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.     

Self-efficacy and social support for psychological well-being of family caregivers of care recipients with dementia with Lewy bodies,Parkinson’s disease dementia,or Alzheimer’s disease

The study identified factors associated with depressive symptoms in family caregivers of persons with dementia with Lewy bodies, Alzheimer’s disease, or Parkinson’s disease dementia. An Internet-based survey generated 606 response sets. Regression analysis indicated that care recipients’ memory and behavioral problems, caregivers’ self-efficacy, grief, and tangible, emotional, and affectionate support were significantly associated with caregivers’ depressive symptoms. Self-efficacy mediated caregiver burden to improve psychological well-being. Social support mediated the association between caregiver grief and psychological well-being. Social workers can provide psychoeducational interventions to help caregivers to manage emotional distress and improve capacity to manage tasks related to the dementia.     

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.     

Racial/ethnic segregation and health disparities: Future directions and opportunities

Health researchers have investigated the association between racial segregation and racial health disparities with multilevel approaches. This study systematically reviews these multilevel studies and identifies broad trends and potential directions for future research on racial segregation and health disparities in the US. After searching databases including CINAHL and MEDLINE, we systematically reviewed 66 articles published between 2003 and 2019 and found four major gaps in racial/ethnic segregation and health disparities: (a) the concept of segregation was rarely operationalized at the neighborhood level, (b) except for the evenness and exposure dimension, other dimensions of segregation are overlooked, (c) little attention was paid to the segregation between whites and non‐black minorities, particularly Hispanics and Asians, and (d) mental health outcomes were largely absent. Future directions and opportunities include: First, other segregation dimensions should be explored. Second, the spatial scales for segregation measures should be clarified. Third, the theoretical frameworks for black and non‐black minorities should be tested. Fourth, mental health, substance use, and the use of mental health care should be examined. Fifth, the long‐term health effect of segregation has to be investigated, and finally, other competing explanations for why segregation matters at the neighborhood level should be answered.     

Differences in soil chemical properties with distance to roads and age of development in urban areas

The main objective of this study was to characterize variation in soil chemical properties with length of urbanization period and distance to roads. Urban boundaries from 1920’s (old), 1960’s (middle) and 2000’s (new) were identified for three cities in northeast Ohio: Massillon, Wooster and Canton. Within each identified historic boundary, soil samples were collected from two road-side and two interior lawns in one public school site in each city. Thus, there were three urban age and two distance-to-roads classes. Soil particle composition and basic chemical properties including pH, available phosphorus (P), exchangeable potassium (K), calcium (Ca), magnesium (Mg), cation exchange capacity (CEC), nitrate, total carbon (C), total nitrogen (N), and soil organic matter (SOM) were measured. Two notable spatio-temporal patterns appeared repeatedly in the data set. First, total C, total N and SOM were higher in the soils of old (>50 years) urban sites than of newly developed sites. Similar, but not always significant, trends in soil pH, and exchangeable Ca were also observed. Second, road-side soils had higher pH, Ca, total C and N than interior sites regardless of urban age. These data indicate that key soil chemical properties can vary in predictable ways with urban age and distance-to-roads classes. Such variations in key soil chemical properties may influence or reflect soil biota and biogeochemical processes in urban soils.     

From ‘sustainable rural communities’ to ‘social sustainability': giving voice to diversity in Mangakahia Valley, New Zealand

Despite widespread interest in the notion of sustainability, little progress has been made towards an understanding of its social dimensions. Nonetheless, the concept of ‘sustainable rural communities’ is embedded in popular, policy and academic discourses, where the needs of ‘rural communities’ are usually equated with those of farm families. Our ethnographic research in Northland, New Zealand illustrates the diverse interests to be found within ‘rural communities'. Interviews and participant observation were undertaken between August 1995 and July 1996 in the Mangakahia Valley. The increasing divergence in the ethnic, class and occupational makeup of the population has brought with it complexities in terms of what can be said to contribute to ‘sustainable rural communities'. We suggest that ‘sustainable rural communities’ be treated as a folk category, and instead, social science discourse should resort to the broader concept of social sustainability, which will have a locally defined content, not a universal definition, but will include elements of livelihood, social participation, justice and equity.     

Penalized likelihood approach for the four-parameter kappa distribution

The four-parameter kappa distribution (K4D) is a generalized form of some commonly used distributions such as generalized logistic, generalized Pareto, generalized Gumbel, and generalized extreme value (GEV) distributions. Owing to its flexibility, the K4D is widely applied in modeling in several fields such as hydrology and climatic change. For the estimation of the four parameters, the maximum likelihood approach and the method of L-moments are usually employed. The L-moment estimator (LME) method works well for some parameter spaces, with up to a moderate sample size, but it is sometimes not feasible in terms of computing the appropriate estimates. Meanwhile, using the maximum likelihood estimator (MLE) with small sample sizes shows substantially poor performance in terms of a large variance of the estimator. We therefore propose a maximum penalized likelihood estimation (MPLE) of K4D by adjusting the existing penalty functions that restrict the parameter space. Eighteen combinations of penalties for two shape parameters are considered and compared. The MPLE retains modeling flexibility and large sample optimality while also improving on small sample properties. The properties of the proposed estimator are verified through a Monte Carlo simulation, and an application case is demonstrated taking Thailand’s annual maximum temperature data.     

Bayesian analysis for incomplete multi-way contingency tables with nonignorable nonresponse

We propose Bayesian methods with five types of priors to estimate cell probabilities in an incomplete multi-way contingency table under nonignorable nonresponse. In this situation, the maximum likelihood (ML) estimates often fall in the boundary solution, causing the ML estimates to become unstable. To deal with such a multi-way table, we present an EM algorithm which generalizes the previous algorithm used for incomplete one-way tables. Three of the five types of priors were previously introduced while the other two are newly proposed to reflect different response patterns between respondents and nonrespondents. Data analysis and simulation studies show that Bayesian estimates based on the old three priors can be worse than the ML regardless of occurrence of boundary solution, contrary to previous studies. The Bayesian estimates from the two new priors are most preferable when a boundary solution occurs. We provide an illustrating example using data for a study of the relationship between a mother's smoking and her newborn's weight.     

A minimum asymptotic mean squared error controller for an IMA(1,1) noise process with a starting offset, and its resetting design

In a discrete-part manufacturing process, the noise is often described by an IMA(1,1) process and the pure unit delay transfer function is used as the feedback controller to adjust it. The optimal controller for this process is the well-known minimum mean square error (MMSE) controller. The starting level of the IMA(1,1) model is assumed to be on target when it starts. Considering such an impractical assumption, we adopt the starting offset. Since the starting offset is not observable, the MMSE controller does not exist. An alternative to the MMSE controller is the minimum asymptotic mean square error controller, which makes the long-run mean square error minimum.Another concern in this article is the un-stability of the controller, which may produce high adjustment costs and/or may exceed the physical bounds of the process adjustment. These practical barriers will prevent the controller to adjust the process properly. To avoid this dilemma, a resetting design is proposed. That is, the resetting procedure in use of the controller is to adjust the process according to the controller when it remains within the reset limit, and to reset the process, otherwise.The total cost for the manufacturing process is affected by the off-target cost, the adjustment cost, and the reset cost. Proper values for the reset limit are selected to minimize the average cost per reset interval (ACR) considering various process parameters and cost parameters. A time non-homogeneous Markov chain approach is used for calculating the ACR. The effect of adopting the starting offset is also studied here.