Factor Structure and Psychometric Properties of the Work-Family Balance Scale in an Urban Chinese Sample

The purpose of this study was to explore the factor structure of the Work-Family Balance Scale (WFBS) and examine its reliability and validity in use in the urban Chinese population. The scale was validated using a sample of 605 urban Chinese residents from 7 cities. Exploratory factor analysis identified two factors: work-family conflict and work-family enrichment. The WFBS showed adequate reliability and concurrent validity. The WFBS is a reliable and valid instrument to measure work-family balance for Chinese working parents. However, further examination of the scale is needed.     

Exploratory analysis of verbal aggression in romantic relationships among unmarried men and women: Predictive patterns by gender and race

This is an exploratory study that examined verbal aggression in romantic relationships among unmarried Black and White women and men as a function of gender and race. We employed an ecological approach to examine the receipt of verbal aggression separately for men and women at the levels of individual, relationship, and community. We also explored whether gender-specific correlates of verbal aggression interacted with race. Analyses were based on a sample of 212 women and 133 men in non-marital romantic relationships recruited from 21 U.S. cities for a larger study. Linear mixed-effects models revealed that factors related to experiencing verbal aggression differed substantially for unmarried women and men in romantic relationships. Interesting racial differences also emerged distinctly for women and men.     

Event-tree analysis with imprecise probabilities

Novel methods are proposed for dealing with event-tree analysis under imprecise probabilities, where one could measure chance or uncertainty without sharp numerical probabilities and express available evidence as upper and lower previsions (or expectations) of gambles (or bounded real functions). Sets of upper and lower previsions generate a convex set of probability distributions (or measures). Any probability distribution in this convex set should be considered in the event-tree analysis. This article focuses on the calculation of upper and lower bounds of the prevision (or the probability) of some outcome at the bottom of the event-tree. Three cases of given information/judgments on probabilities of outcomes are considered: (1) probabilities conditional to the occurrence of the event at the upper level; (2) total probabilities of occurrences, that is, not conditional to other events; (3) the combination of the previous two cases. Corresponding algorithms with imprecise probabilities under the three cases are explained and illustrated by simple examples.     

A gradient Markov chain Monte Carlo algorithm for computing multivariate maximum likelihood estimates and posterior distributions: mixture dose-response assessment

Multivariate probability distributions, such as may be used for mixture dose‐response assessment, are typically highly parameterized and difficult to fit to available data. However, such distributions may be useful in analyzing the large electronic data sets becoming available, such as dose‐response biomarker and genetic information. In this article, a new two‐stage computational approach is introduced for estimating multivariate distributions and addressing parameter uncertainty. The proposed first stage comprises a gradient Markov chain Monte Carlo (GMCMC) technique to find Bayesian posterior mode estimates (PMEs) of parameters, equivalent to maximum likelihood estimates (MLEs) in the absence of subjective information. In the second stage, these estimates are used to initialize a Markov chain Monte Carlo (MCMC) simulation, replacing the conventional burn‐in period to allow convergent simulation of the full joint Bayesian posterior distribution and the corresponding unconditional multivariate distribution (not conditional on uncertain parameter values). When the distribution of parameter uncertainty is such a Bayesian posterior, the unconditional distribution is termed predictive. The method is demonstrated by finding conditional and unconditional versions of the recently proposed emergent dose‐response function (DRF). Results are shown for the five‐parameter common‐mode and seven‐parameter dissimilar‐mode models, based on published data for eight benzene–toluene dose pairs. The common mode conditional DRF is obtained with a 21‐fold reduction in data requirement versus MCMC. Example common‐mode unconditional DRFs are then found using synthetic data, showing a 71% reduction in required data. The approach is further demonstrated for a PCB 126‐PCB 153 mixture. Applicability is analyzed and discussed. Matlab^® computer programs are provided.     

Order-free co-regionalized areal data models with application to multiple-disease mapping

With the ready availability of spatial databases and geographical information system software, statisticians are increasingly encountering multivariate modelling settings featuring associations of more than one type: spatial associations between data locations and associations between the variables within the locations. Although flexible modelling of multivariate point-referenced data has recently been addressed by using a linear model of co-regionalization, existing methods for multivariate areal data typically suffer from unnecessary restrictions on the covariance structure or undesirable dependence on the conditioning order of the variables. We propose a class of Bayesian hierarchical models for multivariate areal data that avoids these restrictions, permitting flexible and order-free modelling of correlations both between variables and across areal units. Our framework encompasses a rich class of multivariate conditionally autoregressive models that are computationally feasible via modern Markov chain Monte Carlo methods. We illustrate the strengths of our approach over existing models by using simulation studies and also offer a real data application involving annual lung, larynx and oesophageal cancer death-rates in Minnesota counties between 1990 and 2000.     

An Eight Component Decision-Making Model for Problem Gambling:A Systems Approach to Stimulate Integrative Research

Problem Gambling (PG) represents a serious problem for affected individuals, their families and society in general. Previous approaches to understanding PG have been confined to only a subset of the psychobiological factors influencing PG. We present a model that attempts to integrate potential causal factors across levels of organization, providing empirical evidence from the vast literature on PG and complimentary literatures in decision-making and addiction. The model posits that components are arranged systematically to bias decisions in favor of either immediately approaching or avoiding targets affording the opportunity for immediate reward. Dopamine, Testosterone and Endogenous Opioids favor immediate approach, while Serotonin and Cortisol favor inhibition. Glutamate is involved in associative learning between stimuli and promotes the approach response through its link to the DA reward system. GABA functions to monitor performance and curb impulsive decision-making. Finally, while very high levels of Norepinephrine can induce arousal to an extent that is detrimental to sound decision-making, the reactivity of the Norepinephrine system and its effects of Cortisol levels can shift the focus towards long-term consequences, thereby inhibiting impulsive decisions. Empirical evidence is provided showing the effects of each component on PG and decision-making across behavioural, neuropsychological, functional neuroimaging and genetic levels. Last, an effect size analysis of the growing pharmacotherapy literature is presented. It is hoped that this model will stimulate multi-level research to solidify our comprehension of biased decision-making in PG and suggest pharmacological and psychological approaches to treatment.     

Old-age support system and policy reform in China

"With the on-going economic transition from the central planned system to a market-oriented system, the population aging process in China is also accelerating, and the support system for the elderly becomes an important issue demanding attention. With data from Chinese national statistics and a regional probability sampling survey in Wuhan Area, two supporting systems for the aged in China are examined in this paper. After a briefing of the population aging process in China, a detailed discussion of the two supporting systems, i.e. the formal and the informal support systems for the aged, including arrangement, operation and funding mechanisms of the systems, is made in terms of policy reform. Then follows an international comparison to highlight features and problems of the Chinese systems. Finally, solutions are proposed for the restructuring of China's pension systems."     

An analysis of baby boom characteristics in China

This article analyzed the 3rd Chinese baby boom and its causes by focusing on the fertility intensity of women and the number of births per year. The 3rd baby boom is expected to end in 2002 and to peak in 1995. The baby boom in urban areas will last a much shorter time than in rural areas. Particular differences are manifested between provinces: 1) The baby boom will last 3-4 years in Beijing, Shanghai, and Tianjin, and will last 10 years in Liaoning, Jilin, and Jiangsu. In other provinces, the boom will last 15-23 years; 2) The peak years are different between provinces; 3) The base number of births curves are province-specific; 4) The absolute base birth numbers are different between provinces (Jiangsu, Shandong, Henan, and Sichuan 700,000; Beijing, Tianjin, Qinghai, and Ningzia 100,000); and 5) The shapes of progressive increases for base number of births are province specific. The increase in the number of childbearing-age women, the unplanned higher parity births, and early marriage and childbearing have all contributed to the current baby boom. Recommendations to control the problem include controlling higher parity births, observing birth spacing, and controlling the age of marriage and childbearing.     

Population control in a market economy

The author explains that it is extremely important to establish a link between the new socialist market economy and control of population reproduction, especially the size of the population. That will determine future directions of reform in population control. Experiences gained from the coastal regions, which initiated reform and opened itself to foreign countries and influences, and from some inland areas which took the lead in establishing a market economy, allow the issue to be viewed in a new light. Sections discuss increasing the weight of benefit regulation with regard to micropopulation control, the regulatory function of the community in intermediary population control, and improving overall regulation with regard to macropopulation control.     

Nonparametric Estimation of Bounded Survival Functions with Censored Observations

Stochastic ordering of survival functions is a useful concept in many areas of statistics, especially in nonparametric and order restricted inferences. In this paper we introduce an algorithm to compute maximum likelihood estimates of survival functions where both upper and lower bounds are given. The algorithm allows censored survival data. In a simulation study, we found that the proposed estimates are more efficient than the unrestricted Kaplan-Meier product limit estimates both with and without censored observations.