Classifying At‐Risk High School Youth: The Influence of Exposure to Community Violence and Protective Factors on Academic and Health Outcomes

Using cluster analysis, 789 predominately Latino and African American high school youth were classified into varying academic at‐risk profiles using self‐reported levels of academic confidence, motivation to attend school, perceived family support, connections with teachers and peers, and exposure to violence. Six clusters emerged, 5 of which were identified as “at‐risk.” The clusters were examined in relation to academic stress, health status, grades, and school retention. Exposure to violence was one distinguishing feature of youth identified as most vulnerable, vulnerable, and resilient; however, youth identified as resilient recorded better academic outcomes.     

New imperfect rankings models for ranked set sampling

Ranked set sampling is a sampling approach that leads to improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. This ranking may be done either by subjective judgment or according to an auxiliary variable, and it need not be completely accurate. In fact, results in the literature have shown that no matter how poor the quality of the ranking, procedures based on ranked set sampling tend to be at least as efficient as procedures based on simple random sampling. However, efforts to quantify the gains in efficiency for ranked set sampling procedures have been hampered by a shortage of available models for imperfect rankings. In this paper, we introduce a new class of models for imperfect rankings, and we provide a rigorous proof that essentially any reasonable model for imperfect rankings is a limit of models in this class. We then describe a specific, easily applied method for selecting an appropriate imperfect rankings model from the class.     

Constituents’ economic interests and senator support for spending limitations

Peltzman [Peltzman, S., 1984. Constituent interest and congressional voting. Journal of Law and Economics 27, 181–210] argues that if constituents’ economic interests have well-defined “winners and losers” and are appropriately measured, then constituents’ economic interests, and not legislator ideology, are the most important determinates of legislator voting. We test Peltzman's theory by examining senatorial voting on three mandated spending limitation bills. We find, consistent with Peltzman's theory, that the ratio of federal spending in a senator's state to federal taxes paid by that state, and not a senator's personal ideology, matters on legislation where there are well-defined economic “winners and losers.” This is particularly important because unlike other constituents’ economic interest measures that only impact a fraction of the constituency, the ratio of federal spending to federal taxes in a state represents the economic interests of all the constituents in a state.     

Gay Affirmative Practice: A Model for Social Work Practice with Gay,Lesbian, and Bisexual Youth

Gay affirmative practice has recently been introduced into the social work literature as a culturally sensitive model for working with gay, lesbian, and bisexual (GLB) adults, however, this model has rarely been applied to practice with GLB youth. In this article, the authors review the literature to present the main tenets of gay affirmative practice, outline the challenges that GLB youth face, and delineate the environmental and individual strengths that can be enhanced to promote well-being. The authors then apply the gay affirmative practice model to GLB youth, offering concrete information about the specific knowledge, attitudes, and skills that social workers should acquire to better serve the unique, yet diverse, needs of GLB youth.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.