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Purpose: Although children with hearing loss (HL) can benefit from cochlear implants (CIs) and hearing aids (HAs), they often show language delays. Moreover, little is known about the mechanisms by which children with HL learn words. One mechanism by which typically hearing (TH) children learn words is by acquiring word learning biases such as the "shape bias," that is, generalizing the names of novel solid objects by similarity in shape. In TH children, the shape bias emerges out of regularities in the early vocabulary and, once acquired, has consequences for subsequent vocabulary development. Method: Here, we ask whether children with HL exhibit similar word learning biases as TH children. In the current study, nineteen 2- to 3.5-year-old children with HL generalized the names of novel objects by similarity in shape or material. We compared their performance to that of 20 TH children matched on age and 20 TH children matched on vocabulary size. Results: Children with HL were significantly less likely than age-matched TH children and vocabulary-matched TH children to generalize novel names to objects of the same shape. However, there was also an interaction such that vocabulary has a stronger effect on novel noun generalization for those with HL than for those who are TH. Exploratory analyses of children with HL reveal similar novel noun generalization and vocabulary sizes in children who use CIs and those who use HAs, regardless of hearing age or degree of HL. Conclusion: Together, the results suggest that, although vocabulary knowledge drives development of the shape bias in general for all children, it may be especially important for children with HL, who are at risk for language delays.

Purpose: The particular statistical approach researchers choose is intimately connected to the way they conceptualize their questions, which, in turn, can influence the conclusions they draw. One particularly salient area in which statistics influence our conclusions is in the context of atypical development. Traditional statistical approaches such as "t" tests or analysis of variance lend themselves to a focus on group differences, downplaying the heterogeneity that exists within so many atypically developing populations. Understanding such variability is important--classification of what a disorder is, an individual's diagnosis, and whether or not a child receives intervention all directly relate to an accurate classification of the disorder and individual's abilities compared to their typically developing peers. Method: Here, we use word learning biases (i.e., shape and material biases) in late-talking children as a sample case and employ a variety of statistical approaches to compare the conclusions those approaches might warrant. Results: We argue that advanced statistical approaches, such as mixed-effects regression, can help us make sense of heterogeneity and are more consistent with a modern dimensional view of language disorders. Conclusions: Accurate characterization of late-talking children (and others at risk for delays) and their prognoses is necessary for accurate diagnosis and implementation of appropriate target interventions. It therefore requires rigorous statistical analyses that can capture and allow for interpretation of the heterogeneity inherent in populations with language delays and disorders.