Research has supported a negative correlation between cigarette smoking and exercise; however, the temporal nature of this association is not obvious. We modeled the relationships among smoking, exercise, and self‐perceived health over time, within a college population. We collected 5 waves of data from 1,023 undergraduate students over a 14‐month period. The results verified that smoking and exercise each made independent contributions in the prediction of health. Smoking was associated with reduced exercise over time, while no evidence was found for the reverse relationship. Our final mediation model demonstrates that smoking is related to poorer self‐perceived health, and that this effect is partially mediated by the fact that smokers are less likely to engage in exercise.

Multiple imputation (MI) and full information maximum likelihood (FIML) are the two most common approaches to missing data analysis. In theory, MI and FIML are equivalent when identical models are tested using the same variables, and when m, the number of imputations performed with MI, approaches infinity. However, it is important to know how many imputations are necessary before MI and FIML are sufficiently equivalent in ways that are important to prevention scientists. MI theory suggests that small values of m, even on the order of three to five imputations, yield excellent results. Previous guidelines for sufficient m are based on relative efficiency, which involves the fraction of missing information (γ) for the parameter being estimated, and m. In the present study, we used a Monte Carlo simulation to test MI models across several scenarios in which γ and m were varied. Standard errors and p-values for the regression coefficient of interest varied as a function of m, but not at the same rate as relative efficiency. Most importantly, statistical power for small effect sizes diminished as m became smaller, and the rate of this power falloff was much greater than predicted by changes in relative efficiency. Based our findings, we recommend that researchers using MI should perform many more imputations than previously considered sufficient. These recommendations are based on γ, and take into consideration one’s tolerance for a preventable power falloff (compared to FIML) due to using too few imputations.

Objective Research demonstrates that interventions targeting multiple settings within a child's life are more effective in treating or preventing conduct disorder. One such program is the Incredible Years Series, which comprises three treatment components, each focused on a different context and type of daily social interaction that a child encounters. This article explores the cost-effectiveness of stacking multiple intervention components versus delivering single intervention components. Method The data involved 459 children, ages 3 to 8, who participated in clinical trials of the Incredible Years Series. Children randomized to one of six treatment conditions received one or more of the three following program components: a child-based program, a parent training program, and a teacher-based program instructing teachers in classroom management and in the delivery of a classroom-based social skills curriculum. Results Per-child treatment costs and child behavior outcomes (observer and teacher reported) were used to generate cost-effectiveness acceptability curves; results suggest that stacking intervention components is likely cost-effective, at least for willingness to pay above $3,000 per child treated. Conclusions Economic data may be used to compare competing intervention formats. In the case of this program, providing multiple intervention components was cost-effective.

The authors describe 2 efficiency (planned missing data) designs for measurement: the 3-form design and the 2-method measurement design. The 3-form design, a kind of matrix sampling, allows researchers to leverage limited resources to collect data for 33% more survey questions than can be answered by any 1 respondent. Power tables for estimating correlation effects illustrate the benefit of this design. The 2-method measurement design involves a relatively cheap, less valid measure of a construct and an expensive, more valid measure of the same construct. The cost effectiveness of this design stems from the fact that few cases have both measures, and many cases have just the cheap measure. With 3 brief simulations involving structural equation models, the authors show that compared with the same-cost complete cases design, a 2-method measurement design yields lower standard errors and a higher effective sample size for testing important study parameters. With a large cost differential between cheap and expensive measures and small effect sizes, the benefits of the design can be enormous. Strategies for using these 2 designs are suggested.