Social Network Blog - g10

For the last assignment, we administered a survey to 20 people, 10 between the ages of 18-22 and the other 10 over the age of 33. The survey combined several of the methodologies and measurements that we have examined in this course this semester including the position generator and name generator. However, unlike the position generator used in the Lin et al. study, our survey did not ask how the person in each job was connected to the participant, and our name generator asked additional questions concerning frequency and duration of relationships, as well as the mediums of communication used.

Position Generator: For this part of the study, I assigned the positions listed on the survey scores of 1-15 (15 being the highest for Judge and 1 the lowest for Laborer). Using this scale, I then calculated the various aspects of the position generator including the extensity (number of positions), upper reachability (score of highest occupational prestige accessible) and range (the distance between highest and lowest score). The results, which are listed below, can be used to examine the weak ties and diversity of one’s social network. Both Burt and Granovetter discuss the role of weak ties as critical to the access of diverse resources, such as information and social capital.

The original reading, “The Position Generator: Measurement Techniques for Investigations of Social Capital,” examines a different measure of personal social networks: the measure of social capital. Social capital focuses on the resources embedded in social structure, the accessibility of these resources and opportunity for resource mobilization.

GENDER:

The Taiwan study we read for class, Lin, Fu & Hsung (2001, p. 67), concluded, “the structure of social capital, while showing superficial similarities, it is essentially different for males and females. Females are generally more disadvantaged in accessing many of the positions.” In their explanation, they suggest that this is due to women’s position in the home as the homemaker. However, the in United States, more and more women are working outside the home, which makes this assertion less relevant. In fact, my data suggests a much different trend, showing that women scored higher than males in all three areas (when not controlling for age).

FEMALE
Extensity: 7.5
Upper Reachability: 13.4
Range: 12

MALE
Extensity: 6.2
Upper Reachability: 12.7
Range: 11.4

When I introduced age into the comparison the results changed slightly, but women still outscored males within the same age grouping. The most notable difference between the males and females in both groups was the extensity of the network: females 18-22 scored 1.5 points higher than males and females 33+ scored 1.3 points higher. This suggests that women in both age ranges have access to a greater variety of occupational positions.

The differences for upper reachability and range were not as significant in the 18-22 group as the 33+, differing by only .2 and .4 respectively. However, there was a significant difference between the upper reachability score of females and males in the 33+ group, with women scoring an astounding 1.7 points higher than males, as well as the range (13.25 v. 11.8). Overall, these findings suggest that women have access to a greater range, number and diversity of positions than men, which challenges the previous findings.

FEMALES 18-22
Extensity: 7.5
Upper Reachability: 12.7
Range: 11.2

MALES 18-22
Extensity: 6
Upper Reachability: 12.5
Range: 10.8

FEMALES 33+
Extensity: 7.5
Upper Reachability: 14.5
Range: 13.25

MALES 33+
Extensity: 6.3
Upper Reachability: 12.8
Range: 11.8

AGE: Our survey also looked at the relationship between age and network composition and diversity. The samples for the survey were divided into the age group of 18-22 and those 33+. As a result, the design produced 2 distinct samples groups to compare. The results suggest that older people have access to more prestigious positions from a broader range than younger participants. The similarity in extensity, despite other differences, is due to the fact that the younger generation can access a similar number of positions, however, they tend to be in the lower range of prestige.

AGE 18-22
Extensity: 6.9
Upper Reachability: 12.6
Range: 11

AGE 33+
Extensity: 6.8
Upper Reachability: 13.5
Range: 12.4

Not surprisingly, the data from my surveys also suggest a relationship between education and network diversity and composition, particularly when comparing those that have completed college to those that only did some (dropped out or are still attending). Respondents that completed college scored higher for all three measures (Extensity, upper reachability and range). The most pronounced differences were extensity. These findings seem logical because one would assume that people who have graduated from college might have access to a greater variety of occupational positions. In fact, the results for the graduate degree participants support this assertion, scoring significantly higher in upper reachability than both college graduates and non-graduates.

COMPLETED COLLEGE
Extensity: 7.1
Upper Reachability: 12.85
Range: 11.67

GRADUATE EDUCATION:
Extensity: 6
Upper Reachability: 14
Range: 12.5

SOME COLLEGE:
Extensity: 6.4
Upper Reachability: 12.8
Range: 11

The Name Generator:

To measure strong ties, we used the Name Generator, which asks the familiar question: “With whom do you discuss important matters?” As we have learned from Wellman and Wortely, most people get a great deal of their social support from a select number of strong ties, which “provide broader support than weaker ties.” (1990, p. 566). There have been some challenges to the validity of the name generator in identifying strong ties. There is the issue concerning the idea of “important matters,” which could vary significantly from person to person. This is also affected by the fact that relationships are becoming increasingly specialized with the introduction of new media and continued advancements in transportation and technology, so that people often rely on a variety of possibly weaker ties to discuss certain things rather than others. It is harder to lump everything into important matters because it is extremely subjective. However, for the purposes of the study, the name generator gives us an idea of the composition of one’s network of close “confidants,” which are very likely to be strong ties. In addition, Marin and Hampton assert in their article, “Simplifying the Personal Network Name Generator Alternatives to Traditional Multiple and Single Name Generators,” the most reliable single name generators are surprisingly to important matters question and “who do you enjoy socializing with?”

We learned earlier from the McPherson et al. study, “Social Isolation in America,” that “the number of discussion partners in the typical American’s interpersonal environment has decreased by nearly one person” (358). This was in reference to a decrease from 2.94 to 2.08. However, my findings are closer to 3 with an average of 2.85 discussion partners. In addition, I found that younger people tended to have larger social networks than older people. The average was 2.3 for participants over 33 and 3.4 for the 18-22. In our earlier readings, Kalmijn also found, “the older people are, the lower the number of friends they report” (241). This relationship may be due to the patterns and trends associated with marriage and cohabitating couples, as they are discussed in Kalmijin’s article. In his study, he found that married and cohabitating couples’ networks tend to blend together and merge stating, “Among married persons, there is a negative association between duration of the marriage and the size of the friendship network” (233). In my study, every member of the 33+ group mentioned their spouse as someone with whom they discuss important matters. This is also consistent with the findings of McPherson et al, who reported an increase in the number of people who list their spouse using the important matters measure.

Another interesting finding related to the older generation is that among their network of close confidants, for those who listed more than one, 96% at least knew each other and 36% were especially close. This may be due to the fact that they are older and thus their contacts have had greater time to meet, or as Kalmijn might suggest, as their networks merge, ties tend to be more overlap. This may also simply be a result of homophily, which assumes, as Fischer explains, “People tend to build networks composed of others very similar to themselves in background, position, personality, and way of life” (1982, p. 6).

There was a considerable level of overlap and subsequent homophily in the younger group, however not as strong. Within the discussion networks, 34% were strangers, 39% knew each other and 27% were especially close. While this is an interesting finding, I think that it is largely due to the bias in my sample. I drew most of the participants from my immediate social network, as a result many of our ties overlap (even close ties). In addition, the college environment may also increase the homophily of ties. The presence of strangers in close discussion networks supports Granovetter’s idea of the forbidden triad between strong ties, which claims that transity does not always guarantee that your friends will be friends, despite the prevalence of the “friends of friends are friends” homophilous nature of social networks. This, it turn, affects network density, with the older generations having smaller, yet denser social networks that the younger sample. The article draws heavily on ideas discussed in earlier readings, by Granovetter and Burt, such as cognitive balance theory and transitivity, which both assert the basic concept that “friends of friends tend to be friends.” This also relates to Burt’s criticism of redundant ties and the role of “egocentrism,” which attributes tie formation to cues from shared backgrounds and interests (73).

There were also significant differences in network size based on gender. Overall females reported more confidants than males: mean of 3.4 (F) and 2.3 (M). When age was considered, these results continued. In the 33+ group males listed an average of 2 confidants and females 2.75. In the 18-22 group, males listed 2.75 and females 3.83. This is consistent with Wellman and Wortely’s idea of women as main sources for emotional aid. As they explain, “Numerous analysts contend that women are more likely than men to provide emotional support”, and that, “women are often the principle emotional supporters of men as well as of other women” (576).

The McPherson et al study also reported a decrease in the modal number of confidants from 3 to an alarming zero. However, my findings suggested nothing of the sort. The median in my sample was 3 and the mode was 2. While no one that I gave the survey to answered zero, my sample is absolutely BIASED.

Homophily:

In this part of the survey, we are examining close ties, which are often homophilous. Granovetter argues, that they are not good sources of information and some resources (i.e. getting a job). While ties between similar people tend to be more empathetic and conducive to social support (Wellman and Wortley, 578), homophilous ties often link us to similar others and redundant ties, who have access to the same information that we do. Thus access to new information and a diversity of resources is threatened by densely knit, homophilous networks.

Another indicator of homophily, is the significant presence of kin in the social networks, particularly in the younger group where 9 out of 10 listed their mother and 5 out of 10 listed a father or sibling. While these types of relationships are generally supportive, there a significant decrease in non-kin confidants may also contribute to an increase in social isolation. McPherson et al explain, “These [non-kin] ties are the most likely to bridge socially distinct parts of the community structure, since we know that marriage and family are more homophilous on class, religion, race and several other social attributes than ties formed in other ways” (359). Thus solely depending on a spouse or parent cuts a person off from other networks, affecting the types of new ties formed outside of your existing network. This is important in the process of socialization and normative pressures, which are primarily transferred through closely-knit interpersonal networks. Fischer supports this assertion, stating, “Most people affect their society only through personal influences on those around them. These personal ties are our greatest motives for action” (3).

The Role of New Media:

In our survey, we also looked at frequency of contact and duration, which are alternative measures of ties strength, as well as 6 various communication mediums. The findings support many of the course readings, such as Baym et al. article, which asserts, “Internet is particularly useful in maintaining long distance relationships (314). For all age groups, but particularly the younger generation, email was used to communicate with close ties that lived far away. Interestingly, email was also used to communicate between confidants that reside locally (i.e. spouses in the same house and friends that live in the same neighborhood). This supports Hampton’s argument that computer-mediated communication is also useful for maintaining local ties within a neighborhood or community. As he explains, “the Internet offers a way of overcoming barriers to local tie formation” (225).

Overall, my findings support the assertion, “the advent of new technologies like print, the telegraph, the telephone, and email may have loosened the bounds of geography by lowering the effort involved in contact, but these new modes have certainly not eliminated the old pattern” (McPherson, Smith-Lovin & Cook, p. 430). Discussion networks were composed of both local and distant contacts, who were contacted via old and new media. When confidants lived in the same household, face-to-face was the primary form of communication followed by telephone, cell phone and email. It is important to examine the phone v. telephone because the younger generation only used cell phone, while the older group used the phone more. Despite the significant differences, this is a limitation of the sample because students at Penn usually do not have landlines (so expensive), and adults often have home phones and office phones.

Lastly, I was surprised to find that no one reported using postal mail, yet it might be annoying to discuss important matters in a written letter, especially when the younger generation are used to immediate response.

Measurement:

This entire study is laden with considerable limitations to the assertions of accuracy and validity. The Zwijze-Koning and De Jong provides an excellent source in the examination of measurement weakness. For example, as a survey, there is the obvious self-report bias, but Zwijze-Koning and De Jong go more in depth, discussing socially desirable response bias,” which may have caused respondents to list the number of people and positions that they thought were socially important. They also mention telescoping and recall, which is a comprehension of time error; respondents often remember an event occurring more recently than it did, making it difficult to establish validity of self-report that asks vague questions, such as “in the last 3 months.” This study uses the time frame of 30 days, but people may have listed communication that took place a month a half before due to simple miscalculation. Another critical source of error is question wording, which often leads to misinterpretation. One of the most interesting conclusions of the study was the discussion of the role of respondent perception or relationships compared with actual relationships. The participants in this survey may no accurately gage how often they talk to people as well as whether they actually discuss important matters. Another example is our discussion of extended kin; while people often name them as strong ties, studies of social support find that they are rarely reported as sources of support that are associated with strong ties (i.e. financial, emotional, companionship, etc.


The use of the position generator is also a source of error because it is so vague. By simply asking a person to if they know anyone by first name in the various positions, people may list people that they do not actually know. As Zwijze-Konign & de Jong explain, “contacts with persons who are nearby or higher in rank are reported more frequently than others” (435). Using the name generator also poses a threat because people may be limited to only 6 people when they actually discuss important matters with more. As Zwijze-Konign & de Jong assert, restricting respondents to a preset number of contacts may cause them to name more or less than the real number (433).

CONCLUSIONS:

On the eve of graduation, this study reminded me just how terrible it can be asking people to participate in a survey. After spending nearly 6 hours on the phone collecting answers from my entire extended family over the age of 33, I realized how important sampling is to the results of a study. However, despite the bias of my sample, the active participation in the process helped solidify my understanding of many of the basic tenants of social network theory.

In the first reading, “Social Resources and Mobility Outcomes: A Replication and Extension,” the authors, Marsden and Hurlbert replicate and extend and previous study by Lin, Ensel and Vaughn concerning the effects of social network resources on finding a job. The study examines job outcomes based on 6 categories” occupational prestige, wages, industrial sector, form size, possession of authority and closeness of supervision. In the study, their findings confirm that the listed outcomes are not affected by incidental selection bias of the controls. However, Marsden and Hurlbert assert that the social resources argument, which states that effects of different social resource measures are largely outcome-specific, so that no single measure is the indicator of social capital. The study also illustrates a lack of a significant correlation of tie strength for both mobility outcomes and/or access to social resources. Interestingly, the study only found the prestige of contact only had an effect on the prestige outcome of the jog change. This seems logical because one would expect a prestigious social contact to have greater access and influence pertaining to prestigious jobs; whereas, someone who does not have a prestigious job would be less likely to have contact or a relationship with other prestigious individuals. This supports Burt’s assertions concerning the importance of who you know and in what positions they reside. Marsden and Hulbert account for this trend based on their findings that “education and respondents prior prestige are the principle correlates of the prestige of accessible contact. In addition, Marsden and Hulbert also assessed the various mobility outcomes, such as wages, in association to social resources, which exhibit no net relationship. For those in the class applying for internships and summer jobs, what do these findings suggest about social resources in the search for jobs? Who are the best people to contact and why?

The second reading, “Social Isolation and the Underclass,” presents a somewhat idealistic examination of social isolation in inner-city black communities as products of class and neighborhood differences. In the study, Fernandez and Harris use a pervious study by Wilson as the theoretical framework for their study, focusing specifically on his assertions concerning the dramatic changes that have occurred in the social structures of the urban ghettos in the last 25 years. Wilson explains that these changes are due to the unique position of ghetto communities at the “intersection of a number of large-scale social and economic processes,” such as shifts in urbanization, suburbanization and the “exodus of the black middle class to the suburbs.” As Wilson explains, “the net results of these changes is that inner-city black communities are in crisis and their residents are in serious danger of forming an ‘underclass’ trapped in a permanent condition of emiseration.” Fernandez and Harris use Wilson’s definition of social isolation as “the lack of contact or of sustained interaction with the individuals or institutions that represent the mainstream society,” and assert, “Social isolation is a key theoretical concept that serves as an alternative to ‘culture of poverty’ explanations of the maintenance and preproduction on the underclass.” They focus on the patterns of interpersonal contact and isolation associated with poverty and the black underclass in comparison to ‘mainstream society,’ which they identify as individuals “who are steadily employed, not involved in public assistance, and who reside in ‘stable areas.’” In their findings they assert that nonworking poor blacks are indeed socially isolated along various dimensions, frequently more than non-working poor. While this is not a surprising finding in the context of our previous readings by Granovetter and Burt, who both assert that weak ties offer the most diverse resources in social networks; studies have consistently found that upper-class, white individuals with higher educations tend to have more diverse social networks. The most interesting findings in their study were the evident gender differences in the patterns of isolation, reporting significant isolation among both poor and non-working poor black women compared to black males of the same status. I usually think of women as more social in general, yet more women were non-working in the study, which would contribute to isolation from resources outside of the community.

While I think that the subject of the study is interesting as well as socially relevant, I had some trouble with the fact that the study used poverty ratings that were 7 years old. I also felt the article proved several things that seem to be common sense. I found myself continually saying “duh” in my head. I am not sure if that is because I have read many studies concerning this topic, thus it seemed repetitive, or if, as I suspect, the study basically replicated obvious social and economic patterns of society, that cannot be explained simply using social network theory. While the authors control for confounding factors to determine statistical, I think that almost every finding suffers from questionable temporal order. It is the chicken or the egg question. It is almost impossible to determine causality. For example, they find that black, poor unemployed women are more socially isolated, but is it because they are black, or poor, or unemployed, or is it all three??? What do you think? How could the study be improved? In addition, what do you think about their idea of redistribution of poverty in non-poor communities? Is it realistic?

Small University Experiment:

I will begin my examination of this study by discussing my personal experience.

My Small World Folder:

I was pleasantly surprised by the relative success of my folder in reaching the target due to the dramatic downsizing in my Penn network in June of 06’. Nevertheless, of the 8 students who were assigned Antonio Polley as the target, my folder was one of the only 2 that successfully reach Polley.

Although I had originally hypothesized that my folder would be passed through 4 intermediaries, the folder actually traveled between 6 people before reaching Polley.

ME (Sr.+, F, SAS, Comm., Student, 4.5 yrs @ Penn)→ Bradley E. (Jr., M, SAS, M, BioChem., Student, 2 yrs @ Penn)→ Amelia F (Jr., F, SAS, BioChem., Student, 2 yrs @ Penn)→ Ayaka Iwata (Jr., F, SAS, BioChem/Hist., Student, 2 yrs @ Penn)→ Troy Messick (M, Wistar, Post-Doctoral Researcher, 4 yrs @ Penn)→ Shawn Blackburn (M, Med, Wistar, Student, 2 yrs @ Penn)→ ANTONIO POLLEY

While I underestimated the length of the chain, I was on the right track when I hypothesized:

“I think that my folder will follow a path primarily through the departments related to science and medicine. I think that it will be passed from the first person to another fellow student closer to the related field of study, but from there I am not sure…The final link in the chain will most likely work in a similar field of research or occupation.”

Link: http://www.mysocialnetwork.net/blog/481/g10/2006/09/connecting_at_penn.html

As shown in the illustration of the pathway, my folder was passed from my initial link Bradley E. to another biochem student. It remained within the biochem department until the 5th link when it crossed the department/occupational boundary and was passed from a student to a post-doctoral researcher in the Wistar institute. From there, the folder was passed to a fellow researcher and med school student in the Wistar student and onto Polley. The Stevenson et al. study noted one of the major challenges in small world studies, particularly in a university setting, is the difficulty “for folders to cross boundaries between professors, staff members, administrators and students” (1997, p. 22). Accordingly, my folder was passed through several students in the same department before finally reaching the Wistar Institute; yet, once the folder crossed the boundary it was only passed through one more intermediary.

One of the most notable characteristics of my folder’s path is the fact that it is relatively gender heterogeneous, containing 3 males and 3 females (excluding Polley): F→M→F→F→M→M. In the Milgram study, “participants were three times as likely to send the folder on to someone of the same sex as to someone of the opposite sex” (1967, p. 65). Moreover, in the Stevenson study, women relied more heavily on gender homophilous ties when passing folders as compared to men, and both sexes relied on homophilous ties when crossing the occupational boundary (1997, p. 23). However, my folder passed between men and women almost equally and the folder was from a female to a male when crossing into the Wistar institute. SO technically, the last three members of the chain were male.

Notably, there was a fair degree of age and baseline homophily in the middle of my chain. Of the first 4 links, I was the only one to pass the folder to a younger person. In fact, the next 3 links were all junior biochem majors. Burt would describe this as a series of redundant ties lacking the efficiency of a structural hole.

The reason I was initially skeptical regarding the possibility of a successful chain is due to the fact that I am a minor anomaly in the Penn network structure. As a 5th year senior, all of my friends, networks connections, etc. graduated last fall, so this assignment forced me to look outside my own class. While Stevenson et al. originally suggested, “the longer the time at the university, the more likely a student is to initiate a successful chain of communication to a target” (1997, p. 24), I thought that the significant downsizing of my network would decrease the likelihood of completion. However, as Granovetter explains, “removal of the average weak tie would do more damage to transmission possibilities that would than average strong one” (Granovetter, 1973, p. 1366); most of the network members who graduated were close ties rather than weak, so I chose a weak tie as my initial intermediary. This may have increased the chance of completion because “weak ties are more likely to link members of different small groups than are strong ones, which tend to be concentrated within particular groups” (Granovetter, 1973, p. 1376).

Aggregated Class Results for Both Targets: Antonio Polley and Susan Yoon

Unlike the Stevenson study, our study had two different targets: Antonio Polley and Susan Yoon. Interestingly, there were considerable differences in completion rates between the two targets. 80% of the folders successfully reached Susan Yoon via a mean of 3.25 links, yet only 25% reached Antonio Polley via a mean of 4.5 links.

Gender of Target:

While I do not think that folders failed reach Antonio because he was male, it did have a significant affect on the gender homophily of the pathways. Of the folders that reached Susan Yoon, only one folder was ever passed to a male (at any point in the chain). Within this group all of the starters were female (we only have one boy in the class), and 84.6% of the successful chains to Yoon were transferred to the same gender. 87.5% of the final Links to Yoon were also female. These results seem to support a relationship between gender and ties as suggested by Milgram and Stevenson, yet it is unreliable due to the lack of a male starter sample from which to draw comparisons. In the Stevenson study, there was considerable gender homophily among undergraduate females (6 of the 8 paths that originated from females were passed to another female). The chains to Polley were more heterogeneous, with only 55.6% of the successful chains being transferred to the same gender, yet it is difficult to draw any conclusions concerning the Polley folders because only 2 reached the target. However, both of the finals links to Polley were the same gender as Polley (M).

Penn Affiliation and Structural Position:

I think that the considerable difference between the targets’ affiliations to Penn played a significant role in the disparate rates of success. Susan Yoon is an assistant professor in the graduate school of Education, while Antonio Polley is a lab technician in the Wistar Institute. As a result, Susan Yoon has a stronger affiliation with Penn academics and occupies a much more visible and prestigious position at the university. Polley, on the other hand, seems to actually not work for Penn directly, but instead is a staff member of an affiliated research institute. Most of the students who had him as a target hypothesized that he had some connection to the med school, yet were severely disappointed to learn that he was much more obscure in the system. However, those who had Susan as a target were able to immediately identify her affiliation and structural position at Penn, in particular, her department. Many of the students actually already knew who she was, where as no one had ever met Polley.

While both targets could potentially be reached through strong and weak ties, it is easier to navigate one’s social network when you have a strong idea of the location your target.
As Kilworth et al. discussed in their article, “The Accuracy of Small World Chains in Social Networks,” people posses limited information concerning the composition of their networks. As a result, we often have difficulty identifying the shortest path, or geodesic, to a target. This relates to the idea of searchability; while shorter paths may exist, they are not always obvious, thus links in a path must rely on their knowledge about their networks to make decisions. In this study, the constraint of available information concerning the targets significantly combined with the relative occupational prestige of the different targets significantly influenced the completion of chains (Fischer, p.4).

Years at Penn/Age:

I do not think that there was a significant relationship between the year of the students and the success of chains. For both Yoon and Polley roughly 40-45% of the completed chains were passed to students in the same class, so I do not think it accounts for the success rates. In addition, our sample was not a diverse as the Stevenson sample, which consisted of an equal distribution of freshman, sophomores, juniors and seniors, where as our class consists exclusively of juniors and seniors (and seniors+). As a result, the Stevenson study showed a different relationship between class year reporting, “Undergraduates were found to prefer to pass small world folders among their own class and did not pass the folders to lower classes.” 60% of the Yoon starters and 29% of the Polley group passed their folders to a younger student.

One of the most critical features missing from the results of our experiment is the idea of “funneling” or channeling, which suggests “folders converge on a small number of sociometric ‘stars’ before reaching the target person” (Stevenson et al., 1997, p. 26). This is a critical contribution of the original Milgram study, as he explains:
There appear to be highly popular channels for the transmission of the chain. Second there is a differentiation among these commonly used channels, so that certain of them provide the chief points of transmission in regard to residential contact, while others have specialized contact possibilities to the occupational domain. (Milgram, 1967, p. 66).
In our experiment, no obvious sociometric stars emerge emerged. In fact, only two folders passed through the same final person before reaching a target (Yoon). However, there was some degree of channeling in that 75% of the folders that reached Yoon were transferred through the FPE, which is in the same school. The folders that reached Polley went through different people and different departments.

Accounting for Differences:

An obvious explanation for the difference between the results of the Stevenson study and our study is the chosen targets. The Stevenson target was the undergraduate dean of the school of management, who was picked because “he was located in the building where most of the classes are held and would be easy to physically access” (Stevenson et al., 1997, p. 25). Our targets, other hand, were chosen because they did not occupy the same geographic space and were much less visible occupationally.

While Stevenson and his participants estimated a 50% completion rate because most of the courses were held in the same building, our estimations were influenced by the fact that this was a graded course assignment. Although it was stipulated that students were not graded on the success of their chains, there is no doubt that most students felt some pressure academically to ensure the completion and were more motivated than a random sample. Due to the visibility and occupational prestige of the Steveson16 of the 60 folders (27%) reached the target with a mean of 1.25 links between the starter and the target. Although they had a lower response rate, the folders that reached the target got there much faster.

Conclusion:

Although they were built upon the same ideas, our study differed in design structure from the original Milgram study. The organizational structure of the University setting focused purely on occupational paths. We were given no information regarding the residences of the targets, thus, the “funneling effect” was limited to occupational or university based affiliations. While the Korte and Milgram study of 1970 identified 3 key persons, or “gatekeepers,” through which the chains would pass through before reaching the target, our experiment had no clear links to the targets. Penn represents what Fischer would describe as a “primary social context” (Fischer, p. 79), with unique structural opportunities and constraints based on the network affiliations.

Question 1:

Who are the five people you interact with most often?

1. Jaime L.: 23 interactions; 2 emails, 9 text messages and 12 cell phone. Close tie that I have known for nearly five years. She is one of my few remaining friends from freshman year that has not graduated. I spend most of my time out of class with Jaime. She lives one block away and is also graduating in December. 21-years-old.
2. Liz M.: 13 interactions; 9 emails, 2 cell phones, and 3 text messages. My sister, who lives in New York City. She is my only relative that lives on the east coast. She is two years older and we share many mutual friends. 24 yrs old.
3. Jarrett W.: 9 interactions; 2 emails, 5 cell phone and Jarrett is a text messages. A friend and close tie who lives in Boston. We have known each other for 4 years (3.5 of the years were at Penn). He just graduated and has joined a band (no real job). Philosophy major. 22 yrs old.
4. Cheryl W.: 8 interactions; 3 emails, 5 cell phone. Cheryl is my mother who lives in California. She is a close tie.
5. Nolie G.: 8 interactions; 5 AIM, 2 Cell and 1 text message. Nolie is my 22-year-old roommate. She is a close tie that I have known for fours years. We have lived together for the past three years. Although we are technically the same year at Penn, she will not graduate until this spring or next fall. Art major.

Who are the five people you interacted with most often for each of the communication mediums used?

Email:
1. Liz M.: 9
2. Cheryl M.: 3
3. Claire H.: 4. Major advisor in the Communications Department and has been at Penn for four months now. She is infamous for her frequent emails concerning academic events and information. Not Close.
4. Alina B.: 2. Alina is a T.A. for a course in which I am enrolled this semester. I have only known her for a few months and we are not close. She is from Eastern Europe.
5. Barnaby L.: 2. He is a friend that I have known for 3 years, but our tie is moderate because we do not discuss important matters. He attended Princeton and now lives in London, where he is originally from. He is 25.
6. Jarrett W.: 2
7. Jaime L.: 2
8. Mel G.: 2. She is my best friend from home that I have known for 9 years. Our tie is definitely close, but we communicate relatively infrequently. Lives in California and 22 yrs old.
9. SAS Faculty: 2. This category represents emails received from the general college listserve.
10. Alina Y.: 2. The head of the Slavic department, whom I have never met. I somehow ended up on a listserve for the Slavic Department events.

Cell Phone:
1. Jaime L.: 12
2. Cheryl W.: 5
3. Jarrett W.: 5
4. Nolie G.: 2
5. Sammy S.: 2. A friend and moderate tie who is a 21-year-old senior at Penn. We have known each other for two years and interact socially. Lives a block away.
6. Annie B.: 2 A friend and moderate tie who is a 21-year-old senior at Penn. We have known each other for 4 years. Lives a block away.

SMS:
1. Jaime: 9
2. Pat M.: 4. Pat is my roommate. I have known him for fours years and he is also a December graduate. He is a close tie. 22 yrs old. Political Science major.
3. Liz M.: 3
4. Trina G.: 2. Trina is my closest friend from Penn who lives in New York City. We have known one another for five years and lived together the first 4 years. She is 23 and from Texas.
5. Bryce L.: 2. A friend and close tie. He is a 21-year-old senior who lives one block away. We have known each other for 4 years.
6. Jarrett W.: 2

Facebook:
1. Mark P.: 2. Friend from California (home). Moderate tie whom I have known for 9 years.
2. Kelsey F.: 2 Friend and moderate tie. Lives in Washington D.C. 22 yrs old. Attended Penn for one semester.
3. Agnes T.: 1 Friend and close tie. Lives in China. Lived together at Penn for 3 years, but she graduated last year. 22yrs old.
4. Bradley E.: 1 Acquaintance and weak tie. Junior at Penn.
5. Chase M.: 1 Friend and weak tie. 23 yrs old and graduated from Penn last year. Lives outside Philadelphia now.
6. Jeff S.: 1 Friend and moderate tie. 22 yrs old and graduated 2006. Lived on my hall freshman year. Lives in Colorado now.
7. Josh Y.: 1 Friend from high school and weak tie.
8. Miles H.: 1 Classmate and weak tie. Senior.
9. Shannon J.: 1 Former roommate and weak tie. Fifth year senior.

AIM:
1. Nolie G.: 5
2. Trina G.: 2
3. Grant G.: 2 Friend and moderate tie. Lives in Washington D.C. Graduated 2007. From the town in California.
4. Pat Hunt.: 2 Friend from California and close tie. Known for 9 years.
5. Bryan M.: 1 Roommate and close tie. Graduated last year, but now attending grad school at Penn.
6. Agnes T.: 1

Question 2:

A. Is there a relationship between the medium of communication used and the strength of the tie?

CellPhone SMS Email AIM Facebook
Close 29 23 24 11 1
Moderate Tie 4 2 2 7
Not Close 18 3

When communicating with the 3 close ties with whom I interact most, Jaime L., my sister Liz and Jarrett W., I used 4 of the 5 mediums (none of them use AIM). This supports Wellman’s assertion, “the stronger the tie, the more media used” (2001). In fact, looking at the overall distribution of interaction, close ties are the only group that I interacted with using all five mediums. Alternatively, for weak ties (not close), I only used 2 new mediums, email and Facebook, which are less personal and most often asynchronous. My cell phone is more intimate because I carry it with me all the time, making me always available (Wellman, 2001, p. 239). I also lose my phone frequently, so I usually only have the numbers of people I contact most often programmed in the address book. As a result, when using my cell phone I primarily contact strong ties [cell phone (88%) and text messaging (92%)].

Overall, I communicate primarily with strong ties (71%), which Wellman and Wortley explain as “[a tie] that has at least two of the characteristics of intimacy, voluntariness and multiplexity” (1990, p. 566). Not surprisingly, most of the close ties that I communicate with on a regular basis, i.e. Jaime, Liz, Jarrett and my mom, each exhibit all three of these qualities, particularly the mutliplexity. A large majority of my personal network recently graduated last year (when I was supposed to graduate), thus I currently depend on a few multiplex close ties for most types of support (rather than a diverse group of specialized weaker ties).

B. The type of support exchanged?

Cell Phone SMS Email AIM Facebook Total
Emotional Aid 4 3 6 2 15
Small Services 8 5 4 4 3 24
Large Services 4 4 8
Companionship 18 17 11 7 8 61
Job Information 1 1
Information: Penn
Events/Announcements 12 12
Academic Course Info 3 3
Other 1 1

For this section I used Wellman and Wortley’s classifications for types of support: emotional aid, small services, large services, companionship, and job information. In addition I added two other categories: 1) Information concerning Penn events and announcements and 2) Academic course information/communication.

Overall, companionship was the most common type of support, accounting for 73% of Facebook, 68% of SMS, 54% of AIM, 53% of cell phone, and 26% of emails. This supports Baym et al. report that the predominant purpose of interaction (phone, face-to-face and Internet) was social. Most of my close ties no longer attend Penn or live outside Philadelphia, thus I depend greatly on new media for companionship. Wellman and Wortley would characterize my network as “spatially diverse,” which they assert contributes to the demand for the Internet and computer mediated communication (2001, p. 228). I use cell phone and SMS most for companionship, which is logical because I almost exclusively communicate with strong ties via these mediums and “strong ties are more likely to provide companionship” (Wellman & Wortley, 1990, p. 567). Cell phone is also a synchronous form of interaction, which provides the added bonus of co-presence to the companionship experience.

Small Services was the next most common type of support and was more equally distributed throughout the mediums (31% AIM, 27% Facebook, 23% Cell phone, 12% SMS, 10% Email). For small services I rely primarily on my roommate Nolie G (5 interactions), Jaime L. (4 ), my sister Liz (4) and my other roommate Pat M. (3), all of whom are strong ties. This supports Wellman and Wortley’s assertion that “strong ties provide significantly more small services” (1990, p. 566). In addition, with the exception of my sister, all of these people live within one block of me, which relates to the ideas that “neighbors are most likely to provide services” (Wellman & Wortley, 1990, p.570), and, “physical access promotes small and large services” (p. 569).

Emotional aid was given and/or received via cell phone, email, SMS and AIM with close ties (totaling 15 interactions). Surprisingly I used email most for emotional aid, which is often criticized as an impersonal medium. My sister and I exchanged the most emotional aid (5 interactions), 4 of which were over email. I do not think that this is an indication of an inherent relationship between email and emotional aid, but rather a unique aspect of my sister and I’s relationship.

Large services were all cell phone and email consisting of 5 interactions with my mom, 2 with my sister and 2 with Jaime. This supports the assertions that mother-daughter ties are especially supportive, as well as the idea that siblings are more likely than friends to provide large services. (Wellman & Wortley, 1990, p. 572-574).

C. The type of relationship?

Cell Phone SMS Email AIM Facebook Total
Parent 5 4 9
Sibling 1 3 9 13
Friend 24 6 11 7 9 57
Classmate 1 1
Acquaintance 1 1 2
Roommate 3 5 6 14
Professor/T.A. 3 3
Penn Faculty 9 9
Penn Organization/Group 3 3

I interact with friends more than any other type of relationship (46% of all communication) and I use all mediums (42% by cell phone, 19% email, 16% Facebook, 12% AIM and 11% SMS). I interact with my roommates the second most (11% of interactions) using the cell phone, SMS and AIM. Most of my interaction with my roommates is for small services (i.e. locking myself out) and physical companionship, so it is easier just to call, text or AIM from wherever I am to get a quick response. I communicate with my sister, only slightly less than my roommates, but we primarily communicate via email (69%), and occasionally text message or call. As Hampton explains, “Asynchronous communication facilitates temporal flexibility: people can read and respond to communication at individually convenient times and places” (Hampton, 2004, p. 226). Email also allows my sister and I to over come the obstacles of geography and time.

My phone demonstrated a relationship to the intimacy of the relationship. My friends, roommates and sister all represent very intimate relationships, thus I support Baym et al.’s report that phone is more likely to be used for intimate relationships.

Not surprisingly, I only use email (44%) and cell phone (56%) to communicate with my parents. As members of an older generation neither of my parents has any idea how to use AIM, text messaging or Facebook.

I interact with professors, T.A.’s and Penn faculty solely through email. I doubt the most professors would enjoy having students call them at home, so email allows both the student and the professor (or other faculty) to communicate on their own schedules without breaking the student-teacher relationship. This supports Baym, Zhang and Lin’s assertion that Internet provides a good channel for “getting schoolwork done and exchanging information” (2004, p.304).

D. Duration of the relationship?

Duration Cell Phone SMS Email AIM Facebook
0-1 years 9 1
1.1-3 years 3 5 3 2 3
3.1-5 years 23 17 6 9 4
5-10 years 1 2 2 3
11+ years 6 3 13

I interact most with people I have known for 3-5 years (48% of all interaction), using all mediums. They comprise 70% of cell phone, 68% of SMS and 69% of AIM. This is not surprising considering that this is how long I have known most of my college friends, who make up a large portion of my personal network. For people I have known less than a year, I interact almost exclusively through email; most of these ties are classmates, faculty and professors from this semester, thus email is the most appropriate means. For people I have know the longest (over 11 years), I communicate primarily through email (60%) and cell phone (27%). In fact, I email people I have known for more than 11 years more than any other group, particularly because this group represents my sister and mother, with whom I frequently exchange email.

E. Distance to the person?

Distance Cell Phone SMS Email AIM Facebook Total
0-5 miles 20 18 16 4 3 61
6-20 miles 1 1
21-200 2 5 13 6 2 28
201-999 5 2 2 9
1000+ 6 9 3 5 23

Nearly 50% of all of my interactions were with people living less than 5 miles away (avg. less than 1 mile). This group represents my local Penn network of friends, classmates, professors, faculty, etc., with whom I interact via cell phone, SMS, email, aim and facebook. As McPherson, Smith-Lovin & Cook explain, “We are more likely to have contact with those who are closer to us in geographic location than those who are distant” (2001, p. 429). While the advent of new technologies may “loosen the bounds of geography,” they will not entirely do away with the traditional pattern (McPherson, Smith-Lovin & Cook, 2001, p. 430). Most notably, cell phone and text massaging were used primarily for local ties (61% of my cell phone interactions and 72% of my text messaging) within 0-5 miles of me. This supports Baym et al.’s finding that telephone communication tends to be local rather than long distant (204, p. 310-311).

Email, on the other hand, was used to contact both local and distant network members, with the most interactions falling in either the 0-5 range or 20-200 (NYC). As Hampton explains, computers allow communication across significant geographic distances as well as supporting local interactions (2004, p. 225).

My data seems to support Baym et al.’s idea that the Internet is particularly useful in maintaining long distance relationships. For people that live over 1000 miles from me, I communicated more via computer mediated communication (17 interactions: 9 emails, 5 Facebook, and 3 AIM) than cell phone (6 interactions).

F. The person’s age or gender?

Cell Phone SMS Email AIM Facebook Total
20-21 18 13 5 2 38
22-23 9 9 5 13 9 45
24-30 1 3 13 17
31-50 5 5
51+ 5 4 9

When using my cell phone and text messaging, I interact most with people that are 20-21 years old. Most of my friends and classmates at Penn are this age; thus they represent my local Penn network. In addition, the person that I communicate with most, Jaime L., is 21.

For people ages 24-30 I primarily communicate via email. This age group represents young professionals and former Penn students, who graduated before 2006. Most people in this group have jobs, thus email is more convenient. For people over 50 (my mom and dad), I use email and cell phone.

G. The similarity of age and gender to your own?

Overall, I communicate most often with people that are my age (22-23), but it is distributed more broadly across all mediums. I communicate most via AIM with this age group (the only group I talk to via AIM), but cell phone, SMS and Facebook are not far behind. Of the 5 people I communicate with most, 2 are in this same group.

Cell Phone SMS Email AIM Facebook
Female 26 17 26 8 4
Male 7 8 9 5 7

For all mediums except Facebook, I interact more with females than males. Thus my diary suggests that most of my network interaction is homophilis in terms of gender, although McPherson’s argues that gender homophilily is typically low among “young, highly educated and Anglos” (me).

H. What if anything dos this say about the role of new media in networks?

My new media use patterns seem to strongly support many of the ideas put forth by Hampton and Wellman. I use email frequently to communicate with close ties, including all of the 5 people I contact most, all of whom represent relationships that were originally formed offline and are place-based, yet are maintained using new media. This supports Wellman’s assertion that “the rapid emergence of computer-mediated communications means that relations in cyberspace are joining with relations on the ground” (228). The asynchronous nature of CMC allows me to interact without the constraints of time and geography, which might severely affect a person in my unique situation (“in between” networks). As Hampton explains, “The internet reduces friction of space” (Hampton, 2004, p. 226). It is not opening my world to a vast heterogeneous population of new ties; instead I use new media to maintain the homophily of my previous Penn 06’ network, as more of a person-to-person network than a place-to-place one.

At the same time, new media are not completely isolating me from my local network, as the Dystopian view suggests, because I frequently interact with people near me and use CMC in my local network, supporting the idea of “glocalization,” or using global technologies for local ties (Hampton, 2004, p. 226). In reference to the Baym et al. article, I think that my diary demonstrates an online social life that has successfully integrated Internet into my interpersonal communication. In this regard, I identify with Wellman and Gulia’s parallel drawn between the telephone and online communication as new technologies. They explain that phone was once exotic, but now people take it for granted and use it frequently to communicate with both distant and local ties. Online interactions may also become normalized as a reasonable way to maintain ties. (348).

Question 3:

What characteristics of the tie and person were most common for those interactions that took place inside your home?

I had 46 interactions in my home, 87% of which were with close ties, 3% moderate and 3% weak ties. Most communication at home is on the cell phone (37%) or email (24%) and text (22%). 8 of the interactions were with people living over 3,000 miles away, 16 with people between 100 and 300 miles away, and 17 with people a mile or less. 27 of these interactions were for companionship, 9 emotional aid and 7 small services. I interacted with Jaime most (12), my sister (7), and Jarrett (5). 52% of my cell communication was at home.

In public places?

The Street: 11 interactions on the street (2 SMS and 9 cell phone). 8 with close ties and 3 moderate. 6 of the interactions were local and the other 5 distant. All but one of the interactions was for companionship. I usually call or text people when I am walking to keep me company.

Class (semi-public): 21 interactions with both close ties (14) and weak ties (6), from a broad age range. This is when I interact most with 24-30 year olds because they are my friends bored at work. I use primarily email (12) and SMS (4) because they are discrete. All female.

Library: (semi-public) I interact in the library more than any other public space (42 interactions via email, SMS, cell, AIM), with friends (23), Penn faculty (5), roommates (4) and parents (4). Of these interactions, 26 female and 11 male. Most live within a mile of me (24), yet 11 interactions were with people over 2,000 miles away. Mostly close and moderate and weak ties.

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

I was surprised to discover that 60% of my total interactions were in public and semi-public places, challenging Putnam’s warning that people are not longer interacting in public. While new media liberate me from the constraints of geography in maintaining distant ties, they also considerably mobilize my communication. While I did use my cell phone more at home (17/33) than other places, 48% of my cell phone interactions were outside my home. Most of my computer-mediated interaction was also outside the home, even though Baym et al. reported that most CMC is based in the home. I do not think that new media are changing the composition of my social network, but rather how, where and when I choose to contact them. With the exception of some Penn Faculty and other university-related contacts, most of the members of my network could have been contacted through traditional media such as mail or landline telephone, but I choose to use other media out of convenience. If anything, I think that new media enhance my ability to control the composition of my network by maintaining ties that I feel particularly useful/important. New media simply allow me to move out of the private home and into public. This portability challenges the traditional boundaries of private and public, as Hampton states, “The growth of mobile phones and wireless computing has brought computer mediated communication out of the home and into the street, but people can cut themselves off from public spaces by creating private spheres of mobile interaction. ( 2004, 219). People interact less with strangers in public spaces; instead forfeit chance encounters for interaction will familiar ties with new media.

Most of my network members are person-to-person interactions, yet were formed as “place-based” relationships, such as college, high school, etc., so I agree with Hampton when he writes, “The reduction in the friction of space enabled by the Internet has not made geography or place irrelevant” (2004, p. 229). While I agree that the past few decades have seen a strong societal trend towards privatization of social interaction that is closely related to technological advancements in communication media, but it cannot be isolated from considerable confounding factors (i.e. transportation, political climate, economy, etc.).

The first reading, “Deviance as a search process,” examines the ideas of deviant Internet and deviant behavior within social networks. A major example used in the article is the search for marijuana. Tepperman mentions many of the ideas we have discussed thus far in the course, such as closed networks, open networks, strong ties, weak ties, etc., and applies them to the search for drugs within a social network, explaining that there are two possible routes: 1) “closing methods” and 2) “following the path.” Closing methods are similar to a scientific study that surveys a wide sample within the population, in this case one’s social network, and narrows the field as it goes. The second process, “following the path,” represents a more small world approach, in which a person chooses the tie that it feels is most likely to be closer to the desired resource, in this example marijuana or knowledge concerning its availability and whereabouts. Can you think of different examples of situations in which you night use each of these search methods?

The second article, “The Social Capital of Opinion Leaders,” by Ronald Burt, presents interesting insight into the role of opinion leaders as “information brokers” in social networks. I was particularly intrigue by Burt’s assertion that these opinion leaders exist “at the edge of things” rather than in the middle, which may seem like the logical assumption. When one thinks of the most influential people, it might seem logical that they would be in the center with strong ties to the most people (as we have discussed, information is trusted most from homophilous close ties). I also enjoyed his analogy of “brokers,” who carry information across social boundaries between groups. This examination is similar to his economic and entrepreneurial interpretation of social capital of strong and weak ties that we read earlier in the semester. He presents opinion leaders as entrepreneurs in the information business, who work between groups, trigger conversation within them, and ensure a smooth transition in the two-step flow of communication, much like a broker in a typical business deal.

The third reading, “Diffusion Networks,” presents an in depth examination of diffusion networks and how they convey innovation education information through opinion leaders. This article presents a slightly different definition of opinion leaders than the previous article, stating, “Opinion leadership is the degree to which an individual is able to influence informally other individuals’ attitudes or overt behavior in a desired way with relative frequency.” This presents a more manipulative interpretation of the role, as an individual who “intentionally” seeks to influence others. Burt’s article focuses more on their position within networks “on the edge” rather than in the middle, portraying as less deliberate role as opinion brokers.

The article does present an excellent examination and clear explanation of homophilly and heterophilly within social networks and the diffusion of information. Not only does the article explain that people who are similar tend to interact, homophilly, but also explains, “Homophilly occurs frequently because communication is more effective when source and receiver are homophilous.” The article continues, “Heterophilous communication between dissimilar individuals may cause cognitive dissonance because an individual is exposed to messages that are inconsistent with existing beliefs, an uncomfortable cognitive state.” This point highlights one of the critical issues that were discussed in last week’s blog comments as well as class discussion, by illustrating the critical communication that is lost when people no longer are forced to interact with opinion leaders of opposite/heterogeneous opinion. New information enters a communication network through contact, at some point, with other heterogeneous nodes within a system, often other opinion leaders. Unfortunately, the Internet and other forms of telecommunication allow people the possibility to create a virtual reality (which often transfer into their physical reality) with minimal exposure to heterogeneous ties. I am not saying that this is the norm, or that it occurs on such an extreme level, but this type of behavior can be seen in many areas. Do you think that it is a problem? Can you think of any specific examples?

I was actually amused by the first article, “Pentagon sets its sight on social networking websites,” in which the author, Paul Marks, examines the Pentagon’s growing interest in the information that people post on various social networking websites. Are they seriously suggesting that an extremely secretive terrorist group is stupid enough to link themselves on a highly visible online network such as Myspace or Facebook? I am much less surprised that the NSA is collecting information about people’s profiles from online sources than I was concerning the phone tapping. Unlike telephones, the Internet is an undeniably public space. Although people often access the net within the comfort of their own private sphere (i.e. home, work, school, dorm), it is still a widely public domain. Credit card companies, online stores and thousands of other websites collect information about a person each time they complete a transaction, subscribe to an online publication, search using a search engine, etc. Things called “cookies” have been around for years now, and websites selling information to advertising agencies is a well-known practice, so I do not understand why people are so surprised that the NSA would utilize the same technology. People just need to learn that the Internet is not as safe as they think it is.

The second reading, “The Structure of the Web,” presented an interesting examination of network analysis applied to a virtual network of ties, rather than the real life/physical communities we have discussed thus far. I would have never thought to examine the Internet using the same ideas of “hubs,” nodes, links, and degrees. I immediately identify the “upstream/downstream” nodes as indegree/outdegree and the core links as highly centralized both in closeness and betweenness. It is interesting to think about the fact that the internet was not geographically or architecturally planned out; instead it grew out of itself, in much the same way as a social network in the actual/physical world. How do you think studying the composition of web structure will provide insight into the study of other social networks?

Wellman’s article, “Physical Place and Cyberspace: The Rise of Personalized Networking,” presented an interesting examination of the role of physical space and computer-mediated communication in computer-supported social networks. Rather than simply studying the effects of computer on other aspects of social interaction, Wellman focuses on how they two realms of interaction coexist in physical and virtual space. He places special emphasis on the access to resources, noting that computer-supported community networks provide a broader bandwidth and greater access to resources, which are improving in quality as new technologies continue to improve (i.e. video communication, and speed). He also discusses the idea of portability, which is an increasingly prominent phenomenon. Many articles have been written concerning the mobility of the cell phone and its intrusion of the private realm (the phone) into the public realm. Wireless Internet is once again challenging society to reorganize its conceptions of public space and interaction because people can now log onto the Internet almost everywhere. People no longer sit in an airport waiting area reading a magazine or talking to the person sitting next to them; instead, people are online checking emails and conducting business transactions. It is incredible that we have this kind of mobility and access to resources, but what are we losing. How important are those conversations in the public space? Are these types of chance encounters replaceable? Are we losing an important contact with our immediate social environment?

Wellman does not bash the Internet like many other authors in this area of study. Instead he argues that people can interact happily online and in ways similar to face-to-face. They can also use online to fill the gaps in a relationship that exists offline.

He posts a popular question form our class discussion that I thought I would throw in so everyone can have a say: “Are relationships based on online communication as authentic and reliable as those in which online communication is only one form of interacting?

The last article, “Spatially Bounded Online Social Networks and Social Capital: The Role of the Facebook,” surprisingly reports that despite potential privacy problems and image control issues, there was a “robust connection between Facebook usage and indicators of social capital” (32). In other words, they argue that Facebook users actually benefit from their participation in the online network community. One of the critical features of Facebook as a unique site is that many of the relationships formed on Facebook are grounded in real-life relationships, from school or other extracurricular activities. These are not just a group of random kids you meet online. It provides excellent resources for homework and university events. It can work as a form of interpersonal communication, messaging, mass messaging, etc. The major limit of this study is that it does not compare Facebook to other similar online network sites. If they did, I think that they would find significant differences, largely due to the school affiliation that Facebook relies on.

How do you think this would affect the findings?

This week’s readings provide informative studies of error, limitations and bias associated with various measures of network composition and social capital in network analysis.
The first reading, “Auditing Information Structures in Organizations: A Review of Data Collection Techniques for Network Analysis,” examines the methodological strengths and weaknesses of six data collection techniques: sociometric questioning, diaries, observation, archival records, ECCO questionnaires, and the small-world technique. What is most interesting in this study is the critical examination of concerns regarding reliability and validity, which have often been understated or under-estimated in many of the studies we have read thus far. For example, while most studies note self-report bias, Zwijze-Koning and De Jong go more in depth, discussing socially desirable response bias,” which refers to the tendency of respondents to answer questions in a what that conforms to dominant belief patterns among groups to which the respondent feels some identification or allegiance. We have already seen examples of this type of behavior in studies concerning adolescent self-report of substance abuse (i.e. smoking, drinking, and marijuana). They also mention telescoping and recall, which is a comprehension of time error; respondents often remember an event occurring more recently than it did, making it difficult to establish validity of self-report that asks vague questions, such as “in the last 3 months,” especially with sensitive topics. Another critical source of error is question wording, which often leads to misinterpretation. Can you think or any examples of this type of effect in our readings thus far? Reactivity is another source of error that they find common in the use diaries, when respondents consciously decide to behave differently because they are recoding the task (i.e. watching less TV when you have to log it in a diary because you are become conscious of how much you are watching). One of the most interesting conclusions of the study was the discussion of the role of respondent perception or relationships compared with actual relationships. We discussed this in class two weeks ago in reference to the teen smoking study, which found that teen smokers reported larger social networks (more ties), but were viewed by others as less desirable friends. Another example is our discussion of extended kin; while people often name them as strong ties, studies of social support find that they are rarely reported as sources of support that are associated with strong ties (i.e. financial, emotional, companionship, etc.
In the second reading, “Simplifying the Personal Network Name Generator Alternatives to Traditional Multiple and Single Name Generators,” the authors, Marin and Hampton, compare “measures of network composition and structure obtained from stand alone generators to measures of a six item multiple name generator” (1). The purpose of the study was to examine the effects of using a single name generator (i.e. the “important matters question” used in the Killworth study we read two weeks ago concerning core discussion networks), rather than the more costly and time consuming multiple name generators, on the reliability and validity of the estimates obtained in various studies of personal networks. The study also tested two new alternative methods, the MMG and MGRI. In the Study, Marin and Wellman examine many of the same limitations and biases discussed in the first reading. For example, they assert, “there is little correlation between routine contacts and those ties that people ten to evaluate as most important,” which is related to the influence of perception. They also explain, “numerous studies question the validity of interaction data when respondents report on contact over extended period of time or over a ‘typical day’ (4),” which is an example of telescoping and recall bias. The study concludes that the most reliable single name generators are surprisingly to important matters question and “who do you enjoy socializing with?” In the end the decision concerning methodology is ultimately left to the researcher based in the context of the specific project and the network properties that are of interest.
The third reading, “The Position Generator: Measurement Techniques for Investigations of Social Capital,” examines a different measure of personal social networks: the measure of social capital. Social capital focuses on the resources embedded in social structure, the accessibility of these resources and opportunity for resource mobilization. The article argues that current measure of social capital do not allow for the development of “an approach that integrates theory and measurement of the concept” (57). The propose a new measurement, ‘the position generator,’ as an alternative to the name generator, which is limited because it tends to elicit strong ties rather than weak, locates access to individuals rather than social positions and is bound to specified content areas (i.e. financial support). Although the study provided ample justification for the method and produce significant findings, there are several sources of error that are not accounted for. For example, the method relies on the self-report of occupations of the respondents’ family, friends and acquaintances. The questionnaire asks several questions concerning the occupations, many of which people may not know. I do not know what most of my friends do, let alone how prestigious they are within their company. I know even less about the occupations of my acquaintances. The study is also limited to Taiwan, although it claims that the findings have been replicated in other countries, they do not provide proof.
The last reading printed out in a foreign language, with English alternating every fourth line. Unfortunately I am in Athens, Greece, so I do not have access to a new version of the article. I hope that this will not affect my grade because the circumstances are out of my control.

1. In their article, “Social Isolation in America: Change in Core Discussion Networks Over Two Decades,” McPherson et al. are hesitant to make any substantial claims concerning the causes of the shrinking discussion networks in the U.S., but they do provide a few suggestions. The most notable trend in the article is the fact that the average number of discussion partners, or confidants, has decreased by almost one person (358). The authors suggest that the demographic characteristics of the country have changed in the past two decades, asserting, “As the population gets older and more racially diverse, we would expect networks to get smaller, since older people and racial minorities have smaller networks, on average” (367). In our earlier readings, Kalmijn also found, “the older people are, the lower the number of friends they report” (241). Lynn Smith-Lovin attributes this trend in aging to the WWII generation of baby boomers, who are reaching the stage in their life cycle when network size begins to decline, causing the mean size for the entire population to drop.

Putnam, on the other hand, suggests “networks are collapsing inward,” due to a decline in civic engagement and membership in voluntary organizations. This suggestion is supported by the McPherson et al. article, which reports a decrease in the number of close core discussion ties from neighborhoods and voluntary organizations. Thus, as the population joins less clubs and organizations and participates less in the community, the opportunity to form non-kin close ties diminishes.

McPherson et al. also note shifts in work, geographic and recreational patterns, which could substantially impact network composition and structure. As we have examined in our readings thus far, there is the trend towards fragmented social networks with highly specialized ties and uni-plex relationships, which could contribute to the decrease in the number of ties with whom you discuss a wide range of important matters. While some authors, including Wellman, suggest that this is largely due to an increase in geographical dispersion. Putnam argues that the rate of geographic moves has not increased in the last two decades; Putnam explains that we are moving less than our parents did, so what else is at work? Privatization? Both Wellman and Putnam suggest that people are privatizing social relationships, meaning that they are moving social interaction out of the public sphere and into the home.

The study also reports a notable increase in the number of people who report discussing important matters with their spouse. While this could be due to a wide range of factors, it is most likely related to the increase in dual income households, which might increase the discussion of important financial matters between spouses. Kalmijn also supports the decrease in the size of social networks of married people, stating, “Among married persons, there is a negative association between duration of the marriage and the size of the friendship network” (233).

2. The article focuses on confidants, which are characterized as a type of close tie. In a study of tie strength and social support, Wellman and Wortely report that this type of tie provides a broad range of social support, including emotional aid, small services and companionship. When a confidant is a parent, which was often the case in the McPherson et al. study, support is broadened to include financial aid and large services, with a decrease in the level of companionship (558). These types of ties are often homophilous, and Granovetter argues that they are not good sources of information and some resources (i.e. getting a job). While ties between similar people tend to be more empathetic and conducive to social support (Wellman and Wortley, 578), homophilous ties often link us to similar others and redundant ties, who have access to the same information that we do. Thus access to new information and a diversity of resources is threatened by densely knit, homophilous networks.

The significant decrease in non-kin confidants may also contribute to an increase in social isolation. McPherson et al. explain, “These [non-kin] ties are the most likely to bridge socially distinct parts of the community structure, since we know that marriage and family are more homophilous on class, religion, race and several other social attributes than ties formed in other ways” (359). Thus solely depending on a spouse or parent cuts a person off from other networks, affecting the types of new ties formed outside of your existing network. This is important in the process of socialization and normative pressures, which are primarily transferred through closely-knit interpersonal networks. Fischer supports this assertion, stating, “Most people affect their society only through personal influences on those around them. These personal ties are our greatest motives for action” (3).

In the radio interview, both Putnam and Smith-Lovin suggest other impacts of these trends, such as an increase in the rate of crime because people feel less safe and have less ties in the community. Putnam also notes a threat to the foundation of democratic institutions, which he argues are based on participation in civic engagement and community structure. There are also several references to effects on mental health and well being, although our readings have not explicitly examined such effects.

McPherson et al.’s article, “Birds of Feather: Homophily in Social Networks,” discusses the principle of homophily, which asserts the “contact between similar people occurs at a higher rate than among dissimilar people” (416). In the article, they examine the sources, types of relationships, and varying dimensions of homophily. The article draws heavily on ideas discussed in earlier readings, by Granovetter and Burt, such as cognitive balance theory and transitivity, which both assert the basic concept that “friends of friends tend to be friends.” This also relates to Burt’s criticism of redundant ties and the role of “egocentrism,” which attributes tie formation to cues from shared backgrounds and interests (73). McPherson and his colleges expand on these concepts, identifying baseline homophily, related to demographic variables that create the opportunity for a potential tie, and inbreeding homophily, which is independent from opportunity (choice to form a tie). Not surprisingly, race and ethnicity contributed most significantly to homophily, along with education, sex age and religion.

I was particularly interested in their discussion of “tie dissolution,” which asserts, “nonhomophilous ties are especially likely to be dropped when they are involved in intransitive friendships” (436). This provides an introduction into the process of co-evolution of friendship networks, which are characterized by both the selective formation and dissolution of ties, and is a nice transition into the next article from this week, “Homophily and Assimilation Among Sport-Active Adolescent Substance Users.” In this study, the authors, Pearson et al., “analyse the co-evolution of social networks and substance use behavior of adolescents and address the problem of separating the effects of homophily and assimilation” (47). Their discussion highlights the process of “friend selection” based on homophily v. behavioral assimilation in relation to substance abuse. In this sense, they examined whether students chose friends based on their similar preferences and habits (homophily) or assimilated to the same substance use behavior of their existing friends (assimilation). The strongest homophily and assimilation were associated with alcohol, which found that “drinkers prefer the same drinking behavior of their friends, as well as exercising strong influence on the dimensions of alcohol use” (57). They attribute this finding to the social dimension of alcohol use. How do you think that this reflects substance use later in life, specifically patterns in college?

This study suffers from several laminations. The sample is drawn from 160 students at a single school in West Scotland, this it cannot be generalized to all adolescents. The study is also based on self-report, which is not always reliable when discussing drugs use in a school setting.

The last two readings focus on the estimation of network size, employing very different methodologies and producing two dramatically different estimations. In the first article, “Estimating the Size of Personal Networks,” Killworth et al. attempt to determine the maximum number of individuals known by an informant. In the study, Killworth and his colleges examine four different methods that have been employed to estimate total size of communication networks, by reevaluating data from three separate previous studies, Jacksonville, FL, Orange County, CA, and Mexico City, Mexico. They use four different elicitors, or modules, to determine network size: 1) Approximation of GSS question; 2) Support Network; 3) the RSW instrument; and 4) FT telephone book instrument. Unlike the Christmas card study by Hill and Dunbar, the Kilworth study used four elicitors simultaneously to determine network size and compared the results to determine reliability. The sample used is also considerably larger (i.e. 98 in Jacksonville, 99 in Mexico City) and the methodology is presumably more random, the list is compiled from a telephone book, although participation was voluntary. Kilworth et al. also did an excellent job of citing possible sources of error, such as the problem of “similar names,” size of telephone book, and randomness of names chosen. One of the most obvious limitations of the study is the dependence on the representativeness of the phonebook because not everyone is listed. They conclude with an approximation of 1,526 individuals in the average U.S. informants’ network. Do you think that this is a reasonable estimate? How can one account for the significant variance between the three different samples, particularly Mexico City?

The second article discussing network size, “Social Network Size in Humans,” by Hill and Dunbar, aims to identify “those people an individual considers important and whose relationship they value” (55). The study uses a unique approach, using the exchange of Christmas cards as a measure of network size. Although the study provides adequate justification for this methodology, I found that the study was plagued by bias and sources of error. For example, the participants were solicited through other participants, making it a convenience sample that relied on snowballing, which are two well-known sources of nonrandom sampling error. As a result, it is very likely that the sample in this study is more homophilous that the rest of the population because, as we discussed earlier with McPherson, Burt and Granovetter, friends of friends are usually friends due to transitivity, making overlap and similar network structure more likely. Another more obvious limitation is the use of Christmas cards, which almost ensures homophily based on religion. Not only are none of the participants the same religion, but also people may not send Christmas cards to all of the important people in their network if they have varying religious beliefs (i.e. not sending an X-mas card to a Jewish friend). How could this study be improved?

The first reading for this week was a study by Elizabeth Bott entitled, “Urban Families: Conjugal Roles and Social Networks.” The study claimed to be an explanatory examination of the relationship between conjugal roles of husbands and wives and the “connectedness” of their social networks, yet the findings and analysis of the study fail to establish a consistent correlation between the two variables. Of the twenty families that participated in the study, only six of them actually fall into two patterns identified in the hypothesis, the other original two do not occur at all. She then creates an intermediate degree and a transitional family group, into which the rest of the families fall. It is as though she makes up the classifications as she goes. This types of ad hoc methodology would be further complicated if the sample in the study increased. Do you think there would be more possibilities, of would the four identified trends hold up?

Bott does provide a nice introduction into the role of social networks, specifically strong ties, in urban cities in her discussion of the role of kin and joint relationships. She explains that urban communities are not as cohesive and urban families are not contained in small locally connected networks. Instead she asserts that their communities are composed of the “network of actual social relationships they maintain, regardless or whether these are defined to the local area or run beyond its boundaries” (373). These networks are often based on personal preference and shared interests.

Claude Fischer presents a consistent description of urban social networks. In his article he explains, “Urbanites will have more varied and distinct social networks than residents of small communities” (11). He attributes this to the concentration of diverse population around a community, which is intensified in urban environments. He challenges the idea that cities have lead to a decline in community and quality of personal networks and asserts, “general quality of personal life n cities and small town may be similar, but the typical style of life differs” (12). The ties that make up social networks are influenced by the primary social contexts of personal relations. For example, kinship involvement is reduced in urban communities, because most kin are located farther away. When people move to cities there is an expansion of the individual’s range of choice in making and maintaining personal relations. He concludes that as urbanism increases, the number of ties in traditional contexts (family, neighbors, etc) decline, while the number of ties in modern contexts (coworkers, co-members of organizations, and just friends) increases. Thus people in the cities are not isolated, but they have different type of networks. This is a similar argument o the earlier reading by Wellman that challenged the idealization of the “golden pastoral past.”

The third reading, “Different Strokes from Different Folks: Community Ties and Social Support,” Wellman and Wortley examine 6 possible explanations for why different types of ties provide different kinds of social support and resources. They conclude that contemporary communities support both the “community saved” argument and the “community liberated argument.” Immediate kin make up the network segment that is densely knit and multiplex, providing a broad range of supportive resources. Friends, coworkers and neighbors make up the other network segment that is sparsely knit and segmented, with different types of ties providing different resources. The degree to which both types of ties provide support is a function of the strength and accessibility of the ties and the relationship. If this study were conducted today (16 years later), would the same patterns and relationships be observed?

The last reading examines the effect of cohabitating relationships on friendship networks, asserting that “the percentage of shared friends and joint contact increases over the life course.” To what extent do you think that this is due to the principle of competition in comparison to the dyadic withdrawal hypothesis? What role does age play in the process? Is it more significant that Kalmijn explains?