect to gender
Local Networks
Network Heterogeneity
Heterogeneity differs by:
Age:
Tends to decrease as we age
Education:
Heterogeneity increases with education
Race:
No differences in age. Minorities tend to have higher race-heterogeneity
(consistent with Blau’s intergroup mixing model) and lower gender heterogeneity.
Size of place:
Large settings tend to be correlated with greater heterogeneity in the
network.
Local Networks
Fischer’s Work.
What does Fischer have to say about Homogeneity in local nets?
Local Networks
Questions that you can ask / answer
Mixing
The extent to which one type of person is tied to
another type of person (race by race, etc.)
Aspects of the local context:
Peer delinquency
Cultural milieu
Opportunities
Social Support:
Extent of resources (and risks) present in a type of
network environment.
Structural context (next class)
Local Networks
Calculating local network information.
1) From data, such as the GSS, which has ego-reported information on alter
2) From global network data, such as Add Health, where you have self-reports on
alters behaviors.
Local Networks
Calculating local network information 1: GSS style data.
This is the easiest situation. Here you have a separate variable for each alter
characteristic, and you can construct density items by summing over the relevant
variables.
You would, for example, have variables on age of each alter such as:
Age_alt1 age_alt2 age_alt3 age_alt4 age_alt5
15
35
20
12
.
You get the mean age, then, with a statement such as:
meanage=mean(Age_alt1, age_alt2, age_alt3, age_alt4, age_alt5);
Be sure you know how the program you use (SAS, SPSS) deals with missing data.
Local Networks
Calculating local network information 2: From a global network.
There are multiple options when you have complete network information.
Type of tie:
Sent, Received, or both?
Once you decide on a type of tie, you need to get the information of interest in a
form similar to that in the example above.
Calculating local network information 2: From a
global network.
An example network:
All senior males from a
small (n~350) public HS.
Calculating local network information 2: From a
global network.
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. This allows you to link each person in the network.
(b) age of each person,
(c) the friendship nominations variables.
•Then you need to:
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.
There is a SAS program described in the exercise that shows you how to do this kind of
work, using the graduate student network data.
Calculating local network information 2: From a
global network.
1) Go over how to translate network data from one program to another
UCINET
PAJEK
2) Go over the use of ego-net macros in SAS