OBJECTIVES: Substance use in the college-age population is an important public health and educational concern. This study compared rates of use among college students and nonstudents, including high school dropouts, from a single data source representative of the nation. METHODS: Rates of use were estimated from the combined National Household Surveys on Drug Abuse from 1991 to 1993. Logistic regression models were used to test the effects of educational status and living arrangement. RESULTS: Educational status and living arrangement were found to be significant predictors of substance use. Rates of illicit drug and cigarette use were highest among high school dropouts, while current and heavy alcohol use were highest among college students who did not live with their parents. CONCLUSIONS: Substantial variation in substance use patterns within the college-age population suggests that overall rates of use for young adults should not be used to characterize specific subgroups of young adults. These data from a single source will thus help planners more clearly distinguish the service needs of the diverse subgroups within this population.

Non-response is a common problem in household sample surveys. The Medical Expenditure Panel Survey (MEPS), sponsored by the Agency for Healthcare Research and Quality (AHRQ), is a complex national probability sample survey. The survey is designed to produce annual national and regional estimates of health-care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian non-institutionalized population. The MEPS sample is a sub-sample of respondents to the prior year's National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS). The MEPS, like most sample surveys, experiences unit, or total, non-response despite intensive efforts to maximize response rates. This paper summarizes research on comparing alternative approaches for modelling response propensity to compensate for dwelling unit (DU), i.e. household level non-response in the MEPS.Non-response in sample surveys is usually compensated for by some form of weighting adjustment to reduce the bias in survey estimates. To compensate for potential bias in survey estimates in the MEPS, two separate non-response adjustments are carried out. The first is an adjustment for DU level non-response at the round one interview to account for non-response among those households subsampled from NHIS for the MEPS. The second non-response adjustment is a person level adjustment to compensate for attrition across the five rounds of data collection. This paper deals only with the DU level non-response adjustment. Currently, the categorical search tree algorithm method, the chi-squared automatic interaction detector (CHAID), is used to model the response probability at the DU level and to create the non-response adjustment cells. In this study, we investigate an alternative approach, i.e. logistic regression to model the response probability. Main effects models and models with interaction terms are both evaluated. We further examine inclusion of the base weights as a covariate in the logistic models. We compare variability of weights of the two alternative response propensity approaches as well as direct use of propensity scores. The logistic regression approaches produce results similar to CHAID; however, using propensity scores from logistic models with interaction terms to form five classification groups for weight adjustment appears to perform best in terms of limiting variability and bias. Published in 2007 by John Wiley & Sons, Ltd.

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Peer tutoring is a simple, low-cost intervention that can be implemented in CS1/2 courses. It is hypothesized that peer tutoring helps students build a sense of community, succeed in course work, and build confidence to take further courses in the major. This paper examines the latter two hypotheses by examining the predicted and actual behavior of students in CS1/2. Course performance improvements were observed, which also strongly influence retention in computing-related courses. The measures also point to further research directions, such as social influences and the impact of peer tutoring relative to office hours or online forums.