The Relation of Career Adaptability to Work–Family Experience and Personal Growth Initiative Among Taiwanese Working Parents

This study aimed to explore the effect of career adaptability on 598 working parents in Taiwan. The results showed that career adaptability served an important role in moderating and mediating the effects between work–family conflict, work–family strength, and personal growth initiative.     

A penalized likelihood method for multi-group structural equation modelling

In the past two decades, statistical modelling with sparsity has become an active research topic in the fields of statistics and machine learning. Recently, Huang, Chen and Weng (2017, Psychometrika, 82, 329) and Jacobucci, Grimm, and McArdle (2016, Structural Equation Modeling: A Multidisciplinary Journal, 23, 555) both proposed sparse estimation methods for structural equation modelling (SEM). These methods, however, are restricted to performing single-group analysis. The aim of the present work is to establish a penalized likelihood (PL) method for multi-group SEM. Our proposed method decomposes each group model parameter into a common reference component and a group-specific increment component. By penalizing the increment components, the heterogeneity of parameter values across the population can be explored since the null group-specific effects are expected to diminish. We developed an expectation-conditional maximization algorithm to optimize the PL criteria. A numerical experiment and a real data example are presented to demonstrate the potential utility of the proposed method.     

Bayesian Diagnostics of Hidden Markov Structural Equation Models with Missing Data

Cocaine is a type of drug that functions to increase the availability of the neurotransmitter dopamine in the brain. However, cocaine dependence or abuse is highly related to an increased risk of psychiatric disorders and deficits in cognitive performance, attention, and decision-making abilities. Given the chronic and persistent features of drug addiction, the progression of abstaining from cocaine often evolves across several states, such as addiction to, moderate dependence on, and swearing off cocaine. Hidden Markov models (HMMs) are well suited to the characterization of longitudinal data in terms of a set of unobservable states, and have increasingly been used to uncover the dynamic heterogeneity in progressive diseases or activities. However, the existence of outliers or influential points may misidentify the hidden states and distort the associated inference. In this study, we develop a Bayesian local influence procedure for HMMs with latent variables in the presence of missing data. The proposed model enables us to investigate the dynamic heterogeneity of multivariate longitudinal data, reveal how the interrelationships among latent variables change from one state to another, and simultaneously conduct statistical diagnosis for the given data, model assumptions, and prior inputs. We apply the proposed procedure to analyze a dataset collected by the UCLA center for advancing longitudinal drug abuse research. Several outliers or influential points that seriously influence estimation results are identified and removed. The proposed procedure also discovers the effects of treatment and individuals’ psychological problems on cocaine use behavior and delineates their dynamic changes across the cocaine-addiction states.     

Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation

There is a recent increase in interest of Bayesian analysis. However, little effort has been made thus far to directly incorporate background knowledge via the prior distribution into the analyses. This process might be especially useful in the context of latent growth mixture modeling when one or more of the latent groups are expected to be relatively small due to what we refer to as limited data. We argue that the use of Bayesian statistics has great advantages in limited data situations, but only if background knowledge can be incorporated into the analysis via prior distributions. We highlight these advantages through a data set including patients with burn injuries and analyze trajectories of posttraumatic stress symptoms using the Bayesian framework following the steps of the WAMBS-checklist. In the included example, we illustrate how to obtain background information using previous literature based on a systematic literature search and by using expert knowledge. Finally, we show how to translate this knowledge into prior distributions and we illustrate the importance of conducting a prior sensitivity analysis. Although our example is from the trauma field, the techniques we illustrate can be applied to any field.     

Estimation of a Latent Variable Regression Growth Curve Model for Individuals Cross-Classified by Clusters

The cross-classified multiple membership latent variable regression (CCMM-LVR) model is a recent extension to the three-level latent variable regression (HM3-LVR) model which can be utilized for longitudinal data that contains individuals who changed clusters over time (for instance, student mobility across schools). The HM3-LVR model can include the initial status on growth effect as varying across those clusters and allows testing of more flexible hypotheses about the influence of initial status on growth and of factors that might impact that relationship, but only in the presence of pure clustering of participants within higher-level units. This Monte Carlo study was conducted to evaluate model estimation under a variety of conditions and to measure the impact of ignoring cross-classified data when estimating the incorrectly specified HM3-LVR model in a scenario in which true values for parameters are known. Furthermore, results from a real-data analysis were used to inform the design of the simulation. Overall, it would be recommended for researchers to utilize the CCMM-LVR model over the HM3-LVR model when individuals are cross-classified, and to use a bare minimum of more than 100 clustering units in order to avoid overestimation of the level-3 variance component estimates.     

Communal coping and rumination in the aftermath of Chile earthquake: Multiple mediation analysis of the relationship between subjective severity and posttraumatic growth

Communal coping and collective participation were recently proposed as a potential mechanism that may favour not only individual's capacity to bounce back but also community cohesion and social well‐being and posttraumatic growth in the aftermath of natural disasters. To date, there is a lack of studies simultaneously assessing the effect of communal coping strategies and cognitive strategies on the development of posttraumatic growth. Therefore, the aim of this study was to examine the role of communal coping strategies and cognitive strategies such as intrusive and deliberated rumination as potential mediators between subjective severity of the event and posttraumatic growth and positive adjustment in the context of natural disaster. The questionnaire was administrated to 225 people affected by the most intense earthquake recorded in Chile in this century. The results of multiple mediation analysis showed that both cognitive strategies, such as deliberative rumination, and communal coping strategies, such as communal positive reappraisal and participation in spiritual rituals, are potential mediators between subjective severity and posttraumatic growth. Overall, the present work offers researchers and professionals interested in this area of study an interesting approach to analyse individual and collective coping strategies and its interrelation.     

Post-combat adaptation: improving social support and reaching constructive growth

Background and Objectives: Posttraumatic stress disorder, a commonly researched mental health outcome associated with trauma, does not develop in the majority of survivors. More common trajectories of adaptation include resilience, and posttraumatic growth (PTG). The objectives of the current study were to: (1) describe posttrauma adaptation profiles in a sample of Israeli male military veterans (N?=?448); and (2) to explore the protective factors that promote constructive PTG within two profiles of posttrauma adaptation.

Methods: The study used secondary data to estimate latent profile mixture models and a series of logistic regression analyses.

Results: Demographic controls, combat related variables, endorsement of coping strategies, and reports of improvement in social support were not significant predictors of constructive growth in the resilient class. However, those in the struggling growth subset of the sample who reported improvement in perceived social support increased the odds of reaching constructive growth.

Conclusion: These findings highlight the importance of tailored clinical interventions that account for more complex profiles of posttrauma adaptation; and further, provide evidence that adaptation takes place over time. Finally, these findings call for future research to continue to explore the quality of PTG and the contexts in which protective factors promote positive adaptation.     

The relationship among resilience,rumination and posttraumatic growth in hemodialysis patients in North China

Research has indicated that clinical serious disease may lead to posttraumatic growth (PTG). However, little is known about PTG among hemodialysis (HD) patients. The study examined the relationship among resilience, rumination and PTG among Chinese HD patients. 196 HD patients were recruited from a tertiary hospital in a Northern city of China between 1 June 2015 and 30 May 2016. Patients were surveyed using the Posttraumatic Growth Inventory-Chinese version, Connor-Davidson Resilience Scale, and Chinese Event Related Rumination Inventory. Correlation analyses showed that resilience was most highly positively correlated with PTG (r = .70, p < .001), deliberate rumination moderately correlated to PTG (r = .50, p < .001), and intrusive rumination was lower negatively related to PTG (r = –.26, p < .001). Regression analyses showed that age, gender, duration of dialysis, resilience and deliberate rumination had significant associations with PTG (β = ?.31, p ＜ .0001; β = –.14, p = .002; β = .10, p = .032; β = .44, p < .001; β = .20, p < .001). They together explained 65% of the total variance in PTG (F [8,195] = 46.74, p < .001). However, intrusive rumination was not associated with PTG (p > .05). The results suggested that resilience and deliberate rumination may be instrumental for PTG improvement.     

Level of service of pedestrian facilities: Modelling human comfort perception in the evaluation of pedestrian behaviour patterns

The paper presents a new methodology for evaluating the quality of operation of pedestrian facilities: the methodology is based on the individual level of comfort perceived by each pedestrian that moves in the area.At each time instant, each pedestrian perceives a comfort level which is a function of the space they feel currently available and of his required space. The required space depends on the subject’s walking direction as well as on physical and psychological factors. The available space depends on the current positions of pedestrians. The proposed methodology quantifies the current discomfort due to pedestrian interactions as a continuous function of the interpersonal distances.The proposed methodology has been applied to empirical data. The experimental data are presented, discussed and compared with widely accepted level of service assessment methods.     

雾霾对地区GDP增长率的影响：抑郁情绪的中介效应

环境污染导致的天气变化对人的身心健康和社会发展都会产生重大影响。本研究以心理学的视角探讨雾霾天气是否会使人产生抑郁情绪,降低工作效率,进而影响社会经济发展。方法：收集2013-2015年中国大陆27个省会城市及4直辖市的PM2.5浓度、抑郁指数及地区生产总值增长率。采用百度统计平台基于关键词“抑郁”搜索量合成的抑郁指数作为抑郁情绪指标。结果表明：（1）2013-2015年各地区的PM2.5浓度与抑郁指数正相关显著(r= .33, p< .01),抑郁指数与地区GDP增长率的负相关显著(r= -.37, p< .01);（2）抑郁指数在PM2.5浓度和地区GDP增长率间起完全中介作用。（3）当lag=-6时,上海市2013年1月到2015年12月PM2.5浓度和抑郁指数的互相关系数最大(rR = .38, p< .05)。结论：雾霾天气可能会使人产生抑郁情绪,进而对经济增长产生负面影响。