Local Networks

1. 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

2. 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

3. 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.

4. Local Networks Distribution of total network size, GSS 1985 Percent

5. 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).

6. Local Networks Proportion Kin, GSS 1985

7. Proportion Kin Age Local Networks Proportion Kin by: Age:

8. 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.

9. 1 2 3 4 5 2 2 1 1 1 2 3 4 5 1 1 R 3 3 1 1 1 4 4 5 5 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. D=0.5

10. Local Networks Density

11. 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.

12. 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

13. 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.

14. Local Networks Fischer’s Work. What does Fischer have to say about Homogeneity in local nets?

15. 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)

16. 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.

17. 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.

18. 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.

19. Calculating local network information 2: From a global network. An example network: All senior males from a small (n~350) public HS.

20. 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.

21. 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