On the design of comparative lifetime studies

In designing a study to compare two lifetime distributions, decisions are required about the study size, the proportion of observations in each group and the length of follow-up period. These aspects of study design are examined using a Bayesian approach in which the expected consequences of a particular choice of design are evaluated by the expected gain in infornlation.     

The robustness of point optimal testing for rosenberg random regression coefficients

The literature on testing for the presence of Rosenberg's (1973) return to normalcy random coefficient model is well developed with both Shively (1988) and Brooks (1993) advocating the use of point optimal tests. This paper explores the robustness of point optimal testing for the Rosenberg alternative to two departures: the special case HildrethHouck (1968) alternative and non-normality in regression disturbances, finding the point optimal testing approach to be fairly robust to both departures.     

Confidence Intervals for Univariate Impulse Responses With a Near Unit Root

This article proposes a method for constructing confidence intervals for the impulse response function of a univariate time series with a near unit root. These confidence intervals control coverage, whereas the existing techniques can all have coverage far below the nominal level. I apply the proposed method to several measures of U.S. aggregate output.     

Inferactive data analysis

We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory and confirmatory data analysis allowing also for Bayesian data analysis. We see this as a useful step in concrete providing tools (with statistical guarantees) for current data scientists. The basis of inference we use is (a conditional approach to) selective inference, in particular its randomized form. The relevant reference distributions are constructed from what we call a DAG-DAG—a Data Analysis Generative DAG, and a selective change of variables formula is crucial to any practical implementation of inferactive data analysis via sampling these distributions. We discuss a canonical example of an incomplete cross-validation test statistic to discriminate between black box models, and a real HIV dataset example to illustrate inference after making multiple queries on data.     

Psychometric validation of the PERMA-profiler as a well-being measure for student veterans

Abstract

Objective: The current study aims to validate the PERMA-Profiler, a well-known well-being measure, among a sample of student veterans. Participants: A sample of 205 student veterans were recruited from universities across the United States. Method: Cross-sectional research design was used in this study. Measurement structure of the PERMA-Profiler was evaluated using exploratory factor analysis (EFA). Convergent, divergent, and criterion-related validity was tested using Pearson correlation coefficients and Kruskal-Wallis test. Results: The EFA results yielded a two-factor solution for student veterans. Factors are named as emotional character strengths and performance character strengths. Conclusions: The PERMA-Profiler is a multidimensional scale with good reliability and acceptable levels of convergent, divergent, and criterion-related validity. The PERMA-Profiler can help researchers and practitioners better gauge well-being in student veterans. Implications will be discussed.     

Learning to Manage the Multirisk World

Risk assessment, perception, and management tend to focus on one risk at a time. But we live in a multirisk world. This essay in honor of the 40th anniversary of the Society for Risk Analysis (SRA) and the journal Risk Analysis suggests that we can—and have already begun to—strengthen risk analysis and policy outcomes by moving from a focus on the single to the multiple—multiple stressors, multiple impacts, and multiple decisions. This evolution can improve our abilities to assess actual risks, to confront and weigh risk-risk trade-offs and innovate risk-superior moves, and to build learning into adaptive regulation that adjusts over time. Recognizing the multirisk reality can help us understand complex systems, foresee unintended consequences, design better policy solutions, and learn to improve.     

Subnational Location Capital: The Role of Subnational Institutional Actors and Socio‐spatial Factors on Firm Location

Firms do not simply locate, but rather seek to accrue location‐based advantages such as knowledge, market insidership and resource utilization. Adopting the lens of social capital, this paper explores how subnational institutional actors facilitate location capital for firms. Using qualitative case study analysis of six multinational companies (MNCs), we highlight the important role of subnational institutional actors in fostering three dimensions of subnational location capital – structural, relational and cognitive. We show that subnational location capital, defined as the economic and social assets accessible through relationships within a subnational location, enable firms to derive advantages via subnational engagement. These findings contribute to the growing literature on the dynamic interaction of firms with subnational location, particularly the nuanced role of subnational institutional actors with MNCs.     

Toward Probabilistic Prediction of Flash Flood Human Impacts

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.     

Compassion Satisfaction,Compassion Fatigue,and Burnout: A Replication Study with Victim Advocates

Abstract

Past research has investigated the rates of compassion satisfaction (CS), compassion fatigue (CF), and burnout within health-care providers. Findings indicate higher levels of CS and lower levels of CF predict lower rates of burnout. The current study extended prior research by replicating past research findings, but with victim advocates. This study enrolled a national sample of 142 victim advocates. A hierarchal linear regression was run to test the research questions. In level 1, years of experience was not significant and only accounted for 1.1% of the variance in burnout. When adding CS, the model became significant (p?<?.001) and CS accounted for an additional 51.8% of the variance. When CF was added to the final model, it was significant (p?<?.001), and accounted for an additional 19.3% of the variance. When comparing the findings to past studies, our findings were similar; however, our study did find that CS and CF accounted for significantly more variance than past studies with health-care providers. Therefore, CS and CF might be greater predictors of burnout in victim advocates than in health-care providers. Future studies should aim to create interventions to increase CS and decrease CF, to reduce burnout.