During the present decade a large body of research has employed confirmatory factor analysis (CFA) to evaluate the factor structure of the Strengths and Difficulties Questionnaire (SDQ) across multiple languages and cultures. However, because CFA can produce strongly biased estimations when the population cross-loadings differ meaningfully from zero, it may not be the most appropriate framework to model the SDQ responses. With this in mind, the current study sought to assess the factorial structure of the SDQ using the more flexible exploratory structural equation modeling approach. Using a large-scale Spanish sample composed of 67,253 youths aged between 10 and 18 years ( M = 14.16, SD = 1.07), the results showed that CFA provided a severely biased and overly optimistic assessment of the underlying structure of the SDQ. In contrast, exploratory structural equation modeling revealed a generally weak factorial structure, including questionable indicators with large cross-loadings, multiple error correlations, and significant wording variance. A subsequent Monte Carlo study showed that sample sizes greater than 4,000 would be needed to adequately recover the SDQ loading structure. The findings from this study prevent recommending the SDQ as a screening tool and suggest caution when interpreting previous results in the literature based on CFA modeling.

Vocabulary size seems to be affected by multiple factors, including those that belong to the properties of the words themselves and those that relate to the characteristics of the individuals assessing the words. In this study, we present results from a crowdsourced lexical decision megastudy in which more than 150,000 native speakers from around 20 Spanish-speaking countries performed a lexical decision task to 70 target word items selected from a list of about 45,000 Spanish words. We examined how demographic characteristics such as age, education level, and multilingualism affected participants' vocabulary size. Also, we explored how common factors related to words like frequency, length, and orthographic neighbourhood influenced the knowledge of a particular item. Results indicated important contributions of age to overall vocabulary size, with vocabulary size increasing in a logarithmic fashion with this factor. Furthermore, a contrast between monolingual and bilingual communities within Spain revealed no significant vocabulary size differences between the communities. Additionally, we replicated the standard effects of the words' properties and their interactions, accurately accounting for the estimated knowledge of a particular word. These results highlight the value of crowdsourced approaches to uncover effects that are traditionally masked by smallsampled in-lab factorial experimental designs.

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Learning a foreign language as an adult is a rewarding but challenging endeavor that entails accruing a massive vocabulary. The literature independently highlights that orthographic similarity and bilingual experience could facilitate foreign vocabulary acquisition. Here, we explored the combined effects of orthographic similarity and bilingual experience on foreign vocabulary learning using behavioral and computational approaches. Experiment 1 compared Spanish monolingual, Spanish-English, and Spanish-Basque bilingual participants when learning an artificial vocabulary with varying orthographic similarity to Spanish. The results indicated that similar words were easier to recognize and produce than dissimilar words, and both bilingual groups outperformed the monolingual group in learning the vocabulary, irrespective of orthographic similarity. In Experiment 2, we developed a neural network model that implemented a unified, distributed, and dynamic view of the orthographic lexicon to explain how these effects could emerge from exposure to bilingual input. We simulated adults’ orthographic lexicons by pre-training this architecture on monolingual and bilingual input. We then tested the monolingual and bilingual versions’ capacity to learn the novel words used in the behavioral task. The simulations reproduced the orthographic similarity effects and showed an overall advantage of experience with bilingual input, as observed in the behavioral results. The present study unifies the seemingly disparate effects of orthographic similarity and bilingual experience under a common computational framework, whereby distributed representations of orthographic word forms are stored in a unified space and dynamically modified by learning experiences.