Social Network Blog - g10

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?

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?

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.

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.