Despite their disadvantaged generalizability relative to probability samples, non-probability convenience samples are the standard within developmental science, and likely will remain so because probability samples are cost-prohibitive and most available probability samples are ill-suited to examine developmental questions. In lieu of focusing on how to eliminate or sharply reduce reliance on convenience samples within developmental science, here we propose how to augment their advantages when it comes to understanding population effects as well as subpopulation differences. Although all convenience samples have less clear generalizability than probability samples, we argue that homogeneous convenience samples have clearer generalizability relative to conventional convenience samples. Therefore, when researchers are limited to convenience samples, they should consider homogeneous convenience samples as a positive alternative to conventional or heterogeneous) convenience samples. We discuss future directions as well as potential obstacles to expanding the use of homogeneous convenience samples in developmental science.

Sampling is a key feature of every study in developmental science. Although sampling has far-reaching implications, too little attention is paid to sampling. Here, we describe, discuss, and evaluate four prominent sampling strategies in developmental science: population-based probability sampling, convenience sampling, quota sampling, and homogeneous sampling. We then judge these sampling strategies by five criteria: whether they yield representative and generalizable estimates of a study’s target population, whether they yield representative and generalizable estimates of subsamples within a study’s target population, the recruitment efforts and costs they entail, whether they yield sufficient power to detect subsample differences, and whether they introduce “noise” related to variation in subsamples and whether that “noise” can be accounted for statistically. We use sample composition of gender, ethnicity, and socioeconomic status to illustrate and assess the four sampling strategies. Finally, we tally the use of the four sampling strategies in five prominent developmental science journals and make recommendations about best practices for sample selection and reporting.

Aims To examine age-18 risk factors for alcohol use and heavy drinking during early (ages 22 and 26) and middle (age 35) adulthood, and for symptoms of alcohol use disorders (AUDs) in middle adulthood. Design Nationally representative samples of US adolescents in their senior year of secondary school (age 18) were followed into middle adulthood. Structural equation models estimated the associations between age-18 characteristics and current drinking and heavy drinking at ages 22, 26 and 35 and symptoms of AUDs at age 35. Participants The sample consisted of 21 137 respondents from 11 senior year cohorts from the Monitoring the Future study. Findings Many predictor variables had stable associations with alcohol use over time, although their ability to explain variance in alcohol use declined with increasing time lags. Being white predicted alcohol use, but not symptoms of AUDs. Parental drinking, risk taking and use of cigarettes and marijuana predicted heavy drinking to age 35. Planning to attend college predicted more heavy drinking at age 22 and less frequent heavy drinking by mid-life. High school theft and property damage predicted later AUD symptoms. Most associations were invariant across gender, with variations typically taking the form of stronger associations between predictors and alcohol use for men. Invariance in findings across cohorts indicates that results reflect general developmental trends rather than specific historically bounded ones. Conclusions Many adolescent individual and contextual characteristics remain important predictors of adult alcohol use and abuse, and their predictive impact varies as a function of age and type of alcohol outcome. These associations are largely equivalent across gender and cohort, thus reflecting robust developmental linkages.

Latent variables are common in psychological research. Research questions involving the interaction of two variables are likewise quite common. Methods for estimating and interpreting interactions between latent variables within a structural equation modeling framework have recently become available. The latent moderated structural equations (LMS) method is one that is built into Mplus software. The potential utility of this method is limited by the fact that the models do not produce traditional model fit indices, standardized coefficients, or effect sizes for the latent interaction, which renders model fitting and interpretation of the latent variable interaction difficult. This article compiles state-of-the-science techniques for assessing LMS model fit, obtaining standardized coefficients, and determining the size of the latent interaction effect in order to create a tutorial for new users of LMS models. The recommended sequence of model estimation and interpretation is demonstrated via a substantive example and a Monte Carlo simulation. Finally, extensions of this method are discussed, such as estimating quadratic effects of latent factors and interactions between latent slope and intercept factors, which hold significant potential for testing and advancing developmental theories.

Alcohol consumption is increasing in the United States, as is alcohol-attributable mortality. Historically, men have had higher rates of alcohol consumption than women, though evidence for birth cohort effects on gender differences in alcohol consumption and alcohol-related harm suggests that gender differences may be diminishing. We review studies using U.S. national data that examined time trends in alcohol consumption and alcohol-related harm since 2008. Utilizing a historical-developmental perspective, here we synthesize and integrate the literature on birth cohort effects from varying developmental periods (i.e., adolescence, young adulthood, middle adulthood, and late adulthood), with a focus on gender differences in alcohol consumption. Findings suggest that recent trends in gender differences in alcohol outcomes are heterogeneous by developmental stage. Among adolescents and young adults, both males and females are rapidly decreasing alcohol consumption, binge and high-intensity drinking, and alcohol-related outcomes, with gender rates converging because males are decreasing consumption faster than females. This pattern does not hold among adults, however. In middle adulthood, consumption, binge drinking, and alcohol-related harms are increasing, driven largely by increases among women in their 30s and 40s. The trend of increases in consumption that are faster for women than for men appears to continue into older adult years (60 and older) across several studies. We conclude by addressing remaining gaps in the literature and offering directions for future research.