Simultaneous edit-imputation and disclosure limitation for business establishment data

Business establishment microdata typically are required to satisfy agency-specified edit rules, such as balance equations and linear inequalities. Inevitably some establishments' reported data violate the edit rules. Statistical agencies correct faulty values using a process known as edit-imputation. Business establishment data also must be heavily redacted before being shared with the public; indeed, confidentiality concerns lead many agencies not to share establishment microdata as unrestricted access files. When microdata must be heavily redacted, one approach is to create synthetic data, as done in the U.S. Longitudinal Business Database and the German IAB Establishment Panel. This article presents the first implementation of a fully integrated approach to edit-imputation and data synthesis. We illustrate the approach on data from the U.S. Census of Manufactures and present a variety of evaluations of the utility of the synthetic data. The paper also presents assessments of disclosure risks for several intruder attacks. We find that the synthetic data preserve important distributional features from the post-editing confidential microdata, and have low risks for the various attacks.     

Hypothesis Tests for Neyman's Bias in Case–Control Studies

Survival bias is a long recognized problem in case–control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or ‘prevalence–incidence bias’. It occurs in case–control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case–control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.     

Measuring stakeholder identification with nonprofit causes: the development and validation of the identification with social causes scale

Nonprofit organizations (NPOs) achieve desired outcomes by fostering organizational identification. However, little research has explored how identification with the cause or the recipients of support relates to identification with the NPO. This study developed the Identification with Social Causes Scale as a mechanism for distinguishing organizational identification from identification with a social group or cause. Data were collected using two groups: the homeless (n = 318) and HIV positive (n = 314) individuals. Exploratory factor analysis and multigroup confirmatory factor analysis yielded a 9-item scale measuring two dimensions: attachment and consubstantiality. Initial construct validity of the scale was established.     

Behavioral economics and the value of a statistical life

Journal of Risk and Uncertainty - There are many possible connections between the value of statistical life (VSL) and behavioral economics. A list of topics includes endowment effects, risk...     

Transition probabilities and ARL performance of Six Sigma zone control charts

In this article, Six Sigma zone control charts (SSZCCs) are proposed for world class organizations. The transition probabilities are obtained using the Markov chain approach. The Average Run Length (ARL) values are then presented. The ARL performance of the proposed SSZCCs and the standard Six Sigma control chart (SSCC) without zones or run rules is studied. The ARL performance of these charts is then compared with those of the other standard zone control charts (ZCCs), the modified ZCC and the traditional Shewhart control chart (SCC) with common run rules. As expected, it is shown that the proposed SSZCC outperforms the standard SSCC without zones or run rules for process shifts of any magnitude. When compared to the other standard ZCCs and the Shewhart chart with common run rules, it is observed that the proposed SSZCCs have much higher false alarm rates for smaller shifts and hence they prevent unwanted process disturbances. The application of the proposed SSZCC is illustrated using a real time example.     

Strong convergence rates of probabilistic integrators for ordinary differential equations

Probabilistic integration of a continuous dynamical system is a way of systematically introducing discretisation error, at scales no larger than errors introduced by standard numerical discretisation, in order to enable thorough exploration of possible responses of the system to inputs. It is thus a potentially useful approach in a number of applications such as forward uncertainty quantification, inverse problems, and data assimilation. We extend the convergence analysis of probabilistic integrators for deterministic ordinary differential equations, as proposed by Conrad et al. (Stat Comput 27(4):1065–1082, 2017. https://doi.org/10.1007/s11222-016-9671-0), to establish mean-square convergence in the uniform norm on discrete- or continuous-time solutions under relaxed regularity assumptions on the driving vector fields and their induced flows. Specifically, we show that randomised high-order integrators for globally Lipschitz flows and randomised Euler integrators for dissipative vector fields with polynomially bounded local Lipschitz constants all have the same mean-square convergence rate as their deterministic counterparts, provided that the variance of the integration noise is not of higher order than the corresponding deterministic integrator. These and similar results are proven for probabilistic integrators where the random perturbations may be state-dependent, non-Gaussian, or non-centred random variables.

     

The racialized patterns of police violence: The critical importance of research as praxis

The tragic killing of George Floyd at the hands of the police resulted in hundreds of thousands of protestors marching in the streets demanding change. The call for change criticized the killing of Blacks by law enforcement and challenged White supremacy as an institution of social control and racial violence. A key component of the marches and protests was a message to the residents of the United States: “Black Lives Matter.” As society grapples with a reckoning, researchers studying police violence for the past 6 decades have been empirically and theoretically debating the reasons why use of force by law enforcement continues to have a higher proportion of Black and Brown victims compared to Whites. Although the research on fatal police killings was studied by only a small number of individuals prior to 2014, after the killing of Michael Brown in Ferguson, Missouri the research in different areas of the country increased rapidly as did the forms of analysis and research disciplines making their own contributions. The Washington Post and Guardian news agencies established that deaths at the hands of the police were occurring for at least 900 to 1100 individual deaths per year for which firearms resulted in the greatest cause of death. As US government agencies failed to produce a national data source on police involved killings, the media took a leading role in providing greater national understanding. The authors examine what role academic researchers contribute to the discussion for solutions, particularly those from marginalized backgrounds. As protestors march, lawyers sue and defend, and politicians create new forms of legislation, researchers need to play a more important role initiating critical studies, making sense of the data, and providing a theoretical framework for which police violence can be understood. This article will provide an overview of the literature on racialized police violence, point out key patterns involving racial and ethnic disparities, and emphasize how researchers can play a more important role in advocating for change.     

The Mediating Role of Emotion Dysregulation in the Relation Between Adult Attachment Insecurity and Depression Among Young Gay and Bisexual Men

Objectives: Attachment insecurity may be more pronounced among young gay and bisexual men (YGBM). Further, attachment insecurity is associated with dysregulated patterns of emotion regulation, which can lead to depression. However, pathways linking attachment insecurity to depression are underexplored among YGBM. Methods: This study used pilot data from N?=?67 YGBM to examine if emotion dysregulation mediated the association between attachment insecurity and depression. Results: Results supported the study hypotheses. Attachment insecurity was associated with emotion dysregulation, which was associated with depression. Conclusion: Findings provide preliminary evidence that emotion regulation training may be a useful approach for addressing depression among YGBM.