Social Network Blog - r10

The General Social Survey was administered to 32 people. 16 were males, 8 of whom were aged 18-22 and 8 of whom were aged 33+. 16 were females, 8 of whom were aged 18-22 and 8 of whom were aged 33+. The following blog posting is an analysis of the results of this study.

Social Support

McPherson et al. found that core discussion group size has decreased from 3 in 1985 to 2 in 2004. Our study found people naming an average of 5.375 people with whom they “discuss important matters.” Even though both McPherson and I used the General Social Survey to obtain our data, our results were markedly different. Perhaps some explanations would be that (a) I targeted a very specific group of college-educated, upper-middle class people only, (b) my probes to get people to name more people were taken very seriously, or (c) our methods of survey administration were different.

Wellman enumerated different types of social support, including emotional support, small services, and companionship. While this questionnaire did not ask respondents to detail the types of support they received from their ties, it is possible to begin to make inferences about social support based on the responses to the name generator and subsequent descriptions of these ties. For example, 50% of respondents aged 18-22 lived in the “same country” (but not the “same state”) as their ties, largely because they were at a university in a different state than their family and friends. One can thus infer that they are receiving more emotional support than day-to-day small services or companionship from their ties.

In addition, it is possible to determine what types of social support people have access to by looking at their responses to the position generator. The participants in my survey had the most access to secretaries, hairdressers, nurses, pharmacists, and judges. By considering the types of resources that people in these occupations can provide, it is possible to infer the types of social support that the participants in the survey could receive. Nevertheless, it is important to remember Wellman’s assertion that “[r]espondents appear to get most of their social support – of all kinds – through their small number of strong ties” (p.566).

Network Size

One measure of network size is the position generator. This survey asked respondents if they knew anyone in each of 15 different occupations. The following analyses assume that the “occupational prestige score” for these 15 occupations constitutes interval-level data.

There appeared to be a modest correlation (Pearson’s r = 0.60) between respondent’s age and access to social capital based on the position generator. However, this number seemed skewed by a few female outliers. When respondents were separated by gender, there was a modest-to-strong correlation (Pearson’s r = 0.74) between male respondents’ age and network size based on the position generator, whereas there was a much weaker correlation (Pearson’s r = 0.47) between female respondents’ age and network size based on the position generator.

By looking at the equations for each gender’s regression line, one can see that the younger females in the sample had networks with a greater weighted prestige score than the younger males (by looking at the y-intercept value). Thus women appear to have more access to social capital. Such findings are inconsistent with Lin et al.’s findings about females’ low weighted prestige scores (relative to males) in Taiwanese society. However, the males in this sample accrued a larger social network at a quicker rate (as determined by the value of the slope).

y = 1.1842x + 9.6453 for males
y = 0.8484x + 26.491 for females

Thus, as people get older, they appear to have access to more personal resources and information. Thus, they are able to obtain “significant information to you before the average person receives it” (Burt, p.71) and “a reliable flow of information to and from those places” (Burt, p.72).

Network Density

Network density was measured by looking at the ties between the people with whom respondents discuss important matters. 76.6% of the relationships were either “especially close” (23.4%) or “know each other” (53.1%). However, 23.4% of the relations qualified as “strangers.” Such a finding runs counter to the theory of the forbidden triad (Granovetter, p.1363), which is the idea that if Person A knows Person B and Person C, then Person B and Person C must know each other, too. The existence of the forbidden triad was roughly as prevalent for both the 18-22 and the 33+ age groups.

As for gender, females had a larger percentage of ties who “knew each other” or were “especially close” (81% for females versus 72% for males). On the other hand, males had a larger percentage of ties who were “strangers” (28% versus 19%). Such a finding is consistent with McPherson et al.’s assertion that girls form smaller, more homogenous groups than do boys (p.423).

Community

I analyzed “community” by looking at the geographic proximity of the respondents and their ties. Respondents between ages 18 and 22, all of whom were current college students, had a majority of their ties (51%) in the “same country;” this statistic represented family and friends living outside of Pennsylvania. Respondents aged 33+ had the largest percentage of ties (82%) in the same state; this finding demonstrates that older populations tend to befriend people based more on proximity. The places where these respondents live tend to be less transient than college students’ locations.

In both age groups, more ties lived outside of respondents’ neighborhood than within it. Such a finding is consistent with Wellman’s statement that “[c]ommunities have moved out of neighborhoods to be dispersed networks that continue to be supportive and sociable” (p.26).

Privatization

“Privatization” refers the trend in which individuals communicate with people in private places (ex. homes) rather than in public places. Although this survey did not measure the location of people’s communication, it is possible to infer the locations in which people have discussions on important matters by looking at respondents’ distance to their ties. Specifically, interactions with people who live in (a) the same house or (b) the same building/dorm are more likely to occur in the house (in private) than interactions with people in the same city or same state. McPherson et al. explain this finding by saying, “We find…a shift away from ties formed in neighborhood and community contexts and toward conversations with close kin (especially spouses)” (p.353).

However, I predict that there is a “tipping point” to this trend, whereby people who live in different states or different countries are more likely to have discussions in private, since (a) if the discussions are in-person, it is after much traveling and (b) if the discussions are over other media, it is possible that those conversations will occur while both people are at home.

Using this logic, one can say that 70% of the ties noted by respondents aged 18 to 22 are between people who communicate in private (same house, same building/dorm, same country, different country) while 37% of the ties noted by respondents aged 33+ are between people who communicate in private. However, one major caveat that exists in this logic is the confounding variable created by the fact that respondents aged 18 to 22 often noted that they live in the “same country” as their parents. This dual-home status complicates analyses purporting to use “distance” to determine “privatization of communication.”

Additionally, it is possible to look at “relationship” as an indicator of where a conversation would take place. Especially for people aged 33+, it is likely that conversations in the home are taking place between spouses or parents and children. Indeed 21% of the ties’ relationships fit into those categories. However, further studies would have to explicitly ask about the location of conversations because there are so many complicating variables.

Network Diversity

It is possible to measure network diversity by looking at the position generator and the number of resources to which people have access.

As demonstrated here, the older group has a more diverse network and thus access to more resources and social capital. There was less of a stark difference between males and females in both age groups.

It is also possible to measure network diversity by looking at the educational and relationship characteristics of the people named by the respondents.

Educational

The above chart demonstrates that males 33+ had the most access to people with a graduate or professional degree, whereas females 33+ had the most ties to people who had a 4-year bachelor’s degree. In the 18-22 age group, comprised of college students, the most ties were to people who were currently finishing their undergraduate degrees. However, it is important to look at not only the level of education of the ties but the overlap between the respondents’ and ties’ levels of education; such a comparison is shown below.

As demonstrated, people tend to “discuss important matters” with people who are similar to themselves in terms of the highest level of education attained. Such a finding may be termed “homophily” and is thus discussed in the “Homophily” section below.

Relationship

People in both age groups spoke with friends more than people with whom they had any other type of relationship. However, it is important to note that an individual may have more friends (unlimited) than other types of relationships (ie. parents, which are often limited to 2). The finding that people are speaking so frequently with their friends is inconsistent with McPherson’s assertion that people are speaking more to kin than non-kin ties; however, it is consistent with Fischer’s statements that “[p]eople usually turn to nonrelatives for sociability and casual assistance” (p.80) and that “modernization and urbanization break down the family through strains that drive members apart and seductions that pull them apart” (p.81).

Strong Ties and Weak Ties

McPherson and Marin/Hampton all explained that the name generator can be used to identify people’s strong ties. It is interesting to note that the findings from this study run contrary to the findings by McPherson, in that there were over 2 times more strong ties named by respondents in this study than the respondents in McPherson’s study. This study’s findings are closer to the Marin/Hampton study’s findings in which respondents listed an average of 4.8 discussion partners (p.8).

Lin et al. explained that the position generator is a way to identify weak ties. It is likely that the people whom an individual knows in various occupations are weak ties. As Granovetter explained, it is these people to whom the respondents may go in order to find information (such as job information) to which that individual would not normally be exposed. In that way, the weak ties form a type of bridge or structural hole, as Burt termed it, between two groups of people.

Thus, one might even say that people can measure social capital by calculating the number (rather than the weighted value) of people who that person knows in the various listed occupations. The older group had almost 70% more (130 versus 77) weak ties, as measured in this way. Measured by gender, however, there was an approximately equal distribution (107 versus 100) of access to resources via weak ties.

Granovetter measured weak and strong ties as a function of the number of times in which individuals communicate in-person (p.1371). Although such a methodology is flawed, it is nevertheless a way of identifying general trends. Measured in this way, females 33+ (who had spoken to their ties using 6 types of media 1,769 days per month) had the strongest ties; males 18-22 had the lowest number of strong ties. The exact calculations are displayed below.

Homophily

Sex

Women and men showed approximately the same amount of gender homophily (69% versus 67%) when listing people in their social networks. The numbers also seem reasonable in light of McPherson et al.’s finding that “[g]ender homophily is lower among the young, the highly educated, and the Anglos” (p.423). Almost all 18-to-22-year-old respondents were young University of Pennsylvania students, and most were Anglo. Most respondents aged 33+ were upper-middle class Anglos. While these respondents did show some gender homophily, there was only about a 2:1 ratio of ties that were same-sex versus ties that were opposite-sex.

Age

Respondents aged 18-22 were much more likely to have ties that were the same age as them than respondents aged 33+ (32% versus 11%).

Respondents aged 18-22 were almost completely unlikely to be older than their ties. Only 14% of respondents aged 18-22 were older than their ties, and 0% were more than 8 years older. This statistic offers a stark contrast to people in the 33+ category, who were almost equally as likely to be older than their ties (44%) as they were to be younger than their ties (45%).

Education

As demonstrated above, people tended to have ties with people who have attained the same level of education as themselves.

Of the ties named by respondents (a) aged 18-22 (b) whose highest level of education was “attended college, not complete,” 56% were listed as “attended college, not complete”

Of the ties named by respondents (a) aged 33+ (b) whose highest level of education was “attended college, not complete,” 67% were listed as “attended college, not complete”

Of the ties named by respondents (a) aged 33+ (b) whose highest level of education was “4 Year Bachelors,” 77% were listed as “4 Year Bachelors”

Of the ties named by respondents (a) aged 33+ (b) whose highest level of education was “graduate or professional degree,” 71% were listed as “graduate or professional degree”

These findings complement McPherson et al.’s findings that “[s]ocial class of origin often determines neighborhood residence; education locates people in school settings; and occupation affects both workplace and voluntary association activity. Therefore, it is not surprising that we find significant homophily on these achieved characteristics as well” (p.426).

The Role of New Media

Note: All values below refer to “average number of days over a 1-month period during which interactions with one’s tie occurred over a given medium.” Thus, for example, if I write “average number of e-mails for females aged 18-22,” it means “the average number of days over a 1-month period during which female respondents aged 18-22 sent e-mails to their ties.”

Written Correspondence (E-mail versus Postal Mail)

The average number of e-mails was 25 times higher than the average number of postal mail messages. E-mail is not only cheaper (in fact, free), but it is more versatile (allowing people to send files directly from their computer for recipients to edit or allowing them to forward information) and convenient (done right at the computer). Thus, respondents in both age groups and both genders used e-mail significantly more often than they used postal mail.

It is surprising to see that respondents in the younger group sent fewer e-mail messages than respondents in the older group. Perhaps younger respondents use e-mail for more functional purposes, sending e-mails to many people – not only those people who they listed as ties. On the other hand, older people may be less likely to use e-mail functionally (to transmit Word documents, e-mail a listserve about a meeting, and all of the other functions discussed in the “New Media Lifestyle” blog posting) but more likely to send jokes and notes to their closest ties.

Telephone Technology (Phone versus Cell Phone)

College students are over 10 times more likely to communicate via cell phone than they are to communicate over landline phones. Respondents aged 33+ are more likely to communicate over regular phones. Such a disparity occurs because many college students “go wireless,” only having a cell phone. Thus while these people are at college, they do not have access to any means of telephone communication other than cell phones.

It is interesting to note, however, that respondents aged 18-22 did not have a significantly higher average number of cell phone calls than respondents aged 33+. Such a finding indicates that people in the 33+ age group are using telephone media, on the whole, more often.

Instantaneous Technology (IM)

Although females aged 18-22 used Instant Messenging (IM) more often than females aged 33+, there is not such a huge gap between the IM use by males ages 18-22 and 33+. Although it is true that the males aged 33+ did use IM more than females 33+, the more significant reason for the closeness in the values for males of both age groups is that males aged 18-22 used IM almost half as often as females in that age group.

Traditional Conversation (In-Person)

There does not appear to be an overarching detrimental effect of technology and privatization on the number of in-person interactions people have, on average, with their ties. It is true that respondents aged 33+ had more interactions with their ties than respondents age 18-22, but I hypothesize that such a result is because people aged 18-22 regularly interact with more people on a college campus than people aged 33+. Such a hypothesis can neither be affirmed nor disconfirmed with data from a study that primed respondents to list no more than 6 ties.

People communicated in-person more frequently than they communicated online. The number of in-person interactions for college students was 1.5 times greater than the number of e-mail interactions for females and 1.7 times greater for males. Such a finding is consistent with Baym’s statement that “[a]though the [college-aged] users were adept at using the internet socially and had integrated it into their daily lives, face-to-face communication clearly remained their dominant mode of interaction” (p.306).

Issues of Measurement as They Pertain to This Survey

1. Convenience Sample. Since the respondents in this survey were collected via a convenience sample, it is not possible to generalize the results. The people who I contacted were almost all college-educated, many with a graduate or professional degree. The survey would not be comprised of as many people with such a socioeconomic status if participants were collected via a random sample.

2. Memory Issues. Many of the numbers given were estimates. For example, many people estimated the number of days in the past month they communicated with their ties using the various media. Additionally – and this is especially true for people as they get older – people estimated their ties’ ages and the number of years that they knew them.

3. “What Comes to Mind.” Many people listed ties that I believe, if they had time to consider the question more, they would not have listed. Some people felt that only “friends” could constitute ties, “forgetting” to list people with whom I personally know they are extremely close. Such a need to immediately answer the survey may have also flawed and skewed the position generator results.

4. Mobility of College Students. College students make up a highly mobile segment of the population, often having at least two places they consider “home.” Many college students expressed discomfort when they had to say that the closest that they lived to their parents was “same country,” especially when their parents’ homes were in New Jersey or New York. However, since most college students took this survey while at college, the results are skewed towards presenting themselves as not living in their parents’ homes.

5. Small Sample. Although in this survey, n=32, such a small sample can only begin to demonstrate any strong trends in the General Social Survey.

Link to Assignment 2, Part 1 Website: Link

Originating Tie Strength

Question #1: What is the strength of the tie between the originating alter and the 2nd alter?

Of the 6 class members for which we have data, no one passed on their folders to extreme ties (either very weak or very strong). Both of the 2nd alters who had strong ties to the originating passed on the folder. Most moderate ties passed on the folder. No weak ties passed on the folder. Thus, “tie strength” appears to positively influence whether or not the folder is passed along. Nevertheless, it is important to recognize that more data points would further illuminate this hypothesis.

Granovetter explained that weak ties are often the sources of information, a type of bridge to groups to whom an individual might otherwise not be connected and from whom an individual might not otherwise get information. It is not possible to determine whether weak ties or strong ties helped move the Polley folders closer to the target, since that data is missing. However, based on the very limited data from the Yoon group, it appears that weak ties between the originating and 2nd alters were less effective than strong ties (since of the chains that were completed, 67% began with weak ties and 80% began with strong ties). Such a finding is consistent with Wellman’s argument that people “appear to get most of their social support – of all kinds – through their small number of strong ties” (p.566). In this case, the social support is coming in the form of “small services,” since passing on the folder was often seen as a favor.

Size of “Small World”

Question #2: What is the average number of links for both (a) completed and (b) incomplete chains?

Completed Chains: 4 1/2
Incomplete Chains: 2 1/3

Both these numbers are lower than the “6 degrees of separation” found by Milgram and Milgram/Korte. Nevertheless, our class’s findings are consistent with Stevenson et al.’s finding that “Small world studies in organizations have shown, given the relatively clear boundaries in organizations, the number of intermediaries between a starter and target is smaller.” Clearly, the University of Pennsylvania contains fewer people than the United States, and this comparison is reflected in the degrees of separation.

An interesting point is that our results demonstrate that, somehow, everyone in the class is connected to Polley. Thus, the theory mentioned in Milgram’s study that perhaps there are clusters of people in the world who are not connected to each other does not apply here. To explain, 2 folders did reach Polley, demonstrating that any of the folders that did not reach him could have originally been given to either Classmate r45 or Classmate g10, allowing the folder to reach Polley along either of the chains emanating from these two people.

Question #3: What percentage of folders reached the target?

2 out of 8 folders (25%)

This low number could be explained because of the strict organizational structure of the university and thus the relative unreachability of the target. In other words, it is likely that Communication majors were largely distanced from a lab technician at the Wistar Institute in terms of age, affiliation, field of study, and (for most people) gender.

In addition, the apathy of students translated into a failure to pass on the folder. Indeed, much anecdotal evidence indicates that alters complained that passing on the folder was “too much work.” In addition, many participants, feeling that they had no idea how to get a folder to someone in an institute as obscure as the Wistar Institute, neglected to continue the chain.

Our number, 25%, is similar to the 28% (44 out of 160) chains that traveled from the starting point in Nebraska to the target in Milgram’s study (p.65).

Homophily

Question #4: Is there “gender” homophily among chain links?

Whereas females passed folders on to alters of the same sex approximately as often as they passed folders on to males (53% versus 47%), the males in the study demonstrated slightly more gender homophily in their choice of people to whom to pass the folder (62.5% versus 37.5%). However, the sample size appears to be too small to make any sweeping statements. Nevertheless, these findings do differ from the finding by Stevenson et al. that “women relied more on homophilous ties to pass folders compared to men.”

In our study, the percentage of transfers to the same gender was 56.52% (55.56% for completed chains and 57.14% for incomplete chains). McPherson argues that “[u]ntil men and women enter the sex segregated voluntary association structure and labor force, most sex homophily is created by inbreeding rather than baseline homophily” (p.422). The results here do demonstrate a roughly equal split between homophilous and heterophilious transfers, demonstrating only a slight impact of this inbreeding homophily.

Question #5: Is there “affiliation” homophily among the chain links?

The folders appeared to be passed largely among students. In fact, folders were only passed to non-students 5 times, and of those 5 times, (a) 2 times occurred when the folder was given to the target person, (b) 2 times, the folder was passed back to a student, and (c) 1 time, the faculty member failed to pass on the folder. Perhaps such decisions were based on familiarity, trust, or convenience.

The abundance of student-to-student passings is indicative of what McPherson termed “status homophily” (p.419), in which people base their similarity on informal, formal, or ascribed status. Indeed, the high number of student-to-student transfers is consistent with McPherson’s comment that “[p]eople who are more structurally similar to one another are more likely to have issue-related interpersonal communication” (p.428). Thus, they are more likely to have had the requisite “several conversations … outside the classroom” (Assignment #2, Part #1) necessary to allow for the formation of a link in the chain.

These results run counter to Korte’s finding that “[t]he target typically occupied a lower status than that of the person who forged the final link” (p.105). Both individuals who forged the final link were students, whereas the target person was staff.

Question #6: Is there “years at Penn” homophily among the chain links?

9 folders were passed on to people who had been at the University of Pennsylvania for the same number of years (same class).
8 folders were passed on to people who had been at the University of Pennsylvania for more years (upper classes).
6 folders were passed on to people who had been at the University of Pennsylvania for fewer years (lower classes).

It appears that people passed folders on to “upper classes” and “lower classes” equally as often (especially considering that 2 of the 8 folders passed on “upper classes” went to Polley). There is a slightly higher incidence of people passing folders on to people in the “same class.” Such results differ from those found by Stevenson et al., who found that no one passed folders on to lower classmen.

Question #7: Is there “school” homophily among the chain links?

Of the 23 links, 16 (70%) were passed on to people in the same school. Such a finding suggests that members of the University of Pennsylvania community may have more interactions with people in the same school as themselves. If this stark division truly exists, it may account for the difficulty students found in reaching an individual who is removed by many degrees (graduate school, affiliate institute) from undergraduate Communication majors.

Such a gap can be liked, in a way, to the gaps that Korte found along racial lines. In our study, it appears that the folder had trouble crossing school lines, whereas in Korte’s study, the folder needed to cross racial lines. In both studies, the cross between groups occured very close to the target person (in terms of the number of links).

Question #8: Is there “department” homophily among the chain links?

In contrast to the high incidence of school homophily, there appears to be a remarkably low incidence of department homophily. Only 5 ties were between people of the same department (2 Biochemistry, 1 Communication and Political Science, 2 Wistar), and 2 of those ties were from one person to Polley. Thus, one can determine that departmentally homophilous ties were effective in completing the chains. Nevertheless, they were not too common along the path of the chain for either completed or incomplete chains

Time

Question #9: How much time does it take between “passings”? Does this differ for completed chains versus incomplete chains?

This data is incredibly flawed, and identifying the flaws may be more telling that analyzing the numbers. For example, the fact that there was both missing data (N/A) and negative numbers demonstrates that our method of determining the number of days between the time a person received a folder and the time he or she passed it on is inherently flawed. In addition, I know that my 2nd alter collected and mailed the cards for himself, Alter #3, and Alter #4. Such beneficence further skews the data. Finally, it is easy to conceive of someone passing on the folder on a different day from the day that he or she mailed the postcard (and the post office stamped it). Thus, the time advance or the time lag further renders this data inconclusive.

It is interesting to note that the chains which were completed had an average “days between ‘passings’” of 2 and 7. Of the other chains, 1 had fewer days than “2 days,” 1 had more days that “7 days,” and the other 4 had between “2 days” and “7 days.” Thus, it appears that completed chains were basically representative of all chains, allowing nothing to really be determined about the difference between “days between ‘passings’” for completed and incomplete chains.

Path of Folder

Question #10: Is there a funnel shape to the folders’ path (meaning that they all converge on a couple of individuals)?

Our data cannot comment on this topic, since only 2 chains were completed, and each of them had a different “2nd to last alter.” Nevertheless, it is interesting to note that the “Susan Yoon” group had 8 completed chains which came through 7 different “2nd to last alters.” Thus, in our two studies, there appears to be no demonstration of the strong funneling effect of which Stevenson et al. and Milgram (p.66) spoke.

Challenging My Own Initial Assumptions

Question #11: Was Dave the best person to originally pick?

As Killworth said, “[I]naccuracy in selection of small world chain intermediaries is predominant” (p.95). After passing my folder to Dave in belief that his work in a laboratory in the School of Medicine would put him (and therefore my folder) en route to Polley, I learned that my two friends Adam and Laura had much better connections to the Wister Institute. Adam mentioned knowing people in the Wister Institute, whereas a simple Facebook search revealed that Laura actually works in the Wistar Institute. I thus demonstrated inadequate choice in both “path accuracy” (Killworth, p.92) and “next person accuracy” (Killworth, p.93). Dave’s stories about having trouble finding someone who would accept the folder – since many potential 3rd Alters felt that they did constitute adequate “next choice accuracy” (Killworth, p.93) – demonstrates an inadequate ability to macroscopically assess the optimal route.

Question #12: Did the folder pass through the chain I had originally anticipated?

I had originally predicted that the folder would go from Dave to his boss at the Medical School to an official at Wistar to Polley. However, I soon learned how unfamiliar I am with the structure of laboratories at the University of Pennsylvania. Dave’s boss did not know anyone who he felt would bring the folder closer to Polley. The other people in Dave’s lab did not feel like they knew anyone either. Not only did Dave thus have trouble finding someone who would accept the folder, but after the three links which Dave oversaw, the folder actually never left his lab.

General Reflections

Question #13: What are some possible causes for the relative success or failure of folder delivery to the two targets based on the aggregate results?

1. Obscurity of Target. Based on the success of the Yoon folder and the failure of the Polley folder, one might conclude that a lab technician is more obscure than a professor in the Education School. Indeed, Stevenson et al. mentioned that “[s]mall world studies in the organizational setting have shown that barriers between professional groups exist and these barriers make it difficult for SW folders (and other communication) to cross these barriers.” Whereas Yoon is an educator, Polley is a lab technician (staff). As students, we are more able to reach an educator, even if she is in a different school, than a lab technician; such a result may be because of our familiarity with the structure of the educational system at the University of Pennsylvania.

2. Macroscopic Insight of Yoon Team. Perhaps the Yoon team picked “better” people to whom to pass their folder. “Better” could be defined by (a) more dedicated, (b) more strategically positioned, or (c) closer ties, (since 63% of completed Yoon folders were “Strong” or “Very Strong” ties).

Question #14: What are some possible reasons for my folder not being delivered?

1. The alters did not care about my project. [This is a true fact, as Dave (Alter #2) said that he had to mail Alter #3’s and Alter #4’s postcards.]

2. People felt that they could not reach Polley, as demonstrated by the fact that Dave had to ask a number of people to participate in the project before he could find someone who agreed to accept the folder.

3. I picked the wrong 2nd Alter (since, as mentioned above, I should have picked Laura or Adam).

Class Results and Stevenson et al. Study

Question #15: What are some of the similarities between class results and the Stevenson et al. study?

1. University Setting

2. Degrees of Separation. Both the Stevenson et al study and our study found fewer than Milgram’s 6 degrees of separation for completed chains. (See Question #16, Part 4, below).

3. Response Rate. Stevenson et al. had a response rate (measured by completed chains) of 27%. Our (Polley) study had a response rate of 25%.

Question #16: What are some of the differences between the class results and the Stevenson et al. study?

1. Class Homophily. Stevenson et al. found that undergraduates were (a) most likely to pass folders on to people of the same class and (b) not at all likely to pass folders on to people in a lower class. Our study of the Polley folder found (a) only a slight favoritism for passing the folder on to people in the same class and (b) the occurrence of people passing the folder on to people in lower classes.

2. Gender Homophily. Stevenson et al. found that women tended to pass the folder on to other women whereas men did not favor homophilous ties as much. Our study found no strong evidence of a preference for homophilous ties.

3. Funneling Effect. Stevenson et al. found that the “folders converge[d] on a small number of sociometric ‘stars’ before reaching the target person.” Our study found no such funneling effect in either the Polley or the Yoon group.

4. Number of Links. Stevenson et al. found that completed chains had an average of 1.25 links. The Polley and Yoon groups found that completed chains had an average of 4.5 and 3.25 links, respectively. Nevertheless, it is important to note that all three of these numbers are smaller than Milgram’s 6 degrees of separation.

5. Postcards. Stevenson et al. discusses how their decision not to attach postcards for intermediaries to send in caused them to have “received much less information on the links between individuals in the chains of communication.” Our study’s methodology included postcards and thus gave us more of the “richer set of data” which Stevenson et al. wanted.

6. Originating Alters. Stevenson et al. gave the folders to people in all grade levels. Our study used our class’s members as originating alters, therefore limiting the scope of the study.

1)

People with whom I interacted the most often:
(1) Evan F. (48 interactions) – Boyfriend, close tie, attends University of Pennsylvania
(2) Susan S. (23 interactions) – Mother, close tie, lives in New Jersey
(3) Gregg G. (21 interactions) – Co-Director of SPEC Connaissance, moderate tie, attends University of Pennsylvania, male, 21-years-old
(4) Dan A. (20 interactions) – Friend and Group Project member, close tie, attends University of Pennsylvania, male, 21-years-old
(5) Max C. (tie, 19 interactions) – Co-Director of SPEC Connaissance, moderate tie, attends University of Pennsylvania, male, 20-year-old
(6) Rachel S. (tie, 19 interactions) – Sister, close tie, lives in New Jersey, 17-year-old

People with whom I interacted the most often via AIM (AOL Instant Messenger):
(1) Polly S. (8 interactions) – Friend, close tie, attends University of Pennsylvania, female, 20-year-old
(2) Jackie W. (3 interactions) – Friend, close tie, attends University of Pennsylvania, female, 21-year-old
(3) Maria L. (2 interactions) – Friend, moderate tie, attends University of Pennsylvania, female, 21-year-old
(Note that I interacted with 10 other people via AIM 1 time each.)

People with whom I interacted the most often via Cell Phone:
(1) Evan F. (26 interactions) – Boyfriend, close tie, attends University of Pennsylvania
(2) Susan S. (14 interactions) – Mother, close tie, lives in New Jersey
(3) Rachel S. (9 interactions) – Sister, close tie, lives in New Jersey
(4) Sydney K. (6 interactions) – Grandfather, close tie, lives in New Jersey
(5) Emily G. (tie, 4 interactions) – Friend, close tie, attends University of Pennsylvania, female, 21-year-old
(6) Janet K. (tie, 4 interactions) – Grandmother, close tie, lives in New Jersey

People with whom I interacted the most often via E-Mail:
(1) Gregg G. (21 interactions) – Co-Director of SPEC Connaissance, moderate tie, attends University of Pennsylvania, male, 21-year-old
(2) Max C. (19 interactions) – Co-Director of SPEC Connaissance, moderate tie, attends University of Pennsylvania, male, 20-year-old
(3) Evan F. (18 interactions) – Boyfriend, close tie, attends University of Pennsylvania
(4) Dan A. (17 interactions) – Friend and Group Project member, close tie, attends University of Pennsylvania, male, 21-year-old
(5) Michelle C. (tie, 15 interactions) – Group Project member, moderate tie, attends University of Pennsylvania, female, 21-year-old
(6) Christina K. (tie, 15 interactions) – Group Project member, moderate tie, attends University of Pennsylvania, female, 20-year-old
(7) Alex A. (tie, 15 interactions) – Group Project member, not close tie, attends University of Pennsylvania, male, 21-year-old

People with whom I interacted the most often via Text Messaging:
(1) Evan F. (tie, 3 interactions) – Boyfriend, close tie, attends University of Pennsylvania
(2) Rachel S. (tie, 3 interactions) – Sister, close tie, lives in New Jersey
(3) Craig S. (1 interaction) – Father, close tie, lives in New Jersey
(Note that I only interacted with 3 people via Text Messaging during this week.)

2a) Relationship between “medium of communication” and “strength of the tie”

AIM and cell phones are mediums which allow for synchronous interactions. Since people are probably more comfortable directly connecting to a close tie, these two mediums are used most often to interact with close ties (AIM, 78%; Cell phone, 83%).

2b) Relationship between “medium of communication” and “type of support exchanged”

I use e-mail to both to correspond with the leadership of SPEC Connaissance and to manage e-mails to this organization’s listserve. Since various Directors, Advisors, Chairs, and Committee members all need to “be on the same page” regarding this organization’s activities, I often send out mass e-mails to exchange such information. In fact, of the 106 e-mails offering “Information (Extracurricular),” 63% were sent to multiple recipients. Likewise, when coordinating with multiple people about a Group Project, I use e-mail as well. Such uses are consistent with both (a) Hampton’s statement that the Internet’s has the “ability to be used as an asynchronous form of communication that can engage others not only one-on-one, but as a broadcast of one-to-many” (p.225-226, 2004) and (b) Baym, Zhang, and Lin’s statement that the Internet was rated as better than other forms of interaction for “getting schoolwork done and exchanging information” (p.304, 2004). Thus, I postulate that e-mail undermines Burt’s theories on structural holes and elimination of redundant ties, since I am not expending extra resources when communicating with multiple people through e-mail.

I noticed that Wellman and Wortley’s (p.562-563, 1990) categories for “type of support” are not fully transferable to “new media” interactions. For example, I doubt that it is too often that people provide financial aid via new media (Do they often transfer money online from their own bank accounts to the others’ bank accounts of others?). In addition, it is impossible to use “new media” to actually ‘do things together,’ a sub-category of Wellman and Wortley’s “companionship” category.

Nevertheless, my results do correspond with their assertion that “[s]trong ties provide broader support than weaker active ties,” (p.566) since 92% of my interactions with “close” ties provided companionship or small services, compared to 13% of my “moderate” ties and 3% of my “not close” ties. (see below)

2c) Relationship between “medium of communication” and “type of relationship”

For everyone with whom I communicate besides my boyfriend, my mother, and my sister, I used e-mail more than any other medium. Because of the high number of daily interactions for which I use “new media,” I value e-mail’s asynchronous and direct nature.

In addition, due to its informal nature, I used AIM more with friends than with professionals. In addition, because of its personal nature, I used AIM with friends rather than with people with whom I have a more instrumental relationship (ie. classmates, delivery people, professionals).

I postulate that I would talk to fewer people if I did not have media such as e-mail and a cell phone. I wonder how McPherson would explain the shrinkage in the size of people’s core discussion networks with the results from my new media diary, in which I talk to my boyfriend and family members more than anyone else. Perhaps she would not classify the discussions that I have with most people as “discussing important matters.”

2d) Relationship between “medium of communication” and “duration of relationship”

I tend to communicate most often with people who I have met within the last year (duration = 0 years) via e-mail. However, I often use this method reactively, since people who do not know me very well (ie. classmates with whom I am working on a group project, Connaissance committee members, professionals) are more likely to contact me via e-mail; then I respond.

In addition, people who have known me my whole life (family) are most likely to speak with me via cell phone (my only telephone) due to their age and the closeness of our tie. However, e-mail is a close second, because I often send pictures to these relatives via e-mail.

Nevertheless, I believe that the “type of relationship” and “type of support” are confounding variables for the relationship between “medium of communication” and “duration of relationship.”

2e) Relationship between “medium of communication” and “distance to the person”

Because I am most likely to communicate via e-mail with other students on the University of Pennsylvania campus, e-mails to people who are less than 1 mile away are the most common method of communication. There is about an equal split between using my cell phone and using e-mail to communicate with people 75-100 miles away (family members in New Jersey and professionals in New York). However, the relationship between “medium of communication” and “distance to the person” is most likely confounded by other variables like “type of relationship” and “type of support.”

2f) Relationship between “medium of communication” and “person’s age or gender”

(Note that “20s,” “30s,” and “40s” refer to people in this decade for whom I did not know an exact age. “N/A” refers to group listserves and various professionals whose ages could not be specified.)

One may expect that older people are more likely to use telephone media to communicate. This seemingly intuitive hypothesis would make sense, since landlines (which have been around for longer than the “new media,” cell phones) can receive telephone calls, whereas to receive e-mail, the sender and receive must both use “new media.” All of my friends, parents, grandfathers, and professionals with whom I communicated besides my two grandmothers could be accessed via both cell phones and e-mail.

However, my grandparents’ are much less likely to use AIM and completely unable to send text messages. Thus, I only used these two media with my friends, sister, and parents.

I communicate most frequently through all media with 21-year-olds and other college-aged students. Because my group projects, extracurricular activities, and immediate friendship networks are comprised of other students of the same age, I am experiencing what McPherson calls baseline homophily (p.419, 2001) regarding age.

There appears to be no relation between “gender” and “medium of communication” besides that I use AIM with more females than males. Since I am female and may communicate on more personal matters with people of my gender, I would use this form of computer-to-computer communication for same-sex conversations. Nevertheless, my overall media use is consistent with the McPherson’s finding that people like me should have “friendship and confidant networks that are relatively sex-integrated” (p.423, 2001).

2g) Relationship between “medium of communication” and “similarity of age and gender to your own”

I appear to use e-mail more frequently than other media of communication to interact with other 21-year-olds. However, this finding may be because I use e-mail most frequently. In fact, of the females with whom I interacted across these various media, 45% of AIM interactions were with 21-year-olds females, 28% of cell phone interactions were with 21-year-old females, and 41% of e-mail interactions were with 21-year-old females. Upon noting that only 38% of my overall interactions with females were with 21-year-old females, I determined that I use AIM and e-mail more than the “expected frequency” and cell phones and text messaging less than the “expected frequency.”

2h) What if anything does this say about the role of new media in our social networks?

New media serve the same purposes as “old media” such as telephone calls and in-person interaction. Although Wellman and Gulia (1999) noted that online relationship development might take longer due to the asynchronous nature of e-mail exchange, their report references Walther, who determined that undergraduates’ “online interactions are as sociable or intimate as in-person interactions[, demonstrating that]…the Net does not preclude intimacy” (p.346). Consistent with Baym, Zhang, and Lin’s comment, my use of the Internet as a social tool is “influenced by [my] relationships, including [my] geographical distance and type, and [my] pre-existing sociability” (p.302, 2004). Finally, my usage patterns correspond to Ellison, Steinfield, and Lampe’s comment that students use “online channel[s] less to meet new people than to intensify and solidify relationships that started offline” (p.32, 2006).

E-mail is unique, because it both (a) directly connects you to a person but (b) has social mores surrounding it that make it appropriate to use e-mail contact “not close” ties. In fact, 85% of the interactions I had with “not close” ties were through e-mail. Thus, the popularity of e-mail could perhaps serve to increase the number of interactions people have with weak ties. Applying the proposed popularity of e-mail to Granovetter’s “strength of weak ties” theory, one may determine that people will have more access to resources to provide them job information than before.

3a) Relationship between “tie characteristics” and “Mindy’s location during interaction”

I appear to communicate most often to close ties and most often in a private (dorm) location.

The reason that most interactions took place in private locations was because of the media to which I had access in these locations. As demonstrated before, I often use “new media” to send and receive information. Such information comes in the form of computer files that I need to either save to or access from my computer. Since I leave my computer in my dorm room, such interactions must therefore occur in my dorm room.

My choice of dorm room for interactions is consistent with Wellman’s statement that “[r]ather than being accessible to others in public places, people now overcome their isolation by getting together in each other’s homes or by telephone and electronic mail” (p.29, 1999). In fact, Baym, Zhang, and Lin’s study found that “online interaction was conducted less frequently in public places than telephone calls and face-to-face interaction” (p.311, 2004).

Thus, location of communication is more related to the medium rather than the type of tie. As can be seen below, the most common type of medium for me to use in a public place is a cell phone, since I can walk and talk simultaneously.

3b) Relationship between “person’s characteristics” and “Mindy’s location during interaction”

For each category of relationship, I am more likely to talk to the person in private than in public. The most common types of people with whom I spoke in public were my boyfriend (with whom I interact frequently throughout the day, when I am away from my dorm), friends (who I am likely to call to make plans or discuss homework), and my sister (who prefers to talk on the phone between 2 PM and 8 PM, when I am going to and from classes and meetings).

3c) What if anything does this say about how new media may change the composition of our social networks?

New media are used to communicate with people while both/all parties to the interaction are not together. Thus, “new media” would appear to be used regardless of context. Nevertheless, it appears that these new media are often used in private, to both (a) communicate with others who the communicator cannot see and (b) communicate away from the presence of a public who is not involved in the conversation.

Consequently, new media are used to seek out certain strong ties rather than speak with ties of all strengths into whom someone might run in the course of a day’s natural activities (ie. on the street). This allows the new media to reinforce stronger ties with specific individuals, an increases privatization.

Nevertheless, although new media could serve as an isolating factor, it did not seem to do so in my case. I was able to speak with many people, even though those individuals were not physically surrounding me. Thus I see new media more as a uniting factor, determined because many interactions did occur between myself and others. Wellman determined that “Cyberspace fights against physical space less than it complements it. Cyberspace is the medium by which people arrange things and fill in the gaps between meetings” (p.247, 2001). Thus, perhaps new media interactions actually supplement our relationships and allow us to have more interactions within our social networks.

Tepperman’s article discusses deviance and how deviant actors often turn to their social networks for both (a) instruments of deviance, such as marijuana and (b) facilitators of deviance, such as a hit man. Tepperman presents deviance as a spectrum, running from the search type, in which effort must be consciously expended to find social support for deviant acts, to the contagion type, where people do not consciously intent to find support for their deviant acts. This dichotomy reminded me of homophily, in which birds of a feather flock together, and assimilation, in which people who are already friends adopt the same (sometimes deviant) behaviors. I ask: Do you think that the “search type” sounds more like homophily, whereas the “contagion type” sounds more like assimilation? Or do you think that these theories do not have any similarity?

Tepperman presents an idea that seems like an application of Wellman: Deviant communities are not confined to place. Tepperman supports this idea by referring to classified advertising for wife swapping and extramarital relations. These deviant behaviors, Tepperman shows, are created by communities consisting of dispersed people. However, I think that it is interesting to note that all of these activities do require in-person communication. Thus, although the deviant community is formed without regard to proximity, it is maintained via face-to-face contact.

Tepperman also demonstrates that trust is a necessary quality for deviant social networks. Killworth’s article had demonstrated that individuals are unable to macroscopically and accurately create a social network chain. However, an individual looking to participate in an illegal activity needs to tell as few people as possible about his or her intentions. Thus, it is even more critical that the deviant passes his or her message via the most direct route. How do you think most deviant actors determine the most optimal, clandestine chain along which to pass their messages? Do you think that this “secret” would benefit other actors, looking to pass along a message but who, according to Killworth, do not determine the best path?

Rogers’s chapter explains opinion leadership, communication networks, and critical mass; he relates these concepts to theories and examples of the diffusion of innovation throughout networks.

When discussing opinion leadership, Rogers brings up the differences between polymorphism and monomorphism. Whereas polymorphic opinion leaders serve as opinion leaders on a variety of topics, monomorphic opinion leaders serve as opinion leaders for only a single topic. This difference made me consider online friendships. Specifically, the literature on online relationships emphasizes how these friendships are usually very specified and built around a common topic. In class, we mentioned how a “bottle cap collector” is more likely to meet other people who are enthusiastic about this hobby over the Internet than he or she is to meet other enthusiasts in person. This relationship appears to be built around a single interest. Thus, I ask: Are opinion leaders online more likely to exhibit polymorphic characteristics or monomorphic characteristics?

When discussing innovativeness and opinion leaders, Rogers mentions how neither opinion leaders nor their followers are innovative in traditional communities but that both opinion leaders and their followers tend to be innovative in modern communities. When reading about this difference, I imagined the structure of traditional and modern communities, envisioning a tribal structure and a busy city structure. I thought that traditional communities are likely to rely more on a leader, such as a religious or spiritual leader, while modern communities are more likely to have different leaders for different activities (a CEO at work, a local mayor, a national president, etc.) due to their massive size. Thus, I wonder: Do you think that traditional communities or modern communities rely more on an opinion leader for guidance and influence?

Another point Rogers brought up is that “[a]n individual is more likely to adopt an innovation if more of the other individuals in his or her personal network have adopted previously” (p.359). Previous articles such as those by Wellman have emphasized that one’s personal network is not necessarily geographically based. However, some innovations that require a critical mass might be constrained, at least in part, by geography. For example, it costs more to make a long-distance phone call than it does to make a local phone call. Do you think that the original adopters of telephone technology were influenced more by others in their geographic network (area code) adopting this technology or by their (perhaps closer) larger social network?

Burt’s report discusses the network structure of interpersonal contagion and research on the network structure of social capital. Interpersonal contagion is understood via two concepts: cohesion (strength of the relationship between two people) and structural equivalence (similarity of network position for two people). Network structure of social capital is discussed in reference to opinion leaders and brokers of information between social group clusters.

One of Burt’s points is that strong relations between weakly equivalent people significantly decrease the amount of time it takes for someone to learn something new (contagion and/or adoption). It is interesting that strongly equivalent people appear to be competitive – and thus lose the ability to share information amongst each other – whereas weakly equivalent people seem to be more cooperative and allow benefits to accrue to each other. This situation reminds me of university applications. I noticed that there was more competition in high school, when everyone was applying to college, than in college, during which everyone’s post-graduation plans are different. Do you think that the level of “equivalence” (weak or strong) between two people has a direct bearing on how much “college/graduate school application help” they offer each other?

In addition, Burt notes that opinion leaders bring new information to a group via cohesion (with people outside the group) and then that the information spreads amongst the group via equivalence (of people within the group). Thus, Burt believes that an opinion leader is a product of network position more than superior authority and attractiveness. However, not everyone within a group is in a structural position to serve as an opinion leader, since many people do not have any types of “bridges” to other social clusters. Therefore, an opinion leader does display many unique characteristics in regards to his or her network position. Do you think that opinion leaders have higher levels of centrality and/or prestige, too? Why or why not?

Kleinberg discusses web pages as if they were nodes in a social network. He looks at the organic growth of the World Wide Web and research that tries to understand if the concepts such as centrality, prestige, in-degree, and out-degree apply to the Internet network. Kleinberg discusses a recent study that found that “the Web contains a large, strongly connected core in which every page can reach every other by a path of hyperlinks” (p.1849). Of course, this notion mirrors Milgram’s notion of six degrees of separation. However, just as people have asked about hypothetical people ‘living on a deserted island’ and how they could possibly be connected to Milgram’s chain, I wonder if such interconnectivity is true of websites. I believe that this is a catch-22, in the sense that only websites that could be found could be used for the study, and the study can only report on websites that could be found.

Consider how human reproduction (which produces the nodes in a social network) can be thought of as the generation of sequential links (parent-child) and how humanity seems to exist in communities, even if some of these communities are isolated. Now consider the argument in this article that the growth of the World Wide Web was decentralized. What do you think is more likely: Milgram’s idea of 6 degrees of separation (a theory of human interconnectedness) or Kleinberg’s suggestion that any website can be reached via a chain of hyperlinks (a theory of webpage interconnectedness)?

Marks looks at the potential of social networking sites to become feeders into huge database sites maintained by agencies such as the National Security Agency. With somewhat of an alarmist approach, he outlines all the ways that the hordes of very personal, descriptive information people post about themselves on websites such as MySpace could be used to create a watchdog database.

Before I get into my analysis, I believe this funny video describes the issue perfectly. Please watch it, because it is really funny: This is so funny!!

Some of the things Marks believes that intelligence and national security agencies could use the databases for is to detect insider trading or highlight groups of terrorists. However, as Krebs’ article explained in the past, terrorists work to keep a low profile by having very dispersed social networks. Granted, such dispersion could be mapped on a social networking site. However, I find it hard to believe that a terrorist would want Friend A to see that he is connected to Friend B, who knows Friend C. The terrorist would not want Friend A to have such clear-cut access to Friend C, according to Krebs’ beliefs. Thus, I feel that social networking sites could not be used for these purposes.

To further bolster this viewpoint, I point to the part of the article where Marks points out someone who explains how “people have to wise up to how much information about themselves they should divulge on public websites” (p.3). However, of course the people with information to hide will realize this first and foremost. As soon as the first few college interns/graduates got fired from internships/jobs for information they posted on Facebook, many people who had lurid information and pictures changed their profiles. This example shows how people with “incriminating” information are the ones who will choose not to post it. I ask: Do you think that there is a direct negative linear correlation between an (a) increase in intelligence and national security agencies’ use of information on social networking sites and (b) how much information people put on those sites?

Ellison’s research project discusses Facebook, a topic of high interest to me and many other college students. It is interesting how Ellison notes that Facebook is often used to maintain offline relationships, especially compared to how often it is used to generate new friendships online. Ellison evaluates how Facebook can be used as a mechanism for creating various forms of social capital, and then she analyzes its effectiveness in those domains.

One comment that is particularly relevant is that, at the time this article was written, Facebook “distingushe[d] itself from other online social networks in that it primarily serves a geographically bound community (the campus)” (p.5). At this point, it has expanded to people who are not necessarily in college, high school, or an organization/company. I ask: If her study was done now, what findings do you feel would be different, regarding the geographic restriction previously characteristic of Facebook?

In addition, Ellison says that there “are some tendencies for Facebook members to report higher satisfaction with MSU life, bridging and bonding social capital” (p.20). (She does note that these findings cannot be statistically significant due to the small number of respondents who were not on Facebook.) I wonder: What do you think is the direction of causality? Specifically, do you think that (a) people who are on Facebook are more likely to have access to social capital and resources or that (b) people who self-select to be on Facebook are those that have higher self-esteem. In other words, does Facebook cause high social capital and satisfaction, or does high social capital and satisfaction cause one to sign up for Facebook?

Ellison also notes that Facebook is used for entertainment purposes more than informational purposes and notes that this fact “at first seemed at odds with [Facebook’s] role in forming and maintaining social capital” (p.28). Her statement implies that social networking and accessing social capital and its resources should be a planned, instrumental purpose. I determined this conclusion by analyzing her link between informational purposes and social capital and the lack of link between entertainment purposes and social capital. I believe that reading about friends’ lives gives you much more knowledge about and consequently access to their resources than a forced goal of “networking.”

Finally, one problem with analyzing this study was that there was an “extremely low incidence of non-members in [their] sample” (p.31). However, I wonder what the actual percentage of college undergraduates who are registered for Facebook is. Specifically, this sample determined that number to be 94%, but what if the national population number were also 94%? If she found a school in which many students were not on Facebook, then that school would be an outlier and produce odd, unrepresentative data. I ask: Do you think that 94% is the national undergraduate student population percentage of Facebook registrants? Why or why not?

Wellman’s comprehensive overview of the impact of online media on interpersonal relationships determines that proximity and in-person contact are not necessary for community-building. He explains how the communication node will be the person, regardless of where that person is located, rather than the “person in the place.” As computers and online access become more ubiquitous, social networks will become a placeless phenomenon. Contrary to other writers’ alarm, Wellman thinks that this is just dandy.

One question he forebodingly asks is, “Does the switch to person-to-person connectivity mean that even stably-married husbands and wives will be in separate communities?” (p.239). I think that to answer this question, it is critical to define ‘community.’ Even though Wellman makes it clear that ‘community’ is not inextricably tied to place, does that mean that people who live in extremely close proximity (i.e. the same household) do not necessarily have to be in the same community? Is ‘community’ only defined by one’s personal interests, hobbies, and favorite topics? I ask: If two people are living together but have different interests and different people with whom they explore these interests, can they still be part of the same community?

Another interesting quote that Wellman mentions is Andrew Odlyzko’s comment that “Our barber and our babysitters will continue to come from places not far away” (p.247). Although it is clear that there are some professions that need in-person communication and contact, it is also true that there are some professions (i.e. telephone operator) that do not need in-person communication and thus can be outsourced. What are some professions that are currently place-based that you could potentially see being performed virtually?

Kronholz tells the story of Shannon, who tracked a chain letter she started as part of a science fair project and became overwhelmed with e-mail replies. Although the article doesn’t say what the chain letter was about, it says that from January 13th to February 5th, she got 160,478 replies. This makes me wonder two things. First, what was the response rate? In other words, how many people got her letter and replied to her? How many received her letter and did not reply to her?

Also, the article notes that people even found her phone number to complain that their e-mails to her were being returned. This, to me, seems preposterous. I can imagine a student who gets an “unable to send e-mail” response when trying to send a project to his or her professor to contact that professor. However, I find it hard to believe that strangers halfway around the world would contact some random girl sending a chain letter. To add to my disbelief, this event occurred in 2003. I believe that people were more tolerant of chain letters when the Internet was newer (early 1990s) but are fed up by now. We’ve heard too many times that by forwarding a random letter, a girl dying of cancer will receive pennies for treatment.

Perplexed and disbelieving, I did some research as to the validity of this article. Apparently, as far as I can see, Snopes thinks that it is true. In addition, a Google search revealed sites in different languages discussing the event.

I want to know: Assuming that this article is true, how can you address the discrepancies between the supposed response rate and the more conceivable notion that people delete almost any e-mails which are not particularly relevant to their everyday lives?

Wellman’s defense of online relationships undermines pundits’ attacks on the legitimacy of online friendships. Whereas critics argue that relationships on the Internet are narrow, unsupportive, weak, and uncommitted, Wellman praises their compatibility with “real-world” communities; opportunities for fostering unique, specialized, close relationships; and function for engendering heterogeneous and homophilous ties between individuals and amongst groups.

One issue I considered during my reading was Latane and Darley’s “bystander intervention” theory and its presence in both real and virtual worlds. This theory says that an individual is more likely to provide help to someone in need when that individual feels that he or she is responsible; such a felt responsibility is often the result of the individual being the only person in the vicinity of the victim. Wellman says that recipients of online requests for help are affected by the theory of “bystander intervention” in the sense that they feel that they are the only ones who may be able to provide help to the person asking the question. I can understand that rationale, for when an individual is sitting in a room alone at his or her computer, that individual really is the only person “in the room” able to help. However, I would have thought that applying the “bystander intervention” theory would result in people not responding to online requests for help. Clearly, a person alone at his or her computer realizes that when he or she communicates in an online forum (i.e. newsgroup), there are many other people who are recipients of those messages (including requests for help). Thus, I would think that the person would be less likely to volunteer assistance, presuming that the hundreds or thousands (or millions!) of other people on the Internet would aid the requester. Thus, I ask: Why does Wellman believe that people, when “interacting”/“operating” in the virtual world, act in ways that would lead one to determine that those people believe that they are the only recipients of “virtual world messages”?

Another question I had concerns people’s demonstration of the norm of reciprocity in the virtual world. The article explains that “[i]t is a general norm of community that whatever is given ought to be repaid, if only to ensure that more is available when needed” (p.342). It goes on to say that there may be less motivation for social support in a virtual community, since ties are impersonal, weak, and not bound by the indirect social pressures inherent in real-world community structures. I was wondering: Is there a type of transitivity in the norm of reciprocity online? In other words, do people offer support online, hoping that if they needed support in the future, they could find it online? Does that make virtual acts of reciprocity more of an act of respect for the institution of the Internet rather than an act to aid an individual?

Baym’s study of the place of online social interaction among the milieu of person’s typical daily communication (especially) multimedia activities determined that the college students in this study have integrated the Internet into their repertoire of communication techniques. However, it also determined that, contrary to Nie et al’s 2002 study, use of the Internet as a communication tool does not diminish the quality of such interactions.

Baym first examined the pervasiveness of the internet. One of the findings he discussed is Dimmick et al’s 2000 study that showed that “[n]early half of their respondents said that they used long-distance telephone calls less, now that they were online” (p.303). I wondered how that fact is impacted by the higher cost of long-distance telephone calls relative to Internet communication. In addition, I wondered how will much will the increased pervasiveness in such technologies as VOIP and Skype impact that this finding?

When analyzing the results from her own study, Baym said that “26.5 percent of online interaction was conducted in the presence of other people, and 73.5 percent of the reported online interactions were conducted without others there” (p.312). I wondered what the distribution would be if this study were conducted with middle school students or high school students. Specifically, I know that in those age demographics, the Internet is often used as a “toy,” with which to play when friends come over to visit. Two friends might pass their time IMing another friend. I ask: Do you think that the use of the Internet in the presence of others would be dramatically increased if this question were asked of a different population? Which other population do you think would provide results most different from these?

Mesch’s study of Israeli adolescents focuses on the creation – rather than just the maintenance – of online ties. His review of the literature indicates that there exist disparate views on the quality of online relationships. While social constructivists see online relationships as high quality as in the social constructivist, the reduced social cues perspective sees them as low quality. Although he looked as the strength of tie and quality of relationship on many levels (i.e. multiplexity, time, closeness), I found the following result particularly interesting: “Adolescents with an online friend reported that this friend was know[n] for a shorter time than face-to-face friends, they discussed fewer topics, and they participated in fewer shared activities” (p.147).

Since the study involved adolescents who were an average age of 15.52, I wonder if this finding has to do with the facts that (a) people that age could not have used the Internet to maintain social relationships for more than a decade and (b) people that age have probably been taught for most of their lives not to “talk to strangers.” Thus, there must be a type of age bias in the number of online relationships someone of this age could be both able and allowed to form. I wonder if this study was done with older people, who are likely not living in the same locale as their childhood friends, would the findings be different?

Hampton questions whether the Internet should be viewed as a means of facilitating relationships within communities or hindering those relationships. By looking at past studies, he determines that the potential for the Internet to provide support between users and to be used as a legitimate form of asynchronous communication should not be overlooked.

When looking at the 1998 Homenet study by Kraut et al, I considered its flaws. These researchers provided people with a free computer, telephone line, and dial-up Internet access and then noticed a decrease in the “amount of time family members spent communicating with each other, the size of participants’ social networks, and psychological well-being” (p.221). To me, it makes sense when you are given a very valuable, new “present,” you would spend more time learning how to use it. Although it was admitted that the decrease in psychological well-being may be attributed to frustration and difficulties learning this new technology, there is no mention of a decrease in time spent with others due to the amount of time necessary to learn how to use the computer. In fact, Kraut noticed that the “negative effects originally attributed to Internet use dissipated over time” (p.223). I ask: Do you think the reason for this effect was because as people learned how to use the computer better, they increased the time they spent communicating in-person?

Another study discussed in this paper is of Netville. One finding says that “[a]ccess to computer-mediated communication in Netville was primarily successful in affording frequent social contact with a high number of what were comparatively weak social ties” (p.227). I wonder: Do you think that the fostering of contact between neighborhoods was a result of (a) access to computer-mediated communication, (b) homophily, in that all community members had unique high-speed Internet access, (c) both, or (d) something else?

Zwijze-Koning’s article discusses the variety of methodologies used for network analysis. They include sociometric questioning, diary research, observation research, analysis of archival records, ECCO analysis, and the small-world technique. The article focuses on the accuracy, reliability, validity, and feasibility of these techniques for collecting good data to be subject to a communication audit.

While reading this article, I thought of Killworth’s article in which he tried to determine the size of individuals’ social networks. However, in organizational studies, the population size is limited to the size of the organization. Therefore, there is a clear maximum network size, although not necessarily a clear network structure. I then contemplated about what additional methodologies might address the flaws that the ones mentioned in Zwijze-Koning’s article mentioned. I thought that an analysis of the “To:” and “From:” e-mails that one sends and receives would be a quite objective way to measure and diagram one’s network. As Zwijze-Koning mentions, self-reports are subject to bias based on who the person most recently has talked to, regardless of if that person is someone who they regularly talk to. Using my experiences working in large corporate settings, I decided that e-mail is the predominant means of communication. Although face-to-face communication is very prominent, it is also true that the people with whom an individual is most likely to communication with in person are likely to (in my experience) also be the people to whom who the person is most likely to send e-mails (at least attached documents and files). I ask: If “Incoming and Outgoing E-mails” were added as a methodology in this study, what do you think Zwijze-Koning would say are its benefits and shortcomings?

While reading Marin and Hampton’s article, I began to wonder why the following two questions were most indicative of people’s social networks: (1) “From time to time, most people discuss important matters with other people. Who are the people with whom you discuss matters important to you?” (p.7-8) (2) “Who are the people you really enjoy socializing with?” (p.8). I then realized that those categories had more people in them, a point identified in the article and consistent with Smith-Lovin’s core discussion networks theories. Indeed, even Wellman’s article would support the idea that there are more people with whom you could talk or hang out than there are people from whom you could ask favors. The former two categories (socialization and discussion) require less “cost” and more mutual benefit from the “friend” than the other categories (getting favors both large and small as well as monetary).

One question I had during the article was why respondents could only name six friends per category. As far as I could see, that would cap one’s social network at 36. Granted, it would be somewhat of an indicator of strong ties. But at the same time, “people with whom you socialize” is likely to be a category that includes weak ties. Nevertheless, it is interesting to note that very few people reached six people in a category. Thus, I wonder: If respondents had not been primed with a cutoff of 6, do you think that they would have been likely to name more people in any/all of the categories?

Lin’s chapter looks as the “position generator” technique of measuring social capital. To me, the position generator technique looks at social capital as it relates to human capital (in the sense that it can refer to organizational and occupational structures), a tactic which can be contrasted with the other methodology, name generators, which looks at social capital as it relates to cultural capital (in the sense that it can refer to subjective, emotional relationships between people). The Taiwan social networks study discussed in this chapter uses the position generator technique to see what types of social capital respondents accrue from their networks.

One finding was that men generate higher returns from knowing people in the labor force, whereas women, tied down to households, are more likely to benefit from the type of community-based support that Wellman discusses (i.e. “small favors”). Lin explains that, “[f]emales are generally disadvantaged in accessing many of the positions, but probably compensate by the roles they play relative to household well-being, such as education for children, health care for family members, and household maintenance” (p.67). I ask: This study uses the position generator to look largely at respondents’ connections to people in various occupations (with various levels of prestige). It was determined that females, by this definition, have less social capital. However, do you think that such a way of operationalizing the name generator skews the study’s focus away from recognizing the value of focusing one’s time and effort on building relationships in the community? Do you think these females would receive more “small favors” and “large favors” (Wellman) or have larger “core discussion networks” (Smith-Lovin)?

Finally, Van Der Gaag emphasizes the importance of multiple generators when introducing the Resource Generator. In contrast to the Name Generator and the Position Generator, the resource generator tries to elicit the people in and qualities of a person’s social network by asking them to name people with various characteristics such as “owns a car” (p.12) or “knows a lot about governmental regulations” (p.12). I think that it is very interesting how these questions can cover different aspects of people. Specifically, the Name Generator article (Marin and Hampton) seemed to focus on six broad categories; the vagueness of this methodology could have limited the number of individuals respondents named. In addition, the Position Generator asks only about occupation, limiting the scope of the applicability of results. Finally, the Resource Generator can combine all sorts of diverse characteristics that both (a) cover a wide range of people yet (b) elicit specific responses/names by people, due to the preciseness of the questions. Above all, I think that the Resource Generator demonstrates the necessity of multiple-item responses when measuring social networks. When contemplating the multiple questions included in such a questionnaire, I wondered: Is there any way to create a comprehensive set of questions to be included in a Resource Generator survey, or will the types of questions/categories covered always be biased by the researcher’s personalities, values, and cultural background?

Wasserman’s article discusses centrality and prestige; it explains how to calculate these two qualities (and more qualities) via a plethora of very complex equations. He uses three diagrams (the star graph, the circle graph, and the line graph; all shown below) to discuss various aspects of centrality and prestige. One question I kept having while reading this article was: Are centrality and prestige good things? Their English vernacular connotation is positive, but in social networks terminology, do they have a connotation, or are they descriptive?

Wasserman wrote that “[t]he star graph is maximally central, since its one central actor has direct contact with all others, who are not in contact with each other” (p.176). I wondered if that is really beneficial – or even possible. If I had six friends who did not know each other – and who did not come to know each other, via my introductions – that means that I would necessarily split my time six ways to hang out with each of them individually. This is inefficient. I also ask: Would such an action result only in the creation of weak ties, since I could not dedicate enough time to each of my six friends?

Then I wondered about the circle graph. Clearly, it is a type of extended example of the forbidden triad, which says that it is impossible for the friends of an individual to not be friends. But is also seems improbable. Can the follow pairs be the friends: A-B, B-C, C-D, D-E, E-F, F-G, G-A. Yes. But with all those ties, you’d assume that there would be additional friendships, perhaps B-D or C-F, for example. But this is not what a circle graph represents. I ask: With these realistic considerations in mind, is the circle graph feasible and legitimate? The line graph is subject to almost all of the same considerations (except G-A).

Additionally, I wondered about directional prestige. If you have hundreds of fans (whose direction is of an arrow away from them and towards you), you clearly have a high prestige value. However, this situation reminds me of Britney Spears’ song “Lucky,” about how she feels lonely even though she is famous. She sings, “She's so lucky, she's a star; But she cry, cry, cries in her lonely heart, thinking; If there's nothing missing in my life; Then why do these tears come at night?” Prestige might not actually be desirable, but rather lonely. I ask: Although prestige has a vernacular connotation of something good, is it really a good personal aspect for a person’s description of his or her social network? Additionally, whereas there is a high likelihood of her friends befriending one another (in a fan club, for example, an idea supported in part by Pearson’s discussion of homophily), someone with such a high prestige ranking would likely be isolated from such ties. Such a situation places network centrality above network prestige in desirability.

Freeman’s article calculates a node’s centrality via three measures: degree, betweenness, and closeness. I wondered if there are times when these three measures come into conflict with one another. Specifically, node A could be connected to nodes B, C, D, and E (4 degrees). However, due to the size of the network, E could have the highest betweenness, even though E has a lower degree than A. I ask: Can you give an example of a situation in which this conflict could occur (in which A has the highest degree but E has the highest betweenness)?

Additionally, I wonder if a node with high structural centrality could use his position deleteriously. Specifically, we have always concentrated on an individual’s ability to communicate a message. We assumed that they were transmitting the correct message. Yet at one point in his article, Freeman discusses how “Bavelas (1948) and Shaw (1954) suggested that when a person is strategically located on the communication paths linking pairs of others, that person is central. A person in such a position can influence the group by withholding or distorting information” (p.221). I ask: Does the position of “central actor” give individuals a lot of power, to use either beneficially or destructively? This question makes me consider tyrants; such leaders have high directional prestige (as discussed by Wasserman above), high in-degree, and are central actors, in a sense. Yet they are bad people. I ask: Does the positive connotation of “central actor” preclude individuals from considering these people to be in a harmful structural position?

Krebs demonstrates that terrorist networks are both similar to and unlike “normal social networks” (p.10). He measures terrorist tie strength via “the amount of time together by a pair of terrorists” (p.2). This measure reminds me of Granovetter’s measure of tie strength; indeed, we discovered that the predominance of communication technologies over transportation means has made it so that ‘time spent together’ is an incomplete measure of tie strength. I ask: Do you think Krebs could have picked another, more appropriate measure of tie strength? What would it be?

Additionally, the pictures in this article made me think of Milgram’s six degrees of separation. Many of the diagrams were very sparse and dispersed, and there were definitely more than six links between individuals. I ask: Do you think that the dearth of information Krebs mentions could be the reason why there were more than six degrees of separation in the diagrams? Or do you think that terrorist networks, in attempting to be clandestine and disconnected, do not exhibit this six degrees of separation characteristic? Or do you think that Milgram’s six degrees of separation theory is faulty?

I disagree with Valente’s finding that popular students smoke more than students who are not calculated to be popular. Besides basing my claim on just a general gut feeling, I have found that his study has some very questionable findings. For example, he says “Opinion leadership is measured as the frequency with which one is nominated as someone others turn to for advice or counsel, whereas popularity is measured as the frequency with which one is nominated as a friend” (p.324). Clearly, Valente is thus using Wasserman’s idea of assimilation and Freeman’s idea of centrality to measure popularity. However, popularity does not necessarily mean idea transmission and copying. Past articles have demonstrated that popular people form clusters and are not likely to be connectors. They are not spreading ideas as much as standing more isolated and behaving in their own way. Thus, it is no surprise that Valente found similar trends between popular people, isolates, and members of specific groups (p.325) – people who are all very much distanced on the social hierarchical ladder. I ask: Do you think that it is odd that Valente found that smoking is common among the most popular people and the least connected people? Do you feel that this finding undermines his conclusion that not only do popular people smoke but that their behavior is likely to influence others’ behavior?

Additionally, I was concerned with how Valente operationalized ‘popular.’ He asked people to name their five closest friends, which sounds more of a test of homophily or assimilation than opinion leadership. I ask: Is the measure of “close friends” a measure that implies reciprocity? Additionally, he asked people to name five students who make the best leaders for a project in class. This measurement is regarding positive characteristics, whereas people who are smoking are emulating negative behavior. I ask: What do you think was Valente’s rationale when equating ‘project leaders’ and ‘popular people’?

Mouttapa looks at the social networks of bullies, victims, and aggressive victims (people who are both bullies and victims). She finds that social cognitive theory best explains friendship networks of bullies; people who were bullies had friends who were also bullies. This finding has extensive verisimilitude. A bully would not be the friend of a victim. Additionally, if you have tough/bully friends, you are more likely to be protected and therefore not be a victim yourself. Thus, her findings appear to be both correlated and causal. I ask: Do you think Wasserman would attribute such findings to homophily or assimilation?

Another interesting finding was the females who are bullies have stronger ties with their friends. I posit that the reason the female bullies have such close friends is due to the nature of female bullying. Specifically, Moutappa writes, “females tend to use indirect forms of bullying (e.g., rumor spreading, social ostracism)” (p.329). To continue such behavior, these females need friends who will listen to the rumors and stand by them when forming the cliques. I ask: Do you therefore think that male bullies who use physical force have less of a need for friends? Or do you think that bullies who use physical force also rely on having a group of friends for intimidation purposes?

1.

(a) “Ecology of how we spend our time” (Smith-Lovin, broadcast). During the radio broadcast, Smith-Lovin said that we are spending more time in the paid labor force and commuting to work. This time was previously spent developing intimate friendships and participating in voluntary organizations. We now have a “much larger array of less interconnected, more geographically dispersed, unidimensional relationships” (p.372-373) rather than a core discussion network. As more women enter the workforce, families are working 10 to 29 more hours per week (p.373). Smith-Lovin notes that this workforce trend is especially prevalent among middle-aged, better-educated, higher-income families, who are historically more likely to join clubs. Thus, time crunch that results from the replacement of time developing close friendships with time spent at work is a reason for the shrinkage in core discussion networks.

(b) Decline in trust. In the radio broadcast, Putnam points out that in the last 30 to 40 years, there has been a general decline in Americans’ trust in institutions such as the government and media. He says that this causes a “long-term drag on your own sense of well-being” (Putnam, broadcast), resulting in a decline in how much trust people have in their friends. The decrease in trust among peers explains why people do not discuss important matters with as many friends.

(c) Changing definition of “discuss” due to new technology. In her article, Smith-Lovin points out that respondents may have interpreted “discuss” as face-to-face conversations, “exclud[ing] other forms of communication that are becoming dominant in our contacts with core confidants” (p.372). If people largely talk about important issues via cell phone and e-mail, but interpret the question to refer exclusively to in-person communication, the size of one’s core discussion network will erroneously shrink.

2.

(a) Material and Emotional Support. By literally and metaphorically ‘bringing you chicken soup when you are sick,’ close friends offer material and emotional support. “Cyber friends can be useful for some purposes but they’re not likely to bring you chicken soup unless you actually know them, face-to-face, in your neighborhood” (Smith-Lovin, broadcast). In fact, as Wellman explained, “friends who are rarely seen or who live far apart tend to have weaker, less supportive ties” (p.581); it is only friends with whom you have a high-quality relationship who will go out of their way to assist you.

(b) Helping in Major Disasters and Minor Emergencies. Smith-Lovin discusses the American government’s failure to aid Hurricane Katrina victims and indicates that strong friendships were the safety net that actually helped these distressed victims. She also mentions that close confidants can be trusted with one’s children and house as well as given power of attorney. However, a loss of trust and consequent loss of such relationships will lessen aid for people facing huge trauma and people in minor binds.

(c) Diversity of Options and Information. Smith-Lovin poses the question of if a person depends just on a spouse or partner, what would they do if something happened to that person. The old saying goes, “Don’t put all of your eggs in one basket,” and decreasing the size of core discussion networks is doing just that. In addition, if someone only discusses serious matters with two individuals, that person is limiting his or her exposure to new ideas.

(d) Physical Health. Social isolation (not knowing neighbors or friends) is as big of a risk factor for death as smoking (Putnam, broadcast) and has deleterious effects on mental health. As Putnam stated, “We can’t outsource our own physical health” (Putnam, broadcast). The shift towards smaller networks poses health risks. Also, when society at large is suffering from poor health, there is a harmful impact on economic and insurance institutions.

(e) Institutional Effectiveness and Societal Safety. Putnam explains that social ties are powerful predictors of crime rates in neighborhoods and in the ways the schools work. In addition, there exists a casual relationship between stronger interpersonal networks and objectively better government functioning (Putnam, broadcast). In her book The Death and Life of Great American Cities, Jane Jacobs presents the idea of ‘street eyes,’ the notion that neighbors who recognize each other can distinguish people who should be on the block from strangers. This informal policing helps make neighborhoods safer. In addition, for the educational system to function most effectively, parents need to be involved in their children’s schools.

(f) Connectors. The fewer people someone knows, the fewer people to whom he or she can be introduced. As Gladwell demonstrated, many of his friends could be traced back to one of his friends, Jacob. He showed that without knowing Jacob, he would never have met “the overwhelming majority of [his] relationships” (part 3). As Smith-Lovin explains, by cutting one’s closest network by one-third, people are limiting their opportunities to meet new people and learn more information.

McPherson’s literature review discussed the numerous studies underlying the old maxim, “Birds of a feather flock together.” This paper made me consider other papers that we have read in the past.

For example, one the points made in this literature review is that “Homophily limits people’s social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience” (p.415). This statement made me consider the immense value of structural holes. If people are homophilous, discussing similar issues in social clusters, then Burt is correct that structural holes in a social network are vital for transmitting new information from one group to the other.

McPherson also brings up the idea that “[h]omophily implies that distance in terms of social characteristics translates into network distance, the number of relationships through which a piece of information must travel to connect two individuals” (p.416). Later, McPherson says that “[r]ace and ethnicity are clearly the biggest divide in social networks today in the United States” (p.420). These statements made me consider Korte’s study in which Milgram’s six degrees of separation study was done across racial lines. However, both the Milgram and Korte studies determined that there are 5 to 6 degrees of separation. I ask: How do you reconcile the idea that (a) people are homophilous and thus the distance between two different people is larger than the difference between two similar people with (b) Milgram’s study involving a white starting person and a white target person produced the same number of degrees of separation as the part of Korte’s study that had a white starting person and a black target person?

Pearson claims that his article is about determining the impact of homophily and assimilation on drinking, smoking (tobacco, marijuana), and sporting behavior. However, I think it is more of a descriptive study of such behaviors among the “West of Scotland” population. His conclusions on homophily (making friends with people who do what you do) and assimilation (doing what your friends do) are indistinct, yet his findings on respondents’ behaviors are more elaborate.

One finding he did have which incorporated ideas of homophily and assimilation was the following: “A non cannabis user with a friend who uses cannabis (or a cannabis user who has non-user friends) is more likely to assimilate (change) his behavior to match that of his friends” (p.55). This made me wonder if there is a “tipping point” (as Gladwell’s book discusses) in the number of friends of an individual that would yield a change in behavior. Specifically, if Keith has 10 friends, how many friends would need to be cannabis smokers for Keith to change his behavior? Is it half (5), more, or less? I ask: Do you think there is a tipping point in homophily and association? Do you think it is a percentage or an absolute number? What would you hypothesize that tipping point value to be?

Hill’s study measures an individual’s social network size by the number of Christmas cards that this individual sends. This study is replete with glaring flaws. Primarily, the study is asking “What is the size of this Christian’s Christian social network?” People of other religions do not send Christmas cards (although some sent Holiday cards), and people who are other religions receive far fewer Christmas cards than someone in a family actually celebrating Christmas. If Hill and Dunbar want to account for age, gender, family size, and even questionnaire structure, how could they forget religion and ethnicity as crucial and seemingly overlooked variables in this experience? I ask: Do you think this study really calculates an individual’s entire network size?

Besides religion, Christmas cards are correlated with economic status. To an albeit small degree (but definitely noteworthy), Christmas cards cost money to send. Therein arises the extraneous variable problem of “How many of my friends are worth the $xx it costs for me to send them this card?” I ask: Do you think that more people would have been sent cards if the cards were sent online (because of cost considerations)? Do you think that fewer people would have received cards if the cards were sent online (because of technological limitations)? I remember how Wellman said that organizational ties offer a little bit of companionship (but no financial assistance, small services, etc.). Thus, I ask: Does sending a card qualify as “financial assistance” or “small services”? If so, does that mean that “organizational ties” (clearly part of one’s social network) were precluded from this data?

Killworth tries to determine the size of an individual’s social network. However, if Lois Weisberg exists, then one must understand that there is no single number that can be assigned to “the size of a person’s network.” Killworth criticizes other studies for their high variability, but it makes sense that different people would answer with different numbers. I even ask: Do you think that if you were asked, on two different days, to list the people in your entire social network, you would come to the same number?

Additionally, phone books (the medium Killworth used when operationalizing the variables for this study) are constrained to a given geographic region. My phone book at home in New Jersey only lists people in my county (and only the ‘listed numbers,’ at that). However, we have determined that proximity is not a measure of network strength, let alone a constraint on network size. Therefore, I ask: Can identifying the number of people in the phone book who you recognize be an adequate way to really measure the full size of your network?