Exploring the Hierarchical Structure of Pacific Identity and Wellbeing

To understand outcomes for Pacific peoples in New Zealand we need to further our psychological knowledge of the relationship between Pacific identity and wellbeing. We map the hierarchical organization of Pacific identity and wellbeing using a novel top-down factor analytic approach applied to the Pacific Identity and Wellbeing Scale (PIWBS; N = 586). Analyses indicated that Pacific identity experiences were organized within two broad dimensions reflecting Identity Engagement and Cultural Wellbeing. Critically, our analysis showed that Religious Centrality and Embeddedness emerged jointly from these dual broad domains. Religious identification provides a bridging link between identity and wellbeing for Pacific peoples. Identifying the relationships of Pacific identity and wellbeing factors, and how religious identification emerges jointly from these two broad domains, provides valuable information in how the Pacific self may be cognitively organized and may assist in future research directions in this area. We assert that this general statistical model provides broad conceptual insights into how Pacific peoples experience their identity and culture, and how this relates to various social indicators of health and wellbeing at a broad, theoretical level. In particular, we offer a conceptual analysis of possible insights from our hierarchical model of identity and wellbeing for understanding Pacific suicide in New Zealand.     

Point Estimates of Test Sensitivity and Specificity from Sample Means and Variances

In a wide variety of biomedical and clinical research studies, sample statistics from diagnostic marker measurements are presented as a means of distinguishing between two populations, such as with and without disease. Intuitively, a larger difference between the mean values of a marker for the two populations, and a smaller spread of values within each population, should lead to more reliable classification rules based on this marker. We formalize this intuitive notion by deriving practical, new, closed-form expressions for the sensitivity and specificity of three different discriminant tests defined in terms of the sample means and standard deviations of diagnostic marker measurements. The three discriminant tests evaluated are based, respectively, on the Euclidean distance and the Mahalanobis distance between means, and a likelihood ratio analysis. Expressions for the effects of measurement error are also presented. Our final expressions assume that the diagnostic markers follow independent normal distributions for the two populations, although it will be clear that other known distributions may be similarly analyzed. We then discuss applications drawn from the medical literature, although the formalism is clearly not restricted to that application.     

Demand for gambling: development and assessment of a gambling purchase task

Self-report purchase tasks are a novel approach examining the reinforcing value of addictive behaviour relative to increasing monetary costs required to access the addictive behaviour (i.e. demand). These measures reveal a positive relationship between the indices of demand and addiction problem severity and can elucidate factors associated with motivation for substance use. Gambling is an addictive behaviour that has not been examined using this paradigm. This study seeks to adapt and examine the purchase task for gambling behaviour. A gambling purchase task was devised that asked individuals how often per month they would gamble at various cover charges. Participants were 73 adults from the community with either gambling disorder (n = 28) or alcohol use disorder (n = 24) or were a healthy control (n = 21). Both the alcohol and gambling purchase tasks were administered. Results demonstrate discriminant validity of the gambling purchase task, as individuals with gambling disorder have significantly greater demand for accessing gambling than other groups. The alcohol purchase task also evidenced discriminant validity in that individuals with alcohol use disorder have significantly greater demand for alcohol than other groups. These findings support the use of the gambling purchase task to assess the demand for gambling.     

Stock investors’ preference for short-term vs. long-term bonuses

Bonuses in the finance sector may be based on too short time intervals for environmental and social factors to be taken into account in investment decisions. We report two experiments to investigate whether investors prefer short-term to long-term bonuses. In Experiment 1 employing 27 undergraduates, preferences were measured for four short-term certain bonuses, evenly distributed across a time interval, and one certain long-term bonus at the end of the time interval. A majority chose the short-term bonuses, and in order for the long-term bonus to be equally preferred it had to be about 40% higher than the four added short-term bonuses. Experiment 2 employing another 36 undergraduates introduced outcome uncertainty that more accurately reflects the choices stock investors face. The participants again choose between a long-term bonus and four distributed short-term bonuses. It was shown that uncertainty made more participants prefer the long-term bonus to the added short-term bonuses than when the outcome was certain. A smaller increase of the long-term bonus of about 20% was now required to make it equally attractive as the four added short-term bonuses.     

A Bayesian approach for zero-modified Skellam model with Hamiltonian MCMC

Statistical Methods & Applications - In this paper is presented the zero-modified poisson difference and present possible applications of them in the analysis of paired count data. The...     

Empirical Likelihood Inference for the Rao‐Hartley‐Cochran Sampling Design

The Hartley‐Rao‐Cochran sampling design is an unequal probability sampling design which can be used to select samples from finite populations. We propose to adjust the empirical likelihood approach for the Hartley‐Rao‐Cochran sampling design. The approach proposed intrinsically incorporates sampling weights, auxiliary information and allows for large sampling fractions. It can be used to construct confidence intervals. In a simulation study, we show that the coverage may be better for the empirical likelihood confidence interval than for standard confidence intervals based on variance estimates. The approach proposed is simple to implement and less computer intensive than bootstrap. The confidence interval proposed does not rely on re‐sampling, linearization, variance estimation, design‐effects or joint inclusion probabilities.     

A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.