The role of titles in enhancing data visualization

Many in the data visualization and evaluation communities recommend conveying the message or takeaway of the visualization in the visualization's title. This study tested that recommendation by examining how informative or generic titles impact a visualization’s visual efficiency, aesthetics, credibility, and the perceived effectiveness of the hypothetical program examined. Furthermore, this study tested how simple or complex graphs, and positive, negative, or mixed results (i.e., valence of the results) affected outcomes. Participants were randomly assigned to one of 12 conditions, representing a 2 (graph: simple or complex) x 2 (title: generic or informative) x 3 (valence: positive, negative, mixed) between-subjects study. The results indicated that informative titles required less mental effort and were viewed as more aesthetically pleasing, but otherwise did not lead to greater accuracy, credibility, or perceived effectiveness. Furthermore, titles did not interact with graph type or the valence of the findings. While the results suggest it is worthwhile to consider adding an informative title to data visualizations as they can reduce mental effort for the viewer, the intended goal of the visualization should be taken into consideration. Considering the goal of the visualization can be a deciding factor of the type of graph and title that will best serve its intended purposes. Overall, this suggests that data visualization recommendations that impact evaluation reporting practices should be scrutinized more closely through research.     

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.     

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.     

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.     

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.     

Data Science: A Powerful Catalyst for Cross-Sector Collaborations to Transform the Future of Global Health—Developing a New Interactive Relational Mapping Tool (Demo)

ABSTRACT

The increasingly complex and rapidly changing global health and socioeconomic landscape requires fundamentally new ways of thinking, acting, and collaborating to solve growing systems challenges. Cross-sectoral collaborations between governments, businesses, international organizations, private investors, academia, and nonprofits are essential for lasting success in achieving the Sustainable Development Goals (SDGs), and securing a prosperous future for the health and well-being of all people (United Nations, n.d United Nations. (n.d.). SDGs: Sustainable development knowledge platform. Sustainable Development United Nations. Retrieved from https://sustainabledevelopment.un.org/ [Google Scholar].). Our aim is to use data science and innovative technologies to map diverse stakeholders and their initiatives around SDGs and specific health targets—with particular focus on SDG 3 (Good Health & Well Being) and SDG 17 (Partnerships for the Goals)—to accelerate cross-sector and multidisciplinary collaborations. Initially, the mapping tool focuses on Geneva, Switzerland as the world center of global health diplomacy with over 80 key stakeholders and influencers present. As we develop the next level pilot, we aim to build on users’ interests, with a potential focus on non-communicable diseases (NCDs) as one of the emerging and most pressing global health issues that requires new collaborative approaches. Building on this pilot, we can later expand beyond only SDG 3 to other SDGs.     

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.     

Power and the perception of social networks

Previous work has led to divergent conclusions about how power affects the accuracy of network perceptions in groups and organizations. This paper develops and tests an argument linking higher power to less accurate network perception. Results from the first experiment showed that, relative to participants primed with high power, those primed with low power had more accurate perceptions of who was tied to whom in novel networks. The second experiment demonstrated that such differences in perceptual accuracy do not emerge for non-social relations.     

Order of recall and meaning of closeness in collecting affective network data

The paper investigates how study participants handle the so-called “hierarchical mapping technique”, an affective name generator developed by Antonucci (1986), which is accompanied by a diagram enabling respondents to compare alters with regard to different degrees of closeness. By applying the thinking-aloud method, we identified three patterns in the order of recalling alters: closeness as overarching schema (with either role relationships or relationship properties as subordinate schema), roles and foci as overarching schema, and a fraying schema. In addition, we investigated how study participants understand and interpret “closeness”. The meanings of closeness can refer to various relationship properties, cultural framing, and relationship dynamics. Results show that specific meanings of closeness are related to different recall patterns. Furthermore, recall patterns vary according to the socio-economic status of the participants. Finally, implications for the construction of name generators and data collection are discussed.     

Different relationships for coping with ambiguity and uncertainty in organizations

This study seeks to shed light on the relationship between the situation and the activation of specific relationships. We hypothesized that the type of uncertainty present in a situation would prompt people to call upon different relationships based on different types of trust: cognitive trust for expertise and affective trust for friendship. We elaborated vignettes as name generators to test whether the colleagues called upon in different situations were perceived as being more friends or more experts. The perceived level of expertise and friendship were evaluated with Likert-style scales. The results support our theoretical argument to the effect that the “activation” of a relationship is influenced by the type of uncertainty a person is confronted to. Situations of information uncertainty elicit recourse to relationships based on expertise while ambiguous situations call for friendship.     

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.     

Are Hearing Voices Networks Compatible with Cognitive Behavioural Therapy for Psychosis?

The most extensively evaluated intervention for auditory hallucinations is Cognitive Behavioural Therapy for Psychosis (CBTp), which is usually delivered in statutory settings by experienced clinicians. In contrast, peer support networks, such as the Hearing Voices Network (HVN), operate informally using peers to normalise and manage voices. This article compares these two approaches to identify points of synergy and difference. Its analysis suggests the two approaches share many features, but also deviate in important ways that could compensate for each other. Instead of competing with each other as might first be thought, they may actually be complementary. Further, HVN may align comfortably with the values and principles of social work. Social workers seeking to engage with voice hearers may be advised to study the underlying principles of each approach to guide the way in which they can capitalise on intervention opportunities to better assist voice hearers.     

Whose results are these anyway? Reciprocity and the ethics of “giving back” after social network research

Bridging the social networks, field methods and ethics literatures, I make the case that the process of reporting research findings is an ethical issue, and recommend elevating it in the research design. I draw on a reflective account of three research experiences with settings in, respectively, online health communities, economic organizations, and the mainstream media. I proceed in steps, discussing release of personal network results to individual participants, of whole network results to the researched community, and finally of general results to wider audiences, under a unifying idea that a reciprocity obligation underlies the reporting process. I claim that communication should follow an iterative rather than a linear approach to reach all relevant stakeholders, thereby mitigating the vulnerabilities that arise from research.     

Evolution of what? A network approach for the detection of evolutionary forces

Structures of evolving populations are traditionally derived from traits of its members. An alternative approach uses network metrics to define groups that evolve jointly. This supposes that selection acts not only on who members are (i.e., traits) but also on to whom they are connected (i.e., interdependent relationships). This paper presents a method to meaningfully quantify differences in evolutionary forces over multiple levels of population taxonomies and tests almost 1000 multilevel partitions of 8 empirical networked populations evolving over time. It shows that multilevel network metrics as selection criteria identifies stronger evolutionary natural selection than trait based population taxonomies.