Cultural Contributors to Ego‐Resiliency and Associations with Depressive Problems in Midwestern Latino Youth

Ego‐resiliency is an enduring psychological construct reflecting how individuals‐adapt to environmental stressors, conflict, and change which is linked to positive adjustment. Ego‐resiliency has not been examined in Latino youth, despite their high risk for mental health problems; nor have cultural precursors to ego‐resiliency been examined. Given these gaps, we examined whether familism values (supportive, obligation, and referent) were associated with ego‐resiliency and, in turn, depressive problems in Latino adolescents across two time points (N = 123, mean age = 11.53). Results indicated that supportive familism was associated positively with ego‐resiliency and negatively with depressive problems. Ego‐resiliency negatively predicted depressive problems across time, controlling for prior levels, suggesting that ego‐resiliency may have long‐term effects on depressive problems in Latino youth.     

What’s in a yardscape? A case study of emergent ecosystem services and disservices within resident yardscape discourses in Minnesota

Urban Ecosystems - Residential yardscapes are at the forefront of human interaction with nature for those living in urban areas across Europe and North America. In recent years a significant amount...     

Enduring not enjoying? Emotional responses to studying abroad among Danish and Chinese students

ABSTRACT

Students’ emotions during stays abroad have mainly been treated as ”culture shock”, where difficulties are interpreted as universal ”stages” towards ”adaptation”. This paper explores how students from different cultural and educational traditions experience studying abroad differently. The study presents a qualitative study of 18 Danish and Chinese students in Chinese and Danish universities and situates itself within the literature on culture shock, student emotions and study-abroad experiences. The study exposes the students’ complex emotions throughout their studies abroad and explains how these emotions relate to processes of self-formation, professional and academic development, increase or decrease in freedom, and student agency.     

Putting people into dynamic places: the importance of specific contexts in understanding demographic responses to changes in the natural environment

Population and Environment -     

Left home high and dry-reduced migration in response to repeated droughts in Thailand and Vietnam

Population and Environment - We investigate the extent to which droughts impact migration responses of rural households in Thailand and Vietnam, as well as the role of underlying mechanisms such as...     

Ethnicity and fertility desires in Ghana

Journal of Population Research - The extant literature shows that fertility desires are an important indicator for understanding and predicting the future course of fertility; however, little work...     

A Decision Model for the Robot Selection Problem Using Robust Regression*

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.     

Regional Differentials in Unemployment Rates: The Economic Causes

Abstract. Recent studies on the causes that bring about the increase in unemployment rates of the Mezzogiorno area and Italy are mainly based upon demographic and social causes. The aim of this study is to verify if they can be also influenced by economic factors. Through a dynamic model with partial adaptive hypothesis, some ways by which demand affects the differences of regional unemployment rates has been tested. In this respect it has been considered whether variations in economic activity can lead to different growth rates of regional production owing to the different industrial structures; whether the employers are influenced by local factors in varying the occupation when the production increases, and finally in which way the regional unemployment rate depends on the excess of desired, over effective, labour demand. Data base are from 1959 to 1976, and the unemployment rate is referred only to unemployed workers that is the better proxy of the unemployment connected with the economic cycle. The results show a slight reactiveness to demand, both in industrial labour demand and supply in the Mezzogiorno.     

Three Recommendations for Improving the Use of p-Values

ABSTRACT

Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer this question and are often misinterpreted in ways that lead to overstating the evidence against the null hypothesis. Even in the “post p?<?0.05 era,” however, it is quite possible that p-values will continue to be widely reported and used to assess the strength of evidence (if for no other reason than the widespread availability and use of statistical software that routinely produces p-values and thereby implicitly advocates for their use). If so, the potential for misinterpretation will persist. In this article, we recommend three practices that would help researchers more accurately interpret p-values. Each of the three recommended practices involves interpreting p-values in light of their corresponding “Bayes factor bound,” which is the largest odds in favor of the alternative hypothesis relative to the null hypothesis that is consistent with the observed data. The Bayes factor bound generally indicates that a given p-value provides weaker evidence against the null hypothesis than typically assumed. We therefore believe that our recommendations can guard against some of the most harmful p-value misinterpretations. In research communities that are deeply attached to reliance on “p?<?0.05,” our recommendations will serve as initial steps away from this attachment. We emphasize that our recommendations are intended merely as initial, temporary steps and that many further steps will need to be taken to reach the ultimate destination: a holistic interpretation of statistical evidence that fully conforms to the principles laid out in the ASA statement on statistical significance and p-values.     

Accurate computation of single and product moments of order statistics under progressive censoring

An accurate procedure is proposed to calculate approximate moments of progressive order statistics in the context of statistical inference for lifetime models. The study analyses the performance of power series expansion to approximate the moments for location and scale distributions with high precision and smaller deviations with respect to the exact values. A comparative analysis between exact and approximate methods is shown using some tables and figures. The different approximations are applied in two situations. First, we consider the problem of computing the large sample variance–covariance matrix of maximum likelihood estimators. We also use the approximations to obtain progressively censored sampling plans for log-normal distributed data. These problems illustrate that the presented procedure is highly useful to compute the moments with precision for numerous censoring patterns and, in many cases, is the only valid method because the exact calculation may not be applicable.