BIAS IN LIST-ASSISTED TELEPHONE SAMPLES

A number of researchers have suggested list-assisted samplingfor the selection of telephone households to overcome some ofthe operational difficulties associated with the Mitofsky-Waksbergmethods of random digit dialing (RDD). An advantage of a list-assistedmethod of RDD is that an equal probability systematic sampleof telephone numbers can be selected and the variances of estimatesfrom such a sample are usually lower than from a clustered designlike the Mitofsky-Waksberg method. The main disadvantage ofthe list-assisted method is that it excludes some householdsfrom the sample, thus creating a coverage bias in the estimates.This article describes research on the coverage bias for a particularmethod of list-assisted sampling. The two key determinants ofcoverage bias are the proportion of households that are noteligible for the sample and the differences in the characteristicsof the covered and not covered populations. The results showthat about 4 percent of all households are excluded in nationalsamples using this method of sampling. Furthermore, they showthat the differences between the covered and uncovered populationsare generally not large. The coverage bias resulting from theseconditions may often be small.     

Netzwerken: Reflexionen und Erfahrungen am Beispiel eines TrainerInnen und BeraterInnen-Netzwerkes

How are social networks organized and how much structure and openess do they have? The ambiguity of networking and the grade of organization are discussed and described theoretically and with an authentic example.     

Empirical likelihood confidence intervals for the mean of a population containing many zero values

If a population contains many zero values and the sample size is not very large, the traditional normal approximation‐based confidence intervals for the population mean may have poor coverage probabilities. This problem is substantially reduced by constructing parametric likelihood ratio intervals when an appropriate mixture model can be found. In the context of survey sampling, however, there is a general preference for making minimal assumptions about the population under study. The authors have therefore investigated the coverage properties of nonparametric empirical likelihood confidence intervals for the population mean. They show that under a variety of hypothetical populations, these intervals often outperformed parametric likelihood intervals by having more balanced coverage rates and larger lower bounds. The authors illustrate their methodology using data from the Canadian Labour Force Survey for the year 2000.     

Inference for the measurement of poverty in the presence of a stochastic weighting variable

Empirical applications of poverty measurement often have to deal with a stochastic weighting variable such as household size. Within the framework of a bivariate distribution function defined over income and weight, I derive the limiting distributions of the decomposable poverty measures and of the ordinates of stochastic dominance curves. The poverty line is allowed to depend on the income distribution. It is shown how the results can be used to test hypotheses concerning changes in poverty. The inference procedures are briefly illustrated using Belgian data. An erratum to this article can be found at     

Statistical Distributions of Daily Breathing Rates for Narrow Age Groups of Infants and Children

Children may be more susceptible to toxicity from some environmental chemicals than adults. This susceptibility may occur during narrow age periods (windows), which can last from days to years depending on the toxicant. Breathing rates specific to narrow age periods are useful to assess inhalation dose during suspected windows of susceptibility. Because existing breathing rates used in risk assessment are typically for broad age ranges or are based on data not representative of the population, we derived daily breathing rates for narrow age ranges of children designed to be more representative of the current U.S. children's population. These rates were derived using the metabolic conversion method of Layton (1993) and energy intake data adjusted to represent the U.S. population from a relatively recent dietary survey (CSFII 1994–1996, 1998). We calculated conversion factors more specific to children than those previously used. Both nonnormalized (L/day) and normalized (L/kg-day) breathing rates were derived and found comparable to rates derived using energy estimates that are accurate for the individuals sampled but not representative of the population. Estimates of breathing rate variability within a population can be used with stochastic techniques to characterize the range of risk in the population from inhalation exposures. For each age and age-gender group, we present the mean, standard error of the mean, percentiles (50th, 90th, and 95th), geometric mean, standard deviation, 95th percentile, and best-fit parametric models of the breathing rate distributions. The standard errors characterize uncertainty in the parameter estimate, while the percentiles describe the combined interindividual and intra-individual variability of the sampled population. These breathing rates can be used for risk assessment of subchronic and chronic inhalation exposures of narrow age groups of children.     

Effects of Marriage and Children on Financial Risk Tolerance: A Synthesis of Family Development and Prospect Theory

Family development and prospect theory were used as a framework to predict variability in individuals' subjective financial risk tolerance within distinct family structures. Gender, age, and income were expected to interact with the main effects of family structure (marital status and children). Theory-generated hypotheses were examined in Study 1 (data from university housing respondents, n = 76) and Study 2 (the 1998 Survey of Consumer Finances, n = 4,305). One family structure main effect (child presence) was significant for investment risk tolerance in both studies. Family structure interactions (marital status × age and child × income) were significant for employment risk (Study 1), and child × age was significant for investment risk in Study 2.