Chapter 15: Ego Networks

15.7 Problems and Exercises

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1. Provide three examples of network studies where a personal network approach is more appropriate than a sociocentric study design. 


Some examples:

  1. The relationship between New York teenager’s social networks and their attitudes towards tobacco use (studying the whole network of all New York teenagers would be a Herculean task and therefore not practical. In addition, attitudes about tobacco may be influenced by parents and adult relatives and the study of teenager’s networks alone wouldn’t capture this).
  2. Syrian refugees in Germany and the relationship between ego network composition and adaptation to life in Germany.
  3. Access to social resources and recovery from Hurricane Maria in Puerto Rico.

2. A researcher decides to limit the number of alters’ ego names to six (alter1, alter2, alter3, and so on). Construct an alter name inter-relator matrix that could be used in a survey to collect alter–alter tie data. 


See Figure 15.2 for an example.


3. A researcher is interested in doing a study of social capital and its relationship to wealth in cattle in a number of small villages in Northern Kenya using both position and resource generators. Provide some examples of the types of positions and resources that the researcher may want to use in the study. Which of the two approaches might be more appropriate for this ethnographic context? Explain your answer. 


Generally, the use of position generators allows for the measurement of social capital as a function of the prestige of the occupations an ego knows (e.g., doctor, lawyer, professor). In Western settings, occupational prestige can be more readily measured and used to measure social capital. The more one knows people in highly prestigious positions or occupations the higher their social capital. This, however, does not often transfer well to Non-Western settings. The types of prestigious positions in a village in Northern Kenya (e.g., tribal elder), for example, may not readily lend themselves to standard measures of occupational prestige. Thus, the resource generator may be more appropriate. For example, practically, the more you know someone who can fix a bike, fix a tractor, and owns a car may be more important in measuring social capital in this context than with knowing someone in particular positions or occupations.

4. Using the E-NET row-wise example file from the chapter “enet_example.txt” open E-NET and click on File|Import and click on Row-Wise selection and click OK. Load the ENET example file and click OK. This is just a simple dataset containing three egos (the attributes for egos should be displayed). Before we can visualize the ego networks we must make certain to filter the Alter-Alter Ties to include only alters that ‘know’ of each other. Go to Alter-Alter Ties and in the box to the upper right (Alter-Alter ties filter criteria) type into the box “knows>0”. This will filter out alter–alter ties in which alters do not know one another. Click on Filter and create the working dataset. To visualize each of the ego networks click on Visualization and the network for one of the egos should be displayed. To move through the visualizations of the ego networks go over to the left or right arrows (upper right) and click to display the next ego network. Go through all three ego networks. How would you describe the differences (or similarities) in network structure across the three networks? We can also compare the structural and compositional characteristics of the ego networks using structural holes and cohesion measures. Go to Analyze|Structural holes and leave the default “None” for each of the tie strength variables and click OK. Which of the three egos has the most social capital according to structural holes theory? 


Input of row-wise file


Alter-Alter ties filter


Visualization of ego network for Ego26


Structural Holes Results


4a. How would you describe the differences (or similarities) in network structure across the three networks? 


Ego31 has the densest ego network while the remaining two egos tend to connect alters that are not connected to one another.

4b. Which of the three egos has the most social capital according to structural holes theory? 


Clearly Ego31 has the least amount of social capital based on the structural holes measure, since the network has higher density and therefor more redundant ties. Both Ego26 and Ego45 have similar profiles across the structural holes measures suggesting relatively similar and higher degrees of social capital.

5. For this problem we will use the GSS dataset used in the column-wise example in the chapter (GSS 1985 network data.xlsx). To load the data into ENET go to File|Import and click on Column-Wise and click OK. Load in the GSS dataset and click on Load. To load the variables for ego, ego–alter and alter–alter go to the Auto button on the bottom left and click (this will automatically load the variables). Once the variables are loaded click OK. Now the data are ready for analysis. One interesting question to examine is do the ego networks show more homophily on the basis of sex or race? To exam this go to Analyze|Homophily. In the middle box highlight SEX=SEX and click on the right arrow to the right and move it to the box to the right. Do the same for the RACE=RACE variable. Once loaded click OK. A text output file will open showing the E-I index for sex and race for the entire sample. Also a set of measures were calculated for each ego including the E-I index and the proportion of alters in the ego network of the same sex and race. Does homophily by sex and race in ego networks differ? Also, what is the relationship between the “same proportion” variable and the E-I index for sex and race? 


Answer 1:There is much stronger homophily by race than by sex suggesting that egos interact more with members of their own race than across races. This is less so for sex. It is readily apparent from the comparison of the E-I index with the measure “same proportion” that they are related. A same proportion value of 50.0 corresponds to an E-I index value of 0.0 (50 percent same race), while a same proportion value of 100 corresponds to an E-I index of -1.0 (100 percent same race).

Results for E-I Index analysis


Comparison of E-I index and “Same Proportion” for each ego.


Answer 2: There is more homophily by race than by sex

6. Provide examples of the types of analyses and hypothesis testing you can conduct with the data generated in Problem 5 above. 


There are a number of further analyses that can be done using the “Analyze” list in E-Net. Network variables such a homophily, heterogeneity, composition, and density can be used in combination with demographic and other non-network variables in order to test any number of hypotheses. For example, measures produced by E-Net can be used in UCINET or exported to other statistical packages to test any number of statistical  hypotheses about variation in social capital (based on the structural holes measure) across groups, such as race.