Nourishing the Social Innovation Debate with the “Social Technology” South American Research Tradition

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Inspired by the South American research tradition known as “social technology,” this article proposes an...     

Developing a screening questionnaire for problem drinking in college students.

The CAGE questionnaire is one of several brief screening instruments that has been successful in detecting alcoholism in a general population. There is evidence, however, indicating that none of these instruments has sufficient validity for detecting the less severe pattern of problem drinking that is more typical of college students. This study analyzed survey data, including CAGE questions, to identify items that would have sufficient test operating characteristics more valid for screening problem drinkers. Problem and normal drinking patterns were defined by combining certain categories of quantity-frequency data with categories of alcohol-related problems. Problem and normal drinkers were identified as those students at the ends of the quantity-frequency/alcohol-problems continuum. Differences between the two groups in response frequencies to a 17-item alcohol-use survey were examined using chi-square analyses. Five items showing highly significant differences were identified, and sensitivity, specificity, and positive predictive values were calculated at possible cutoff points. These items were (1) a positive response to any 2 of the 4 CAGE items, (2) reporting rarely or never choosing nonalcoholic beverages at social events, (3) driving under the influence at least 6 to 10 times or more in the last year, and (4) having started regular use of alcohol before the college years. At the recommended cutoff score for a positive test, the items have a sensitivity of 88%, specificity of 87%, and positive predictive value of 52%. When used for screening in a population similar to this, it will fail to identify 12% of the problem drinkers and will falsely classify 13% of all normal drinkers as problem drinkers.(ABSTRACT TRUNCATED AT 250 WORDS)     

To Join or Not to Join: Gay-Straight Student Alliances and the High School Experiences of Lesbian,Gay, Bisexual,and Transgender Youths

This retrospective study investigates 79 lesbian, gay, bisexual, and transgender (LGBT) college students’ experiences attending high schools with gay-straight student alliances (GSAs). Responses to two, open-ended survey questions were analyzed thematically to identify reasons that underlie GSA non-membership and to model facets of GSAs that may impact the GSA-related experiences of LGBT youths. Three frames of reference for understanding GSA non-membership and perceptions of GSAs emerged. Implications for predicting GSA membership and identifying protective mechanisms of GSAs are outlined. Suggestions to help GSA leaders and advisors enhance specific aspects of GSAs that appear to be associated with positive perceptions of GSAs are also provided.     

Time-management strategies used by households with home-based work

A sample of 899 households in which at least one member is engaged in home-based work is used to analyze two time-management strategies used to respond to the demands of home-based work. Analyses reveal that, first, personal time is reallocated more than additional help is obtained for either the home-based work or household production and, second, that different strategies are used depending on whether the household manager is also the home-based worker. Respondents holding both roles report reallocating personal time more often than respondents who are not home-based workers; the reverse holds for obtaining additional help. The results suggest that households generating higher incomes in which home-based work is a full-time occupation are more likely to use time-management strategies than those in which incomes are lower and the home-based work is part-time.Journal Paper Number J-14861 Iowa Agriculture and Home Economics Experiment Station, Project Number 2857. This article reports results from the Cooperative Regional Research Project, NE-167, entitled, At-Home Income Generation: Impact on Management, Productivity and Stability in Rural and Urban Families, partially supported by the Cooperative States Research Service, U. S. Department of Agriculture, and the Experiment Stations at the University of Hawaii, Iowa State University, Lincoln University (Missouri), Michigan State University, Cornell University (New York), The Ohio State University, The Pennsylvania State University, Utah State University, and the University of Vermont. This article was accepted in 1992 under the editorship of Charles B. Hennon.Her current research work includes an analysis of family resource management in Mexico and of housing conditions in rural areas. She is also involved in the study of household members who work at home for pay and their associated management practices and coping strategies. She received her Ph.D. from The Pennsylvania State University in 1970.She received her M.S. from Iowa State University in May 1992. This paper is based on her M.S. thesis.Her current research interests include household asset and debt formation, working families and employers' benefits, and home-based employment. She received her Ph.D. from Purdue University in 1978.Her current research work includes such topics as divorce settlements, at-home income generation, and management practices of households engaged in home-based employment. Her Ph.D. degree was received from Cornell University in 1978.     

Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models

Bayesian analysis often concerns an evaluation of models with different dimensionality as is necessary in, for example, model selection or mixture models. To facilitate this evaluation, transdimensional Markov chain Monte Carlo (MCMC) relies on sampling a discrete indexing variable to estimate the posterior model probabilities. However, little attention has been paid to the precision of these estimates. If only few switches occur between the models in the transdimensional MCMC output, precision may be low and assessment based on the assumption of independent samples misleading. Here, we propose a new method to estimate the precision based on the observed transition matrix of the model-indexing variable. Assuming a first-order Markov model, the method samples from the posterior of the stationary distribution. This allows assessment of the uncertainty in the estimated posterior model probabilities, model ranks, and Bayes factors. Moreover, the method provides an estimate for the effective sample size of the MCMC output. In two model selection examples, we show that the proposed approach provides a good assessment of the uncertainty associated with the estimated posterior model probabilities.

     

Demographic Characteristics as Predictors of Quality of Life in a Population of Psychiatric Outpatients

Studies examining relationships between demographic variables in a general population of psychiatric outpatients and quality of life (QOL), in which QOL was assessed according to current recommendations, have not been performed yet. The aim of this study was to examine one particular aspect of this relationship: the question to what extent QOL scores can be predicted by demographic variables. In a sample of adult Dutch psychiatric outpatients (n=495), demographics were recorded and the participants completed a questionnaire for measuring QOL (WHOQOL-100). The relationships of the demographic variables with the WHOQOL-100 domains Social Relationships and Environment, were stronger than those with the domains Physical Health and Psychological Health. The latter had only significant relationships with educational level and sick leave, which explain little of the variance of the concerning QOL domain. In general, the demographic characteristics used, explained only a relatively small part of the variance in QOL scores. An exception was sick leave, which, in participants with a job, explained an extensive part (27.4%) of the variance of scores on the domain Physical Health.