Effects of Time-Varying Parent Input on Children's Language Outcomes Differ for Vocabulary and Syntax.

Abstract: The 30- million- word gap , the quantified difference in the amount of speech that children growing up in low- resourced homes hear compared to their peers from high- resourced homes, is a phrase that has entered the collective consciousness. In the discussion of quantity, the complex and nuanced environments in which children learn language were distilled into a singular metric— number of words. In this article, we propose examining children’s language environments by focusing on what caregivers communicate to children and how they communicate it. Focusing on the features of the language environment promotes a more inclusive approach to understanding how children learn and the diverse contexts in which that learning occurs.

TL;DR: This article investigated the effect of a parent-implemented intervention in increasing late talkers' expressive skills through modifications in structural and functional features of parental speech input and found that the intervention positively affected parents' use of responses and expansions of children's verbal initiatives, as well as the parental amount of talking over reading, whereas no structural features of parent input resulted modified.

TL;DR: English vocabulary growth in preschool-aged children and infants adopted from China and Eastern Europe and comparing it to speech produced by their adoptive mothers was correlated with maternal input features, suggesting input effects on language outcomes persist even in the absence of genetic confounds.

TL;DR: The authors compare different approaches for estimating cross-lagged effects with a cross- lagged panel design under a causal inference perspective, and show that the latent variable-type approaches strongly depend on the specific parametric assumptions, and produce biased estimates under different data-generating scenarios.

TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.

TL;DR: Mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs.

TL;DR: In this paper, the authors introduce marginal structural models, a new class of causal models that allow for improved adjustment of confounding in observational studies with exposures or treatments that vary over time, when there exist time-dependent confounders that are also affected by previous treatment.

TL;DR: A meta-analysis of the results shows that early math skills have the greatest predictive power, followed by reading and then attention skills, while measures of socioemotional behaviors were generally insignificant predictors of later academic performance.