A national survey of alcohol screening and referral in college health centers

To determine the extent and nature of alcohol screening and referral services provided by college health centers, the authors conducted a state-stratified, random sampling of 25% of 327 4-year accredited US colleges and universities with health centers. Of the 249 survey respondents, 32% routinely screened students for alcohol use. Urban, public, and large institutions were most likely to screen routinely. Only 11.7% of the sample reported they used standardized instruments, predominantly the CAGE. The health centers used an average of 3.4 referral options, but only 27.5% offered students access to campus programs specifically designed for students who are substance abusers. Findings suggest that the majority of college health centers are not providing routine alcohol screening for students or using standardized screening instruments. In addition, students are often referred to services that may be inappropriate or ineffective in addressing the needs of college drinkers.     

Aging of the human adrenal cortex

The most striking age-related change in the human adrenal cortex is the decline in secretion of dehydroepiandrosterone and its sulfate, steroids synthesized by the inner zone of the cortex, the zona reticularis. Because these steroids are of essentially unknown function, the importance of this age-related change is the subject of considerable debate. It is likely that the age-related change in these steroids results from loss of zona reticularis cells or impairment of their function. During aging, cumulative damage to the zona reticularis could occur through ischemia-related infarcts and other causes of cell death. Cellular senescence could contribute to a loss of the ability of the tissue to replace lost cells. In contrast, feedback mechanisms that regulate adrenocortical growth cause compensatory local tissue hyperplasias called nodules. The effect of imperfect repair of damage combined with compensatory overgrowth in the form of nodules leads to an increasingly abnormal tissue architecture.     

Factors influencing middle and high schools' active parental consent return rates

The authors examined factors influencing the return rates for attempting to collect active parental consent forms from 21,123 students in the 7th through 10th grades in 41 middle and high schools. Overall return rates from middle schools were higher than from high schools. Schools that offered high levels of staff support for collecting consent forms had higher return rates. Procedures where the consent form was attached to a school form that parents had to complete and return to the school yielded the highest return rate. Implications for how researchers can obtain a high parent consent form return rate are discussed.     

Evolutionary and Cross-Cultural Perspectives on Gambling

Few attempts have been made to incorporate evolutionary and cross-cultural perspectives on gambling. This paper begins with the assumption that gambling represents a risky endeavor undertaken for the purpose of winning stakes. This assumption leads to the derivation from evolutionary theory of two hypotheses concerning the socio-demographic characteristics of gamblers: (1) gambling should be over-represented among males, and (2) among young adults. To test these hypotheses, data are drawn from three sources. A cross-cultural sample of 60 societies reveals that males are more often identified as gamblers than females, and these results do not appear to reflect reporting biases. The data are insufficient to enable conclusions about age patterns associated with gambling within this cross-cultural sample. Nationally representative studies of problem and pathological gamblers drawn from seven nation states show that such gamblers tend to be over-represented by young males, as predicted. Lastly, available demographic data on casino gambling hint at sex differences in the games played and the stakes wagered, but require further research for robust conclusions to be drawn.     

Profiles of Housing Cost Burden in the United States

In this study, the ordered logistic regression model indicates that renters suffer a greater housing cost burden than homeowners and that the lower the income of the household, the larger the portion of income that is spent for housing. Further, the model also shows that the burden of high housing costs falls disproportionately on certain groups of American households. Compared to the reference group, two single-risk groups (Asian-American households and households with three or more children) and two dual-risk groups (female-headed households with three or more children and minority households with three or more children) tend to have a higher risk of excessive housing costs. Elderly households and three elderly-related groups (female-headed elderly households, minority elderly households, and female-headed minority elderly households), however, tend to have a lower risk of housing cost burden than other households. The housing cost burden of the reference group is comparable to the housing cost burden of each of the following groups: for single female-headed households; Black households; Hispanic households; and female-headed minority households. The policy implications of these findings are discussed in the paper.     

Computing location depth and regression depth in higher dimensions

The location depth (Tukey 1975) of a point relative to a p-dimensional data set Z of size n is defined as the smallest number of data points in a closed halfspace with boundary through . For bivariate data, it can be computed in O(nlogn) time (Rousseeuw and Ruts 1996). In this paper we construct an exact algorithm to compute the location depth in three dimensions in O(n2logn) time. We also give an approximate algorithm to compute the location depth in p dimensions in O(mp3+mpn) time, where m is the number of p-subsets used.Recently, Rousseeuw and Hubert (1996) defined the depth of a regression fit. The depth of a hyperplane with coefficients (1,...,p) is the smallest number of residuals that need to change sign to make (1,...,p) a nonfit. For bivariate data (p=2) this depth can be computed in O(nlogn) time as well. We construct an algorithm to compute the regression depth of a plane relative to a three-dimensional data set in O(n2logn) time, and another that deals with p=4 in O(n3logn) time. For data sets with large n and/or p we propose an approximate algorithm that computes the depth of a regression fit in O(mp3+mpn+mnlogn) time. For all of these algorithms, actual implementations are made available.     

Three Likelihood-Based Methods For Mean and Covariance Structure Analysis With Nonnormal Missing Data

Survey and longitudinal studies in the social and behavioral sciences generally contain missing data. Mean and covariance structure models play an important role in analyzing such data. Two promising methods for dealing with missing data are a direct maximum-likelihood and a two-stage approach based on the unstructured mean and covariance estimates obtained by the EM-algorithm. Typical assumptions under these two methods are ignorable nonresponse and normality of data. However, data sets in social and behavioral sciences are seldom normal, and experience with these procedures indicates that normal theory based methods for nonnormal data very often lead to incorrect model evaluations. By dropping the normal distribution assumption, we develop more accurate procedures for model inference. Based on the theory of generalized estimating equations, a way to obtain consistent standard errors of the two-stage estimates is given. The asymptotic efficiencies of different estimators are compared under various assumptions. We also propose a minimum chi-square approach and show that the estimator obtained by this approach is asymptotically at least as efficient as the two likelihood-based estimators for either normal or nonnormal data. The major contribution of this paper is that for each estimator, we give a test statistic whose asymptotic distribution is chi-square as long as the underlying sampling distribution enjoys finite fourth-order moments. We also give a characterization for each of the two likelihood ratio test statistics when the underlying distribution is nonnormal. Modifications to the likelihood ratio statistics are also given. Our working assumption is that the missing data mechanism is missing completely at random. Examples and Monte Carlo studies indicate that, for commonly encountered nonnormal distributions, the procedures developed in this paper are quite reliable even for samples with missing data that are missing at random.     

Penalized Pseudolikelihood Inference in Spatial Interaction Models with Covariates

Given spatially located observed random variables ( x , z = {( x _i , z _i )} _i , we propose a new method for non-parametric estimation of the potential functions of a Markov random field p ( x | z ), based on a roughness penalty approach. The new estimator maximizes the penalized log-pseudolikelihood function and is a natural cubic spline. The calculations involved do not rely on Monte Carlo simulation. We suggest the use of B-splines to stabilize the numerical procedure. An application in Bayesian image reconstruction is described.