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This study investigates body image concerns and disordered weight- and shape-related behaviors across adolescence and young adulthood in males and how patterns vary by sexual orientation. Participants were 5,388 males from the U.S. national Growing Up Today Study. In 2001, 2003, and 2005 (spanning ages 15-20 years), participants reported sexual orientation, past-year desire for toned/defined muscles and concerns with weight and shape, and past-year binge eating, restrictive dieting, purging (vomiting or laxative use), and use of products to increase muscularity (e.g., creatine, steroids). Latent class analyses identified 2 patterns at ages 15-16 years and 3 patterns at 17-18 and 19-20 years: healthy (all ages; low body image concerns and weight- and shape-related behaviors; 54-74% of observations), muscle-concerned (ages 17-18 and 19-20; relatively high muscularity concern and product use; 18-21% of observations), and lean-concerned (all ages; relatively high weight and shape concern, dieting, and binge eating; 19-28% of observations). Latent transition analyses revealed that sexual minority males (i.e., mostly heterosexual, gay, and bisexual) were more likely than completely heterosexual males to be lean-concerned at ages 17-18 and 19-20 years and to transition to the lean-concerned class from the healthy class. There were no sexual orientation differences in odds of being muscle-concerned. Both heterosexual and sexual minority males are at risk for presenting body image concerns and weight- and shape-related behaviors that may have deleterious health consequences. Results suggest the need for screening for concerns and behaviors related to leanness and muscularity in early adolescence among all males, regardless of sexual orientation.

Developmental scientists frequently seek to understand effects of environmental contexts on development. Traditional analytic strategies assume similar environmental effects for all children, sometimes exploring possible moderating influences or exceptions (e.g., outliers) as a secondary step. These strategies are poorly matched to ecological models of human development that posit complex individual by environment interactions. An alternative conceptual framework is proposed that tests the hypothesis that the environment has differential (nonuniform) effects on children. A demonstration of the utility of this framework is provided by examining the effects of family resources on children's academic outcomes in a multisite study (N = 6,305). Three distinctive groups of children were identified, including 1 group particularly resilient to influence of low levels of family resources. Predictors of group differences including parenting and child demographics are tested, the replicability of the results are examined, and findings are contrasted with those obtained with traditional regression interaction effects. This approach is proposed as a partial solution to advance theories of the environment, social ecological systems research, and behavioral genetics to create well-tailored environments for children. (Contains 2 figures, 5 tables and 4 footnotes.)

Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this article, the authors present several currently available modeling options, all of which make appropriate distributional assumptions for the observed categorical data. Three are based on the generalized linear model: a hierarchical generalized linear model, a growth mixture model, and a latent class growth analysis. They also describe a longitudinal latent class analysis, which requires fewer assumptions than the first 3. Finally, they illustrate all of the models using actual longitudinal adolescent alcohol-use data. They guide the reader through the model-selection process, comparing the results in terms of convergence properties, fit and residuals, parsimony, and interpretability. Advances in computing and statistical software have made the tools for these types of analyses readily accessible to most researchers. Using appropriate models for categorical data will lead to more accurate and reliable results, and their application in real data settings could contribute to substantive advancements in the field of development and the science of prevention. (Contains 11 figures, 6 tables, and 14 footnotes.)