Sampling Strategies for Toxic Air Contaminants

Annual concentrations of toxic air contaminants are of primary concern from the perspective of chronic human exposure assessment and risk analysis. Despite recent advances in air quality monitoring technology, resource and technical constraints often impose limitations on the availability of a sufficient number of ambient concentration measurements for performing environmental risk analysis. Therefore, sample size limitations, representativeness of data, and uncertainties in the estimated annual mean concentration must be examined before performing quantitative risk analysis. In this paper, we discuss several factors that need to be considered in designing field-sampling programs for toxic air contaminants and in verifying compliance with environmental regulations. Specifically, we examine the behavior of SO2, TSP, and CO data as surrogates for toxic air contaminants and as examples of point source, area source, and line source-dominated pollutants, respectively, from the standpoint of sampling design. We demonstrate the use of bootstrap resampling method and normal theory in estimating the annual mean concentration and its 95% confidence bounds from limited sampling data, and illustrate the application of operating characteristic (OC) curves to determine optimum sample size and other sampling strategies. We also outline a statistical procedure, based on a one-sided t-test, that utilizes the sampled concentration data for evaluating whether a sampling site is compliance with relevant ambient guideline concentrations for toxic air contaminants.     

Grounded Neural Networking: Modeling Complex Quantitative Data

The latest advances in artificial intelligence software (neural networking) have finally made it possible for qualitative researchers to apply the grounded theory method to the study of complex quantitative databases in a manner consistent with the postpositivistic, neopragmatic assumptions of most symbolic interactionists. The strength of neural networking for the study of quantitative data is twofold: it blurs the boundaries between qualitative and quantitative analysis, and it allows grounded theorists to embrace the complexity of quantitative data. The specific technique most useful to grounded theory is the Self‐Organizing Map (SOM). To demonstrate the utility of the SOM we (1) provide a brief review of grounded theory, focusing on how it was originally intended as a comparative method applicable to both quantitative and qualitative data; (2) examine how the SOM is compatible with the traditional techniques of grounded theory; and (3) demonstrate how the SOM assists grounded theory by applying it to an example based on our research.     

Surname analysis for estimating local concentration of Hispanics and Asians

Surname analysis is a potentially useful technique for identifying members of particular racial, ethnic, or language communities within a population. We review the existing state of the art for identifying persons of Hispanic or Asian origin, based on surnames distinctive of each group. We describe the logic of surname analysis, profile several available surname dictionaries, and illustrate their applications in local redistricting. Results of our ongoing validation studies suggest promising future directions for improving accuracy and broadening applications.This article is based on a paper presented at the annual Population Association of America meetings at Cincinnati, 1 April 1993.     

Race and prevailing wage laws in the construction industry: Comment on Thieblot

Using the 1990 Census of Population, Thieblot (1999) argued that black employment and the strength of the prevailing wage laws are correlated and that repeal of these laws would lead to higher black employment in construction. Analysis of the 1970 Census of Population reveals that the relative abundance of black construction workers in the group of states that eventually would repeal their laws preceded those repeals and that this is mainly due to the characteristics of Southern labor markets. This and other research cast doubt on both the strength and the existence of such a correlation.     

Optimal sampling for repeated binary measurements

The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling cost, a term related to prediction, and if relevant, a term related to learning about a treatment effect To this end, they use a nonparametric probability model relying on a minimal number of assumptions. They show how their assumption of partial exchangeability for the binary sequence of data allows the sampling distribution to be written as a mixture of homogeneous Markov chains of order k. The implementation follows the approach of Quintana & Müller (2004), which uses a Dirichlet process prior for the mixture.     

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.     

Age Cohort, Ancestry, and Immigrant Generation Influences in Family Relations and Psychological Well-Being among Black Caribbean Family Members

Immigration is contributing to the U.S. population becoming increasingly ethnically diverse. This article examines the role of family relations and well-being among different generations of Black Caribbean immigrants. Family disruptions, such as migration, can have complex effects on the support networks and emotional well-being of family members. Data from a recently completed national study of American Blacks in the United States, however, reveal significant similarities across ancestry and immigrant status in family contact, solidarity and well-being. It is concluded that intrafamilial relations may serve to overcome barriers of geographical distance in providing comparable levels of contact, solidarity, and well-being for both U.S.-born and immigrant Black Caribbean family members of different generations. Because of the increasing numbers of immigrant elders, these sources of family support will become increasingly more important in bridging the gaps between government resources and needed assistance in an aging society.