Stressful and positive experiences of women who served in Vietnam

Experiences of women who served during the Vietnam War have been described in interviews/anecdotal reports but rarely in empirical literature. Potential positive (versus negative) aspects of service or its impact on well-being are seldom considered. We describe stressful and positive experiences reported by approximately 1,300 female military personnel, Red Cross workers, and others deployed to Vietnam. Prominent stressful (e.g., negative living/working conditions) and positive (e.g., interpersonal relationships) themes and differences based on trauma history, Vietnam experiences, and group membership are explored. We evaluate associations between themes and psychological well-being. Findings provide insight into experiences of this understudied group of women.     

Same-sex marriage laws and demand for mortgage credit

Marriage for same-sex couples was only permitted in a limited number of states prior to the U.S. Supreme Court’s 2015 decision in Obergefell v. Hodges. We exploit panel variation across states prior to the Supreme Court decision to investigate the effect of marriage laws on demand for mortgage credit. Identification relies on the fact that states permitted same-sex marriage at different points in time, often through court order whereby the outcome and timing of ruling was unknown. We estimate that states permitting same-sex marriage experienced a 6–16% increase in same-sex mortgage applications after the policy was implemented. Federal recognition of marriage is associated with a stronger effect than state same-sex marriage prior to the repeal of the Defense of Marriage Act, but the effect of state-recognized marriage is also stronger than anti-discrimination policies in housing. Our findings provide important insight not only to the housing choices of same-sex households but the impact of marriage on all households.     

Where Does Religion Matter Most? Personal Religiosity and the Acceptability of Wife‐beating in Cross‐National Perspective

Does religion justify violent acts against wives, or does it reduce approval of this type of intimate partner violence? We examine whether personal religiosity raises or lowers the acceptability of wife‐beating. In addition, we investigate how the relationship between personal religiosity and attitudes toward wife‐beating differs depending on the overall normative context of the country where a person lives. Using multilevel modeling with data from the fifth wave of the World Values Survey (2005–2008), we find that greater individual‐level religiosity reduces the acceptability of wife‐beating. More importantly, cross‐level interactions show that these reductions are greatest in countries where there is a general lack of normative restraint as measured by the “anomie” scale. These observations suggest that religiosity may influence an individual's norms the most in countries where secular controls are absent or weak.     

Laplace based approximate posterior inference for differential equation models

Ordinary differential equations are arguably the most popular and useful mathematical tool for describing physical and biological processes in the real world. Often, these physical and biological processes are observed with errors, in which case the most natural way to model such data is via regression where the mean function is defined by an ordinary differential equation believed to provide an understanding of the underlying process. These regression based dynamical models are called differential equation models. Parameter inference from differential equation models poses computational challenges mainly due to the fact that analytic solutions to most differential equations are not available. In this paper, we propose an approximation method for obtaining the posterior distribution of parameters in differential equation models. The approximation is done in two steps. In the first step, the solution of a differential equation is approximated by the general one-step method which is a class of numerical numerical methods for ordinary differential equations including the Euler and the Runge-Kutta procedures; in the second step, nuisance parameters are marginalized using Laplace approximation. The proposed Laplace approximated posterior gives a computationally fast alternative to the full Bayesian computational scheme (such as Makov Chain Monte Carlo) and produces more accurate and stable estimators than the popular smoothing methods (called collocation methods) based on frequentist procedures. For a theoretical support of the proposed method, we prove that the Laplace approximated posterior converges to the actual posterior under certain conditions and analyze the relation between the order of numerical error and its Laplace approximation. The proposed method is tested on simulated data sets and compared with the other existing methods.     

Career Maturity and Quality of Life in Korean Adolescents: A Cross‐Lagged Panel Design

This study investigated the effectiveness of a short‐term career exploration program for enhancing career maturity and quality of life in Korean adolescents. It further tested causality between career maturity and quality of life defined as school satisfaction and subjective well‐being. We used a cross‐lagged panel design to examine the links among career maturity, subjective well‐being, and school satisfaction, measured both before and 4 weeks after a career exploration program for 224 Korean middle school students. Study results indicated that 4 weeks after the program implementation, there were significant increases in career maturity, subjective well‐being, and school satisfaction. A significant causal relationship from the pretest score of career maturity to the posttest score of school satisfaction was found. Results suggest that the implementation of a short‐term career exploration program designed to increase career maturity also significantly promotes school satisfaction. Limitations of the study are discussed along with suggestions for future research.     

Investigating dependence between frequency and severity via simple generalized linear models

Recently, a body of literature proposed new models relaxing a widely-used but controversial assumption of independence between claim frequency and severity in non-life insurance rate making. This paper critically reviews a generalized linear model approach, where a dependence between claim frequency and severity is introduced by treating frequency as a covariate in a regression model for severity. As an extension of this approach, we propose a dispersion model for severity. For this model, the information loss caused by using average severity rather than individual severity is examined in detail and the parameter estimators suffering from low efficiency are identified. We also provide analytical solutions for the aggregate sum to help rate making. We show that the simple functional form used in current research may not properly reflect the real underlying dependence structure. A real data analysis is given to explain our analytical findings.     

Efficient and flexible model-based clustering of jumps in diffusion processes

Jump–diffusion processes involving diffusion processes with discontinuous movements, called jumps, are widely used to model time-series data that commonly exhibit discontinuity in their sample paths. The existing jump–diffusion models have been recently extended to multivariate time-series data. The models are, however, still limited by a single parametric jump-size distribution that is common across different subjects. Such strong parametric assumptions for the shape and structure of a jump-size distribution may be too restrictive and unrealistic for multiple subjects with different characteristics. This paper thus proposes an efficient Bayesian nonparametric method to flexibly model a jump-size distribution while borrowing information across subjects in a clustering procedure using a nested Dirichlet process. For efficient posterior computation, a partially collapsed Gibbs sampler is devised to fit the proposed model. The proposed methodology is illustrated through a simulation study and an application to daily stock price data for companies in the S&P 100 index from June 2007 to June 2017.     

Affect in Daily Career Decision Self‐Efficacy and Career Choice Anxiety

The authors investigated the impact of emotional intelligence on 2 career decision variables: daily career decision self‐efficacy and daily career choice anxiety. They also examined the mediating effects of daily positive affect on these variables. At baseline, 103 Korean undergraduate and graduate students completed questionnaires about emotional intelligence. Using a daily diary method, the authors also collected data on participants’ daily positive affect, daily career decision self‐efficacy, and daily career choice anxiety for 21 consecutive days. Hierarchical linear model analyses indicated that emotional intelligence predicted daily career decision self‐efficacy and daily career choice anxiety. These findings suggest that fostering emotional intelligence and daily positive affect could help students determine their future career. For individuals experiencing difficulty in the career decision process, counselors may foster career decision self‐efficacy and reduced career choice anxiety via enhancing clients’ emotional intelligence and daily positive affect.     

Productive Resistance within the Korean Public Sector: Exploring Organisational Culture

The article examines how South Korean civil servants responded to the introduction of pay for performance. Drawing upon 31 in-depth interviews with career civil servants, it identifies what became known as 1/n, a form of ‘discreet resistance’ that emerged and evolved. The analytical framework allows productive resistance to be seen as ebbing and flowing during organisational change that sees institutionalisation, deinstitutionalisation and re-institutionalisation. In understanding the cultural context of organisational resistance the contribution is three-fold. First, a nuanced definition and understanding of productive resistance. Second, it argues that productive resistance must be seen as part of a process that does not simply reflect ‘offer and counter-offer’ within the change management process. Thirdly, it identifies differences within groups and sub-cultures concerning commitment towards resistance and how these fissures contribute towards change as new interpretive schemes and justifications are presented in light of policy reformulations.     

Bootstrap-Based T 2 Multivariate Control Charts

Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop univariate control charts to monitor a single process, no effort has been made to integrate the effectiveness of the bootstrap technique with multivariate control charts. In the present study, we propose a bootstrap-based multivariate T ² control chart that can efficiently monitor a process when the distribution of observed data is nonnormal or unknown. A simulation study was conducted to evaluate the performance of the proposed control chart and compare it with a traditional Hotelling's T ² control chart and the kernel density estimation (KDE)-based T ² control chart. The results showed that the proposed chart performed better than the traditional T ² control chart and performed comparably with the KDE-based T ² control chart. Furthermore, we present a case study to demonstrate the applicability of the proposed control chart to real situations.