Local Networks Overview

1. Local Networks Overview • Overview • Personal Relations: • GSS Network Data • To Dwell Among Friends • Questions to answer with local network data • Mixing • Local Context • Social Support • Strategies for Analysis • Content • Structure

2. Local Networks GSS 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 GSS 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. Increase in Social Isolation Local Networks GSS Networks Network Size X1985: 2.9 X2004: 2.1 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. IF LESS THAN 5 NAMES MENTIONED, PROBE: Anyone else?

5. Local Networks GSS 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 GSS Networks Proportion Kin, GSS 1985 (Based on Marsden, 1985 GSS data)

7. Proportion Kin Age Local Networks GSS Networks Proportion Kin by: Age: (Based on Marsden, 1985 GSS data)

8. Local Networks GSS 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 GSS 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 GSS Networks Density

11. Local Networks GSS Networks Network Structure Summary

12. Local Networks Core Discussion Nets Network Structure Summary Non-Kin 1985 Non-Kin 2004 Kin 1985 Kin 2004

13. Local Networks To Dwell Among Friends Network Size One of the best-known books on the stuff of local social networks; framed as an attempt to test the idea that cities are socially isolating.

14. Local Networks To Dwell Among Friends Network Composition One of the best-known books on the stuff of local social networks; framed as an attempt to test the idea that cities are socially isolating.

15. Local Networks To Dwell Among Friends Network Composition (non-kin) One of the best-known books on the stuff of local social networks; framed as an attempt to test the idea that cities are socially isolating.

16. Local Networks To Dwell Among Friends One of the best-known books on the stuff of local social networks; framed as an attempt to test the idea that cities are socially isolating.

17. Local Networks Fischer’s Work. What does Fischer have to say about Age homogeneity in local nets? (hidden)

18. Fischer’s Work. What does Fischer have to say about marital homogeneity in local nets? (hidden)

19. Fischer’s Work. What does Fischer have to say about the size of local nets (by context)? (hidden)

20. Fischer’s Work. What does Fischer have to say about the density of local nets (by context)? (hidden)

21. Fischer’s Work. What does Fischer have to say about the % of Kin (by context & Relation type)? (hidden)

22. Fischer: the effect of urbanism. “Urbanism generally increases people’s access to people like themselves and to people unlike themselves -- simply by increasing the sheer numbers of both. For people whose status is a majority or plurality… the increase in similars is of marginal importance; such people are available almost anywhere, and especially in small communities. The increase in dissimilars .. That urbanism brings may be of more consequence since they are now available in large numbers for the first time. The result: in cities majority people may know more minority people; they tend toward networks heterogeneity, if in any direction at all. For people in minority status…the increase in dissimilar others is of little importance; such unlike people are all around them in most places. The increase in people similar to themselves is is more consequential since they now have a large pool of like people to choose from for the first time. The result: In citis minority people know more minority people like themselves; they tend toward network homogeneity.

23. Local Network Analysis • Local network analysis uses data from a simple ego-network survey. These might include information on relations among ego’s contacts, but often not. Questions include: • Population Mixing • The extent to which one type of person is tied to another type of person (race by race, etc.) • Local Network Composition • Peer behavior • Cultural milieu • Opportunities or Resources in the network • Social Support • Local Network Structural • Network Size • Density • Holes & Constraint • Concurrency • Dyadic behavior • Frequency of contact • Interaction content • Specific exchange behaviors

24. Local Network Analysis Introduction • Advantages • Cost: data are easy to collect and can be sampled • Methods are relatively simple extensions of common variable-based methods social scientists are already familiar with • Provides information on the local network context, which is often the primary substantive interest. • Can be used to describe general features of the global network context • Population mixing, concurrency, activity distribution (limited) • Disadvantages • Treats each local network as independent, which is false. • The poor performance of ‘number of partners’ for predicting STD spread is a clear example. • Impossible to account for how position in a larger context affects local network characteristics. “popular with who” • If “structure matters”, ego-networks are strongly constrained to limit the information you can get on overall structure

25. Local Network Analysis Network Composition Perhaps the simplest network question is “what types of alters does ego interact with”? Network composition refers to the distribution of types of people in your network. • Networks tend to be more homogeneous than the population. Using the GSS, Marsden reports heterogeneity in 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

26. Local Network Analysis General Questions 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)

27. Local Network Analysis Mechanics 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.

28. Local Network Analysis Mechanics 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.

29. Local Network Analysis Mechanics 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.

30. Local Network Analysis Mechanics An example network: All senior males from a small (n~350) public HS. Calculating local network information from a global network.

31. Local Network Analysis Mechanics 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.

32. Local Network Analysis Mechanics – if time… 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