December 10, 2006

social networks - the last entry!

Once I started analyzing the data, age was the variable that showed the most differences between the groups that were surveyed.

In terms of network size, as defined by those with whom people listed as discussing important matters with, it seems that younger people have a larger network size than older people: the average for the 18-22 age group was ~5 people, whereas with the 33+ age group was ~2-3 people. In their article “Social Isolation in America”, McPherson et al find that “the number of discussion partners … has decreased by nearly one person (from a mean of 2.94 to a mean of 2.08) (257-8). This tendency can be seen among the older subjects, especially with the male subjects since their average was ~2 people. However, the McPherson et al results do not match to the results found with the younger subjects, as their average is even above the mean found decades ago. On the other hand, it should be mentioned that the McPherson et article’s mean was based on “the typical American’s interpersonal environment” (258), and the subjects in my survey have a strong international tendency, with many of them residing in different countries, and this may potentially make the means not comparable with the McPherson et al averages.
The argument that Kalmijn puts forward about how married and cohabiting couples’ networks tend to blend and merge could be an explanation as to why older people (since 90% of them are married) have a less social network size compared to young people. Further evidence for this trend could be given by the fact that the people that older subjects have listed tend to know each other, and even, the large majority are especially close to each other, 70% as well. This is not the case for young subjects, in which strangers make up 23%, people knowing each other 55% and the especially close 22%. Thus, homophily and transitivity seem to be low for young individuals when it comes to network composition.

An interesting point that I found in my data is the fact that all groups mentioned more women than males when listing people with whom they discussed important matters with. Wellman and Wortley point out that “numerous analysts contend that women are more likely than men to provide emotional support”, and that “women are often the principal emotional supporters of men as well as of other women” (576). This is clearly shown by the data in my survey in which ~63% of the people in general with whom subjects talked about important matters with, and thus gave social support, were women.

Homophily could be detected in various aspects of the survey data. For the older subjects, the age of the people mentioned strongly matched those of the subjects: except for 4 subjects who mentioned their children among others, and 1 subject whose wife is significantly younger, all the other people mentioned were +/-2 years from the subject’s age. This tendency is similarly portrayed in the younger subjects as well: when excluding the family members listed, everyone else was +/-2 years from the subject’s age. McPherson, Smith-Lovin and Cook says that “homophily on age can be stronger than any other dimension” (424) which is supported by the data from my surveys. Education matched the subject’s for 65% of the people mentioned in the older generation, and for those in the younger generation, it was 100% match, if the parents and family members were excluded. McPherson, Smith-Lovin and Cook also say that “all educational groups show inbreeding tendencies” (427), and this is also supported by the data. On the other hand, there was no evidence for McPherson et al’s statement that “more highly educated people have more people to talk to about things that are important to them” (362) since, not only are the college students showing the largest social network size, but even among older subjects, this tendency did not show.

In accordance to the McPherson et al article, 90% of the older subjects mentioned their spouse as one of the people with whom they discussed important matters with. (The only person who didn’t has never been married). This strong tendency to mention their spouses, plus the fact that most of the subjects’ friends reside in the same neighbourhood/city/state could be related back to McPherson et al’s point that privatization seems to be increasing. Moreover, it should be noted that 3 older subjects mentioned friends and all 3 co-listed these friends as either co-workers or neighbours. Also, the remaining 70% mentioned other family members, and these family members were also listed as being especially close to each other. All of this could be used as evidence to support the privatization argument as well. Based on the Fischer reading, it could be said that the family members who have been mentioned enjoy each other’s company, and thus make an effort to seek each other out, especially since everyone who mentioned other family are living in big cities (and sometimes even in differing part of the country or even different countries).

However, the privatization argument does not apply to the data collected from the younger subjects. Only 37% of all people were listed to reside in the same house/building/dorm or neighbourhood, and even 25% of all people listed resided in foreign countries. When it comes to students who attend college, distance and proximity does not seem to matter when it comes to having someone to talk important matters with. Also, maybe the way in which college students reside within a campus, with other students, staff and faculty, makes the campus feel like a community and thus, privatization is not an option that would arise. It should also be noted that 90% of the young subjects mentioned one of their parents, which could add to the long distance factor since it is usually the case (in my sample) that the subjects were currently attending a college far away from home. This goes against Granovetter’s argument that proximity and frequency of interaction matter when it comes to strengthening of ties and also Wellman when he said that most of our social support is within 300 miles of where we live. Instead, the data favors our discussion in class, where we, college students, said that proximity and frequency of interaction are poor predictors of tie strength.

In terms of means of communication, both young and older subjects’ in person communication was related to distance: the farther away people resided from the subject, the fewer days they interacted with in person. However, generation gaps could be seen between the use of old media and new media. The use of main-line phones was much higher among older subjects than younger subjects, and contrastingly, the use of cellphones was much higher among younger subjects than older ones. Young subjects could be potentially drawn by the mobility that cellphones provide, as mentioned as Wellman, in which they can talk to whoever they want, whenever and wherever they wanted to since college is a time in which time schedules are less set compared to older people’s schedules.
Just as the Baym et al article states, young people used both email and IM to interact with each other, with a slight heavier use of email than IM, especially with older people. Also, these authors say that “internet is particularly useful in maintaining long distance relationships” (314) which fits neatly into the data collected from younger people, in which we can see that younger subjects had fewer people living near them than older people.
However, the results were different for older subjects: they rarely used IM to interact with those with whom they talked important matters with, and used email in those cases in which there were overlapping roles, especially for those individuals who were also listed as co-workers. This unusual heavy use of email to interact with co-workers was also present among young people, which suggests that, as Baym et al’s data shows, that email is a highly used mean of online communication that can be used for multiple ends, which makes email a perfect match to address the issues and problems that arise because of the high level of multiplexity (Mesch and Talmud) in college students’ lives.
One interesting point that should be noticed is the fact that for both older and younger subjects, women tended to use more means of communication to get in touch with people. This could potentially be an explanation as to why McPherson et al found that women tended to have “larger networks than men” (372), since, as Hampton says, “the Internet offers is a way of overcoming barriers to local tie formation” (225) as well as “providing access to an even larger, more heterogeneous population” (225).

The position generator was used to assess the access to social capital that the subjects had. In this domain, it wasn’t the age difference that made an impact, it was gender: the average score for older males was 93, young males 70.2, older females 69.2 and young females 42.2. The position generator is said to tap into people’s weak ties and thus measure network diversity as well. Granovetter and Burt place strong emphasis on the importance of weak ties in terms of the social resources that these can provide, especially with job opportunities. However, the fact that the directions defined someone that you know was “anyone that you have known by their first name” could potentially involve the mentioning of strong ties as well as weak ties: knowing someone by their first name, in addition to knowing that person’s occupation seems to imply a stronger relationship than what would be considered a weak tie.
In their article, Lin, Fu and Hsung say that for women especially, education was related to social capital, and this trend is supported by the data from the survey: older females (all of which had at least a Bachelor’s degree) had substantially higher occupational prestige score than younger females did. Education also seems to be related for males, even if the authors have not found such a relationship in their article; this difference could be because of the different cultures in which the surveys were administered to.

Some of the measurement issues that came up during the survey administration were those that Zwijze-Koning and De Jong mentioned in their article: since the survey was a form of sociometric questioning, validity issues arose, especially with the position generator answers since social desirability could have affected the truthfulness of the answers given. Added to this, the fact that my particular survey was administered to international people, the questions could not have translated well, and even with those who took the survey in English, the interpretation to some of the questions were different, especially when it came to defining the relationship between the people that they mentioned. Furthermore, since our survey had the same features and questions as the GSS, the measurement issues that McPherson et al bring up are also applicable here.

Posted by y1 on December 10, 2006 8:30 PM | | Comments (0)

December 4, 2006

last blog~ social prestige and social inequality

Marsden and Hurlbert’s article is an extension to previous studies that deal with job-matching, occupation status attainment and income. Their analysis is on the 1970 Detroit Area study, with a focus “on the transition leading to the first job in the current firm” (1042, with which they mean to address some of the methodological criticism that had been raised to previous studies dealing with similar topics of interest.

Their finding that “effects of social resources measures are, for the most part, outcome-specific” (1054) is interesting. Could this be that, contrary to what Killworth et al have found, people are indeed pretty accurate in choosing links and thus are able to find contacts that provide them with very specific and desired outcomes? Or could this be a result of a possible limit-ness in searching jobs through contacts? The authors even find that the industrial sector of the contact that one reaches out to will probably be the industrial sector in which one works, and also, this will involve close supervision. Not only that, those contacts that are the most influential are helpful in providing entry to small firms. Thus, how helpful is it really to access jobs through contacts? Do contacts really provide with the great jobs that they’re supposed to be providing?

The article by Fernandez and Harris talks about social isolation in the underclass and how this adds and is related to social inequality. They address the different concepts that Wilson proposed in his model which says that the underclass is socially isolated. The authors’ findings of gender differences within the non-working poor, and what these gender differences implied in terms of how socially isolated one is was very interesting. It seems that black poor nonworking women are more socially isolated than black poor nonworking men, especially in terms of contacts since apparently, poor non working women have different social structures than poor non working men. Why and how do you think these differences emerge? And how do you think it’s possible that these differences diminish one poverty does not become such a strong and defining factor in one’s life?

One criticism of the article, which the authors themselves bring up, is the lack of cultural variety. Their study is only based on a sample of black people, which limits not only the generalizability of the results, but also the credibility, since it could be said that the results of the study were present due to characteristics of how African Americans relate to each other, which could potentially differ to how Whites, Latinos and/or Asians relate to each other.

Posted by y1 on December 4, 2006 9:41 PM | | Comments (1)

November 30, 2006

small university experiment - part III

link:
http://www.mysocialnetwork.net/blog/481/y1/2006/09/lets_get_delivered.html

The results of our Small University Experiment are in, and several points of interest have emerged from these. Overall, it was striking to see the differences in the rate of success for the two target people in our study: Susan had an 80% success rate, whereas Antonio only had 25%. When making a comparison to Stevenson et al’s study, who carried out a similar Small University Experiment, their results were a 27% success rate, which resembles Antonio’s success rate and also Milgram and Korte’s (22%), more than Susan’s.

The number of total mean links was different as well: Susan’s 3.25 links and Antonio’s was 4.5 links (Stevenson et al only provide the number for completed chains). Stevenson et al say that “more chains are likely to successfully reach their target in SW studies in organizations as compared to the larger society” (5). Could it be said that the Education School is considered an organization, given its smaller size and relative “isolation”, whereas the Medical School would be “the larger society”, which would partially explain the differences in success rate?

Another point that Stevenson et al make is that “upper-class students were more involved in the completed chains” (6). Our results show a similar patter: except for g23, whose success involved freshmen, mostly upperclassmen were involved in the experiment as a whole, for both successful and unsuccessful chains as well. As the authors mention, this could speak to the relative isolation of freshmen from the rest of the university: this fact is especially pronounced in our case since our experiment was carried out during the Fall semester, a period in which freshmen are still trying to adjust to the new life in college (whereas the Stevenson et al study was conducted in the Spring).

The authors’ second hypothesis states that “folders are more likely to be passed within a class than between classes and occupational groups in a university” (3), which was supported by their results. However, the same pattern could not be seen in our data: there was no real “hierarchy of student communication links” (6), since in general, we passed it to people in our same year, ~40% for both Antonio and Susan, lower classes, ~30% for Antonio, 60% for Susan, and higher classes, ~30% for Antonio. Thus, it would seem that, in terms of class and occupational groups, our results go against this type of homophily and McPherson et al. On the other hand, our results are in line with Milgram’s finding that there is occupational similarity between alters and the target.

However, our results and Stevenson et al’s do agree in that the folder tended to reside within students until it was passed to graduate students, staff or faculty members, followed by the target. There was no regression in terms of hierarchical status. Everett says that “homophily and effective communication breed each other” (306); thus it makes sense that, students, who are homophilous in terms of occupational status, would have kept the folders among themselves to ensure that the folder would reach a faculty, graduate or staff member adequately and not have to regress to the students, since these two groups would have a harder time communicating effectively based on their occupational heterophily. Unfortunately, there doesn’t seem to be any data explaining what characteristics these gatekeepers had since there is no pattern to be detected in terms of years at Penn, school, department nor gender.


There were also some interesting similarities and differences between the results of our own targets. In the case of Susan, a high percentage of the (completed) last intermediate links shared school: 75%, 12.5% shared the same department and 37.5% shared the same affiliation. In Antonio’s case, 0% shared school, 50% shared the same department and 0% shared affiliation. This marked difference could be explained by the sizes of the schools in which the targets resided. Since the Education School is smaller and contains a less variety of departments, the probability that someone will share departments is higher than the Medical School.

Gender was one aspect in which strong homophily was found. Stevenson et al found a strong homophilous tendency among women undergraduates: “6 out of the 8 paths that originated from undergraduate female students went to other females”. (7). Our date shows a similar patter: not only was the last intermediary for completed chains of the same sex in 87.5% cases for Susan, and 100% for Antonio, but also, in the total percentage of transfers to same gender, Susan’s binder went through members of the same gender in 85.7% of the links and for Antonio, it was 50% of the links. Also, it is interesting to see that, even if the majority of starters were women, depending on the target person, the proportion of members of the same gender adjusted to the target person. For example, for Susan, almost everyone was a female, whereas for Antonio, we see a higher presence of males than in Susan’s case. However, as addressed in my blog for part I, this tendency can just be a result of the fact that the Education school has a higher number of women than men, whereas the Medical school is more balanced in its proportions, instead of being a consequence of homophily. Therefore, it would seem that, unlike Stevenson who said that “women relied more on homophilous ties to pass folders compared to men” (8), the gender homophily is more based upon the sex of the target, like Milgram had stated.

From the data that is available, it seems that for Susan, most of the completed binders used a moderate, strong, or very strong tie strength as their 2nd alter. Only 2/8 (25%) chose a weak and very weak tie. This preference for stronger tie strengths may be, as stated on part I, due to the fact that strong ties may feel more responsibility towards and task, and thus make an extra effort to make sure to not only deliver the binder to the next person, but to also think and pick the next alter because s/he was thought to be helpful, instead of just randomly choosing someone, like McPherson et al say. Further evidence is presented from the fact that, from those uncompleted chains for both Antonio and Susan, 100% of the weak ties only made it to one link from the starting alter, whereas 4/6, 66.6%, of the strong/moderate ties made it further than 1 tie. It was unfortunate that Antonio’s data for the tie strength for the 2nd alters was missing. However, as for uncompleted folders, there was a mixture of weak and moderate/strong ties, but even in this case, there seemed to be a preference for the latter.

One of the hypothesis that I brought up on my first blog dealt with the target’s race and how this could have potentially affected (or not) the success of the binder reaching them. However, since there is not information available as to the race/ethnicity of the intermediaries nor the starters, this aspect could not be analyzed.

As hypothesized in the first blog, the binders that were completed were mostly transferred to the target through someone in the same affiliation/department. Only 3/10 (30%) of them weren’t transmitted through someone from the same school as the target. As for the hierarchical transition that was initially predicted, I was surprised to find that it wasn’t the case in which there was a status descent, as Milgram and Korte’s study had stated. There were transfers from students, staff and faculty members, with slightly more student transfers that then latter two groups. Moreover, the hypothesis that people who had been longer at the Penn community would be more likely to make the deliveries wasn’t sustained either, since the length of time of the last alters varied considerably, even within people of the same affiliation.

Despite Milgram’s finding that there were individuals in one’s network that played the role of “principal point[s] of mediation between [one] and a larger world” (66), our data does not support this; there doesn’t seem to be any “funneling effect” (Korte and Milgram, 104). There was only one person, June C, present in Susan’s chains of binder, who delivered two folders to her. Since there is no further information about her, it is hard to determine the reason why she delivered two out of the eight binders that reached Susan.


There are several explanations for the failures of some of the binders. As Killworth et al state in their article, people are very inaccurate when choosing the right intermediate links. This may have led to some erroneous choices, which, in the case of Antonio who is part of a larger school, could have decided the folder’s success. Another cause might have been the fact that, since people are busy, the fact that there wasn’t anything rewarding for them, especially those in the latter part of the chain, might have been an extra challenge in the success. This is especially true for weak ties, since the delivery of a folder does take up time, and more importantly, individuals have to get together in order to pass the folder along: this might have been a turn off for those links who thought about passing the folder to a weak tie. Furthermore, the fact that undergraduates don’t have access to staff, faculty or graduate students that readily might have been a problem, especially if these were weak ties. This might have helped if facebook could have been used to contact these possible alters. Ellison et al found that undergraduate students use facebook heavily as means of keeping in touch and communicating with weak ties. Since the instructions only allowed contact and passing of the binder with people that one somewhat already knew (since there should have been several conversations out of the classroom with these individuals, and Ellison et al found that facebook is not good to initiate new conversations and create ties with strangers), the use of facebook could have been potentially beneficial for students when trying to make initial contact and/or to schedule a meeting. However, since this hadn’t been an available option, this could be an explanation as to why so many binders died with students in Antonio’s case.


Several causes for the success of binders can be accounted for as well. To begin with, the fact that starters were upperclassmen could have aided in the success since upperclassmen tend to know more people around campus, including both strong and weak ties, which according to Granovetter and Burt, can serve as useful information sources. This is further supported by the fact that the only two folders that reached Antonio were started by the super-seniors in our class; if Antonio was a hard to reach target, it would make sense that, under this line of thought, super-seniors would be the ones to know more people who could help reach him.


As for the success of my own binder, one reason that could explain this could be the fact that, knowing Killworth et al’s findings, I made a conscious decision to find someone who was a strong tie, since I wanted the person to make an extra effort in delivering the folder and giving the choice of the 3rd alter some thought, as McPherson et al have stated, as well as someone who knew someone within the School of Education. Moreover, the 3rd alter’s status as a student within the Education school might have been of potential benefit since Susan is an assistant faculty member, which means that she might have taught classes that are smaller in nature compared to big lectures, and thus gotten to know some of her students better. Furthermore, as Milgram noted on his 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” (65), and this is shown with my data: all five alters females, which could have enhanced the chances of the folder reaching Susan.

Posted by y1 on November 30, 2006 11:06 AM | | Comments (0)

November 27, 2006

health and social networks

Dickens et al study the relationship between having (or lack of) a close confidant to the development of future/further cardiac events. Their initial hypothesis looked at whether “depression, lack of social support, or both before MI” (518) could be associated with further developments of adverse events, but their results showed only the lack of social support was associated with this. I was surprised to find out that depression after the initial MI was related to increased mortality, but not depression before the initial MI. It would have been interesting to see whether the depression that developed after the initial MI was also related to lack of social support or not. If the authors had found a relationship, then I believe this could have been used as further evidence to support their claim that lack of social support does indeed have very negative effects on the probability of developing future adverse events.

Cohen and Brissette investigated whether having a greater network diversity helps boost one’s immune system. They found that indeed, it is the diversity of one’s network that matters when it comes to fighting colds, not the number of members that one has in one’s social network. It was very disappointing that the authors could not determine what factors within one’s network diversity were the ones responsible of decreasing one’s susceptibility to colds. They briefly mention different possibilities, “such as network density, weak ties and structural holes” (9). How do you think these possibilities could potentially positively aid one’s immune system?

Q: The authors have mentioned that “for these types of studies, concise instruments are at a premium and intensive measurement is reserved for the rare cases…” (11). What kind of measurements that we have studied could be helpful in solving/dealing with this aspect of measurement that could potentially lead to mis-measurements?

The last reading for this week dealt with the structure in which romantic and sexual relationships among adolescents at a high-school, Jefferson. The authors’ main finding is that the network structure of this high-school’s romantic and sexual relationships is one that follows a spanning tree format, in which a key norm rules the way in which these relationships limit themselves (and thus form the tree): a norm against second partnerships. I was a little surprised at the only explanation that the authors provided for this norm that they found; the fact that adolescents are aware, and care about what others think about their relationships and how this in turn, affects their status among their fellow adolescents. I do agree that this could be a reason behind such tendency, but I somehow feel that there should be other explanations too that the authors failed to mention in their paper. What other reasons do you think could account for such a norm to develop within the romantic and sexual relationships in the high-school?

It is interesting that the authors make a point to differentiate adult network structures to adolescent network structures, and how this difference might lead to the need of having different approaches when dealing with health problems and issues in each of these structures. I do believe that even in the adult world, people are aware of how others think of them, and thus, the way in which their relationships, both romantic and sexual, develop is, in a way, dictated by the status that the relationship and the partner provides, just like in the adolescent world. Even if this presence might not be as prominent in the adult world, it would seem that the conclusion that they drew was a little hasty. If adults’ relationships are also influenced by “local status” (79), how would this influence the way in which adults ultimately decide on partners?

Posted by y1 on November 27, 2006 5:07 PM | | Comments (0)

November 15, 2006

New media's invasion!

(1)
5 people with whom interacted the most often:
1. Daniel – 41 interactions; age 20; male; romantic partner; strong tie strength; known each other for ~1 year; currently resides in Lyon, France (distance: 3000+ miles)
2. HoJun – 25 interactions; age 20; male; sibling; strong tie strength; known each other for ~20 years; currently resides in Atlanta, GA (distance: 800+ miles)
3. Ainsley – 20 interactions; age 20; female; housemate/same organization (sorority)/friend; strong tie strength; known each other for ~2 years; currently live in the same house (distance: 0 miles)
4. Abby – 16 interactions; age 20; female; same organization (sorority)/friend; strong tie strength; known each other for ~2 years; currently resides in Barcelona, Spain (distance: 3000+ miles)
5. Nati – 12 interactions; age 21; female; friend; strong tie strength; known each other for ~2 years; currently resides in Washington DC (distance: 150+ miles)

Most frequent interactions per media:
1] Cellphone
1. Daniel – 13 interactions
2. Nati – 9 interactions
3. Ainsley – 7 interactions
3. Clara – 7 interactions; age 20; female; friend; strong tie strength; known each other for ~3 years; distance: 0.1 miles
5. Emo (Aunt) – 6 interactions; age 44; female; other relative; strong tie strength; known each other for ~21 years; currently resides in South Korea (distance: 3000+ miles)

2] SMS
1. HoJun – 8 interactions
2. Daniel – 3 interactions
3. Ainsley – 1 interaction
3. Nati – 1 interaction
3. Joaquin – 1 interaction; age 21; male; friend; strong tie strength; known each other for ~6 years; currently resides in Barcelona, Spain (distance: 3000+ miles)

3] Email
1. Ainsley – 12 interactions
2. Daniel – 9 interactions
3. Kate F – 7 interactions; age 19; female; housemate/same organization (sorority)/friend; strong tie strength; known each other for ~2 years; resides in same house (distance: 0 miles)
4. Jacki I – 6 interactions; age 20; female; housemate; moderate tie strength; known each other for ~3 months; resides in same house (distance: 0 miles)
5. Kelly J – 5 interactions; age 20; female; same organization (sorority)/friend; moderate tie strength; known each other for ~2 years; distance: 0.1 miles
5. Christina S – 5 interactions; age 20; female; same organization (sorority)/friend; strong tie strength; known each other for ~2 years; distance: 0.1 miles
5. Sarah G – 5 interactions; age 21; female; housemate; moderate tie strength; known each other for ~3 months; resides in same house (distance: 0 miles)

4] IM
1. Daniel – 4 interactions
2. Joaquin – 2 interactions
3. Abby – 1 interaction
3. Dad – 1 interaction; age 51; male; parent; strong tie strength; known each other for ~21 years; resides in Buenos Aires, Argentina (distance: 3000+ miles)
3. Ale A – 1 interaction; age 20; female; friend; strong tie strength; known each other for ~10 years; currently resides in Buenos Aires, Argentina (distance: 3000+ miles)
3. Uncle – 1 interaction; age 42; male; other relative; strong tie strength; known each other for ~21 years; currently resides in South Korea (distance: 3000+ miles)

5] Skype
1. Daniel – 2 interactions
2. Ale A – 1 interaction

6] Facebook message
1. Daniel – 6 interactions
2. HoJun – 1 interaction
2. Katie D – 1 interaction; age 20; female; same organization (sorority)/friend; moderate tie strength; known each other for ~2 years; distance : 0.1 miles
2. Marisa – 1 interaction; age 19; female; same organization (sorority)/friend; moderate tie strength; known each other for ~2 years; currently resides in Padova, Italy (distance: 3000+ miles)

7] Facebook Wall
1. Abby – 2 interactions
2. Nati – 1 interaction
2. Liz Lee – 1 interaction; age 19; female; same organization (sorority); moderate tie strength; known each other for ~1 year; distance: 0.1 miles
2. InHee – 1 interaction; age 20; male; friend; strong tie strength; known each other for ~3 years; distance: 0.1 miles
2. Clara – 1 interaction
2. Kevin – 1 interaction; age 21; male; acquaintance; weak tie strength; known each other for ~1 year; distance: 0.1 miles
2. Greg M – 1 interaction; age 20; male; friend; weak tie strength; known each other for ~3 years; distance: 0.1 miles
2. Maria DM – 1 interaction; age 22; female; same organization (sorority); weak tie strength; known each other for ~1 year; distance: 200+ miles
2. Cici Z – 1 interaction; age 20; female; same organization (sorority)/friend; moderate tie strength; known each other for ~2 years; currently resides in London, UK (distance: 3000+ miles)
2. Dan S – 1 interaction; age 21; male; friend; moderate tie strength; known each other for ~1 year; distance: 0.1 miles
2. Cara Hoy – 1 interaction; age 20; female; acquaintance; weak tie strength; known each other for ~1 year; distance: 0.1 miles
2. Marisa – 1 interaction

8] Skype Out
1. Daniel – 1 interaction
1. Nati – 1 interaction

9] Facebook Poke
1. Hojun – 11 interactions
2. Abby – 7 interactions
3. Daniel – 5 interactions
4. Clara – 4 interactions

10] Facebook picture (comment)
1. Dan S – 1 interaction

11] Blog posting
1. r5 – 1 interaction; N/A; N/A; classmate; weak tie strength; known each other for ~3 months; distance: 0.1 miles
1. r14 – 1 interaction; N/A; N/A; classmate; weak tie strength; known each other for ~3 months; distance: 0.1 miles

(2a)
For most of the communication media there seems to be a relationship with the strength of the tie. Weak ties, all of who were known from an offline setting, were mostly reached through the use of facebook. In their study of facebook, Ellison et al found that facebook is a great way to “capitalize on weak ties and convert latent ties to weak ties” (29) and this is clearly supported by my data. In contrast, strong and moderate ties made use of various media. Once again, facebook was also used for these ties, mainly to “intensity and solidify relationships that started offline” (Ellison et al, 32). Another interesting finding is that the stronger the tie, the more media used, especially those media that required an immediate and synchronous type of communication, as well as a significant amount of time (and maybe resource) investment, such as cell phones, texts, Skype and IM. Since “multiplexity increases ties strength” (Mesch and Talmud, 139), the more activities and interests shared, the stronger the tie, and thus, the more media used in order to address these different activities and interests accordingly. Email is the only media that didn’t show a relationship with tie strength; it was used for all three ties, although my pattern of usage suggests that it was mainly used with strong and moderate ties. Baym et al find that “email was the main internet medium for social interaction” (313) which could be why email isn’t necessarily associated with a tie strength and widely used across different strengths.

(2b)
Emotional aid and companionship were related to the use of almost all media. As Baym et al argue, “people will incorporate the internet into their social lives in ways that fulfill their particular social needs” (315), which suggests that different media are used to fulfill these supports from different people. Email was once again strongly associated with information exchange, and it was also the medium through which the only small service was exchanged.

(2c)
Synchronous media was associated with all types of relationships, except for classmates, acquaintances and professors. These latter were related to strong usage of email, and facebook was especially used for acquaintances. For all other types of relationships, cell phone was the preferred medium, followed by email, facebook, texts and IM/Skype. This pattern could not only be due to the type of relationship, but be associated with the tie strength as well. The romantic partner and sibling were the two type of relationships that made use of a wide range of varied media. Wellman and Wortley say that “siblings are similar to friends in providing emotional support” (574) which, related to the answer in (2b), could be why such varied media was used between us. McPherson et al find that spouses are among the few people with whom important matters are discussed with. Thus, it would seem to make sense that such an important tie would be accessed through different media.

(2d)
There was no clear-cut relationship between duration of relationship and medium used. It’s not so much about the duration of the relationship but more about the strength of the tie.

(2e)
Distance did not seem to play such an important role in determining the type of media employed. Cell phone was the most common medium used for both people in proximity and long distance, followed by email and facebook. Since everyone abroad with whom I have contact with are strong ties, it might be, again, that tie strength is the variable that is playing an important part in determining the usage of media instead of distance. This is consistent with Wellman’s theory that “people usually obtain support, companionship, information and a sense of belonging from those who do not live within the same neighbourhood or even within the same metropolitan area” (233): distance is not what matters since nowadays, the advanced technology for transportation and communication help maintain and support strong ties, wherever they are. Hampton points out that “while computer-mediated communication further reduces the friction of space, it can also afford local interactions” (225), which is a trend that can be seen in my data as well. Overall, the usage of Internet mediated communication (such as email and facebook) was for both long-distance ties, as well as locally proximal ties, since “the Internet supports “glocalization” ” (Hampton, 226).

(2f)
Age did seem to play a role when deciding what medium to use. Older people in my social network tended to make use of the cellphone to get in contact with me. This may be so due to the fact that the cellphone resembles the traditional mainline phone in its purpose and use. However, it should also be pointed out that those older individuals did contact me via computer-mediated communications, such as email and IM, but these two were males and both did it while they were at work, suggesting a relationship between their use of specific media and their environment since in other settings, such as when they were at home, they contacted me via cellphone instead. Consistent with Hampton’s argument that “a large proportion of those who emailed family members did so to seek advice and social support” (224), my email interactions with the older people (both family members) was to obtain emotional support and small services (advice in this case). Furthermore, compared to younger people, older individuals used less of a variety of media, but this fact could have been due to other external factors, such as their lack of accounts in facebook. Younger people did not show a specific relationship with any specific type of medium, and instead made use of a variety of media, and again, these choices were mediated by the strength of the tie.
Gender did not seem to have relationships with media use. However, it is true that the people with whom I have engaged with in the analyzed week are predominantly women, which provides evidence for the tendency of homophily (according to McPherson et al, baseline homophily in this case), and also to the fact that “emotional support [is] substantially provided by women” (Wellman and Wortley, 581), (which is in fact, the highest support received and given through my communication with these people.

(2g)
There was a tendency for age-similarity among the people in my network, as well as, as mentioned on (2f) a tendency for gender-based homophily. However, these similarities did not have a relationship with the medium of communication employed. The same media were used for both older and similar-aged groups, as well as gender.

(2h)
The role that new media plays in our social networks seems to be considerable, and it is a “legitimate, supportive means of social contact”, as well as “one form of communication amongst many” (Hamptom, 223). The variety of communication options play a special role with those individuals with whom face-to-face contact is difficult but with whom strong ties are still shared, since they provide ways in which such relationship can still be sustained and continued. (Ellison et al). Strength of tie seems to be the most important variable in determining what type of medium will be used, especially when it comes to the choice of asynchronous and synchronous media. Email is the exception to this, but this can be explained by the fact that it “can engage others not only one-on-one, but as a broadcast of one-to-many” (Hamptom, 226), which is a very useful and practical feature in communicating with multiple people in short amounts of time and in an economic way (this factor being especially important for college-aged students).

(3)
Both strong and weak ties were contacted from home and public places, and none of the personal characteristics listed played a role in deciding whether the interactions were carried out from home or public places. In general, most of the interactions were carried out from home, and most of the strong tie interactions were carried out from home as well, whereas weak ties were carried equally in home and public settings. I believe that this is the case because strong ties have been predominantly contacted through cellphones. According to Wellman, “mobile-phone users can choose where they call from, but they have less control over where they receive calls” (239). Thus, the home setting may liberate this latter problem, which is why cellphone interaction with strong ties has predominantly taken place there. However, it is also the case that cellphone’s do indeed “afford person-to-person contact […and] liberation from both place and group” (Wellman, 239), and thus are employed in public settings with strong ties as well. Age could potentially be a personal characteristic that played a role in determining the interaction setting: except for one interaction which took place in public space through cellphone, all the other interactions with older people were at home. Once again, this supports Wellman in that cellphones free people from being bound to a specific location for communicating and open possibilities to the public locations as well. However, the age factor could also have been the result of the difference in time since all the older people reside in other countries and time zones.

New media’s changing the composition of our social networks mainly by blurring the boundaries between public and private settings. As Wellman says, new media “use shifts community ties from linking people-in-places to linking people wherever they are. Because the connection is to the person and not to the place, it shifts the dynamics of connectivity from places to individuals” (238). And this is exactly what’s happening. Especially with cellphones (although they will soon be joined by “portable” computers), people have access to other people wherever they are, and thus change the composition of our networks because it will increase and change the connectivity and the type of interaction and relationship with individuals instead of with groups of individuals, such as household (Wellman). Fischer says that people in urban settings choose which kin they want to spend more time with. New media may just be offering the exact same thing, but to a broader audience: by allowing connectivity within individuals and not groups, we may be able to choose and select those people with whom we want to establish what kind of social tie and support, and when.

Posted by y1 on November 15, 2006 8:38 PM | | Comments (0)

November 13, 2006

diffuse, diffuse, diffuse~

In “Diffusion Networks” chapter 8, Everett talks about how interpersonal communication, mainly opinion leaders, affect the diffusion of information in networks. In doing so, he touches on different topics that affect such diffusion, such as homophily and heterophily, opinion leadership and the characteristics of leaders, the concept of critical mass, etc. He also provides different generalizations that he draws from other researches, which help further understand and sustain his argument that opinion leaders do matter in the diffusion in communication networks.

One of his generalizations with which I didn’t agree with was generalization 8-12: “Individuals tend to be linked to others who are close to them in physical distance and who are relatively homophilous in social characteristics” (341). In previous readings and weeks, we have seen that physical distance is not such an important factor in deciding whether individuals are linked to others or not. Especially with the introduction of new technological advances, such as the Internet, cellphone, and other portable electronic devices, physical proximity is a very inaccurate predictor of the structure of one’s network. Also, some individuals, namely those who can be denominated as bridges and/or structural holes within a network, are not mainly linked to others who are homophilous; they also have links with people who are heterophilous, and these are the links that allow these people to fulfill the structural position that they do.

Q: “The fact that certain innovations are adopted by clusters of individuals suggests that interpersonal networks among neighbors are powerful influences on individual decision to adopt” (335). If neighbors are important influences on innovation adoption, how would the fact that people are decreasing their interactions with neighbors affect adoption of innovations? Would people get their information from other sources? Would they stop adopting innovations? Would there be no more clustering of adoption of innovations?

Tepperman makes an interesting application of social networks and their analysis in the search of deviance, giving an example of how theoretical concepts and studies, such as the study of social networks, can be used and applied for meaningful and practical reasons. He explains the “features of a deviant search” and how these affect the way in which individuals engaging in the deviant search are affected by them and how they change their strategies in acting and looking in their social networks for the deviant object. The author mentions that during a blind search, a breadth-first search for a deviant object “will always find a shortest-length path”. However, wouldn’t this mean that the individual who is engaging in such an act also runs the risk of exposing himself to many people at the same time? If an individual carried out a depth-first method instead of a breadth-first search, then, even if the search ends at a dead-end, the individual will not have exposed him/herself to that many people. So maybe, even if a breadth-first search does yield the shortest path, it might not be the safest path, especially for someone who is searching a deviant object.

Q: We have just read about how new media can be an access to new sources of information by meeting new people and gaining access to otherwise limited data. How would a deviant search take place in an online community? Would the steps and factors listed by the author in the article be used in an online setting as well?

Burt also deals with the role that opinion leaders play in diffusion of innovations. He draws a relationship between the study of diffusion and social capital research by pointing out the resemblance of opinion brokers (in diffusion research) and network entrepreneurs (in social capital research). He begins by differentiating cohesion and equivalence, and how these affect contagion, and then applies the theory to studies that were carried out as evidence. The first study that Burt mentions corresponds to the adoption of medical innovations, and concludes that 1) “equivalent physicians followed one another in their adoptions of the new drug” (44) and 2) cohesion is irrelevant where equivalence makes its strongest predictions” (44). Could this finding be due to the fact that the study tracked a profession in which equivalence plays a strong role? Do you think that the same/different results would have been found if the study had been carried out regarding diffusion and contagion in other professions/situations in which equivalence didn’t play such an important role?

Posted by y1 on November 13, 2006 11:55 PM | | Comments (4)

November 5, 2006

computerized networks

Kleinberg and Lawrence’s paper gives an overview of how the web works. The authors explain the different components of the web, “the core, upstream, downstream and tendril regions” (1849) at the local level, and also how the web behaves like a network at a more global level. Based on the explanations and descriptions of how the web works, what are some of the ways in which people/users might use its structure to their advantage? The authors also mention that an “analysis of the Web’s structure can help to define topics and social groupings of interest to its denizens” (1850). What are some of the strengths and limitations that would come along from studying social networks and communities through the use of the Web?

In his article, Marks describes how the Pentagon’s National Security Agency has started looking at the information that people post about their social networks on the Internet, and what kind of implications this could have on our lives. Even though as of now the NSA can only connect people through the data that individuals post on the internet, with advances in internet technology, very specific and personal information, such as financial transactions could be tracked. However, it makes me wonder how feasible this really is. As long as people are careful and don’t make too much information available on the Internet, I feel that, at least as of now, the amount of “connecting the dots” that the NSA can do is limited. And, even if advances of the internet technology do make more personal information available, new technology also means new and/or revised privacy policies which might limit the level of access that the NSA can have. However, this is also depends on whether people are aware of these privacy policies and the rights that they have to protect their privacy, which are not widespread knowledge. If what Marks says is true and future advances will mean exposure of private information, would this have an effect on individuals’ social networks?

Ellison et al studied the role that facebook plays in the “social capital formation and maintenance, integration into college life and psychological well-being” of students at Michigan State University. The researchers found that facebook is being extensively used as a tool to maintain and develop current offline relationships as well as old relationships, such as high school relationships. These relationships can be both strong or weak ties, with facebook playing a special role in stimulating latent ties into weak ties. Why do you think that, unlike the articles that we read for last week, facebook is predominantly used to maintain ties instead of creating new ties?
One of the weaknesses of the study, which the authors admit as their limitation, is that it was only carried out at one school, MSU. Being a state university, this may limit the generalizability of the study results to other schools. Also, the article was published in June, 2006. Since then, facebook has undergone a massive change and included numerous features that let users “monitor and follow” what their friends and others are doing with their lives and facebook use. Do you think these changes would have an impact on the results found by these authors if the survey were to be carried out now? What kind of impact do would you say that these changes have had on users’ social networks and the way in which they relate to people, both online and offline?

Wellman extensively overviews the way in which “affordances in computer-supported interpersonal communication affect the ways in which people connect with each other” (229). He states that the technological advances have provided individuals with new resources that move networks from being place-based to person-based, developing “person-to-person connectivity” (238). His article brings up several questions and issues that have been raised with the expansion of technology-based relationships and how these would impact offline relationships.
I think that his comment that “cyberspace fights against physical space less than it complements it” (247) is a very accurate description of how computer mediated relationships are being integrated to offline relationships. Nowadays, these cyberspaces are filling in the gaps in time that we don’t spend physically at work, school, with friends or family. They provide alternative ways in which we can still interact with these people and organizations, allowing people to be constantly aware and on top of things that are going on around them. What kind of impact would this have on people’s existing relationship and in the formation of new relationships?

Posted by y1 on November 5, 2006 3:54 PM | | Comments (2)

October 22, 2006

new measures

In their article, Zwijze-Koning and DeJong go over the different communication audit currently present in the field for the study of communication within organizations. They present the diverse facts of organizational communication, and both advantages and disadvantages of the different data collection techniques currently present in the field, both individually and comparing these. The way in which they broke down the article and presented/explained the different techniques was very thorough and clear, and the comparison that they provided of different techniques might make this paper very useful for a researcher when s/he decides which technique to use to measure organizational communication. However, it would have been interesting if the authors had also given different ways in which the techniques could have been combined together, instead of just comparing them. The only time the authors mention this possibility is indirectly, while citing another study in which sociometric questioning and archival data contributed to each other’s results instead of having been used as means of comparison. Why do you think that comparing methods has been more popular than having methods complement each other? In what circumstances would contribution of methods be better than the comparison of techniques?

The next two articles for this week touched upon different ways in which social capital can be measured using different techniques. Lin et al introduced The Position Generator as a useful social capital measure, and then applied it to measurement of social capital in Taiwan. They find that there is a gender difference in the structure of social capital and in access to social capital, with females being at a disadvantage, and that weak ties are used to access social capital in both sex entrepreneurs. I wasn’t surprised to read that females were at a disadvantage in terms of accessing social capital in Taiwan, since this society seems to access social capital mainly through weak ties, and has traditionally favored females who stay at home. Education was found to be have an impact on whether women would also get access to social capital, which seems logical as education opens the possibility for people to meet others and form ties, both strong and weak.
Q: If the study were to be done in the US, in what ways/dimensions do you think that the results would differ to those found by Lin et al?

In “The Resource Generator”, Van Der Gaag and Snijders, present a new measurement instrument for social capital, the Resource Generator, by combining the advantages of two already existing measures of social capital, the Name Generator/Interpreter, its ability to get detailed resource information, and the Position Generator, its internal validity and the fact that it is an economic way to conduct research. The resource generator “asks about access to a fixed list of resources, each representing a vivid concrete subcollection of social capital, together covering several domains of life” (4). They also introduce a “new method to aggregate social capital items into a set of multiple measures” (19). The authors’ finding that social capital is related to personal resources seems to be logical. If the Resource Generator yields the same kind of results as the name generator and the position generator, and also have some of the same methodological problems, such as biasing/influencing the responses to the questions depending on the wording (15), then how is the resource generator a better measure of social capital compared to the other measures?

Hampton and Marin introduce the MMG and the MGRI as alternative measures of network composition. These provide reliable estimates for all/most measures of network composition, which means that they outperform the single measure generators, such as the name generator. The authors found that single generators found strong correlations for “discussion” and “socializing” only, whereas MMG provided a stronger correlation, and MGRI an even stronger one. When comparing these two alternatives, it seems that the MGRI is a stronger measurement than the MMG. The authors used “discussion” and “socializing” as the two generators as the basis for the MMG in their study. I wonder what results they would have gotten if they had decided to use two different generators, generators that had not provided strong correlations during the initial part of the study, as the basis for the MMG. Would they have found similar correlations? Would these have differed? How could this data be interpreted?

Posted by y1 on October 22, 2006 9:15 PM | | Comments (6)

October 16, 2006

Centrality in social networks

Wasserman and Faust introduce the concepts of centrality and prestige, and how these differ in what they measure, to our study of social networks. The authors state that degree, closeness and betweenness are the three level indices that can be applied to measure the centrality of an actor and/or of a group, and also present a fourth one, information as another index. In the “Centrality and Prestige” paper, the notion of centrality and prestige are presented as being key in the study of social networks, and also, the role that these central people play seems to be fundamental to the transportation and diffusion of information. However, in “The Strength of Weak Ties”, Granovetter states that the first adopters of a novel innovation are not the central actors of a network, but the isolate/marginal individuals of a network. And it is after this initial group that the central figures adopt the innovation and help diffuse it throughout the rest of the network. Thus, in what cases would being a central figure not be advantageous and/or play such a fundamental role in social networks?

Freeman gives a very clear and thorough explanation of the different terminologies applied to the study of networks in terms of centrality and geodesics (“the shortest paths linking a given pair of points” (218)). He uses both graphs and mathematical equations to support and provide proof of how it is that these measures would be determined. However, his work was a little hard to follow since there weren’t any examples to which he applied the theoretical concepts to. Unlike the Wasserman and Faust paper in which the authors analyzed the different Italian families based on the concepts that they presented, Freeman’s paper was very much theoretical, which makes one wonder how feasible and practical it would be to apply those theories and terms when analyzing real social networks. Social networks between individuals are both complex and differ across groups of people, and it is generally the case that when trying to apply broad theoretical concepts to real examples, one comes across many different obstacles and holes in the theories that can’t explain how it is that the network being studies is spread out.

A good example of this extra step could be seen in Krebs’ paper in which he tried to analyze the network patterns that under laid the 9/11 attack. In “Uncloaking Terrorist Networks”, the author explains the different steps that he had to go through to gather data for his analysis, and also the difficulties that he encountered while doing so. He finds that the people involved in the attack had very sparse ties to one another, and that the entire plan involved people who didn’t know each other very well, and who also had no complete idea of the entire plan itself. The only person who had access to every piece of data of the plan was the central figure and he scored “the highest on all network centrality metrics” (7). If most of the people involved in the attack did not have a complete idea of the plan, how and who would you interview in order to reach the central figure?

In “Do popular students smoke”, Valente et al set out to find out whether there was an association between popularity and tendency to smoke, whether this changed by ethnicity and gender, and whether isolates were also associated with a tendency to smoke. It was interesting to see that both isolates and popular kids had a higher tendency to smoke. Since both groups tend to want to deviate from the norms and rules, it could be said that their higher tendency is a form of “rebelling” against the established societal norms, which maintains the popular kids popular, and the isolates, isolated.
Why do you think that “the association between popularity and susceptibility and smoking was statistically significant for Latina girls” (327)?

Mouttapa et al studied whether the social networks of bullies, victims and aggressive victims differed from each other, and whether these varied by gender and ethnicity. It was very interesting that the authors decided to differentiate victims from aggressive victims, since, as the results, show, these two groups clearly have different social network compositions and respond differently to being bullied. The results of the study show that the friends of bullies and aggressive victims tended to be more aggressive (although this held only for females in the aggressive victims case) and the friends of victims tended to be less aggressive. The authors say that based on the results, social cognitive theory was sustained since those who were aggressive tended to be associated with friends that were aggressive as well. Could this tendency be explained by homophilly as well?
Also, the authors found that bullies were not more likely to be central figures in their networks. Why do you think this is the case?

Posted by y1 on October 16, 2006 11:32 PM | | Comments (2)

October 11, 2006

our core social networks

1) Geography could be an explanation for the changes reported by McPherson et al. More people are living in suburbs and/or cities, and the neighbourhood context as it used to be is hard to find nowadays. Even Bott, with her small sample of households, found only one family that was living in such a close community setting. Related to this is another likely explanation which is that communities are changing in their composition of people. People move around a lot, and as Bott mentions it in her article, the more highly educated and those with more professional jobs tend to do so more often in search for jobs. In the WUNC interview, Smith-Lovin said that community structure, voluntary associations, kind of neighborhoods and the ties to neighborhoods are all interconnected, which means that the more diverse and varied neighbourhood structure, the less connection people feel towards the neighbourhood, and this leads to shifts of core discussion networks away from those in neighborhoods. Added to this, the fact that people are turning to close kind for core discussion networks could be related to Fischer’s idea that there is a kin selectivity present in urban settings. If we choose that we keep in touch with, then this means that we like these ties more than others, and therefore, it is more likely that we will turn to these ties for close relationships and support. Furthermore, based on Bott’s idea that marriages that have join conjugal role relationships have a more dispersed network, if it is indeed true that people are moving around a lot, and thus close friendships are more scattered than before, then spouses will most likely confide in each other for support since they already do discuss important matters of the household together anyway.

2) The social ties in one’s core network tend to provide, according to Wellman and Wortley, emotional support (family and friends), companionship (friends), small services (family, friends and neighbors), and big services (family, especially parents for financial services). These relationships “are central in social influences and normative pressures” (McPherson et al, “Social Isolation in America”, 355)

Since people are shifting their core networks from community contexts to kin, this could affect the resource to small services that, according to Wellman, neighbors and friends are most likely to provide because of their proximity to the node. The lack of access to small services, such as looking after one’s house or borrowing a cup of sugar, could affect one’s everyday life, since it’s very unlikely and impractical for one to have to rely on a far-away kin during an emergency situation, for example. Also, companionship and emotional support could potentially be affected since, if people are relying on their spouses more than before, this could be a problem for the female spouse, especially since Bott says that the emotional support that females give cannot be replaced by men and one’s kin, such as parents, might not be as readily available to give emotional support because of the changes in community structure that have taken place.

According to the weak tie argument forwarded by Granovetter and Burt, the fact that people are more involved with close kin and spouses, this lowers the chances of forming new, socially diverse ties since these individuals are very strong ties. Moreover, Kalmijn says that as people start dating and moving in together, their social networks start to overlap, especially in the weak ties, which could potentially tamper/limit with the ability to form new social ties.

A potential impact that this chance could have in one’s everyday life was mentioned by a caller to WUNC, Laura, who said that since her mother passed away, she hasn’t had anyone close with whom she could talk. This could be a problem if close kin ties die/die off, and/or if the spouse is causing a problem in one’s life, since there would be no other individuals with whom one could get support from because of the changes in the network structure.

For society at large, as neighbourhood ties decrease, then people’s interest about their community might decrease as well. In the WUNC interview, Putnam says that trust towards the government/friends/media has generally gone down, and this affects the way in which governmental structures function. Smith-Lovin also gives the example of Katrina during the interview, saying that the increased percentage of social isolation affected the way in which people responded and helped each other during the disaster, and moreover, affected the efficacy with which the government responded to the disaster.

Posted by y1 on October 11, 2006 1:04 AM | | Comments (0)