A note on social capital and network content

As a guide to selecting name generators for social capital research, I use network data on a probability sample of heterogeneous senior managers to describe how they sort relations into kinds, and how the kinds vary in contributing to social capital. Managers sort relations on two dimensions of strength - intimacy (especially close versus distant) versus activity (frequent contact with new acquaintances versus rare contact with old friends) - and with respect to two contents - personal discussion (confiding and socializing relations) versus corporate authority (the formal authority of the boss and informal authority of essential buy-in). Comparing name generators for their construct validity as indicators of social capital, I compute network constraint from different kinds of relations, and correlate constraint with early promotion. The correlation is strong for the network of personal relations, zero for the network of authority relations, and strongest for personal and authority relations together. I close with research design recommendations for selecting name generators.     

Weak ties and the core discussion network: Why people regularly discuss important matters with unimportant alters

Researchers have paid increasing attention to the core discussion network, the set of friends and family people turn to when discussing important matters. For nearly thirty years, social network researchers have argued that the network is composed of ego's closest or most important alters. This assumption, however, has not been tested empirically. Using original data on an online representative quota survey of 2000 respondents, I find that 45% of the core discussion network is composed of people whom respondents do not consider important to them. In fact, the core discussion network includes doctors, co-workers, spiritual leaders, and other alters whom ego confides in without feeling emotionally attached to. I examine what respondents consider important matters and why they approach weak ties to discuss these. Placing emphasis on the process through which ego mobilizes alters, I develop two theoretical perspectives, which focus on how people identify those appropriate to a topic and how they respond to opportunities in interactional contexts. Findings suggest that ego discusses important matters with non-close alters at times because they are known to be knowledgeable (targeted mobilization) and at times because they are available when important issues arise (opportune mobilization). Results suggest that recent findings about changes in the core discussion network of Americans are consistent with several different possibilities about the nature of strong ties, including those in which there has been no change at all.     

Vorwort

Although the concept of tie strength plausibly extends opinion leadership theory, prior studies have indicated that tie strength is of minor importance for opinion leadership. The paper argues that strong ties and weak ties play major roles in the process of opinion leadership that prior studies have insufficiently differentiated due to methodological limitations. Against this background, a methodologically more rigorous reassessment of the association between tie strength and opinion leadership is conducted. The reassessment centers on the combination of ego-centered network analyses with statistical methods of multilevel analysis. These are applied to original data of a study by Schenk (2006) on financial opinion leadership in Germany (n?=?10,100 survey respondents) which has previously been considered as an important indication for tie strength’s minor importance. Empirical results show that strong ties and weak ties are remarkably more important in the process of opinion leadership than previously known. Opinion leaders benefit from strong ties, because personal influence can be carried through them comparatively easily. By contrast, weak ties foster opinion leadership in a structural manner by catalyzing the influence that opinion leaders can exert through strong ties.     

Missing data in networks: exponential random graph (p1) models for networks with non-respondents

Survey studies of complete social networks often involve non-respondents, whereby certain people within the “boundary” of a network do not complete a sociometric questionnaire—either by their own choice or by the design of the study—yet are still nominated by other respondents as network partners. We develop exponential random graph (p¹) models for network data with non-respondents. We model respondents and non-respondents as two different types of nodes, distinguishing ties between respondents from ties that link respondents to non-respondents. Moreover, if we assume that the non-respondents are missing at random, we invoke homogeneity across certain network configurations to infer effects as applicable to the entire set of network actors. Using an example from a well-known network dataset, we show that treating a sizeable proportion of nodes as non-respondents may still result in estimates, and inferences about structural effects, consistent with those for the entire network.If, on the other hand, the principal research focus is on the respondent-only structure, with non-respondents clearly not missing at random, we incorporate the information about ties to non-respondents as exogenous. We illustrate this model with an example of a network within and between organizational departments. Because in this second class of models the number of non-respondents may be large, values of parameter estimates may not be directly comparable to those for models that exclude non-respondents. In the context of discussing recent technical developments in exponential random graph models, we present a heuristic method based on pseudo-likelihood estimation to infer whether certain structural effects may contribute substantially to the predictive capacity of a model, thereby enabling comparisons of important effects between models with differently sized node sets.     

The content of the General Social Survey discussion networks: an exploration of General Social Survey discussion name generator in a Chinese context

This study focuses on the discussion network name generator used in the 1985 General Social Survey (GSS). Based on the data from a Chinese survey, it explores the content of the discussion networks by examining the overlapping of names generated by the GSS discussion name generator and the exchange name generators. It finds that the GSS discussion question has generated a range of social ties, which accounts for an important part of a Chinese personal network. Specifically, the people with whom the Chinese respondents discussed important matters are also likely to spend leisure time with the respondents and to be the confidants for personal matters. Some of them are expected to offer substantial help or to possess important social resources; however, they are least involved in what are considered family affairs.     

Eliciting representative samples of personal networks

In this paper we introduce and evaluate a method for eliciting a representative sample of total personal networks. First names were used as a cue to elicit a sample of 14 alters from 712 respondents through a telephone interview. Network characteristics for each respondent were calculated as averages and proportions across the 14 alters. These were compared to other studies using more specialized network generators. Our method produced results which are logically consistent with those expected from a generator that elicits a sample from the total rather than a specialized subset of the total network. The proportions of kin relations, average tie strength and frequency of contacts are found to be lower than network generators designed to elicit networks of social support. Given our conclusion that the sample is representative of the total network, we examine the varying characteristics of respondents and their networks based on the domination of a particular relation type in their network. This analysis provides answers to such questions as ‘What characteristics of respondents account for the proportion of family relations in their network?‘ and ‘What are the similarities between respondents whose networks are made up of mostly work-related relations?’     

A note on scaling the general social survey network item response categories

The idea of structural balance is used to suggest quantitative intervals between relationship strength response categories in the GSS network data. In contrast to an assumption of equal intervals between the categories of relationship strength, the intervals appear quite unequal. Relations with discussion partners “less close” to their respondent than other cited discussion partners are about 0.17 the strength of relations with “especially close” discussion partners. The middle category of relations between discussion partners appear to be little more than acquaintance relations; about 0.2 of the distance from people who are “total strangers” to people who are “especially close”.     

A note on strangers,friends and happiness

Using network data obtained in the 1985 General Social Survey, expressions of happiness are shown to increase with the size of a person's discussion network and decrease with the prevalence of strangers in the network. The density of especially close relations in the network has no direct effect on happiness. It is the negative impact of strangers rather than the positive impact of close relations that determines expressions of happiness. The network size and stranger effects remain strong even after respondent differences in socioeconomic status, age, sex, race, and domestic situation are held constant. However, it is clear that an almost certain route to strengthening the network measures to predict well-being lies in studying how happiness varies with the position of a spouse or other domestic partner in the respondent's network.     

Network sampling coverage II: The effect of non-random missing data on network measurement

Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data.     

Neighborhood disadvantage, network social capital, and depressive symptoms

Research on why neighborhood disadvantage matters for health focuses on the capacity of neighborhoods to regulate residents' behavior through informal social control. The authors extend this research by conducting a multilevel analysis of data from a 1995 telephone survey of 497 residents of 32 neighborhoods in a U.S. city. The authors find that network social capital mediates the contextual effect of neighborhood disadvantage on depressive symptoms and that health effects of network social capital persist when perceived neighborhood disorder, a standard indicator of low informal social control, is controlled for. The findings demonstrate the value of a conceptualization and measurement of network social capital that (1) considers ties that transcend neighborhood boundaries, (2) investigates health benefits of network social capital in the forms of closure and embedded support resources and range and embedded instrumental resources, and (3) uses network data on specific network members with strong and weak ties to respondents.     

Beyond interviewer effects in the standardized measurement of ego-centric networks

The effects which interviewers exert on the collection of ego-centric networks have recently come into the focus of methodological considerations. Studies consistently show that the size of networks varies depending on the interviewer. We would like to expand on this research strand by pointing to different aspects which have so far gone unremarked in the discussion. First, size is mainly analysed as a network measure which is influenced during data collection, while other common measures such as network density or composition have not received sufficient consideration. Second, large-scale surveys using face-to-face interviews usually allocate interviewers to a single sampling point. Differences between sampling points (locality effects) are attributed to interviewer effects. Hence, we disentangle the effects of the locality and interviewer. Third, the discussion on interviewer effects often follows an “actor-oriented” consideration of how data collection situations are structured by interviewers. Expanding this approach from a relational perspective, we consider the relationship between the interviewers and respondents and whether this relationship influences the collection of network data. To test our hypotheses about the influence of interviewers, the locality and the interviewer-respondent relationship on different network measures, we use data from the 2010 German General Social Survey (n = 2827 respondents, n = 220 interviewers). The multilevel analyses show that the relationship between the interviewer and the respondent is not very relevant. Furthermore, the analyses show that interviewers have an influence on the network size but not on measures of their composition. However, evidence on the prevalence of locality or interviewer effects is mixed. Finally, homophilous interviewer-respondent relationships have very little effect on network characteristics. We find evidence of training and fatigue effects on network size. However, much of the variation in network size caused by the interviewer still remains unexplained. We draw conclusions on how to organize interview situations in surveys.     

A sign of the times? Weak and strong polarization in the U.S. Congress, 1973–2016

Claims that the United States Congress is (becoming more) polarized are widespread, but what is polarization? In this paper, I draw on notions of intergroup relations to distinguish two forms. Weak polarization occurs when relations between the polarized groups are merely absent, while strong polarization occurs when the relations between the polarized groups are negative. I apply the Stochastic Degree Sequence Model to data on bill co-sponsorship in both the U.S. House of Representatives and U.S. Senate, from 1973 (93rd session) to 2016 (114th session) to infer a series of signed networks of political relationships among legislators, which I then use to answer two research questions. First, can the widely reported finding of increasing weak polarization in the U.S. Congress be replicated when using a statistical model to make inferences about when positive political relations exist? Second, is the (increasing) polarization observed in the U.S. Congress only weak polarization, or is it strong polarization? I find that both chambers exhibit both weak and strong polarization, that both forms are increasing, and that they are structured by political party affiliation. However, I also find these trends are unrelated to which party holds the majority in a chamber.     

Beyond answers: dimensions of the advice network

In this paper, we report the result of a research project investigating social aspects of knowledge sharing and development. Prior research in a consulting firm revealed that respondents recognized five kinds of informational benefits when consulting others: solutions, meta-knowledge, problem reformulation, validation and legitimation. We employed these dimensions in a systematic network analysis of a different sample of people (human resource managers in a large conglomerate), using each of the five benefits as kinds of social relations. Two general research questions guided the analysis. First, how are these relations related to each other (multiplexity)? Do individuals obtain all of the benefits from the same individuals, or do they create balanced portfolios of complementary contacts that provide different benefits? Second, what properties and shapes do the networks induced by these relations form (structure)? What is the basis for who is tied to whom on each relation? The fundamental result emerging from both research questions is that the five relations seem to form a unidimensional scale such that a contact who provides any given benefit is also very likely to provide all the benefits that are lower on the scale. Position on this scale seems to index underlying dimensions of social solidarity rather than individual attributes such as status. Consequently, relations at the end of the scale (e.g. legitimation) were more homophilous and proved to be strongly diagnostic of subgroup boundaries, a fact which could be quite useful in consulting or other applied contexts. This research contributes to the literature on knowledge management by revealing diverse ways that consulting others facilitates knowledge creation and utilization. The research also contributes to social network analysis by examining meanings and relationships among social relations, an area that is understudied. We found that the five benefits, treated as social relations, formed an entailment structure consistent with a Guttman scale. We also found that relations lower in the scale flowed smoothly across historical organizational boundaries whereas relations higher in the scale did not.     

Social networks and Internet connectivity effects

This paper explores the impact of communication media and the Internet on connectivity between people. Results from a series of social network studies of media use are used as background for exploration of these impacts. These studies explored the use of all available media among members of an academic research group and among distance learners. Asking about media use as well as about the strength of the tie between communicating pairs revealed that those more strongly tied used more media to communicate than weak ties, and that media use within groups conformed to a unidimensional scale, showing a configuration of different tiers of media use supporting social networks of different ties strengths. These results lead to a number of implications regarding media and Internet connectivity, including: how media use can be added to characteristics of social network ties; how introducing a medium can create latent tie connectivity among group members that provides the technical means for activating weak ties, and also how a change in a medium can disrupt existing weak tie networks; how the tiers of media use also suggest that certain media support different kinds of information flow; and the importance of organization-level decisions about what media to provide and promote. The paper concludes with a discussion of implications for Internet effects.     

Social networks,stress and child abuse

The study investigated the relationship between situational stress, strength of informal social networks and maternal child abuse. Structured interviews were conducted with 41 abusive mothers and 59 non-abusing mothers using an author-developed instrument to measure social network strength and situational stress. Abusing mothers, on the average, reported significantly weaker, less supportive informal social networks than the non-abusing mothers. Both the neighbor-friend networks and the kinship networks of the non-abusing mothers were found to be stronger than those of the abusing mothers. The data also supported the positive association of situational stress with child abuse. Both situational stress and strength of social network proved to be significant predictors of abuse. The findings supported the hypothesized mediating effect of strong social networks upon the relationship between situational stress and child abuse. Mothers living in highly stressful life situations who reported strong social networks were less likely to be abusers than mothers living in high stress situations who reported weak social networks. The mediating functions of social networks are proposed, and the implications of the findings for interventions with high risk parents to prevent child abuse are discussed.     

Who’s missing out? The impact of digital networking behavior & social identity on PR job search outcomes

This study investigates how different aspects of social identity correlated with online networking and job search success. Undergraduates and recent graduates seeking public relations roles between 2020 and 2021 were surveyed (N = 355) for their early career job-seeking practices. The pre-existing lack of diversity in PR and the tendency to favor job candidates who are similar to the decision-maker means that the move to virtual relationship building and vetting may be especially impactful for diverse applicants. The results showed that networking was extremely important as over 94% of respondents found their position via networking of some sort. Respondents found roles most commonly via LinkedIn (57.5%), followed by other social media (49.9%). LinkedIn network size was positively correlated with more job offers, whereas Twitter and Facebook network sizes were insignificant. Those who identified as white used LinkedIn more and had more job offers than those who identified as BIPOC. Veterans and those receiving need-based assistance also reported having fewer job offers. This paper extends the strength of weak ties theory by demonstrating the importance of weak ties in digital networking in the PR industry. This research suggests that maintaining weak ties via LinkedIn is valuable for success in digital networking, particularly by growing a robust LinkedIn network and maintaining an active profile. Practical advice for PR educators and students illustrates how to enhance LinkedIn use to promote equity in digital networking. Moreover, the PR field may promote diversity among applicants and hires through mentoring, recruiting, and growing their own diverse digital network.     

Measurement error in network data: A re-classification

Research on measurement error in network data has typically focused on missing data. We embed missing data, which we term false negative nodes and edges, in a broader classification of error scenarios. This includes false positive nodes and edges and falsely aggregated and disaggregated nodes. We simulate these six measurement errors using an online social network and a publication citation network, reporting their effects on four node-level measures – degree centrality, clustering coefficient, network constraint, and eigenvector centrality. Our results suggest that in networks with more positively-skewed degree distributions and higher average clustering, these measures tend to be less resistant to most forms of measurement error. In addition, we argue that the sensitivity of a given measure to an error scenario depends on the idiosyncracies of the measure's calculation, thus revising the general claim from past research that the more ‘global’ a measure, the less resistant it is to measurement error. Finally, we anchor our discussion to commonly-used networks in past research that suffer from these different forms of measurement error and make recommendations for correction strategies.     

Using modern methods for missing data analysis with the social relations model: A bridge to social network analysis

Social network analysis identifies social ties, and perceptual measures identify peer norms. The social relations model (SRM) can decompose interval-level perceptual measures among all dyads in a network into multiple person- and dyad-level components. This study demonstrates how to accommodate missing round-robin data using Bayesian data augmentation, including how to incorporate partially observed covariates as auxiliary correlates or as substantive predictors. We discuss how data augmentation opens the possibility to fit SRM to network ties (potentially without boundaries) rather than round-robin data. An illustrative application explores the relationship between sorority members’ self-reported body comparisons and perceptions of friends’ body talk.     

Soziale Beeinflussung in Mikronetzwerken am Beispiel der Erhebung von Markenpräferenzen mit dem „Tischmodell“

Brand preferences in a homogeneous student milieu are analyzed in order to model how social influence affects people’s views and opinions. In this study the individual person is not considered as unit of analysis, but rather the people eating together at a table in a canteen. In classical surveys the opinions of the respondents should be independent from one another. In contrast to standard surveys the study presented here explicitly aims to examine the influence of a micro network on the attitudes of the constituent actors. To measure this effect an overall degree of conformity is calculated based on all table communities. Simulations are used to estimate the influence of the relations constituting the table community on the agreement of brand preferences among the table companions. Similar to the well-known bootstrapping procedure, the respondents are randomly grouped into table communities, where the distribution of group sizes matches the observed distribution for every resampling. By controlling for the socio-demographic structure of the table communities it is possible to estimate the effect of the micro-networks on the agreement of brand preferences. It turns out that the relevance of the “network effect” remains, even if the distribution of socio-demographic factors such as age, gender and field of study were held constant within the table communities. The presented “table model” can be applied to many problems.