Internet-Initiated Sexual Assault Among U.S. Adolescents Reported in Newspapers, 1996–2007

This article provides an assessment over time of the incidence of newspaper-reported Internet-initiated sexual assaults among U.S. adolescents undergoing adjudication from 1996 to 2007. Of 812 newspaper reports of adjudicated Internet-initiated sexual assault, most (79.2%) victims were female, and the median age was 14 years. The incidence rate of these reports increased over the 12-year period for females but remained steady for males. The frequency of these assaults was much less than reported for other types of sexual assaults in this age group. These estimates hopefully will assist in a greater understanding of these assaults, aid in interventions to decrease their occurrence, and guide effective policymaking that will reduce all types of sexual assault among adolescents.     

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.     

Eigenvector centrality and structural zeroes and ones: When is a neighbor not a neighbor?

There will be occasions in which a researcher wants to ignore some dyads in the computation of centrality in order to avoid biased or misleading results. This paper presents a principled way of computing eigenvector-like centrality scores when some dyads are not included in the calculations.     

Degree-like centrality with structural zeroes or ones: When is a neighbor not a neighbor?

In the field of social network analysis, there are situations in which researchers hope to ignore certain dyads in the computation of centrality to avoid biased or misleading results, but simply deleting these dyads will result in wrong conclusions. There is little work considering this particular problem except the eigenvector-like centrality method presented in 2015. In this paper, we revisit this problem and present a new degree-like centrality method which also allows some dyads to be excluded in the calculations. This new method adopts the technique of weighted symmetric nonnegative matrix factorization (abbreviated as WSNMF), and we will show that it can be seen as the generalized version of the existing eigenvector-like centrality. After applying it to several data sets, we test this new method's efficiency.     

Structural effects of network sampling coverage I: Nodes missing at random

Network measures assume a census of a well-bounded population. This level of coverage is rarely achieved in practice, however, and we have only limited information on the robustness of network measures to incomplete coverage. This paper examines the effect of node-level missingness on 4 classes of network measures: centrality, centralization, topology and homophily across a diverse sample of 12 empirical networks. We use a Monte Carlo simulation process to generate data with known levels of missingness and compare the resulting network scores to their known starting values. As with past studies (0035 and 0135), we find that measurement bias generally increases with more missing data. The exact rate and nature of this increase, however, varies systematically across network measures. For example, betweenness and Bonacich centralization are quite sensitive to missing data while closeness and in-degree are robust. Similarly, while the tau statistic and distance are difficult to capture with missing data, transitivity shows little bias even with very high levels of missingness. The results are also clearly dependent on the features of the network. Larger, more centralized networks are generally more robust to missing data, but this is especially true for centrality and centralization measures. More cohesive networks are robust to missing data when measuring topological features but not when measuring centralization. Overall, the results suggest that missing data may have quite large or quite small effects on network measurement, depending on the type of network and the question being posed.     

Nurturing the Seeds of Restorative Justice

Abstract

Communities are looking for community-building responses to the issue of crime. Traditional social and political discourses have presented only two responses to crime, “get tough” or rehabilitate offenders. An alternative view has begun to emerge in community criminal justice discourse and practice. Restorative justice emphasizes the restoration of relationships and community peace that are damaged by the harm of crime, and the repair of these social injuries. This article presents the findings of a pilot study that was conducted to assess a community's openness to restorative justice principles.     

La victime et le policier : point de vue profane et point de vue professionnel sur la délinquance

Comparing surveys of victims with police statistics illustrates the differences between lay and professional views of crime. Victims’expectations and the police handling of cases do not always match. The determinants of victims’ decisions to report incidents to the police are briefly summarised; and the ways the police classify victims’account and handle their complaints, examined. This analysis is based on two sets of data: a victimisation survey of a sample of 10?504 persons aged 15 and older, drawn from the Île-de-France Region; the police statistics corresponding to the same area.     

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.     

Do required fields in online surveys in the social sciences impair reliability?

ABSTRACT

The present study is the first to examine empirically whether required fields in online surveys impair reliability and response pattern, as participants forced to respond to all items may provide arbitrary answers. Two hundred and thirteen participants completed a survey consisting of six questionnaires testing personal and social issues and perceptions. They were randomly assigned to one of two versions of the survey: optional-fields (N = 104) or required-fields (N = 109). Comparison of the Cronbach’s alpha of the two versions revealed identical reliability values for all questionnaires, save for somatization, where a minor difference was found. Confirmatory factor analysis showed no difference in the factor structure of the two versions, and no differences were found by Bayesian t-test and Levene’s test for equality of variances. The findings suggest that required fields do not impair reliability or change the response pattern, and therefore can be used in online surveys to prevent missing data.     

Radical/Critical Social Work with Young Offenders: Challenges and Possibilities

In the UK, particularly in England, youth crime is perceived as a serious social problem, which is always near the top of the political agenda. Since the early 1990s, ‘populist punitiveness’ (Bottoms, 1995), amounting to varying degrees of punishment and control, has been key for addressing the problem. This culminated in New Labour's flagship Crime and Disorder Act 1998 and thereafter increasing concern with anti-social behaviour. The Conservative-led coalition is continuing in this vein. It is a ‘get tough’ approach in which the role of social work has been sidelined. In this article, I argue that such an approach is counterproductive as evidenced by the riots of August 2011 in London and other major cities. Rather than notions of punishment and control being to the fore, attention should be paid to the social and economic conditions that shape young people's lives and behaviour. For social workers, this involves relationship building with young offenders and their families and this is where a radical/critical work practice comes in. It is an emancipatory practice, which resists the neoliberal present and has some vision of a more socially just and equal future world.     

Bayesian analysis for partially observed network data,missing ties,attributes and actors

We consider partially observed network data as defined in Handcock and Gile (2010). More specifically we introduce an elaboration of the Bayesian data augmentation scheme of Koskinen et al. (2010) that uses the exchange algorithm (Caimo and Friel, 2011) for inference for the exponential random graph model (ERGM) where tie variables are partly observed. We illustrate the generating of posteriors and unobserved tie-variables with empirical network data where 74% of the tie variables are unobserved under the assumption that some standard assumptions hold true. One of these assumptions is that covariates are fixed and completely observed. A likely scenario is that also covariates might only be partially observed and we propose a further extension of the data augmentation algorithm for missing attributes. We provide an illustrative example of parameter inference with nearly 30% of dyads affected by missing attributes (e.g. homophily effects). The assumption that all actors are known is another assumption that is liable to be violated so that there are “covert actors”. We briefly discuss various aspects of this problem with reference to the Sageman (2004) data set on suspected terrorists. We conclude by identifying some areas in need of further research.