Bayesian Probabilistic Projections of Life Expectancy for All Countries

We propose a Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world to 2100. Such forecasts would be an input to the production of probabilistic population projections for all countries, which is currently being considered by the United Nations. To evaluate the method, we conducted an out-of-sample cross-validation experiment, fitting the model to the data from 1950–1995 and using the estimated model to forecast for the subsequent 10 years. The 10-year predictions had a mean absolute error of about 1 year, about 40 % less than the current UN methodology. The probabilistic forecasts were calibrated in the sense that, for example, the 80 % prediction intervals contained the truth about 80 % of the time. We illustrate our method with results from Madagascar (a typical country with steadily improving life expectancy), Latvia (a country that has had a mortality crisis), and Japan (a leading country). We also show aggregated results for South Asia, a region with eight countries. Free, publicly available R software packages called bayesLife and bayesDem are available to implement the method.     

Interpretive Engagement and the Study of Civil Society Networks: An Illustration of Interpretive Methods

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This article explores the use of interpretive methodologies to study civil society networks within the field of third...     

Mechanisms of religious trauma amongst queer people in Australia’s evangelical churches

Clinical Social Work Journal - Christian communities teaching traditional theology and ethics, which treat diverse sexualities and gender expansive identities as sinful, can be places where...     

Factors impacting the knowing-doing gap in sexual harassment training

We examined the gaps between research-based sexual harassment training practices human resource (HR) managers believe their organization should use and the practices their organizations actually use (knowing-doing gaps). We studied individual (attitudes about academics) and organizational predictors (senior management support, managerial rewards, and organizational resources) of gaps at the pre-training, training design and delivery, and post-training stages. Organizational factors generally reduced gaps at the pre-training and post-training stages, while the individual factor marginally increased the gap at the pre-training stage. The knowing-doing gap for training design and delivery practices was not predicted by any of the individual or organizational variables.     

A temporal investigation of the direct,interactive, and reverse relations between demand and control and affective strain

Abstract

Few studies have tested how stressors affect outcomes over time. We sought to extend the literature by means of a longitudinal study testing for direct, interactive, and causal relations between demands and control and affective strain. We extended prior work testing causal relationships for Karasek's (1979 Karasek, R.A. Jr. 1979. Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly, 24: 285–307. [Crossref], [Web of Science ®] , [Google Scholar]) Job Demand-Control (JDC) model by examining both the effects of demands and control on strain and in turn the effects of strain on demand and control. We tested our hypotheses using hierarchical linear modelling with a military sample of 1539 soldiers who completed six waves of survey data at 3-month time lags. The results replicate earlier cross-sectional studies reporting effects of work characteristics on strain; however, in our study these effects did not persist past three months. The results also provide evidence for reverse causal effects such that higher strain was associated with higher subsequent work overload and lower control over a six month time period. Similar to past research, we did not find support for the interactive effects of work overload and control on strain. We discuss the implications of our findings for theory and practice (such as the optimum time for applying interventions during the management of change), especially in terms of understanding the specific time lags for different stress–strain associations and the need for additional theories to explain reverse relationships.     

Relationships between psychological,physical, and behavioural health and work performance: A review and meta-analysis

The relationships between health and job performance problems have received increased attention in business and scientific communities. This paper attempts to synthesize theoretical and empirical work in this arena. First, the theoretical links between health and work performance are presented. This is followed by a meta-analysis of the relations between psychological, physical, and behavioural health variables and work performance criteria. Meta-analytic results from 111 independent samples obtained from a search of the literature indicate that psychological health, in the form of psychological well-being, depression, general anxiety, and life satisfaction, is a moderate-to-strong correlate of work performance. Associations between physical health, particularly somatic complaints and hypertension, and performance were weak-to-moderate. Regarding health behaviour, alcohol consumption, and smoking were weakly and sleep problems moderately associated with performance problems. Effect sizes sometimes differed across performance dimensions and data sources. The results are consistent with the notion that ill-health is associated with substantial reductions in work performance. This implies that interventions to improve health may have an impact on performance. However, as most of the research in this area has been cross-sectional, more longitudinal research is needed to test theoretical and alternative causal explanations for the relations summarized in this review.     

Power,Positionality, and the Ethic of Care in Qualitative Research

Qualitative Sociology - Building on the definition offered by Aspers and Corte, I argue that qualitative research is not qualitative simply because it encodes for the ability “to get...     

Modeling Electricity Price Using A Threshold Conditional Autoregressive Geometric Process Jump Model

Electricity market prices are highly volatile and often have high spikes. Both government authorities and market participants require sophisticated models and techniques for forecasting future prices and managing relevant financial risks in such a volatile market. This article extends the conditional autoregressive geometric process (CARGP) model (Chan et al., 2012 Chan, J. S.K., Lam, C. P.Y., Yu, P. L.H., Choy, S. T.B., Chen, C. W.S. (2012). A Bayesian conditional autoregressive geometric process model for range data. Computat. Statist. Data Anal. 56:30063019.[Crossref], [Web of Science ®] , [Google Scholar]) to the CARGP model with thresholds and jumps, which is abbreviated as CARGP-TJ model in this article. We will demonstrate that the proposed CARGP-TJ model not only captures the unique features of the electricity price but also performs better than other existing models. For robustness consideration, a heavy-tailed error distribution is adopted. Model implementation relies on the powerful Bayesian Markov chain Monte Carlo simulation techniques via WinBUGS software. The analysis of the daily maximum electricity prices of the New South Wales, Australia reveals that the proposed CARGP-TJ model captures the price spikes well for both in-sample estimation and out-of-sample forecast.