Positive and Negative Aspects of Transracial Adoption: An Exploratory Study From Korean Transracial Adoptees' Perspectives

Participants, 92 Korean-born transracial international adoptees, responded to two open-ended questions designed to explore both positive and negative aspects of transracial adoption. Almost all of them reported experiencing both benefits and challenges, suggesting that transracial adoption is more complex than the polarized “success or failure” paradigm that underlies much of the transracial adoption research. Participants described negative features that are consistent with themes in the extant literature, including racism, alienation from their White families and peers, unfamiliarity with Korean culture, and identity confusion. They also described positive aspects that have been less frequently explored, such as greater learning and awareness and advantages of belonging to multiple cultures. Of interest, facets that some adoptees considered to be positive aspects of transracial adoption were considered by others to be negative aspects.     

Computationally and statistically efficient model fitting techniques

A significant challenge in fitting metamodels of large-scale simulations with sufficient accuracy is in the computational time required for rigorous statistical validation. This paper addresses the statistical computation issues associated with the Bootstrap and modified PRESS statistic, which yield key metrics for error measurements in metamodelling validation. Experimentation is performed on different programming languages, namely, MATLAB, R, and Python, and implemented on different computing architectures including traditional multicore personal computers and high-power clusters with parallel computing capabilities. This study yields insight into the effect that programming languages and computing architecture have on the computational time for simulation metamodel validation. The experimentation is performed across two scenarios with varying complexity.     

Testing Variability in the Two-Sample Case

A number of robust methods for testing variability have been reported in previous literature. An examination of these procedures for a wide variety of populations confirms their general robustness. Shoemaker's improvement of the F test extends that test use to a realistic variety of population shapes. However, a combination of the Brown–Forsythe and O'Brien methods based on testing kurtosis is shown to be conservative for a wide range of sample sizes and population distributions. The composite test is also shown to be more powerful in most conditions than other conservative procedures.     

Good Health is Not the Same as a Good Life: Survey Results from Brandon,Manitoba

The aim of this investigation was to obtain some baseline self-reported data on the health status and overall quality of life of a sample of residents of the city of Brandon, Manitoba aged 18 years or older, and to measure the impact of a set of designated health determinants, comparison standards and satisfaction with diverse domains of life on their health and quality of life. In May and June 2010, 2,500 households from the city of Brandon, Manitoba were randomly selected to receive a mailed out questionnaire and 518 useable, completed questionnaires were returned. Baseline health status data were obtained using the 8 SF-36 dimensions of health and 13 items from the United States Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System. Determinants of health and overall quality of life included measures of socializing activities, a Good Neighbourhood Index, Social Support Index, Community Health Index, a measure of free-time exercise levels, health-related behaviours, use of drugs, health care issues, a set of domain-specific quality of life items, a set of measures concerning criminal victimization, worries and behaviours concerning victimization and the basic postulates of Multiple Discrepancies Theory. Overall life assessment, dependent variables included Average Health, happiness, a single item measure of satisfaction with life as a whole, a single item measure of satisfaction with the overall quality of life, the Satisfaction With Life Scale, Contentment with Life Assessment Scale and a Subjective Wellbeing Index. Using multiple regression, we explained as much as 75% of the variance in Subjective Wellbeing scores and as little as 45% in happiness scores. Four clusters of health determinants explained from 20% (Happiness) to 44% (Average Health) of the variance in the dependent variables. Adding comparison standards and domain satisfaction scores to the set of health determinants increased our total explanatory power by only 2% points for Average Health (from 44 to 46%), but more than doubled our explanatory power for Happiness (from 20 to 45%) and for satisfaction with the overall quality of life (from 31 to 67%). As well, our explanatory power for the single item of Life Satisfaction increased from 34 to 66%, for the Satisfaction With Life Scale from 39 to 74%, for the Contentment With Life Assessment Scale from 36 to 60%, and for Subjective Wellbeing from 42 to 75%. This provided very clear evidence that self-perceived good health is not equivalent to perceived quality of life, confirming evidence reported in our earlier studies. The three most important take-home messages from this investigation are (1) in assessing the relative influence of any alleged determinants of health and the quality of life, different sets of alleged determinants will appear to be more or less influential for different dependent variables. Therefore, (2) researchers should use diverse sets of determinants and dependent variables and (3) it is a big mistake to use measures of health status as if they were measures of the perceived quality of life.     

Pairwise testing of group mean vectors in MANOVA with small samples

Several, multivariate, pairwise, multiple comparison procedures are proposed as follow-ups for a significant multivariate analysis of variance. The Peritz procedure is generalized from univariate to several multivariate applications. Procedures are evaluated using overall power, any-pair power and all-pairs power applied to mean vectors with common sample sizes of 4, 5, and 9. Monte Carlo simulation demonstrated greater power than previously proposed univariate procedures in many conditions especially for all-pairs power. The multivariate Peritz procedure based on the Lawley–Hotelling trace was found to be most powerful in many conditions.     

Bridges to Sobriety: Testing the Feasibility and Acceptability of a Mobile App Designed to Supplement an Adolescent Substance Use Disorder Treatment Program

Clinical Social Work Journal - Adolescent substance use is a growing problem that causes a myriad of negative outcomes. Using substances during adolescence can lead to decreased executive...     

Selective Decisions: Challenging Orthodox Analyses of the Hiring Process

Employee recruitment and selection decisions are increasingly framed in terms of explanatory variables and interactions that span multiple levels of analysis. This paper reviews the gradual transition in thinking from predominantly micro-level explanations, to frameworks more accepting of the importance of both micro- and macro-level explanations. The latter accommodate the influences of and interactions between, for example, the individual characteristics of candidates; the cognitive processes of key actors or decision-makers; organisational characteristics; collective processes; and wider environmental contextual factors which constrain and shape an organisation's or manager's decision alternatives. The result is more complex cross-level theorising about the hiring process. The paper also goes beyond cross-level interactions between multiple variables in considering selection as a social relational phenomenon. Meso-level theories present epistemological alternatives to the dominant micro-level research tradition in selection and potentially more parsimonious and powerful micro–macro bridging concepts than cross-level interactionist perspectives. It is argued that cross-disciplinary perspectives, although still problematic, are valuable additions to the individualistic paradigm which is conventionally used to understand employee recruitment and selection.