Lost in transition: Illicit substance use and services receipt among at-risk youth in the child welfare system

This study examined the use of mental health and substance abuse services among adolescents in the child welfare system (CWS) who reported use of illicit substances. 1004 adolescents age 11–15 years at baseline were followed for 5–7 years, over five waves of data collection. Shortly after the investigation for maltreatment (baseline), 69.1% of youths using illicit substances received mental health and/or substance abuse outpatient specialty services. By the last follow-up, during the transition to adulthood, only 21.5% of young adults using illicit substances received outpatient specialty services. Youth who used illicit substances were more likely to receive outpatient and inpatient specialty services than non-users at the time of contact with the CWS (mostly baseline), but this difference faded over the follow-up period. By 5–7 years follow-up, there was no significant difference in specialty services receipt for illicit substances users versus non-users. Predictors of outpatient service use at most waves were having Medicaid, mental health needs, and having recently seen a school counselor or primary care physician. Among illicit substance users transitioning to adulthood, African American youths were less likely to receive outpatient specialty services than White youths. These findings reveal a need for more attention to illicit substances use among youth in the CWS, better cross agency integration, and special attention to the needs of transition-age youth to better connect them with services as they age out of the CWS.     

Media Images of the Poor

This article provides a comprehensive overview of research that has examined the content and prevalence of stereotypic media images of the poor. Research examining televised images and print media are reviewed. An analysis of media framing as well as classist, racist, and sexist imagery is provided. Additionally, to assess media depictions of the poor in the wake of welfare reform, 412 newspaper articles about poverty and welfare published during a 3-month period in 1999 were content analyzed. Although most articles were neutral in tone and portrayed the difficulties facing welfare recipients and the poor sympathetically, they did little to contextualize poverty or illuminate its causes. These findings are discussed in terms of their context and political function.     

Sequential parameter structure,conditional inference,and likelihood drop

Successive tests of hypotheses, as exemplified with an analysis of variance table, impose a set theoretic structure on the parameter space and yet allow much arbitrariness in the definition of nuisance parameters. Two major types of statistical model, the exponential and transformation, are shown to have by basic theory well defined conditional testing procedures. The two types of testing procedure are then shown to have opposite forms of set theoretic structure on the sample space, and to differ sharply from the commonly used deviance or likelihood drop methods. The two types of model have the normal linear model as the intersection model and the two opposite forms of testing procedure manage to coincide by product space structure and independence. Details of the two types of testing procedure are discussed, related to the arbitrariness in nuisance parameter definition, and organized to provide a general-case pattern for the development of conditional procedures as an alternative to the default likelihood-drop methods.     

Some alternatives to Edgeworth

The Edgeworth expansion is well known as a means for obtaining approximate tail probabilities from information concerning the moments of the distribution. Recent saddlepoint and asymptotic methods lead to several alternative approximations. These alternatives are developed and compared by means of average relative error.     

Non-nested linear models: A conditional confidence approach

The comparison of nested linear models with normal error is well standardized in the common procedures of the analysis of variance. This article considers the comparison of two non-nested linear models that have the same parameter dimension; the comparison is made on the assumption that the true mean lies somewhere in the linear span of the two models. The analysis leads to a precision-based conditional confidence interval for the unsigned angular direction of the true mean, and this in turn provides a confidence assessment of the two directions that correspond to the two models being compared. The confidence interval is an approximate conditional interval (given the distance of the estimate from the intersection of the hypotheses), and its length as a fraction of π indicates the precision of the confidence procedure. The method provides a conditional-inference alternative to a confidence interval available by Creasy-Fieller analysis.     

Mixtures,embedding, and ancillarity

Ancillary statistics, proposed by Fisher (1925), can be constructed by forming a mixture model (Birnbaum 1962) or can be extracted or derived from a transformation-parameter model (Peisakoff 1951, Fraser 1961) or from the corresponding error-based structural model (Fraser 1968, 1979); these latter models involve an implicit mixture structure. Compound models with ancillaries can also be formed by a cross embedding, discussed from a technical viewpoint in this paper. Of the 25 examples in Buehler (1982), 22 are mixtures or implicit mixtures and 3 correspond to cross embedding. The cross embedding examples exemplify the nonuniqueness difficulties with ancillaries. This paper discusses a simple and two generalized versions of cross embedding but makes no general valuations of these for statistical inference; their role within inference is discussed in Evans, Fraser, and Monette (1984, 1985).     

Further remarks on asymptotic normality of likelihood and conditional analyses

Under weak conditions the normalized likelihood with or without weight function almost surely converges to a normal density function: for a real parameter or a vector parameter; with or without the assumption of independent identical distributions. Applications arise for confidence intervals, confidence distributions, structural distributions. and conditional analyses with transformation and structural models.     

Linear calibra and conditional inference

Approximate conditional inference is developed for the linear calibration problem. It is shown that this problem can be transformed so that the primary parameter is an angle, the nuisance parameter is a radial distance, and the density is rotationally symmetric. Were the nuisance parameter known, exact location confidence intervals would be available by location of structural arguments. A confidence distribution is used to average out the nuisance parameter yielding an approximate confidence interval that involves a precision indicator derived from the radial distance. Some difficulties with the ordinary solution are avoided by the conditional procedure.