Stochastic blockmodels: First steps

A stochastic model is proposed for social networks in which the actors in a network are partitioned into subgroups called blocks. The model provides a stochastic generalization of the blockmodel. Estimation techniques are developed for the special case of a single relation social network, with blocks specified a priori. An extension of the model allows for tendencies toward reciprocation of ties beyond those explained by the partition. The extended model provides a one degree-of-freedom test of the model. A numerical example from the social network literature is used to illustrate the methods.     

A goodness-of-fit index for blockmodels

The ‘fit’ between blockmodels and data networks is extended from a binary to a continuous concept. An index of goodness-of-fit for α-fit blockmodels is proposed, based on the purity (density of 1's or 0's) of the submatrices of the data matrix(es) after the blockmodel partition is imposed. The characteristics of this index are compared with those of correlation coefficient. Some applications are described.     

Stochastic a posteriori blockmodels: Construction and assessment

In 1983, Holland, Laskey, and Leinhardt, using the ideas of Holland and Leinhardt, and Fienberg and Wasserman, introduced the notion of a stochastic blockmodel. The mathematics for stochastic a priori blockmodels, in which exogenous actor attribute data are used to partition actors independently of any statistical analysis of the available relational data, have been refined by several researchers and the resulting models used by many. Attempts to simultaneously partition actors and to perform relational data analyses using statistical methods that yield stochastic a posteriori blockmodels are still quite rare. In this paper, we discuss some old suggestions for producing such posterior blockmodels, and comment on other new suggestions based on multiple comparisons of model parameters, log-linear models for ordinal categorical data, and correspondence analysis. We also review measures for goodness-of-fit of a blockmodel, and we describe a natural approach to this problem using likelihood-ratio statistics generated from a popular model for relational data.     

Non-response in social networks: The impact of different non-response treatments on the stability of blockmodels

Discerning the essential structure of social networks is a major task. Yet, social network data usually contain different types of errors, including missing data that can wreak havoc during data analyses. Blockmodeling is one technique for delineating network structure. While we know little about its vulnerability to missing data problems, it is reasonable to expect that it is vulnerable given its positional nature. We focus on actor non-response and treatments for this. We examine their impacts on blockmodeling results using simulated and real networks. A set of ‘known’ networks are used, errors due to actor non-response are introduced and are then treated in different ways. Blockmodels are fitted to these treated networks and compared to those for the known networks. The outcome indicators are the correspondence of both position memberships and identified blockmodel structures. Both the amount and type of non-response, and considered treatments, have an impact on delineated blockmodel structures.     

Actor non-response in valued social networks: The impact of different non-response treatments on the stability of blockmodels

Social network data usually contain different types of errors. One of them is missing data due to actor non-response. This can seriously jeopardize the results of analyses if not appropriately treated. The impact of missing data may be more severe in valued networks where not only the presence of a tie is recorded, but also its magnitude or strength. Blockmodeling is a technique for delineating network structure. We focus on an indirect approach suitable for valued networks. Little is known about the sensitivity of valued networks to different types of measurement errors. As it is reasonable to expect that blockmodeling, with its positional outcomes, could be vulnerable to the presence of non-respondents, such errors require treatment. We examine the impacts of seven actor non-response treatments on the positions obtained when indirect blockmodeling is used. The start point for our simulation are networks whose structure is known. Three structures were considered: cohesive subgroups, core-periphery, and hierarchy. The results show that the number of non-respondents, the type of underlying blockmodel structure, and the employed treatment all have an impact on the determined partitions of actors in complex ways. Recommendations for best practices are provided.     

一种调压撬配置的新方式

针对部分可间断供气的用户使用常规2+0调压撬时,其中备用调压路设备利用率不高,造成投资成本增加的情况,该文提出了一种新的改进方式,可降低调压站箱尺寸、节省投资费用。     

Teaching social workers to use psychological assessment data

Psychological assessments of children and parents are frequently used by social service caseworkers when making case planning decisions. Often, however, the two disciplines--psychology and social work--have difficulty collaborating in and coordinating their respective work. Caseworkers may lack formal training in how to understand and use psychological reports. Psychologists may lack formal training in forensic assessment and in understanding the constraints of the social service and child welfare systems. This article describes how caseworkers may become more sophisticated consumers of psychological assessments, and how collaborative relationships between evaluating psychologists and caseworkers can be fostered.     

Using logic programming to facilitate qualitative data analysis

Conventional word-processing programs and LOGLISP, a system which integrates logic programming with LISP, form the basis for QUALOG, a set of computer programs which facilitates the qualitative data analysis process. These programs aid the researcher's conceptual work in data analysis in four areas: 1) formatting the data as text files; 2) creating, organizing, and storing a knowledge base of information about the data; 3) testing knowledge-base queries relevant to working hypotheses for confirming and for disconfirming information; and 4) creating, organizing, and storing files for the researcher's memoranda.     

Finite Normal Mixture SEM Analysis by Fitting Multiple Conventional SEM Models

This paper proposes a two-stage maximum likelihood (ML) approach to normal mixture structural equation modeling (SEM), and develops statistical inference that allows distributional misspecification. Saturated means and covariances are estimated at stage-1 together with a sandwich-type covariance matrix. These are used to evaluate structural models at stage-2. Techniques accumulated in the conventional SEM literature for model diagnosis and evaluation can be used to study the model structure for each component. Examples show that the two-stage ML approach leads to correct or nearly correct models even when the normal mixture assumptions are violated and initial models are misspecified. Compared to single-stage ML, two-stage ML avoids the confounding effect of model specification and the number of components, and is computationally more efficient. Monte-Carlo results indicate that two-stage ML loses only minimal efficiency under the condition where single-stage ML performs best. Monte-Carlo results also indicate that the commonly used model selection criterion BIC is more robust to distribution violations for the saturated model than that for a structural model at moderate sample sizes. The proposed two-stage ML approach is also extremely flexible in modeling different components with different models. Potential new developments in the mixture modeling literature can be easily adapted to study issues with normal mixture SEM.     

Developing ethical approaches to data and civil society: from availability to accessibility

This research note reflects on the gaps and limitations confronting the development of ethical principles regarding the accessibility of large-scale data for civil society organizations (CSOs). Drawing upon a systematic scoping study on the use of data in the United Kingdom (UK) civil society, it finds that there are twin needs to conceptualize accessibility as more than mere availability of data, as well as examine the use of data among CSOs more generally. In order to deal with the apparent “digital divide” in UK civil society – where, despite extensive government rhetoric about data openness, organizations face not only the barriers of limited time, funds, and expertise to harness data but also the lack of representation within existing data – we present a working model in which ethical concerns accompanying data utilization by civil society may be better accounted. This suggests there is a need for further research into the nexus of civil society and data upon which interdisciplinary discussion about the ethical dimensions of engagement with data, particularly informed by insight from the social sciences, can be predicated.     

When data justice and environmental justice meet: formulating a response to extractive logic through environmental data justice

ABSTRACT

Environmental data justice (EDJ) emerges from conversations between data justice and environmental justice while identifying the limits and tensions of these lenses. Through a reflexive process of querying our entanglement in non-innocent relations, this paper develops and engages EDJ by examining how it informs the work of the Environmental Data & Governance Initiative (EDGI), a distributed, consensus-based organization that formed in response to the 2016 US presidential election. Through grassroots archiving of data sets, monitoring federal environmental and energy agency websites, and writing rapid-response reports about how federal agencies are being undermined, EDGI mobilizes EDJ to challenge the ‘extractive logic’ of current federal environmental policy and data infrastructures. ‘Extractive logic’ disconnects data from provenance, privileges the matrix of domination, and whitewashes data to generate uncertainty. We use the dynamic EDJ framework to reflect on EDGI’s public comment advising against the US Environmental Protection Agency’s proposed rule for Transparent Science. Through EDJ, EDGI aspires to create new environmental data infrastructures and practices that are participatory and embody equitable, transparent data care.     

Fitting Demand and Supply: How Identification Brings Appeals and Motives Together

In this article, participants in two demonstrations are compared. The demonstrations took place in two different squares in Amsterdam, on the same day, opposing the same governmental policy. Everything was the same except the organizers and their appeals: labor unions with an appeal in terms of threatened interests, on the one hand, and an anti-neoliberalism alliance with an appeal in terms of violated principles on the other. We hypothesize that social cleavages shape mobilizing structures and mobilization potentials. Thereby, this study takes an important yet rarely tested assumption in social movement literature seriously: namely, that grievances are socially constructed. If indeed grievances are socially constructed, one would expect that organizers rooted in different cleavages issue different appeals that resonate with different motives. What made individuals who were protesting the same governmental policy participate in one square rather than in the other? Organizational embeddedness, identification, and appeals that resonate with people's grievances provide the answer to that question. To test our hypotheses, we conducted surveys at both demonstrations; survey questionnaires were randomly distributed. The findings supported our assumptions regarding the influence of the diverging mobilizing contexts on the dynamics of protest participation and revealed the crucial role of identity processes.     

Fitting it all in: how mothers' employment shapes their school engagement

Although incompatibilities between work and home life are well studied, less is known about the implications of employment for another key life role, particularly for working mothers: being a ‘school-engaged parent’. Using data from in-depth interviews with 17 employed mothers in a mid-size Midwestern city, recruited from a diverse sample of 95 survey-taking parents, we examined the mechanics of how mothers' employment conditions shaped their involvement in their children's schools. We observed patterns between occupational status – professional and low-wage jobs, particularly – and when and how mothers engaged. Some with job schedule flexibility and paid time off were more often and easily able to participate in school activities, while others faced barriers to or negative consequences from using such supports. Several mothers lacked any time-related accommodations from their jobs. Yet all mothers pushed themselves to be involved, even as they had to make hard calculations about their work lives to do so. The findings extend research on the ‘life’ side of work–life research and point to the limits of U.S. education reform's emphasis on family engagement, suggesting that varied bundles of employment conditions stratify parents' school participation in ways that may be difficult for schools to accommodate.