1. Local Networks Overview Personal Relations: Core Discussion Networks Getting Deals Done Questions to answer with local network data Mixing Local Context Social Support Collecting (Ego-)Network Data Cloning Headless Frogs Examples Effect of missing data Strategies for Analysis Content Structure Software: UCINET & PAJEK

2. 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 Core Discussion Nets

3. 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 Core Discussion Nets

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

5. 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 Core Discussion Nets

6. Proportion Kin, GSS 1985 Local Networks Core Discussion Nets

7. Proportion Kin by: Age: Age Proportion Kin Local Networks Core Discussion Nets

8. 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 Core Discussion Nets

9. 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. R 2 3 1 4 5 1 2 3 4 5 1234512345 1 1 1 1 1  =0.5 2 3 1 4 5 Local Networks Core Discussion Nets

10. Density Local Networks Core Discussion Nets

11. 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 Core Discussion Nets

12. 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 Core Discussion Nets

13. 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 Core Discussion Nets

14. Social Network Data Cloning Headless Frogs How good is the name generator? Bearman and Parigi ask about what is being captured in the GSS name generator, which because of it’s placement in the GSS has become a standard question. Others have done this, and found that the resulting list of names does not differ significantly (see Straits 2000). Bearman & Parigi argue that to understand the network, you need to understand what it is people are really talking about. The basic assumption of the GSS question is that people talk about important matters to people who are important to them.

15. Social Network Data Cloning Headless Frogs Key Questions: 1)What do people talk about? 2)Why do so many people not report talking about anything with anybody? 3)Given the heterogeneity of the topics discussed, is there a foundation from which one could use the GSS data to describe anything meaningful about core discussion networks? 4)Is there a pattern of topics to alters and how does this affect comparative analyses?

16. Social Network Data Cloning Headless Frogs Key Questions: 1)What do people talk about? & Who did they talk to? Note that the topic was heavily dependent on the questionnaire order. In this survey, it was the first question.

17. Social Network Data Cloning Headless Frogs Key Questions: 1)Why do so many people not report talking about anything with anybody?

18. Social Network Data Cloning Headless Frogs talks about what with who? Connections are significant cells from table 5.

19. Social Network Data Cloning Headless Frogs Who talks about what? Connections are large values from figure 1. Males Females

20. Social Network Data Cloning Headless Frogs 1)Why do so many people not report talking about anything with anybody? 44% report nobody to talk to More likely to be without spouses, unemployed and non-white 56% report nothing important to talk about.

21. Social Network Data Cloning Headless Frogs

22. Social Network Data Cloning Headless Frogs End result suggest using questions that are linked directly to conversation domains of substantive interest. Or, more generally, defining relationships that are of importance for your topic of study.

23. Local Networks Getting Deals Done If networks provide resources and “social capital,” then different types of networks should matter for business transactions. -Builds on a long line of research about risk control, managing uncertainty and information gathering through networks. -The argument turns on a subtle difference: the networks that help you get information about a deal (and thus bring a deal to the table) are not the same as the networks that generate approval for the deal, and in fact might work in different ways.

24. Local Networks Getting Deals Done Outline of the argument & findings.

25. Local Networks Getting Deals Done Claims the results suggest a paradox: a) Uncertain deals require strong networks b) But embeddedness in strong networks makes it less likely a deal will close.* This leads them to the ‘multiple lens’ hypothesis. That decisions are best made when subject to information that comes from multiple, disconnected sources. (*note this works on two different notions of ‘strong’ in the actual models, so there’s some empirical slippage here…)

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

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

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

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

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

31. 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. (hidden)

32. Existing Sources of Social Network Data: There are lots of network data archived. Check INSNA for a listing. The PAJEK data page includes a number of exemplars for large-scale networks. 1-Mode Data Local Network data: Fairly common, because it is easy to collect from sample surveys. GSS, NHSL, Urban Inequality Surveys, etc. Pay attention to the question asked Key features are (a) number of people named and (b) whether alters are able to nominate each other. Social Network Data Network Data Sources: Existing data sources

33. Existing Sources of Social Network Data: 1-Mode Data Partial network data: Much less common, because cost goes up significantly once you start tracing to contacts. Snowball data: start with focal nodes and trace to contacts CDC style data on sexual contact tracing Limited snowball samples: Colorado Springs drug users data Geneology data Small-world network samples Limited Boundary data: select data within a limited bound Cross-national trade data Friendships within a classroom Family support ties Social Network Data Network Data Sources: Existing data sources

34. Existing Sources of Social Network Data: 1-Mode Data Complete network data: Significantly less common and never perfect. Start by defining a theoretically relevant boundary Then identify all relations among nodes within that boundary Co-sponsorship patterns among legislators Friendships within strongly bounded settings (sororities, schools) Examples: Add Health on adolescent friendships Hallinan data on within-school friendships McFarland’s data on verbal interaction Electronic data on citations or coauthorship (see Pajek data page) See INSNA home page for many small-scale networks Social Network Data Network Data Sources: Existing data sources

35. Boundary Specification Problem Network methods describe positions in relevant social fields, where flows of particular goods are of interest. As such, boundaries are a fundamentally theoretical question about what you think matters in the setting of interest. See Marsden (19xx) for a good review of the boundary specification problem In general, there are usually relevant social foci that bound the relevant social field. We expect that social relations will be very clumpy. Consider the example of friendship ties within and between a high- school and a Jr. high: Social Network Data Network Data Sources: Collecting network data

36. a)Network data collection can be time consuming. It is better (I think) to have breadth over depth. Having detailed information on <50% of the sample will make it very difficult to draw conclusions about the general network structure. b)Question format: If you ask people to recall names (an open list format), fatigue will result in under-reporting If you ask people to check off names from a full list, you can often get over-reporting c) It is common to limit people to a small number if nominations (~5). This will bias network measures, but is sometimes the best choice to avoid fatigue. d) Concrete relational indicators are best (who did you talk to?) over attitudes that are harder to define (who do you like?) Social Network Data Network Data Sources: Collecting network data

37. Boundary Specification Problem Social Network Data Network Data Sources: Collecting network data While students were given the option to name friends in the other school, they rarely do. As such, the school likely serves as a strong substantive boundary

38. Local Network data: When using a survey, common to use an “ego-network module.” First part: “Name Generator” question to elicit a list of names Second part: Working through the list of names to get information about each person named Third part: asking about relations among each person named. Social Network Data Network Data Sources: Collecting network data GSS Name Generator: “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 Note there are significant substantive problems with this name generator

39. Electronic Small World name generator: Social Network Data Network Data Sources: Collecting network data

40. Local Network data: The second part usually asks a series of questions about each person GSS Example: “Is (NAME) Asian, Black, Hispanic, White or something else?” Social Network Data Network Data Sources: Collecting network data ESWP example: Will generate N x (number of attributes) questions to the survey

41. Local Network data: The third part usually asks about relations among the alters. Do this by looping over all possible combinations. If you are asking about a symmetric relation, then you can limit your questions to the n(n-1)/2 cells of one triangle of the adjacency matrix: Social Network Data Network Data Sources: Collecting network data 1 2 3 4 5 1234512345 GSS: Please think about the relations between the people you just mentioned. Some of them may be total strangers in the sense that they wouldn't recognize each other if they bumped into each other on the street. Others may be especially close, as close or closer to each other as they are to you. First, think about NAME 1 and NAME 2. A. Are NAME 1 and NAME 2 total strangers? B. ARe they especially close? PROBE: As close or closer to eahc other as they are to you?

42. Local Network data: The third part usually asks about relations among the alters. Do this by looping over all possible combinations. If you are asking about a symmetric relation, then you can limit your questions to the n(n-1)/2 cells of one triangle of the adjacency matrix: Social Network Data Network Data Sources: Collecting network data

43. Snowball Samples: Snowball samples work much the same as ego-network modules, and if time allows I recommend asking at least some of the basic ego-network questions, even if you plan to sample (some of) the people your respondent names. Start with a name generator, then any demographic or relational questions. Have a sample strategy Random Walk designs (Klovdahl) Strong tie designs All names designs Get contact information from the people named Snowball samples are very effective at providing network context around focal nodes. Detailed treatments of snowball sampling estimates are given in Frank (). Social Network Data Network Data Sources: Collecting network data

44. Snowball Samples: Social Network Data Network Data Sources: Collecting network data

45. Complete Network data Data collection is concerned with all relations within a specified boundary. Requires sampling every actor in the population of interest (all kids in the class, all nations in the alliance system, etc.) The network survey itself can be much shorter, because you are getting information from each person (so ego does not report on alters). Two general formats: Recall surveys (“Name all of your best friends”) Check-list formats: Give people a list of names, have them check off those with whom they have relations. Social Network Data Network Data Sources: Collecting network data

46. Social Network Data Network Data Sources: Collecting network data Complete network surveys require a process that lets you link answers to respondents. You cannot have anonymous surveys. Recall: Need Id numbers & a roster to link, or hand- code names to find matches Checklists Need a roster for people to check through

47. Social Network Data Network Data Sources: Missing Data Whatever method is used, data will always be incomplete. What are the implications for analysis? Example 1. Ego is a matchable person in the School Ego M M M M Out Un True Network Ego M M M M Out Un Observed Network Un Out

48. Example 2. Ego is not on the school roster M M M M M Un True Network M M M M M Un Observed Network Un Social Network Data Network Data Sources: Missing Data

49. Example 3: Node population: 2-step neighborhood of Actor X Relational population: Any connection among all nodes 1-step 2-step 3-step F 1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 3.1 3.2 3.3 F 1 2 3 4 5 1 2 3 4 5 6 7 8 1 2 3 FullFull (0) Full F F (0) F (0) Full (0) Unknown UK Full (0) Social Network Data Network Data Sources: Missing Data

50. Example 4 Node population: 2-step neighborhood of Actor X Relational population: Trace, plus All connections among 1-step contacts F 1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 3.1 3.2 3.3 F 1 2 3 4 5 1 2 3 4 5 6 7 8 1 2 3 FullFull (0) Full Unknown F F (0) F (0) Full (0) Unknown UK Full (0) 1-step 2-step 3-step Social Network Data Network Data Sources: Missing Data

51. Example 5. Node population: 2-step neighborhood of Actor X Relational population: Only tracing contacts F 1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 3.1 3.2 3.3 F 1 2 3 4 5 1 2 3 4 5 6 7 8 1 2 3 FullFull (0) UnknownFull Unknown F F (0) F (0) Full (0) Unknown UK Full (0) 1-step 2-step 3-step Social Network Data Network Data Sources: Missing Data

52. Example 6 Node population: 2-step neighborhood from 3 focal actors Relational population: All relations among actors FullFull (0) Full (0) Full (0) Full (0) Unknown UK Full (0) Full Focal 1-Step 2-Step 3-Step Focal1-Step2-Step3-Step Social Network Data Network Data Sources: Missing Data

53. Example 7. Node population: 1-step neighborhood from 3 focal actors Relational population: Only relations from focal nodes FullFull (0) Unknown Full (0) Full (0) Full (0) Unknown UK Full (0) Full Focal 1-Step 2-Step 3-Step Focal1-Step2-Step3-Step Social Network Data Network Data Sources: Missing Data

54. Local Network Analysis Introduction 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

55. 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

56. 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

57. 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 Network Analysis General Questions

58. 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 Network Analysis Mechanics

59. 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 2012. 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 Network Analysis Mechanics

60. 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. Local Network Analysis Mechanics

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

62. 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. 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). Local Network Analysis Mechanics

63. 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 Local Network Analysis Mechanics