Demographic and Socio-economic Influences on Recent British Marital Breakdown Patterns

The advantages of large-scale multi-purpose surveys compared with official divorce records for examining marital breakdown are assessed, and the extent of under-recording of the concept of breakdown in the latter source is estimated. Demographic and socio-economic differentials in breakdown are examined and the former are found to be generally more powerful. A proportional-hazards life-table model is used to establish the impact of childlessness on divorce in a more satisfactory way than hitherto. Among fertile couples, the length of the first birth interval is found to be particularly important as a risk factor influencing breakdown. Controlling for demographic factors, such as age at marriage and fertility status, is shown to modify the observed crude differences between social classes. Housing tenure and personal factors associated with the couple's individual circumstances are more important than social class in explaining marital breakdown, with age at marriage retaining a strongly persisting and remarkably constant effect whatever other variables are included in the analysis.     

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.     

Modelling Households: A Synthesis

The 1973 U.S. National Survey of Family Growth is used to examine the effects of removing number and timing failures from the reproductive histories of various cohorts of white and black married women. Blacks are more fertile than whites primarily because of their greater unwanted fertility. Removing number and timing failures from the past reproductive histories of American women would have reduced their fertility considerably. These reductions would have been greater for blacks than for whites and would be greater if some wanted pregnancies had continued to terminate in foetal loss.     

The Dual-Factor Model of Mental Health: A Short-Term Longitudinal Study of School-Related Outcomes

The Dual-Factor Model of Mental Health, proposed by Greenspoon and Sasklofske (Soc Indic Res 54:81–108, 2001), suggested that student mental health should be assessed on a dimension of psychopathology as well as a dimension of subjective well-being (SWB). Greenspoon and Sasklofske (Soc Indic Res 54:81–108, 2001) argued that measuring both psychopathology and SWB allow mental health professionals to have a more complete understanding of the child’s psychological health. The present exploratory study provided a further evaluation of the usefulness of the Dual-Factor model in understanding adolescents’ SWB using both person-centered and variable-centered analyses. Using person-centered analyses, we explored whether group membership revealed differential changes in middle school students’ GPAs as well as emotional, cognitive, and behavioral engagement across a 5-month time period. Furthermore, we used variable-centered analyses to examine whether middle school students’ levels of SWB at Time 1 predicted changes in our criterion variables. Specifically, we assessed whether SWB at Time 1 predicted student school engagement and GPA at Time 2 above and beyond their levels of internalizing and externalizing behaviors at Time 1, levels of student engagement and grades at Time 1, and demographic variables. Results of the person-centered analyses found statistically significant differences across the four groups identified in the Dual-Factor Model for emotional, cognitive and behavioral engagement as well as GPA. The results from the variable-centered analyses suggest subjective well-being to be a significant predictor of emotional, cognitive and behavioral engagement beyond measures of psychopathology. These results provide additional support for use of the Dual-Factor Model.     

Predicting social influence with faction sizes and relative status

Building on a recent theoretical development in the field of sociological social psychology, we develop a formal mathematical model of social influence processes. The extant theoretical literature implies that factions and status should have non-linear effects on social influence, and yet these theories have been evaluated using standard linear statistical models. Our formal model of influence includes these non-linearities, as specified by the theories. We evaluate the fit of the formal model using experimental data. Our results indicate that a one-parameter mathematical model fits the experimental data. We conclude with the implications of our research and a discussion of how it may be used as an impetus for further work on social influence processes.     

Archival research on integrated reporting: a systematic review of main drivers and the impact of integrated reporting on firm value

Journal of Management and Governance - Integrated reporting (IR) represents an innovative approach to business reporting especially by Public Interest Entities (PIEs). In addition to financial...     

Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes

ABSTRACT

This work presents advanced computational aspects of a new method for changepoint detection on spatio-temporal point process data. We summarize the methodology, based on building a Bayesian hierarchical model for the data and declaring prior conjectures on the number and positions of the changepoints, and show how to take decisions regarding the acceptance of potential changepoints. The focus of this work is about choosing an approach that detects the correct changepoint and delivers smooth reliable estimates in a feasible computational time; we propose Bayesian P-splines as a suitable tool for managing spatial variation, both under a computational and a model fitting performance perspective. The main computational challenges are outlined and a solution involving parallel computing in R is proposed and tested on a simulation study. An application is also presented on a data set of seismic events in Italy over the last 20 years.     

On kernel machine learning for propensity score estimation under complex confounding structures

Post marketing data offer rich information and cost-effective resources for physicians and policy-makers to address some critical scientific questions in clinical practice. However, the complex confounding structures (e.g., nonlinear and nonadditive interactions) embedded in these observational data often pose major analytical challenges for proper analysis to draw valid conclusions. Furthermore, often made available as electronic health records (EHRs), these data are usually massive with hundreds of thousands observational records, which introduce additional computational challenges. In this paper, for comparative effectiveness analysis, we propose a statistically robust yet computationally efficient propensity score (PS) approach to adjust for the complex confounding structures. Specifically, we propose a kernel-based machine learning method for flexibly and robustly PS modeling to obtain valid PS estimation from observational data with complex confounding structures. The estimated propensity score is then used in the second stage analysis to obtain the consistent average treatment effect estimate. An empirical variance estimator based on the bootstrap is adopted. A split-and-merge algorithm is further developed to reduce the computational workload of the proposed method for big data, and to obtain a valid variance estimator of the average treatment effect estimate as a by-product. As shown by extensive numerical studies and an application to postoperative pain EHR data comparative effectiveness analysis, the proposed approach consistently outperforms other competing methods, demonstrating its practical utility.     

Sins of the parents: Self-control as a buffer between supervisors' previous experience of family undermining and subordinates' perceptions of abusive supervision

Drawing upon social learning theory, the intergenerational transmission of violence hypothesis, and research on self-control, we develop a model of the relationships among previous experiences of family undermining, self-control, and abusive supervision. We tested the model with data obtained from supervisor–employee matched pairs in Study 1 and matched triads in Study 2. Results revealed that: 1) supervisors who experienced higher levels of family undermining (whether reported by the immediate supervisor or a sibling) during childhood are more likely to engage in abusive supervisory behaviors as adults; and 2) this relationship is moderated such that it is stronger for supervisors with low self-control. Overall, our results highlight the role of self-control in mitigating the impact of supervisors' previous experiences of family undermining on subordinate perceptions of abusive supervision, even after controlling for previously established antecedents.