Matching methods such as nearest neighbor propensity score matching are increasingly popular techniques for controlling confounding in nonexperimental studies. However, simple k:1 matching methods, which select k well-matched comparison individuals for each treated individual, are sometimes criticized for being overly restrictive and discarding data (the unmatched comparison individuals). The authors illustrate the use of a more flexible method called full matching. Full matching makes use of all individuals in the data by forming a series of matched sets in which each set has either 1 treated individual and multiple comparison individuals or 1 comparison individual and multiple treated individuals. Full matching has been shown to be particularly effective at reducing bias due to observed confounding variables. The authors illustrate this approach using data from the Woodlawn Study, examining the relationship between adolescent marijuana use and adult outcomes. Keywordslongitudinal studies; long-term consequences; observational study; propensity score; substance use In nonexperimental studies, researchers are often interested in examining the effect of some event or treatment (e.g., substance use) on an outcome (e.g., educational attainment). This is done by comparing individuals who experienced that event or treatment (e.g., substance users) to individuals who did not (e.g., nonusers). In experimental studies with random assignment, treatment and control groups are similar on all background characteristicsobserved and unobserved-as a consequence of the randomization, allowing for straightforward comparison of outcomes. In contrast, in nonexperimental studies, the treatment and comparison individuals may differ significantly on background characteristics -some that are observed and others that may be unknown. For example, substance users and nonusers are likely to be different on characteristics such as family history of drug use as well as on individual behaviors such as aggression. Thus, any difference in outcomes between the two groups may be due to these background covariates or to the treatment itself Author ManuscriptAuthor Manuscript Author ManuscriptAuthor Manuscript (i.e., substance use). The question then is how best to compare substance users with nonusers to clearly separate the effects of substance use from any of these other differences in background characteristics.Matching methods, such as nearest neighbor propensity score matching, are increasingly popular techniques for controlling for observed confounding variables when estimating causal effects in nonexperimental studies. The goal of matching methods is to ensure that the distributions of observed covariates in the treatment and comparison groups are similar, replicating what would have occurred had the treatment been randomly assigned, at least with respect to the observed covariates. Although regression has often been used to adjust for background differences and estimate causal effects in nonexperimental studies, it relies heavily...

IMPORTANCE Social media use may be a risk factor for mental health problems in adolescents. However, few longitudinal studies have investigated this association, and none have quantified the proportion of mental health problems among adolescents attributable to social media use.OBJECTIVE To assess whether time spent using social media per day is prospectively associated with internalizing and externalizing problems among adolescents.

No abstract

Purpose To improve understanding of long-term socioeconomic consequences of teen parenting for men and women. Methods Analysis is based on the Woodlawn Study, a longitudinal study of an African American cohort from a socially disadvantaged community in Chicago; data were collected at childhood (N=1,242), adolescence (N=705), young adulthood (age 32, N=952), and midlife (age 42, N=833). This analysis focused on the 1050 individuals with data on teen parenting. We used propensity score matching to account for differences in background characteristics between teenage parents and their peers and multiple imputation to account for differential attrition. Results The regression models on matched samples showed that at age 32, in comparison to non-teen mothers, teenage mothers were more likely to be unemployed, live in poverty, depend on welfare, and have earned a GED or completed high school compared to finishing college. At age 32, teen fathers were more likely to be without a job compared to non-teen fathers. At age 42, the effect of teen parenting for women remained statistically significant for education and income. There were no significant associations between teen parenting and outcomes for men at age 42. Conclusions Socioeconomic consequences of teenage parenting among African Americans from disadvantaged background seem to be primarily concentrated in women and persist throughout adulthood. In addition to promoting the delay of parenting after the teenage years, it is critical to provide programs at early stages in the life course to mitigate the negative socioeconomic consequences of teenage motherhood as effects for women are broad.