Race–gender Differences in Risk and Protective Factors among Youth in Residential Group Homes

Analysis reported here examined youth in residential group home placements in order to better understand how sexual behaviors, drug use, and environmental experiences differ by race and gender subgroups. Data were collected from 336 youth aged 14–21 residing in one of 41 group homes in Maryland. Chi square and logistic regression analyses were done to determine differences on study variables among the race-gender groupings. Results demonstrate not only significant health concerns overall but notable differences were also found between race-gender subgroups. Findings suggest that found race-gender subgroup differences might be informative for tailoring programming for youth of residential group home settings.     

On the construction of a class of efficient row-column designs

In this paper a method for the construction of a class of row-column designs with good statistical properties and high efficiency is presented. The class of designs produced is shown to exhibit balance, orthogonality and adjusted orthogonality. The efficiencies of these designs are investigated in detail, and they are shown to be very high, and possibly maximal in some cases.     

THE DIFFUSION OF MEDICAL TECHNOLOGY: SOCIAL NETWORK ANALYSIS AND POLICY RESEARCH

The purpose of this article is to demonstrate how social network analysis can be used to provide information for policy decisions pertaining to physicians' adoption and utilization of new medical technology. Interviews and hospital records were used to obtain data on refenals, consultations, discussions, and on-call coverage; utilization of a computer-based hospital information system; and personal and practice characteristics from 24 physicians who belong to a private group practice. The results of a block-model analysis suggest that the physician's relative position in the network is an important determinant of his/her participation in the diffusion process. A number of policy implications related to the introduction of new medical technology into practice settings are discussed.     

Moments of the sampled autocovariances and autocorrelations for a Gaussian white-noise process

Cumulants, moments about zero, and central moments are obtained for the mean-corrected serial covariances and serial correlations for series realizations of length n from a white-noise Gaussian process. All first and second moments (and some third, fourth, and higher moments) are given explicitly for the serial covariances; and the corresponding moments for the serial correlations are derived either explicitly or implicitly.     

Outcomes assessment of role-play scenarios for teaching responsible conduct of research

We describe the summative assessment of role-play scenarios that we previously developed to teach central topics in the responsible conduct of research (RCR) to graduate students in science and engineering. Interviews with role-play participants, with participants in a case discussion training session, and with untrained students suggested that role-playing might promote a deeper appreciation of RCR by shifting the focus away from wanting to simply "know the rules." We also present the results of a think-aloud case analysis study and describe the development of a behaviorally-anchored rating scale (BARS) to assess participants' case analysis performance.     

The Effects of the Registration System on the Seasonality of Births: The Case of the Soviet Union

Scientists study seasonality in order to understand the effect of environmental, biological, and social factors on demographic events. Poor data quality can also affect seasonal variation in mortality and fertility statistics. The influence of error on seasonal data becomes crucial as researchers analyze timing and spacing of events in time-series analyses. In this study we examine the reported number of births by month in the Soviet Union for 1950, 1955, and 1958–85. The lowest number of births has typically occurred in December, and the highest in January. This seasonal pattern is not consistent with any plausible biological or behavioural explanation. It is probably an artifact of attributing births that actually occurred during the preceding December or earlier, to January. This implies that Soviet statistical practice has not followed the stated policy of attributing births to the period (day, month, year) in which they occur. A substantial reduction between the 1950s and 1985 in the December-January peak in reported births implies marked improvement in Soviet vital registration statistics. The Soviet case shows that characteristics of the registration system can impart a particular seasonal pattern to demographic data. It also shows that officially prescribed procedures are not always followed in data generation, even in centrally planned economies. Researchers should carefully examine the quality of seasonal data before concluding that the data reflect real variations in demographic behaviour.     

Hypothesis Tests for Neyman's Bias in Case–Control Studies

Survival bias is a long recognized problem in case–control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or ‘prevalence–incidence bias’. It occurs in case–control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case–control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.     

Quantifying uncertainty in the biospheric carbon flux for England and Wales

Summary.  A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO₂) concentrations in the atmosphere. Vegetation can extract CO₂ through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 10¹² g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered.     

Ambiguity aversion in multi-armed bandit problems

In multi-armed bandit problems, information acquired from experimentation is valuable because it tells the agent whether to select a particular option again in the future. This article tests whether people undervalue this information because they are ambiguity averse, or have a distaste for uncertainty about the average quality of each alternative. It is shown that ambiguity averse agents have lower than optimal Gittins indexes, appearing to undervalue information from experimentation, but are willing to pay more than ambiguity neutral agents to learn the true mean of the payoff distribution, appearing to overvalue objectively given information. This prediction is tested with a laboratory experiment that elicits a Gittins index and a willingness to pay on six two-armed bandits. Consistent with the predictions of ambiguity aversion, the Gittins indexes are significantly lower than optimal and willingnesses to pay are significantly higher than optimal.