Using concept mapping as a planning tool: Child welfare citizen review panels

Citizen Review Panels (CRPs) are groups of citizen volunteers authorized by U.S. federal law to examine state child welfare agencies. These groups inspect policies and practices related to child protection responsibilities and are tasked with making recommendations for systemic improvement. Despite the federal mandate for each state to develop a CRP and the potential of these groups to positively impact child welfare practices, there is a dearth in the literature related to CRPs. Consequently, planning and evaluation processes of these groups vary widely. This study reports on the use of concept mapping (CM) to outline a framework for planning and subsequently evaluating the CRP in one southeastern state. CM is a mixed-method research approach that uses multi-dimensional scaling and hierarchical cluster analyses to explore an area of study. Through these analyses, the method creates visual depictions of conceptual relationships between ideas. Data yielded a seven cluster concept map that CRP members (N = 36) utilized for planning processes, and subsequently for developing an internal evaluation tool. Results from this study offer a unifying framework by which CRPs, and similar groups in other areas can utilize for planning and evaluation purposes. After a review of pertinent literature on CRPs, this article explicates CM processes utilized in this study, describes results, discusses lessons learned, and outlines apposite areas for future CRP research.     

Does geographical proximity matter in determining the profitability of banks?

Despite the existence of a burgeoning literature on bank profitability, yet, none of them gave due consideration to geographical proximity. I fulfill such a gap by analyzing the effects of COVID-19 on the profitability of top-rated banks. Findings confirm the prevalence of spatial dependence at both the global and sub-global with feedback effects being systematically higher than spillover effects. My study uncovers evidence of a COVID-19 induced decline in asset utilization. Findings advocate sharing economy as a potential tool to banks in combating any future pandemic risk with regionalized approach to supervision being deemed better than its globalized counterpart.     

The impact of social power and influence on the implementation of innovation strategies: A case study of a UK mega infrastructure construction project

Influence plays a key role in reaching consensus among multiple actors involved in project-based decision-making processes. While prior literature devotes considerable attention to describing influence, little attention has been paid to influence at the individual level of the strategic project manager within the context of megaprojects. This research intended to fill this knowledge gap by identifying and describing the influence strategies that a strategic project manager applies when implementing innovation strategies on megaprojects. A qualitative case study was used to examine the complex social processes involved in a major UK capital investment programme. The findings underline a critical subset of influence strategies, notably higher-management support, inspirational appeal and bargaining. The study proposes a utilitarian structure of social power comprising selective, supportive and executory power bases.     

Integrating Family Resilience and Family Stress Theory

The construct, family resilience, has been defined and applied very differently by those who are primarily clinical practitioners and those who are primarily researchers in the family field. In thisarticle, the family resilience perspective is integrated with conceptual definitions from family stress theory using the Family Adjustment and Adaptation Response (FAAR) Model in an effort to clarify distinctions between family resiliency as capacity and family resilience as a process. The family resilience process is discussed in terms of (a) the meaning of significant risk exposure (vs. the normal challenges of family life) and (b) the importance of making conceptual and operational distinctions between family system outcomes and family protective processes. Recommendations for future family resilience research are discussed.     

Modelling daily multivariate pollutant data at multiple sites

Summary. This paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4-year period. Such multiple-pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6-day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling.     

Modelling heterogeneous space–time occurrences of earthquakes and its residual analysis

Summary. Earthquake intensities are modelled as a function of previous activity whose specific form is based on established empirical laws in seismology, but whose parameter values can vary from place to place. This model is used for characterizing regional features of seismic activities in and around Japan, and also for exploring regions where the actual seismicity rate systematically deviates from that of the modelled rate.     

Diagnostics for dependence within time series extremes

Summary. The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence. We present a range of new diagnostic tools for assessing whether these assumptions are appropriate and for identifying structure within extreme events. These tools are based on tail characteristics of joint survivor functions but can be implemented by using existing estimation methods for extremes of univariate independent and identically distributed variables. Our diagnostic aids are illustrated through theoretical examples, simulation studies and by application to rainfall and exchange rate data. On the basis of these diagnostics we can explain characteristics that are found in the observed extreme events of these series and also gain insight into the properties of events that are more extreme than those observed.     

Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.     

Consistent maximum likelihood estimation of a unimodal density using shape restrictions

It is well known that the unimodal maximum likelihood estimator of a density is consistent everywhere but at the mode. The authors review various ways to solve this problem and propose a new estimator that is concave over an interval containing the mode; this interval may be chosen by the user or through an algorithm. The authors show how to implement their solution and compare it to other approaches through simulations. They show that the new estimator is consistent everywhere and determine its rate of convergence in the Hellinger metric.