A Comparison of Adaptive Strategies and Patterns of Victimization among Homeless Adolescents and Adults

One hundred and fifty-six homeless adolescents and 319 homeless adults interviewed directly on the streets and in shelters were compared for backgrounds of abuse, adaptations to life on the streets, and rates of criminal victimization when on the streets. Homeless adolescents were more likely to be from abusive family backgrounds, more likely to rely on deviant survival strategies, and more likely to be criminally victimized. A social learning model of adaptation and victimization on the streets was hypothesized. Although the model was supported for both homeless adults and adolescents, it was more strongly supported for adolescents than adults, and for males than females regardless of age.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.