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Specific Federal Emergency Management Authority (FEMA) policies and procedures impacted vulnerable populations both positively and negatively after Hurricane Andrew. Findings from 130 structured interviews with a randomized sample of victims sug-gest that in many cases FEMA policies were unclear, poorly explained, too rigid, and required a high level of middle-class fmancial management skills to comply w i t h eligibility requirements. These problems resulted in multi-generational families living together in one dwelling, and applicants from different cultural and ethnic backgrounds obtaining fewer services than they were entitled to receive. The outcome of the legal action (in which this study was used as evidence) that resulted in reparations to 21,000 Hurricane Andrew applicants and changes made in FEMA policies and procedures are discussed. fAnicle copies ovoiloble for a fee fmm The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: ge~info@howorth.com]Humcane Andrew was one of Florida's worst and the most expensive hunicane ever. Andrew hit South Florida on August 24, 1992. With sustained winds of over I50 mph, this humcane caused 40 deaths, seriously damaged more than 75,000 homes, and resulted in $30 billion in damages. More than a million people were without electricity, telephone, and water. The temperatures were in the mid 90's during the day and mid 80's at night (Clifford, Leen, & Doig, 1992).Often, after the shock of a catastrophic natural or human made disaster,

The principal barriers to universal screening for the cooccurring disorders of mental illness and substance abuse are training, time, cost, and a reliable and valid screen. Although many of the barriers to universal screening still remain intact, the lack of a cooccurring screen that is effective and can be administered in a cost efficient way is no longer an obstacle. This study examined the reliability, factor structure, and convergent validity of the 15-item AC-OK Cooccurring Screen. A total of 2,968 AC-OK Cooccurring Screens administrated to individuals who called or went to one of the nine participating mental health and substance abuse treatment facilities were administrated and analyzed. Principal axis factor (PAF) analysis was used in the confirmatory factor analysis to identify the common variance among the items in the scales while excluding unique variance. Cronbach's Alpha was used to establish internal consistency (reliability) of each subscale. Finally, the findings from the AC-OK Cooccurring Screen were compared to individual scores on two standardized reference measures, the addiction severity index and the Client assessment record (a measure of mental health status) to determine sensitivity and specificity. This analysis of the AC-OK Cooccurring Screen found the subscales to have excellent reliability, very good convergent validity, excellent sensitivity, and sufficient specificity to be highly useful in screening for cooccurring disorders in behavioral health settings. In this study, the AC-OK Cooccurring Screen had a Cronbach's Alpha of .92 on the substance abuse subscale and a Cronbach's Alpha of .80 on the mental health subscale.

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In preparation for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the climate community will run the Coupled Model Intercomparison Project phase 5 (CMIP-5) experiments, which are designed to answer crucial questions about future regional climate change and the results of carbon feedback for different mitigation scenarios. The CMIP-5 experiments will generate petabytes of data that must be replicated seamlessly, reliably, and quickly to hundreds of research teams around the globe. As an end-to-end test of the technologies that will be used to perform this task, a multidisciplinary team of researchers moved a small portion (10 TB) of the multimodel Coupled Model Intercomparison Project, Phase 3 data set used in the IPCC Fourth Assessment Report from three sources-the Argonne Leadership Computing Facility (ALCF), Lawrence Livermore National Laboratory (LLNL) and National Energy Research Scientific Computing Center (NERSC)-to the 2009 Supercomputing conference (SC09) show floor in Portland, Oregon, over circuits provided by DOE's ESnet. The team achieved a sustained data rate of 15 Gb/s on a 20 Gb/s network. More important, this effort provided critical feedback on how to deploy, tune, and monitor the middleware that will be used to replicate the upcoming petascale climate datasets. We report on obstacles overcome and the key lessons learned from this successful bandwidth challenge effort.

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Typology development using discriminant analysis is extremely valuable in learning more about heterogeneous client populations of interest to social workers. However, the discriminant analysis procedure is confined to situations in which individuals can be categorized into subgroups by the social work researcher. Classifying individuals by demographic variables such as gender or services utilized is straightforward and presents few problems. However, when more subjective categories are used to identify subgroup membership, problems may arise. There are many populations, such as the sexually abused, alcohol and drug abusers, sex offenders, and so on, who may avoid answering direct questions or deliberately give incorrect answers on questions formulated to classify them. This places in question the results of the discriminant analysis. In this article, data on 258 runaway and homeless youth are used to demonstrate how cluster analysis can be employed to create a classification variable needed to perform discriminant analysis. First, the data are submitted to cluster analysis to identify the groups in the population. Then, utilizing the cluster groups as the classifying variable needed to perform a discriminant analysis, the discriminantfunctions are extracted to develop a typology. The limitations of cluster analysis and this approach are discussed.The purpose of this article is to demonstrate an approach that employs both cluster and discriminant function analysis to develop a typology of a heterogeneous population. These two procedures are combined because of limitations that often hamper typology development in social research.When the research effort is to differentiate subgroups in a population and to predict the group to which a case belongs, discriminant analysis is one of the best statistical techniques available. Discriminant analysis uses a linear combination of predictor variables to classify each case into one of several groups that are identified by the classification variable. For a better understanding of the role of these variables, the classification variables can be thought of as the dependent variable. This variable identifies group member-