Group-level Outcomes Research Articles

A Markov chain analysis of the dynamics of homophily

Abstract Many networks are made up of different nodes types, which are determined by a set of common quantitative or qualitative node properties. Understanding the effects of homophilic relationships, that is, the tendency of nodes to establish links to other nodes that are alike, requires formal frameworks that explain how local decision-making mechanisms contribute to the formation of particular network structures. Based on two simple stochastic mechanisms for establishing links, this article introduces a model that explains the emergence of homophily as an aggregate group and network level outcome. We characterize the dynamics of homophily and present conditions that guarantee that the amount of homophily exceeds the expected amount of a purely random decision-making process. Moreover, we show that the proposed model resembles patterns of homophily in a citation network of political blogs. Finally, we use the model to design a non-homophilic node detection algorithm for identifying nodes that establish connections without a particular preference for either node type.

Brief cognitive analytic therapy for adults with chronic pain: a preliminary evaluation of treatment outcome.

Cognitive analytic therapy (CAT) has diversified in its application to a broad range of presenting difficulties and formats and there has been growing interest in its use in clinical health settings including Pain Management Services. Despite its growing popularity, to date, no studies have examined the application of CAT for chronic pain. The purpose of this study was to examine the effectiveness of time-limited, individual CAT for adults with chronic pain as delivered in a publicly funded Community Pain Management Service. This was an observational service evaluation of treatment practice within a routine clinical setting. Participants were 53 adults with chronic pain who completed eight sessions of individual therapy. Individual and group-level outcomes on self-reported distress, pain-related self-efficacy, as well as changes in healthcare utilisation pre- and post-intervention were examined. Large pre-post effects on wellbeing (d = 1.50) and self-efficacy (d = 1.13) were observed, while effects on healthcare utilisation were medium to large (d = 0.67). Moreover, reliable change and clinical significance analyses demonstrated that results were clinically meaningful. The majority of clients (67.9%) showed reliable improvement following the CAT intervention and 28% achieved both reliable and clinically significant improvements in psychological distress. Approximately 4 of 10 clients (43.5%) showed reliably higher perceived self-efficacy at the end of therapy. The results of this evaluation suggest that brief CAT is an encouraging intervention for adults with persistent pain. While preliminary, our findings provide support for a brief approach to persistent pain that focuses not on controlling or eliminating pain but on elucidating and revising unhelpful relational patterns that may impinge upon one's pain management. Directions for future research are suggested.

Multilevel Structural Equation Modeling for Cross-National Comparative Research

This contribution focuses on a model that is gaining currency in cross-national research, namely multilevel structural equation modelling (MSEM). Similarly to standard multilevel modelling (MLM), this model distinguishes between various levels of analysis (e. g., individuals nested within countries) and, in doing so, takes the hierarchical structure of cross-national data into account. However, MSEM incorporates a latent-variable approach into the multilevel framework, making it possible to assess the measurement quality of latent constructs. As such, MSEM is a synthesis of structural equation modeling (SEM) and MLM that combines the best of both worlds. The MSEM approach makes it possible to model multilevel mediations and group-level outcomes, and therefore provides a more complete representation of Coleman’s bathtub model. This contribution presents the statistical and conceptual background of MSEM in a formal but accessible manner. The paper discusses applications of MSEM that are particularly useful for cross-national comparative research (CNCR), namely two-level confirmatory factor analysis (CFA), multilevel mediation models, and models for group-level outcomes. A practical step-by-step strategy on how MSEM can be used for applied research is provided and illustrated by means of a didactical example.

Modeling Schistosoma japonicum Infection under Pure Specification Bias: Impact of Environmental Drivers of Infection.

Uncertainties in spatial modeling studies of schistosomiasis (SCH) are relevant for the reliable identification of at-risk populations. Ecological fallacy occurs when ecological or group-level analyses, such as spatial aggregations at a specific administrative level, are carried out for an individual-level inference. This could lead to the unreliable identification of at-risk populations, and consequently to fallacies in the drugs’ allocation strategies and their cost-effectiveness. A specific form of ecological fallacy is pure specification bias. The present research aims to quantify its effect on the parameter estimates of various environmental covariates used as drivers for SCH infection. This is done by (i) using a spatial convolution model that removes pure specification bias, (ii) estimating group and individual-level covariate regression parameters, and (iii) quantifying the difference between the parameter estimates and the predicted disease outcomes from the convolution and ecological models. We modeled the prevalence of Schistosoma japonicum using group-level health outcome data, and city-level environmental data as a proxy for individual-level exposure. We included environmental data such as water and vegetation indexes, distance to water bodies, day and night land surface temperature, and elevation. We estimated and compared the convolution and ecological model parameter estimates using Bayesian statistics. Covariate parameter estimates from the convolution and ecological models differed between 0.03 for the nearest distance to water bodies (NDWB), and 0.28 for the normalized difference water index (NDWI). The convolution model presented lower uncertainties in most of the parameter estimates, except for NDWB. High differences in uncertainty were found in night land surface temperature (0.23) and elevation (0.13). No significant differences were found between the predicted values and their uncertainties from both models. The proposed convolution model is able to correct for a pure specification bias by presenting less uncertain parameter estimates. It shows a good predictive performance for the mean prevalence values and for a positive number of infected people. Further research is needed to better understand the spatial extent and support of analysis to reliably explore the role of environmental variables.

Lower temperatures decrease worker size variation but do not affect fine-grained thermoregulation in bumble bees

Phenotypic variation within biological systems is ubiquitous and often assumed to be adaptive in social insects. Local environmental factors, such as temperature, may affect the phenotypic variation produced, and yield insights into the mechanisms that generate this group-level outcome. For instance, fine-scale heterogeneity in temperature across the nest may generate phenotypic variation by affecting larval development directly, or indirectly by inducing changes in the behavior of group members. To understand the role of temperature in producing phenotypic variation, we studied whether microclimatic differences within the nest could produce the worker size variation commonly observed in bumble bee (Bombus impatiens) colonies. We also tested if changes to the ambient temperature that colonies are exposed to influences the size variation they produced. We recorded brood temperature via thermal imaging and measured thorax widths of workers produced by colonies kept at ambient temperatures of 30 and 20 °C. Overall, average brood temperature did not differ across the nest even while the average temperature of non-brood structures (i.e., honeypots) interspersed among the brood decreased towards the periphery, demonstrating that bees were able to regulate brood temperature at a fine scale. However, we found that the size variation produced was sensitive to ambient temperature and increased under warmer temperatures. These results demonstrate that bumble bees have a unique method of fine-grained thermoregulation, where they regulate brood temperature but not the regions between brood. Additionally, while temperature is not the mechanism that produces size variation, it indirectly influences the mechanism that does. Morphological variation among group members affects group performance. We studied how the thermal environment experienced by developing workers, i.e., brood, influences the between-worker size variation produced within B. impatiens colonies. We found that temperature only indirectly influences size variation, as size variation increased under warmer nest temperatures, yet the temperature of brood did not differ across the nest. Our finding that workers maintain a uniform environment for the brood differs from previous results examining feeding rates across the nest and suggests that while the same workers tend to feed and thermoregulate the brood, they do so according to different rules and that by influencing worker behaviors, such as feeding rate, temperature affects size variation.

Reconceptualizing church-based mental health promotion with African Americans: A social action theory approach

ABSTRACTAfrican Americans are at risk for poor mental health outcomes and disparities in treatment. Because of the importance of religion in the lives of many African Americans Church-Based Health Promotion (CBHP) programs have emerged as a promising way to address disparate mental health issues affecting this population. Many of the theories currently used to ground CBHP efforts focus on individual level health behavior change. This study suggests a re conceptualization of CBHP to include a focus on family, group, congregation, and community level outcomes as opposed to individual health behaviors to maximize the impact on African Americans. The case is made for the use of Social Action Theory by examining several mental health promotion programs, their theoretical foundations, and suggested SAT adaptations. Implications for the development of CBHP interventions directed towards African Americans and a focus on group and community level outcomes are discussed.

Change at the Micro, Meso, and Macro Levels: Connecting Across Research in US Public Education

The last several decades of research and practice in US public education have become increasingly focused on the design and implementation of reform efforts that seek to address inequities in academic performance observed in school organizations across the country. Central to these efforts is an interest in organizational change as new programs and interventions alter the ways and means schools go about their work. While a plethora of approaches to change in education exist, less attention has been paid to drawing connections across varying efforts in the field. One way to address this challenge is to cast a lens on change-oriented programs in education from the vantage point of multiple levels of analysis. Given that the theoretical and methodological traditions of micro, meso, and macro level empirical work varies, our understanding of whether organizational change in education is most effective when focusing on individual, group, or system level outcomes is limited. In this symposium, three works of empirical research in public education will be presented, each situated at a different level of analysis. The presentations highlight a range of change relevant outcomes such as individual work-related non-cognitive perceptions, group stereotypes, and organizational ambidexterity. By putting work at multiple levels in conversation with each other, a clearer understanding of the advantages and challenges of doing work at each respective level of analysis can be made clearer, thereby enhancing our understanding of how and when to use certain organizational change approaches. Career-related Non-cognitive Outcomes: A Network Analysis of a Higher Education Intervention Presenter: Cortney Evans; Harvard U. Presenter: Monica Higgins; Harvard U. Presenter: David Sherer; Harvard U. The Paradox of Stereotypes in Education Reform Presenter: Renee M. Rinehart; Harvard Graduate School of Education The Ambidextrous School System: Case Studies of Process Management Programs in Education Presenter: Maxwell Yurkosfsky; Harvard U. Presenter: Amelia Peterson; Harvard U. Presenter: Kim Furmin; Harvard U. Presenter: Andrew Volkert; Harvard U. Presenter: Jal Mehta; Harvard U.

When Extremists Win: Cultural Transmission Via Iterated Learning When Populations Are Heterogeneous.

How does the process of information transmission affect the cultural or linguistic products that emerge? This question is often studied experimentally and computationally via iterated learning, a procedure in which participants learn from previous participants in a chain. Iterated learning is a powerful tool because, when all participants share the same priors, the stationary distributions of the iterated learning chains reveal those priors. In many situations, however, it is unreasonable to assume that all participants share the same prior beliefs. We present four simulation studies and one experiment demonstrating that when the population of learners is heterogeneous, the behavior of an iterated learning chain can be unpredictable and is often systematically distorted by the learners with the most extreme biases. This results in group-level outcomes that reflect neither the behavior of any individuals within the population nor the overall population average. We discuss implications for the use of iterated learning as a methodological tool as well as for the processes that might have shaped cultural and linguistic evolution in the real world.

How the Distribution of Member Expectations Influences Cooperation and Competition in Groups: A Social Relations Model Analysis of Social Dilemmas.

Group outcomes are difficult to model and predict using individual-level metrics. We use shared cognition concepts and the social relations model to predict cooperative group outcomes in two social dilemmas to test whether social projection or consensus among group members would best predict cooperation. Group-level variance components derived from the social relations model were used as indices of social projection and consensus. Groups played five rounds of two social dilemmas and predicted their partners' behavior on the following round. Results demonstrate that participants expect other group members to respond to the situation like the participant will and that shared expectations predict less competitive group behavior, but only when group members' expectations have little to no variance. This article demonstrates the utility of the social relations model in predicting group-level outcomes from individual inputs, as well as providing novel findings regarding the complex effects of shared expectations on collective outcomes.

Predicting group-level outcome variables: An empirical comparison of analysis strategies.

This study provides a review of two methods for analyzing multilevel data with group-level outcome variables and compares them in a simulation study. The analytical methods included an unadjusted ordinary least squares (OLS) analysis of group means and a two-step adjustment of the group means suggested by Croon and van Veldhoven (2007). The Type I error control, power, bias, standard errors, and RMSE in parameter estimates were compared across design conditions that included manipulations of number of predictor variables, level of correlation between predictors, level of intraclass correlation, predictor reliability, effect size, and sample size. The results suggested that an OLS analysis of the group means, with White's heteroscedasticity adjustment, provided more power for tests of group-level predictors, but less power for tests of individual-level predictors. Furthermore, this simple analysis avoided the extreme bias in parameter estimates and inadmissible solutions that were encountered with other strategies. These results were interpreted in terms of recommended analytical methods for applied researchers.

Positive imagined contact is actively chosen: Exploring determinants and consequences of volitional intergroup imagery in a conflict-ridden setting

Past research has ascertained the benefits of involuntary, “forced” exposure to positive imagined contact. This research explored determinants and consequences of actively chosen imagined contact in a setting of entrenched intergroup conflict. In Study 1, when given an unvalenced visualisation scenario enabling participants to steer the visualisation in any direction they wanted, Turkish Cypriots visualised an intergroup interaction nondistinguishable in quality to that of those assigned to a positive scenario. In Study 2, when asked to actively choose between visualising a positive or a negative intergroup interaction, Turkish Cypriots disproportionally preferred positive over negative contact. The chosen visualisation reflected mood and valenced confirmation biases and resulted in virtuous (vs. vicious) effects on group-level outcomes. These findings shed a first light on the psychological underpinnings of volitional intergroup imagery and indicate that intergroup imagery is a safe way of engaging with the outgroup even in contexts of entrenched conflict.

Costs of task allocation with local feedback: Effects of colony size and extra workers in social insects and other multi-agent systems.

Adaptive collective systems are common in biology and beyond. Typically, such systems require a task allocation algorithm: a mechanism or rule-set by which individuals select particular roles. Here we study the performance of such task allocation mechanisms measured in terms of the time for individuals to allocate to tasks. We ask: (1) Is task allocation fundamentally difficult, and thus costly? (2) Does the performance of task allocation mechanisms depend on the number of individuals? And (3) what other parameters may affect their efficiency? We use techniques from distributed computing theory to develop a model of a social insect colony, where workers have to be allocated to a set of tasks; however, our model is generalizable to other systems. We show, first, that the ability of workers to quickly assess demand for work in tasks they are not currently engaged in crucially affects whether task allocation is quickly achieved or not. This indicates that in social insect tasks such as thermoregulation, where temperature may provide a global and near instantaneous stimulus to measure the need for cooling, for example, it should be easy to match the number of workers to the need for work. In other tasks, such as nest repair, it may be impossible for workers not directly at the work site to know that this task needs more workers. We argue that this affects whether task allocation mechanisms are under strong selection. Second, we show that colony size does not affect task allocation performance under our assumptions. This implies that when effects of colony size are found, they are not inherent in the process of task allocation itself, but due to processes not modeled here, such as higher variation in task demand for smaller colonies, benefits of specialized workers, or constant overhead costs. Third, we show that the ratio of the number of available workers to the workload crucially affects performance. Thus, workers in excess of those needed to complete all tasks improve task allocation performance. This provides a potential explanation for the phenomenon that social insect colonies commonly contain inactive workers: these may be a ‘surplus’ set of workers that improves colony function by speeding up optimal allocation of workers to tasks. Overall our study shows how limitations at the individual level can affect group level outcomes, and suggests new hypotheses that can be explored empirically.

Predictors of Responsivity to Interdisciplinary Pain Management.

This study investigated for whom interdisciplinary pain management (IPM) is most effective. Identification of predictors of treatment responsivity would facilitate development of patient-treatment matching algorithms to optimize outcomes. Repeated measures prospective study of consecutive admissions to a two-week IPM program. Brisbane Pain Rehabilitation Service in Brisbane, Australia. A total of 163 adults referred for chronic pain management. Self-report questionnaires and measures of physical performance were obtained at program entry and completion. Group-level analyses were performed using standard parametric statistics. Individual-level change was assessed using recommended criteria. Multivariate analysis of variance and logistic regression were used to examine outcomes and predictors of response. Significant improvements were observed across psychological, social, and physical outcome domains. Up to 50% of participants had clinically meaningful improvements, while less than 10% deteriorated. Higher baseline depression, anxiety, stress, and pain catastrophizing scores predicted better group-level outcomes (Ps < 0.05). Participants with higher baseline depression scores were most likely to show significant individual-level improvement on at least one outcome (Ps < 0.05). Participants with nociceptive pain were more than four times more likely than those with neuropathic pain to show clinically meaningful improvement on multiple outcomes, while those participants who were older were more likely to be multidomain responders. Physical, psychological, and social outcomes all improved in a significant proportion of participants following the IPM. High baseline depression was a clinically reliable predictor of individual-level improvement. Individuals with nociceptive pain and those who were older, respectively, showed the largest response across multiple outcomes and domains.

Assessing participants’ perceptions on group-based principles for action in community-based health enhancing physical activity programmes: The APEF tool

In community-based health enhancing physical activity (CBHEPA) programmes, group-based principles for action such as active participation, enjoyment, and fostering group processes are widely advocated. However, not much is known about participants’ perceptions of these principles as there are no assessment tools available. Therefore, this article describes the development of the APEF (Active Participation, Enjoyment, and Fostering group processes) tool and reports on its implementation in a Dutch CBHEPA programme. Indicators for the principles have been identified from literature research, interviews with professionals, and secondary analysis of three group interviews with 11 practitioners. To address the identified indicators, the APEF tool was developed, pretested, and used in 10 focus groups with 76 participants.The APEF tool consists of eight statements about group-based principles for action, on which CBHEPA participants vote, followed by in-depth discussion. The voting procedure engages participants. Spider diagrams visualise participants’ perceptions of group-based principles.The APEF tool addresses the challenge of relating group level outcomes to individual outcomes such as physical activity behaviour. The tool facilitates as well as evaluates group-based principles for action, it stimulates dialogue and is culturally sensitive, but it needs strong facilitating skills to manage group dynamics.

Individual-level movement bias leads to the formation of higher-order social structure in a mobile group of baboons

In mobile social groups, influence patterns driving group movement can vary between democratic and despotic. The arrival at any single pattern of influence is thought to be underpinned by both environmental factors and group composition. To identify the specific patterns of influence driving travel decision-making in a chacma baboon troop, we used spatially explicit data to extract patterns of individual movement bias. We scaled these estimates of individual-level bias to the level of the group by constructing an influence network and assessing its emergent structural properties. Our results indicate that there is heterogeneity in movement bias: individual animals respond consistently to particular group members, and higher-ranking animals are more likely to influence the movement of others. This heterogeneity resulted in a group-level network structure that consisted of a single core and two outer shells. Here, the presence of a core suggests that a set of highly interdependent animals drove routine group movements. These results suggest that heterogeneity at the individual level can lead to group-level influence structures, and that movement patterns in mobile social groups can add to the exploration of both how these structures develop (i.e. mechanistic aspects) and what consequences they have for individual- and group-level outcomes (i.e. functional aspects).

Interactions between the developmental and adult social environments mediate group dynamics and offspring traits in Drosophila melanogaster

Developmental conditions can strongly influence adult phenotypes and social interactions, which in turn affect key evolutionary processes such as sexual selection and sexual conflict. While the implications of social interactions in phenotypically mixed populations at the individual level are increasingly well known, how these effects influence the fate of groups remains poorly understood, which limits our understanding of the broader ecological implications. To address this problem we manipulated adult phenotypes and social composition in Drosophila melanogaster – by experimentally manipulating the larval density of the group-members – and measured a range of group-level outcomes across the lifespan of groups. Adult groups composed of exclusively low larval-density individuals showed high courtship levels, and low early reproductive rates, group growth rates, offspring mass and offspring eclosion success, relative to high larval-density or mixed larval-density groups. Furthermore, high larval-density groups had lower survival. Offspring mass increased with time, but at a reduced rate in groups when male group members (but not females) were from a mixture of larval-densities; peak reproductive rates were also earlier in these groups. Our results suggest that that variation in developmental conditions experienced by adult group members can modify the reproductive output of groups.

Sequential social experiences interact to modulate aggression but not brain gene expression in the honey bee (Apis mellifera)

BackgroundIn highly structured societies, individuals behave flexibly and cooperatively in order to achieve a particular group-level outcome. However, even in social species, environmental inputs can have long lasting effects on individual behavior, and variable experiences can even result in consistent individual differences and constrained behavioral flexibility. Despite the fact that such constraints on behavior could have implications for behavioral optimization at the social group level, few studies have explored how social experiences accumulate over time, and the mechanistic basis of these effects. In the current study, I evaluate how sequential social experiences affect individual and group level aggressive phenotypes, and individual brain gene expression, in the highly social honey bee (Apis mellifera). To do this, I combine a whole colony chronic predator disturbance treatment with a lab-based manipulation of social group composition.ResultsCompared to the undisturbed control, chronically disturbed individuals show lower aggression levels overall, but also enhanced behavioral flexibility in the second, lab-based social context. Disturbed bees display aggression levels that decline with increasing numbers of more aggressive, undisturbed group members. However, group level aggressive phenotypes are similar regardless of the behavioral tendencies of the individuals that make up the group, suggesting a combination of underlying behavioral tendency and negative social feedback influences the aggressive behaviors displayed, particularly in the case of disturbed individuals. An analysis of brain gene expression showed that aggression related biomarker genes reflect an individual’s disturbance history, but not subsequent social group experience or behavioral outcomes.ConclusionsIn highly social animals with collective behavioral phenotypes, social context may mask underlying variation in individual behavioral tendencies. Moreover, gene expression patterns may reflect behavioral tendency, while behavioral outcomes are further regulated by social cues perceived in real-time.

Modeling collective action through media to promote social change and positive intergroup relations in violent conflicts

Does social influence exerted through role modeling of collective action impact social change in contexts that are not conducive to collective action, such as long-lasting violent conflicts? We examined this question in two field experiments in the Eastern Democratic Republic of Congo. We created two versions of an episode of an existing media intervention (a show aiming to promote positive social change), in which fictional characters either planned collective action (role modeling condition), or did not plan action (control condition) to address grievances. In Study 1, role modeling affected individual-level outcomes: it increased perceived collective efficacy and willingness to take action, but exacerbated intergroup attitudes and reduced tolerance. Study 2 tested the influence of role modeling on a group-level outcome (group discussions). Discussions following the role modeling show focused less on grievances, and included more positive lessons of the show, as well as more statements about collective efficacy and collective action. The findings highlight the influence of role modeling of collective action through media on efficacy and action for social change, but caution against unintended consequences on intergroup attitudes.

Employee creativity and innovation in organizations

PurposeThe purpose of this paper is twofold. First, the study considers research on creativity and innovation in the field of general management and hospitality. Second, the paper develops a theoretical model to integrate individual- and group-level creativity particularly for service organizations.Design/methodology/approachThis paper provides a comprehensive, albeit non-inclusive, review of research on creativity and innovation in organizations. The review reveals that hospitality research on creativity and innovation has not matched the new advances in management research, particularly the multilevel nature of creativity and the outcomes of creativity. Thus, to advance research in hospitality, this paper proposes a multilevel model of creativity based on a strategic contingency power theory. This model examines how individual- and group-level uncertainties hinder creativity. Moreover, the model also considers several uncertainty coping strategies and examines individual- and group-level outcomes of creativity.FindingsThe proposed theoretical model integrates individual- and group-level uncertainty determinants of creativity and yields a multilevel approach to creativity. Several testable hypotheses are proposed.Research limitations/implicationsThis paper highlights the strategic contingency power approach between individual- and group-level uncertainties in creativity. Uncertainty coping practices that alleviate the negative effects of uncertainties on creativity will be useful to managers and service organizations.Originality/valueThe proposed model provides plausible guidelines that advance creativity research in hospitality management.

Investigating the Relationship between Team Cohesion and Self-Presentation among Different Competitive Team Sports of Ethiopian Universities

One of the most constantly studied constructs in team dynamic research is cohesiveness, because it’s not only associated with group level outcomes such as performance but also with individuals outcomes. Self-presentation involves the selective presentation of particular characteristics of oneself that would make the desired impression on others. The main purpose of this paper is to investigate the relationship between self-presentation and team cohesion among different competitive team sports of Ethiopian Universities. It is the hypothesis that high cohesion would be associated with low self-presentational concerns. The sample consisted of 108 athletes (68 male, 40 female) from 11 different universities among 33, with the average of 20.94 ± 1.95 years of age, using random sampling technique. The data was obtained using Group Environmental Questionnaire (GEQ), Self-presentation in Sport Questionnaire (SPSQ) and the Sport Anxiety Scale (SAS) for team cohesion, self-presentation and competitive anxiety, respectively. The findings show that the task and social cohesion are negatively related to self-presentational concerns in sports, explaining 4% of variance, which means higher perceptions of cohesion, are associated with low self-presentational concerns. Keywords : Team cohesion, Self-presentation, Competitive anxiety, Team sports, Ethiopia.