Segregation in social networks based on acquaintanceship and trust

Racial Disparities in Medical Crowdfunding: The Role of Sharing Disparity and Humanizing Narratives

Americans have increasingly turned to online crowdfunding to pay for healthcare costs, but our understanding of the inequalities in medical crowdfunding remains limited. This study investigates racial disparities in medical crowdfunding outcomes and examines the role of communication in amplifying, altering, or even reducing the disparities. Using data from 1,127 medical crowdfunding campaigns on GoFundMe, the study found that beneficiaries of color received significantly fewer donations than their White counterparts. The differences in donations between racial groups were partly attributable to sharing disparities. Campaigns for beneficiaries of color were shared less via e-mail or social media than campaigns for White beneficiaries. Campaign narratives with more humanizing details about beneficiaries were associated with more donations. However, humanizing details did not predict more shares, nor were they linked to smaller disparities in campaign outcomes between racial groups. Post-hoc analyses showed that more humanizing details were linked to fewer campaign donations for male beneficiaries of color. The findings contribute to the scholarship addressing the intersections of communication and health inequality on digital platforms.

The rise of online dating and racial homogamy in marriage

The rise of online dating has the potential to transform marriage outcomes, as it may alter how individuals are matched with partners. To capture the population-level effects of the rise of online dating, we examine how changes in marital racial homogamy from 2008 to 2016 are associated with changes in online dating within local dating markets. We use data from Google Trends and the American Community Survey with fixed-effects regression models to control for differences across dating markets. Our results suggest that the rise of online dating has not substantially influenced trends in racial homogamy, either nationally or within metropolitan areas.

The Spread of Beliefs in Partially Modularized Communities

Many life-influencing social networks are characterized by considerable informational isolation. People within a community are far more likely to share beliefs than people who are part of different communities. The spread of useful information across communities is impeded by echo chambers (far greater connectivity within than between communities) and filter bubbles (more influence of beliefs by connected neighbors within than between communities). We apply the tools of network analysis to organize our understanding of the spread of beliefs across modularized communities and to predict the effect of individual and group parameters on the dynamics and distribution of beliefs. In our Spread of Beliefs in Modularized Communities (SBMC) framework, a stochastic block model generates social networks with variable degrees of modularity, beliefs have different observable utilities, individuals change their beliefs on the basis of summed or average evidence (or intermediate decision rules), and parameterized stochasticity introduces randomness into decisions. SBMC simulations show surprising patterns; for example, increasing out-group connectivity does not always improve group performance, adding randomness to decisions can promote performance, and decision rules that sum rather than average evidence can improve group performance, as measured by the average utility of beliefs that the agents adopt. Overall, the results suggest that intermediate degrees of belief exploration are beneficial for the spread of useful beliefs in a community, and so parameters that pull in opposite directions on an explore-exploit continuum are usefully paired.

Faculty Experiences Related to Career Advancement and Success in Academic Medicine

Phenomenon: Faculty career success in academic medicine is complex. Dynamic interactions among faculty and between faculty and their socio-cultural working environments contribute to the mystique of academic advancement. There are few empirical studies that analyze the social practice of faculty career advancement in academic medicine, especially those that focus on relations between individual and environmental contexts. This study aimed to investigate various faculty members' experiences around career advancement in a medical school. Through the analytical lens of Bourdieu's theory of practice, we focused on the relationship among individuals, positions, and environmental contexts.Approach: Following a general process of interpretive grounded theory, we collected faculty members' perceptions and experiences related to their career development and advancement via in-depth semi structured-interviews of 23 faculty at a medical school in the United States. We analyzed the interview transcripts using thematic and constant-comparison analyses, informed by Bourdieu's theory of practice emphasizing the concepts of habitus, field, doxa, illusio, and capital.Findings: While there was a general perception of collaborative success in the school, access to resources seemed to be unequally distributed and linked to faculty positions. Career opportunities, such as leadership and promotion, were mostly granted by leaders based on interpersonal relationships (social capital). Clinical faculty's limited access to professional development activities (cultural capital), including research, limited their likelihood for promotion (symbolic capital) at the school. An institutional emphasis on clinical productivity reinforced clinical faculty's constraints on academic scholarship, which led to perceived inequities by clinical faculty. Participants also perceived subtle gender bias and an unequal distribution of power among the specialties, which contradicted a culture of collaboration and support in the school.Insights: Complex power structures influence faculty career success. Unequal access and differential power among faculty positions resulted in disparities in career advancement. Greater transparency, equity, and inclusivity are obvious suggestions for change to allow all faculty to benefit from essential resources and career building opportunities. Furthermore, building high-quality relationships between leaders and faculty and mutual respect between specialties are essential to sustaining an organizational culture conducive to career success for all faculty. Faculty will benefit from a greater awareness of the structural and normative aspects of a medical school that are less obvious and more difficult to change. This is the value of applying Bourdieu's theory of practice to the socio-cultural dynamics of the medical school.

Consistently estimating network statistics using aggregated relational data

Collecting complete network data is expensive, time-consuming, and often infeasible. Aggregated Relational Data (ARD), which ask respondents questions of the form "How many people with trait X do you know?" provide a low-cost option when collecting complete network data is not possible. Rather than asking about connections between each pair of individuals directly, ARD collect the number of contacts the respondent knows with a given trait. Despite widespread use and a growing literature on ARD methodology, there is still no systematic understanding of when and why ARD should accurately recover features of the unobserved network. This paper provides such a characterization by deriving conditions under which statistics about the unobserved network (or functions of these statistics like regression coefficients) can be consistently estimated using ARD. We first provide consistent estimates of network model parameters for three commonly used probabilistic models: the beta-model with node-specific unobserved effects, the stochastic block model with unobserved community structure, and latent geometric space models with unobserved latent locations. A key observation is that cross-group link probabilities for a collection of (possibly unobserved) groups identify the model parameters, meaning ARD are sufficient for parameter estimation. With these estimated parameters, it is possible to simulate graphs from the fitted distribution and analyze the distribution of network statistics. We can then characterize conditions under which the simulated networks based on ARD will allow for consistent estimation of the unobserved network statistics, such as eigenvector centrality, or response functions by or of the unobserved network, such as regression coefficients.

Network ethnic homophily and injection equipment sharing among Latinx and White non-Latinx people who inject drugs

Latinx people who inject drugs (PWID) are less likely to engage in injection equipment sharing, but are more vulnerable to injection drug use (IDU)-related morbidity and mortality than Whites. Identifying subgroups of Latinx PWID who do engage in equipment sharing and likely bear the brunt of this health burden is a priority. Ethnic disparities may reflect contextual drivers, including injection networks. Latinx PWID with low ethnic homophily (the proportion of individuals with the same ethnic background) may be more likely to share equipment due to forced distancing from health-protective ethnocultural resources and power imbalances within injection networks. The current study offers a framework and examines how associations between network ethnic homophily and injection equipment sharing differ among 74 Latinx and 170 non-Latinx White PWID in the Chicagoland area (N = 244). Latinx had less homophilous than non-Latinx Whites (p <.001). Ethnic homophily was protective for equipment sharing among Latinx (OR = 0.17, 95%CI [0.77, 0.04], p = .02), but not non-Latinx Whites (OR = 1.66, 95%CI [0.40, 6.93], p = .49). Our findings implicate the need for targeted cultured interventions that focus on Latinx PWID who are more vulnerable to morbidity and mortality, potentially due to less access to ethnic peers.

Homophily and acrophily as drivers of political segregation

Political segregation is an important social problem, increasing polarization and impeding effective governance. Previous work has viewed the central driver of segregation to be political homophily, the tendency to associate with others who have similar views. Here we propose that, in addition to homophily, people's social tie decisions are driven by political acrophily, the tendency to associate with others who have more extreme political views (rather than more moderate). We examined this using a paradigm in which participants share emotions and attitudes on political policies, observe others' responses and choose which others to affiliate with. In four studies (N = 1,235), both liberal and conservative participants' social tie decisions reflected the presence of acrophily. We found that participants who viewed peers who expressed more extreme views as more prototypical of their political group also tended to engage in greater acrophily. These studies identify a previously overlooked tendency in tie formation.

Tell me who's your neighbour and I'll tell you how much time you've got: The spatiotemporal consequences of residential segregation

Relying on data from the United States Census and the American Time Use Survey (2010-2017), we examine how residential segregation influences per capita discretionary time availability in Los Angeles, New York City and Miami. We find a sizable disadvantage of being Latinx for discretionary time availability. Non-Latinx Whites have 182 extra hours of per capita discretionary time per year than do Latinx individuals. Both within-neighbourhood and adjacent-neighbourhood influences matter. In most neighbourhoods, segregation is correlated with having more discretionary time. Individuals in highly segregated areas have approximately 80 more hours of discretionary time per year than those living in diverse areas. This suggests that in addition to socioeconomic, cultural and well-being benefits, ethnic enclaves may also impart temporal advantages. However, we find that there may be diminishing marginal returns with increasing segregation in surrounding areas. Sociodemographic characteristics explain over one-quarter of the variance between segregation and discretionary time availability.

Teachers Supporting Teachers: A Social Network Perspective on Collegial Stress Support and Emotional Wellbeing Among Elementary and Middle School Educators

School mental health practitioners and researchers are increasingly concerned about educator job-related stress and its implications for teacher burnout, teaching efficacy, turnover, and student outcomes. Educators’ collegial networks in their schools are natural resources for stress support, yet little is known about the extent to which educators seek support from their colleagues in managing their stress and whether these relationships promote their emotional wellbeing. Utilizing peer nomination and self-report data from 370 educators in 17 elementary and middle schools, we found patterns in whom educators nominated as a source of stress support. Specifically, educators more often nominated colleagues who worked in the same role, grade, and/or subject, and those similar in age and who had similar or more experience. Furthermore, men and educators of color more often nominated same-gender and same-race colleagues, respectively, whereas these trends were not observed for women or White educators. However, the prevalence of these characteristics among colleagues nominated as a source of stress support was not often significantly associated with educators’ stress and burnout. Rather, educators’ level of burnout was positively related to the burnout among those in their stress support networks. In addition, educators’ stress and burnout were positively related to the stress and burnout of their colleagues with whom they spent the most time. These findings highlight how educators’ perceptions of stress and burnout may be shared within their collegial networks and have implications for a role for colleagues in teacher stress-reduction and wellbeing-focused interventions.

Quantifying social segregation in large-scale networks

We present a measure of social segregation which combines mobile phone data and income register data in Oslo, Norway. In addition to measuring the extent of social segregation, our study shows that social segregation is strong, robust, and that social networks are particularly clustered among the richest. Using location data on the areas where people work, we also examine whether exposure to other social strata weakens measured segregation. Lastly, we extend our analysis to a large South Asian city and show that our main results hold across two widely different societies.

Political coherence and certainty as drivers of interpersonal liking over and above similarity

Affective polarization and political segregation have become a serious threat to democratic societies. One standard explanation for these phenomena is that people like and prefer interacting with similar others. However, similarity may not be the only driver of interpersonal liking in the political domain, and other factors, yet to be uncovered, could play an important role. Here, we hypothesized that beyond the effect of similarity, people show greater preference for individuals with politically coherent and confident opinions. To test this idea, we performed two behavioral studies consisting of one-shot face-to-face pairwise interactions. We found that people with ambiguous or ambivalent views were nonreciprocally attracted to confident and coherent ingroups. A third experimental study confirmed that politically coherent and confident profiles are rated as more attractive than targets with ambiguous or ambivalent opinions. Overall, these findings unfold the key drivers of the affability between people who discuss politics.

The University as a Source of Social Capital in Chile

This paper investigates the structure and composition of the social network formed on the campus of the Faculty of Economics and Business of Diego Portales University, Chile, exposing a series of characteristics that are aligned with similar research in the field of networks. We use a model of social networks formation in order to understand socioeconomic and academic factors that predict the formation of friendship between two students. Specifically, we test empirically our model, using students' administrative information. Of special interest is the impact of the length of stay of the students in the university, with which we refer to the years completed in the degree course, in the probability of establishing friendship ties where being socioeconomically different is a condition. The mechanism behind a result like this is the sense of belongingness that being part of the same institution may induce amongst students. By means of counterfactual simulations we found evidence in favor that passing through the university increases the probability of forming friendship networks, which can mean a kind of social capital, thus reducing socioeconomic segregation from the Chilean school system. Given the importance of this finding, we believe that policies that increase the sense of belongingness such as cultural events, leaderships programs, and community should be implemented on university campuses.

Unequal treatment toward copartisans versus non-copartisans is reduced when partisanship can be falsified

Studies show that Democrats and Republicans treat copartisans better than they do non-copartisans. However, party affiliation is different from other identities associated with unequal treatment. Compared to race or gender, people can more easily falsify, i.e., lie about, their party affiliation. We use a behavioral experiment to study how people allocate resources to copartisan and non-copartisan partners when partners are allowed to falsify their affiliation and may have incentives to do so. When affiliation can be falsified, the gap between contributions to signaled copartisans and signaled non-copartisans is eliminated. This happens in part because some participants—especially strong partisans—suspect that partners who signal a copartisan affiliation are, in fact, non-copartisans. Suspected non-copartisans earn less than both partners who signal that they are non-copartisans and partners who withhold their affiliation. The findings reveal an unexpected upside to the availability of falsification: at the aggregate level, it reduces unequal treatment across groups. At the individual-level, however, falsification is risky.

Exploring qualities present in current versus dissolved cross-group friendships

In general, cross-group friendships are less stable than same-group friendships. What conditions are present in currently existing versus dissolved cross-group friendships? In order to examine qualities that may influence cross-group friendship stability we compared current and dissolved friendships, including cross-group friendships. Cross-group friendships exist in various group domains, some more easily categorizable than others. That is, sometimes it is easy to tell that a relationship is cross-group (e.g., cross-race), and other times this is less clear (e.g., cross-socio-economic status). Thus, we compared current and dissolved friendships across both a more and a less easily categorizable group domain. In this study, participants reported on their current and dissolved friendships, and we found that, overall, friendship influencing qualities such as closeness, similarity, and social network integration (i.e., becoming friends with the friends of one’s own friends) were present to a greater extent in current versus dissolved friendships. This was the case for both cross-group and same-group friendships. These qualities may influence cross-group friendship stability.

Interfirm Ties Between Ventures and Limited Partners of Venture Capital Funds: Performance Effects in Financial Markets

We argue that strong indirect ties are conducive to the transfer of private information, which provides an advantage in identifying profitable investment opportunities. In our context, a strong indirect tie is generated between an investor and a focal firm if the investor was a limited partner of the focal firm’s lead venture capital fund. We suggest that an investor can access private information on the focal firm’s underlying value through its strong indirect tie to the focal firm via the focal firm’s lead venture capitalist. Supporting our theory, we show that after the focal firm’s initial public offering, the investor with a strong indirect tie to the focal firm receives high risk-adjusted return when the investor chooses to invest in the focal firm’s stock in the stock exchange market. We also show that the investor’s private information attained through its strong indirect tie to the focal firm is more valuable (i) when there is higher exogenous market uncertainty and (ii) when the investor faces higher information asymmetry.

Mortgage Discrimination and Racial/Ethnic Concentration Are Associated with Same-Race/Ethnicity Partnering among People Who Inject Drugs in 19 US Cities

Racial/ethnic homophily in sexual partnerships (partners share the same race/ethnicity) has been associated with racial/ethnic disparities in HIV. Structural racism may partly determine racial/ethnic homophily in sexual partnerships. This study estimated associations of racial/ethnic concentration and mortgage discrimination against Black and Latino residents with racial/ethnic homophily in sexual partnerships among 7847 people who inject drugs (PWID) recruited from 19 US cities to participate in CDC's National HIV Behavioral Surveillance. Racial/ethnic concentration was defined by two measures that respectively compared ZIP code-level concentrations of Black residents to White residents and Latino residents to White residents, using the Index of Concentration at the Extremes. Mortgage discrimination was defined by two measures that respectively compared county-level mortgage loan denial among Black applicants to White applicants and mortgage loan denial among Latino applicants to White applicants, with similar characteristics (e.g., income, loan amount). Multilevel logistic regression models were used to estimate associations. Interactions of race/ethnicity with measures of racial/ethnic concentration and mortgage discrimination were added to the final multivariable model and decomposed into race/ethnicity-specific estimates. In the final multivariable model, among Black PWID, living in ZIP codes with higher concentrations of Black vs. White residents and counties with higher mortgage discrimination against Black residents was associated with higher odds of homophily. Living in counties with higher mortgage discrimination against Latino residents was associated with lower odds of homophily among Black PWID. Among Latino PWID, living in ZIP codes with higher concentrations of Latino vs. White residents and counties with higher mortgage discrimination against Latino residents was associated with higher odds of homophily. Living in counties with higher mortgage discrimination against Black residents was associated with lower odds of homophily among Latino PWID. Among White PWID, living in ZIP codes with higher concentrations of Black or Latino residents vs. White residents was associated with lower odds of homophily, but living in counties with higher mortgage discrimination against Black residents was associated with higher odds of homophily. Racial/ethnic segregation may partly drive same race/ethnicity sexual partnering among PWID. Future empirical evidence linking these associations directly or indirectly (via place-level mediators) to HIV/STI transmission will determine how eliminating discriminatory housing policies impact HIV/STI transmission.

Secularity, religiosity, and health: Physical and mental health differences between atheists, agnostics, and nonaffiliated theists compared to religiously affiliated individuals

Extensive literature in the social and medical sciences link religiosity to positive health outcomes. Conversely it is often assumed that secularity carries negative consequences for health; however, recent research outlining different types of secular individuals complicates this assumption. Using a national sample of American adults, we compare physical and mental health outcomes for atheists, agnostics, religiously nonaffiliated theists, and theistic members of organized religious traditions. Results indicate better physical health outcomes for atheists compared to other secular individuals and members of some religious traditions. Atheists also reported significantly lower levels of psychiatric symptoms (anxiety, paranoia, obsession, and compulsion) compared to both other seculars and members of most religious traditions. In contrast, physical and mental health were significantly worse for nonaffiliated theists compared to other seculars and religious affiliates on most outcomes. These findings highlight the necessity of distinguishing among different types of secular individuals in future research on health.

Whose Moral Community? Religiosity, Secularity, and Self-rated Health across Communal Religious Contexts

Scholars have long theorized that religious contexts provide health-promoting social integration and regulation. A growing body of literature has documented associations between individual religiosity and health as well as macro-micro linkages between religious contexts, religious participation, and individual health. Using unique data on individuals and county contexts in the United States, this study offers new insight by using multilevel analysis to examine meso-micro relationships between religion and health. We assess whether and how the relationship between individual religiosity and health depends on communal religious contexts. In highly religious contexts, religious individuals are less likely to have poor health, while nonreligious individuals are markedly more likely to have poor health. In less religious contexts, religious and nonreligious individuals report similar levels of health. Consequently, the health gap between religious and nonreligious individuals is largest in religiously devout contexts, primarily due to the negative effects on nonreligious individuals' health in religious contexts.

A Mechanistic Model of Human Recall of Social Network Structure and Relationship Affect

The social brain hypothesis argues that the need to deal with social challenges was key to our evolution of high intelligence. Research with non-human primates as well as experimental and fMRI studies in humans produce results consistent with this claim, leading to an estimate that human primary groups should consist of roughly 150 individuals. Gaps between this prediction and empirical observations can be partially accounted for using “compression heuristics”, or schemata that simplify the encoding and recall of social information. However, little is known about the specific algorithmic processes used by humans to store and recall social information. We describe a mechanistic model of human network recall and demonstrate its sufficiency for capturing human recall behavior observed in experimental contexts. We find that human recall is predicated on accurate recall of a small number of high degree network nodes and the application of heuristics for both structural and affective information. This provides new insight into human memory, social network evolution, and demonstrates a novel approach to uncovering human cognitive operations.

LATENT SPACE MODELS FOR MULTIVIEW NETWORK DATA

Social relationships consist of interactions along multiple dimensions. In social networks, this means that individuals form multiple types of relationships with the same person (e.g., an individual will not trust all of his/her acquaintances). Statistical models for these data require understanding two related types of dependence structure: (i) structure within each relationship type, or network view, and (ii) the association between views. In this paper, we propose a statistical framework that parsimoniously represents dependence between relationship types while also maintaining enough flexibility to allow individuals to serve different roles in different relationship types. Our approach builds on work on latent space models for networks [see, e.g., J. Amer. Statist. Assoc.97 (2002) 1090-1098]. These models represent the propensity for two individuals to form edges as conditionally independent given the distance between the individuals in an unobserved social space. Our work departs from previous work in this area by representing dependence structure between network views through a multivariate Bernoulli likelihood, providing a representation of between-view association. This approach infers correlations between views not explained by the latent space model. Using our method, we explore 6 multiview network structures across 75 villages in rural southern Karnataka, India [Banerjee et al. (2013)].

Quantity Versus Quality: A Survey Experiment to Improve the Network Scale-up Method

The network scale-up method is a promising technique that uses sampled social network data to estimate the sizes of epidemiologically important hidden populations, such as sex workers and people who inject illicit drugs. Although previous scale-up research has focused exclusively on networks of acquaintances, we show that the type of personal network about which survey respondents are asked to report is a potentially crucial parameter that researchers are free to vary. This generalization leads to a method that is more flexible and potentially more accurate. In 2011, we conducted a large, nationally representative survey experiment in Rwanda that randomized respondents to report about one of 2 different personal networks. Our results showed that asking respondents for less information can, somewhat surprisingly, produce more accurate size estimates. We also estimated the sizes of 4 key populations at risk for human immunodeficiency virus infection in Rwanda. Our estimates were higher than earlier estimates from Rwanda but lower than international benchmarks. Finally, in this article we develop a new sensitivity analysis framework and use it to assess the possible biases in our estimates. Our design can be customized and extended for other settings, enabling researchers to continue to improve the network scale-up method.

Does Racial Isolation in School Lead to Long-Term Disadvantages? Labor Market Consequences of High School Racial Composition

School racial composition has modest effects on test score gaps, but evidence of a longer-term impact is scarce. Perpetuation theory suggests that blacks who attend schools with higher proportions of white classmates may have better job outcomes. Multilevel analyses of two national longitudinal surveys reveal no effects of high school racial composition on occupational status, employment, or annual earnings for blacks or whites. For other minority groups, attending schools with more whites impedes occupational advancement. For all groups, however, school racial composition predicts workplace racial composition: Whites who attend high schools with higher proportions of white students have higher proportions of white coworkers, while nonwhites who attend schools with higher proportions of whites have fewer same-race coworkers. The findings are modest in size but robust to alternative specifications, and sensitivity analyses support a causal interpretation for same-race coworkers. These results support perpetuation theory for workplace composition but not for stratification outcomes.

Secrets and Misperceptions: The Creation of Self-Fulfilling Illusions

This study examines who hears what secrets, comparing two similar secrets-one that is highly stigmatized and one that is less so. Using a unique survey representative of American adults and intake forms from a medical clinic, I document marked differences in who hears these secrets. People who are sympathetic to the stigmatizing secret are more likely to hear of it than those who may react negatively. This is a consequence of people not just selectively disclosing their own secrets but selectively sharing others' as well. As a result, people in the same social network will be exposed to and influenced by different information about those they know and hence experience that network differently. When people effectively exist in networks tailored by others not to offend, then the information they hear tends to be that of which they already approve. Were they to hear secrets they disapproved of, then their attitudes might change, but they are less likely to hear those secrets. As such, the patterns of secret hearing contribute to a stasis in public opinion.

Redefining neighborhoods using common destinations: social characteristics of activity spaces and home census tracts compared

Research on neighborhood effects has focused largely on residential neighborhoods, but people are exposed to many other places in the course of their daily lives-at school, at work, when shopping, and so on. Thus, studies of residential neighborhoods consider only a subset of the social-spatial environment affecting individuals. In this article, we examine the characteristics of adults' "activity spaces"-spaces defined by locations that individuals visit regularly-in Los Angeles County, California. Using geographic information system (GIS) methods, we define activity spaces in two ways and estimate their socioeconomic characteristics. Our research has two goals. First, we determine whether residential neighborhoods represent the social conditions to which adults are exposed in the course of their regular activities. Second, we evaluate whether particular groups are exposed to a broader or narrower range of social contexts in the course of their daily activities. We find that activity spaces are substantially more heterogeneous in terms of key social characteristics, compared to residential neighborhoods. However, the characteristics of both home neighborhoods and activity spaces are closely associated with individual characteristics. Our results suggest that most people experience substantial segregation across the range of spaces in their daily lives, not just at home.

Humans use compression heuristics to improve the recall of social networks

The ability of primates, including humans, to maintain large social networks appears to depend on the ratio of the neocortex to the rest of the brain. However, observed human network size frequently exceeds predictions based on this ratio (e.g., "Dunbar's Number"), implying that human networks are too large to be cognitively managed. Here I show that humans adaptively use compression heuristics to allow larger amounts of social information to be stored in the same brain volume. I find that human adults can remember larger numbers of relationships in greater detail when a network exhibits triadic closure and kin labels than when it does not. These findings help to explain how humans manage large and complex social networks with finite cognitive resources and suggest that many of the unusual properties of human social networks are rooted in the strategies necessary to cope with cognitive limitations.

The Embedded Self

Despite the fact that key sociological theories of self and identity view the self as fundamentally rooted in networks of interpersonal relationships, empirical research investigating how personal network structure influences the self is conspicuously lacking. To address this gap, we examine links between network structure and role identity salience. We identify two features of personal networks that potentially affect how social ties shape identity salience: (1) proportion and strength of ties to role-based others (RBOs) and (2) embeddedness of RBOs, or the breadth of access that a role-based group has to the rest of an individual’s network. Across three role identities (student, religious, and work), we find that our measure of embeddedness predicts role identity salience but that the proportion and strength of ties do not. Thus, our study does not support the proposition that identity salience is a product of an individual’s social and emotional attachment to role-based groups. Rather, our findings suggest that a role identity becomes more salient as role-based others become more tightly woven into an individual’s social fabric.

LATENT DEMOGRAPHIC PROFILE ESTIMATION IN HARD-TO-REACH GROUPS

The sampling frame in most social science surveys excludes members of certain groups, known as hard-to-reach groups. These groups, or sub-populations, may be difficult to access (the homeless, e.g.), camouflaged by stigma (individuals with HIV/AIDS), or both (commercial sex workers). Even basic demographic information about these groups is typically unknown, especially in many developing nations. We present statistical models which leverage social network structure to estimate demographic characteristics of these subpopulations using Aggregated relational data (ARD), or questions of the form "How many X's do you know?" Unlike other network-based techniques for reaching these groups, ARD require no special sampling strategy and are easily incorporated into standard surveys. ARD also do not require respondents to reveal their own group membership. We propose a Bayesian hierarchical model for estimating the demographic characteristics of hard-to-reach groups, or latent demographic profiles, using ARD. We propose two estimation techniques. First, we propose a Markov-chain Monte Carlo algorithm for existing data or cases where the full posterior distribution is of interest. For cases when new data can be collected, we propose guidelines and, based on these guidelines, propose a simple estimate motivated by a missing data approach. Using data from McCarty et al. [Human Organization60 (2001) 28-39], we estimate the age and gender profiles of six hard-to-reach groups, such as individuals who have HIV, women who were raped, and homeless persons. We also evaluate our simple estimates using simulation studies.

The influence of baseline marijuana use on treatment of cocaine dependence: application of an informative-priors bayesian approach

BACKGROUND

Marijuana use is prevalent among patients with cocaine dependence and often non-exclusionary in clinical trials of potential cocaine medications. The dual-focus of this study was to (1) examine the moderating effect of baseline marijuana use on response to treatment with levodopa/carbidopa for cocaine dependence; and (2) apply an informative-priors, Bayesian approach for estimating the probability of a subgroup-by-treatment interaction effect.

METHOD

A secondary data analysis of two previously published, double-blind, randomized controlled trials provided complete data for the historical (Study 1: N = 64 placebo), and current (Study 2: N = 113) data sets. Negative binomial regression evaluated Treatment Effectiveness Scores (TES) as a function of medication condition (levodopa/carbidopa, placebo), baseline marijuana use (days in past 30), and their interaction.

RESULTS

Bayesian analysis indicated that there was a 96% chance that baseline marijuana use predicts differential response to treatment with levodopa/carbidopa. Simple effects indicated that among participants receiving levodopa/carbidopa the probability that baseline marijuana confers harm in terms of reducing TES was 0.981; whereas the probability that marijuana confers harm within the placebo condition was 0.163. For every additional day of marijuana use reported at baseline, participants in the levodopa/carbidopa condition demonstrated a 5.4% decrease in TES; while participants in the placebo condition demonstrated a 4.9% increase in TES.

CONCLUSION

The potential moderating effect of marijuana on cocaine treatment response should be considered in future trial designs. Applying Bayesian subgroup analysis proved informative in characterizing this patient-treatment interaction effect.

Systems science methods in public health: dynamics, networks, and agents

Complex systems abound in public health. Complex systems are made up of heterogeneous elements that interact with one another, have emergent properties that are not explained by understanding the individual elements of the system, persist over time, and adapt to changing circumstances. Public health is starting to use results from systems science studies to shape practice and policy, for example in preparing for global pandemics. However, systems science study designs and analytic methods remain underutilized and are not widely featured in public health curricula or training. In this review we present an argument for the utility of systems science methods in public health, introduce three important systems science methods (system dynamics, network analysis, and agent-based modeling), and provide three case studies in which these methods have been used to answer important public health science questions in the areas of infectious disease, tobacco control, and obesity.