Network recall among older adults with cognitive impairments

Although it is widely accepted that personal networks influence health and illness, network recall remains a major concern. This concern is heightened when studying a population that is vulnerable to cognitive decline. Given these issues, we use data from the Social Network in Alzheimer Disease project to explore similarities and discrepancies between the network perceptions of focal participants and study partners. By leveraging data on a sample of older adults with normal cognition, mild cognitive impairment, and early stage dementia, we explore how cognitive impairment influences older adults’ perceptions of their personal networks. We find that the average individual is more likely to omit weaker, peripheral ties from their self-reported networks than stronger, central ties. Despite observing only moderate levels of focal-partner corroboration across our sample, we find minimal evidence of perceptual differences across diagnostic groups. We offer two broad conclusions. First, self-reported network data, though imperfect, offer a reasonable account of the core people in one’s life. Second, our findings assuage concerns that cognitively impaired older adults have skewed perceptions of their personal networks.     

Using logic model and visualization to conduct portfolio evaluation

Portfolio evaluation is the evaluation of multiple projects with a common purpose. While logic models have been used in many ways to support evaluation, and data visualization has been used widely to present and communicate evaluation findings, adopting logic models for portfolio evaluation and using data visualization to share findings simultaneously is surprisingly limited in the literature. With the data from a sample portfolio of 209 projects which aims to improve the system of early care and education (ECE), this study illustrated how to use logic model and data visualization techniques to conduct a portfolio evaluation by answering two evaluation questions: “To what extent are the elements of a logic model (strategies, sub-strategies, activities, outcomes, and impacts) reflected in the sample portfolio?” and “Which dominant paths through the logic model were illuminated by the data visualization technique?” For the first question, the visualization technique illuminated several dominant strategies, sub-strategies, activities, and outcomes. For the second question, our visualization techniques made it convenient to identify critical paths through the logic model. Implications for both program evaluation and program planning were discussed.     

A latent space model for cognitive social structures data

This paper introduces a novel approach for modeling a set of directed, binary networks in the context of cognitive social structures (CSSs) data. We adopt a relativist approach in which no assumption is made about the existence of an underlying true network. More specifically, we rely on a generalized linear model that incorporates a bilinear structure to model transitivity effects within networks, and a hierarchical specification on the bilinear effects to borrow information across networks. This is a spatial model, in which the perception of each individual about the strength of the relationships can be explained by the perceived position of the actors (themselves and others) on a latent social space. A key goal of the model is to provide a mechanism to formally assess the agreement between each actors’ perception of their own social roles with that of the rest of the group. Our experiments with both real and simulated data show that the capabilities of our model are comparable with or, even superior to, other models for CSS data reported in the literature.     

Network cohesion and social support

This article analyses the effects of personal network cohesion on different types of social support using two dimensions of cohesion: network closure (defined as a tightly knit set of actors around the ego) and cliquishness (defined as the extent to which an actor is connected with a number of cohesive sub-sets of alters). Data were obtained from a personal networks’ survey conducted in Catalonia (Spain), which was completed by 441 adults and gathered information about exchange of social support in networks made of 30 alters. A multilevel analysis disentangles the effects on support of these two structural dimensions at the network-level from compositional effects at the network and tie-level. The results show that network closure does not play a relevant role in support once confounders at the network and tie levels are controlled for. However, cliquishness has a significant association with labor-related support and housing support, net of statistical controls. Implications of these results in network research are discussed.     

Network knowledge and the use of power

Complementing recent work on the effects of power on network perceptions, we offer a theory specifying how knowledge of network structures and exchange processes differentially affect the use of power by advantaged and disadvantaged positions. We argue that under certain conditions, network knowledge is beneficial to occupants of low-power positions, but not to occupants of high-power positions. Any low-power actor can benefit from having superior information, but if all low-power actors have equally sound knowledge, then all are worse off—a type of social trap. We tested these arguments by manipulating power and the availability of information on network structure and exchange processes in an experimental exchange network setting. The results were supportive.     

A comparative study of social network models: Network evolution models and nodal attribute models

This paper reviews, classifies and compares recent models for social networks that have mainly been published within the physics-oriented complex networks literature. The models fall into two categories: those in which the addition of new links is dependent on the (typically local) network structure (network evolution models, NEMs), and those in which links are generated based only on nodal attributes (nodal attribute models, NAMs). An exponential random graph model (ERGM) with structural dependencies is included for comparison. We fit models from each of these categories to two empirical acquaintance networks with respect to basic network properties. We compare higher order structures in the resulting networks with those in the data, with the aim of determining which models produce the most realistic network structure with respect to degree distributions, assortativity, clustering spectra, geodesic path distributions, and community structure (subgroups with dense internal connections). We find that the nodal attribute models successfully produce assortative networks and very clear community structure. However, they generate unrealistic clustering spectra and peaked degree distributions that do not match empirical data on large social networks. On the other hand, many of the network evolution models produce degree distributions and clustering spectra that agree more closely with data. They also generate assortative networks and community structure, although often not to the same extent as in the data. The ERGM model, which turned out to be near-degenerate in the parameter region best fitting our data, produces the weakest community structure.     

Theorizing issue-driven public attention and expectations in audience responses to corporate sociopolitical activism: A mixed-method analysis

This study develops an issue-driven framework to theorize the role of public attention, support, and expectations in audience responses to corporations’ engagement in contested issues. Based on a mixed-method study using an online survey (N = 817), we investigate how two forms of expectations—normative and predictive—are associated with motivating cognitions, attitudes toward the social impacts of corporate sociopolitical activism (CSA), and buying intentions. Thematic analysis reveals a four-quadrant typology characterized by varying degrees of public attention and support: legitimate engagement, identify alignment, emergent norm, and obscure practice. The structural equation modeling results demonstrate that predictive expectations are a consistent antecedent to the perceived company–issue fit, perceived issue salience, and issue position. In contrast, the associations between normative expectations, the perceived company–issue congruence, and cognitive involvement are contingent on issue types. Perceived advocacy fit and cognitive involvement positively predict attitudes toward the social impacts of CSA. Buycotting intentions, however, are only determined by individuals’ issue position.     

The effects of consideration of future consequences and CSR fit in stigmatized industries: Perceived CSR motives as mediators

This study explored the effects of fit between corporations and their corporate social responsibility (CSR) initiatives in stigmatized industries. Specifically, it examined how these effects vary depending on stakeholders’ consideration of future consequences (CFC) in influencing individuals’ attitudes toward CSR and corporations and purchase intentions; further, it explored whether such interaction effects are mediated by three underlying perceived motives. The study involved a between-subjects experiment with 144 college students. The results showed that high-CFC individuals perceived corporations with strong fit to their CSR initiatives as more stakeholder- and self-serving. The interaction effect also was mediated by stakeholder-serving motives in influencing purchase intentions and by self-serving motives in influencing CSR and corporation-related attitudes.     

The elementary-school functioning of children with maltreatment histories and mild cognitive or behavioral disabilities: A mixed methods inquiry

This mixed methods inquiry examined the school functioning of elementary school-aged children with maltreatment histories and mild cognitive or behavioral disabilities. Quantitative analyses of linked social service and education administrative data bases of 10,394 children in Minnesota with maltreatment histories indicated that 32% were eligible for special education services. Of those children with maltreatment histories and identified disabilities, 73% had mild cognitive or behavioral disabilities. The most frequent primary disabilities categories were specific learning disabilities (33%) and emotional/behavioral disabilities (27%). Children with maltreatment histories and mild cognitive or behavioral disabilities scored significantly below children with maltreatment histories and no identified disabilities on standardized assessments of math and reading, and this gap increased with grade level for math. Qualitative interviews with 22 child welfare professionals and 15 educators suggested why some children with maltreatment histories, especially those with mild cognitive or behavioral disabilities, struggle in school. Risks to school functioning included children's and families' multiple unmet basic and mental health needs which can mask or overshadow children's mild disabilities; poor cross systems collaboration between child welfare, education and mental health systems; and inadequate funding, especially for mental health services. Protective factors included child engagement in school, parent engagement with child welfare services and a professional culture of cross-systems collaboration. Implications are discussed for holistic child, family and system-level interventions.     

Estimating network structure via random sampling: Cognitive social structures and the adaptive threshold method

This paper introduces and tests a novel methodology for measuring networks. Rather than collecting data to observe a network or several networks in full, which is typically costly or impossible, we randomly sample a portion of individuals in the network and estimate the network based on the sampled individuals’ perceptions on all possible ties. We find the methodology produces accurate estimates of social structure and network level indices in five different datasets. In order to illustrate the performance of our approach we compare its results with the traditional roster and ego network methods of data collection. Across all five datasets, our methodology outperforms these standard social network data collection methods. We offer ideas on applications of our methodology, and find it especially promising in cross-network settings.     

Social creativity as a function of agent cognition and network properties: A computer model

Inventions – concepts, devices, procedures – are often created by networks of interacting agents in which the agents can be individuals (as in a scientific discipline) or they can themselves be collectives (as in firms interacting in a market). Different collectives create and invent at different rates. It is plausible that the rate of invention is jointly determined by properties of the agents (e.g., their cognitive capacity) and by properties of the network of interactions (e.g., the density of the communication links), but little is known about such two-level interactions. We present an agent-based model of social creativity in which the individual agent captures key features of the human cognitive architecture derived from cognitive psychology, and the interactions are modeled by agents exchanging partial results of their symbolic processing of task information. We investigated the effect of agent and network properties on rates of invention and diffusion in the network via systematic parameter variations. Simulation runs show, among other results, that (a) the simulation exhibits network effects, i.e., the model captures the beneficial effect of collaboration; (b) the density of connections produces diminishing returns in term of the benefits on the invention rate; and (c) limits on the cognitive capacity of the individual agents have the counterintuitive consequence of focusing their efforts. Limitations and relations to other computer simulation models of creative collectives are discussed.     

Variations in network boundary and type: A study of adolescent peer influences

This study compares variation in network boundary and network type on network indicators such as degree and estimates of social influences on adolescent substance use. We compare associations between individual use and peer use of tobacco and alcohol when network boundary (e.g., classroom, entire grade in school, and community) and relational type (elicited by asking whom students: (a) are friends with, (b) admire, (c) think will succeed, (d) would like to have a romantic relationship with, and (e) think are popular) are varied. Additionally, we estimate Exponential Random Graph Models (ERGMs) for 232 networks to obtain a homophily estimate for smoking and drinking. Data were collected from a cross-sectional sample of 1707 adolescents in five high schools in one school district in Los Angeles, CA. Results of logistic regression models show that associations were strongest when the boundary condition was least constrained and that associations were stronger for friendship networks than for other ones. Additionally, ERGM estimations show that grade-level friendship networks returned significant homophily effects more frequently than the classroom networks. This study validates existing theoretical approaches to the network study of social influence as well as ways to estimate them. We recommend researchers use as broad a boundary as possible when collecting network data, but observe that for some research purposes more narrow boundaries may be preferred.     

How activating cognitive content shapes trust: A subliminal priming study

The activation of cognitive contents plays a prominent role in social psychological research. Yet, so far this has received little attention in economics. In our research we connect a standard social psychological manipulation to activate cognitive content (a trust vs. distrust priming manipulation) to a classic paradigm from economics (a trust game). Our findings demonstrate that subliminally activating the concept of trust (vs. distrust) leads participants to judge a series of strangers as more (vs. less) trustworthy. Moreover, our research shows for the first time that such a subliminal priming manipulation shapes the subsequent sending behavior in a fictitious version of a standard economic trust game. This suggests that psychological priming techniques allow new insights into what determines beliefs in economic games.     

Context matters: Using mixed methods timelines to provide an accessible and integrated visual for complex program evaluation data

The need for conducting evaluations which reflect of the influence of context on complex programs is increasingly recognized in the field of evaluation. Better data visualization techniques for connecting context with program evaluation data are needed. We share our experience developing a mixed methods timeline to visualize complexity and context with evaluation data. Mixed methods timelines provide a meaningful way to show change over time in both a visually stimulating and accessible format for evaluation audiences. This paper provides an innovative example of using mixed methods timelines to integrate evaluation data with key program activities and milestones, while also showing internal and external contextual influences in one cohesive visual. We present methods and best practices for collecting contextual data and for incorporating a variety of data sources into such a visual. We discuss several strategies to collect and organize context related data including: qualitative interviews, program materials, narrative reports, and member checking with stakeholders and staff. Gathering multiple perspectives is essential to better capture the multi-layered elements of program activities and context.     

Network dynamics and its impact on innovation outcomes: R&D consortia in the Dutch water sector

In this study, we explore the relationship between inter-organizational network dynamics and innovation outcomes. We focus on node turnover and argue that both cluster and broker dynamics can range from low (stable) to high (volatile), resulting in differentiated outcomes. The data comprises 318 consortium members participating in 104 R&D consortia forged in a 23-year period in the Dutch water sector. Our analysis reveals two equifinal combinations (stable brokers – volatile clusters and volatile brokers – stable clusters) that both generate significantly higher innovation outcomes compared to networks with low, moderate, or high dynamics across the entire network.     

Network structure and team performance: The case of English Premier League soccer teams

A defining feature of a work group is how its individual members interact. Building on a dataset of 283,259 passes between professional soccer players, this study applies mixed-effects modeling to 76 repeated observations of the interaction networks and performance of 23 soccer teams. Controlling for unobserved characteristics, such as the quality of the teams, the study confirms previous findings with panel data: networks characterized by high intensity (controlling for interaction opportunities) and low centralization are indeed associated with better team performance.     

Gender bias in the classroom: A network study on self and peer ability attribution

Stereotypes can contribute to the gender gap in STEM by shaping people’s expectations on their own and others’ performance. When gender is salient, expectations on task performance might reflect gender constructs even when information on individual abilities is available. We tested this hypothesis in a network study on students from ten high school classes in Milan, Italy. We asked the students to choose the four best candidates from their classmates for three hypothetical inter-class competitions in reading, math, and science. Results showed that females were more likely to be nominated for the reading competition but less likely for science. We did not find any statistically significant results for the math competition. We also found that female students were less likely to nominate themselves for any competition, regardless of the subject, even controlling for their own performance and self-concept.     

大学生网络政治参与:阻碍因素、形成机理与引导对策

网络技术的迅猛发展强烈地冲击着社会政治生活,对当代大学生产生重要影响。处于这个时代的大学生缘于网络呈现政治意识自觉拓展、群体效应的酝酿、实践场域与能力扩展以及诉求多元化与渠道缺失等特点,其形成过程表现为以热点追踪为信息源、话题讨论、观念内化、行为实践由外至内再外化的过程。针对大学生网络政治参与的现状及机理,应从完善法律法规,加强政府网络信息机制建设;优化网络政治环境,加强对大学生网络热点话题的管理;加强网络疏导,引导大学生网络群体活动有序开展;加强网络自律,树立网络名人、榜样示范效应这四个维度来建设,促进大学生网络政治参与和谐进行。     

Co-evolution of conventions and networks: An experimental study

We study the emergence of conventions in dynamic networks experimentally. Conventions are modeled in terms of coordination games in which actors can choose both their behavior and their interaction partners. We study how macro-level outcomes of the process in terms of Pareto-efficiency and heterogeneity depend on initial conditions. Moreover, we examine the underlying processes at the microlevel. Predictions are derived from a game-theoretic model which is applied to our experimental conditions by means of computer simulation. The results provide mixed support for the macro-level hypotheses, and indicate possible directions to improve the model at the micro level.