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Web-CDI: A system for online administration of the MacArthur-Bates Communicative Development Inventories

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Bergmann,  Christina
Language Development Department, MPI for Psycholinguistics, Max Planck Society;

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Rowland,  Caroline F.
Language Development Department, MPI for Psycholinguistics, Max Planck Society;

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Citation

DeMayo, B., Kellier, D., Braginsky, M., Bergmann, C., Hendriks, C., Rowland, C. F., et al. (2021). Web-CDI: A system for online administration of the MacArthur-Bates Communicative Development Inventories. PsyArXiv, 10.31234/osf.io/8mjx9. doi:10.31234/osf.io/8mjx9.


Cite as: http://hdl.handle.net/21.11116/0000-0007-D41D-A
Abstract
Understanding the mechanisms that drive variation in children’s language acquisition requires large, population-representative datasets of children’s word learning across development. Parent report measures such as the MacArthur-Bates Communicative Development Inventories (CDI) are commonly used to collect such data, but the traditional paper-based forms make the curation of large datasets logistically challenging. Many CDI datasets are thus gathered using convenience samples, often recruited from communities in proximity to major research institutions. Here, we introduce Web-CDI, a web-based tool which allows researchers to collect CDI data online. Web-CDI contains functionality to collect and manage longitudinal data, share links to test administrations, and download vocabulary scores. To date, over 3,500 valid Web-CDI administrations have been completed. General trends found in past norming studies of the CDI are present in data collected from Web-CDI: scores of children’s productive vocabulary grow with age, female children show a slightly faster rate of vocabulary growth, and participants with higher levels of educational attainment report slightly higher vocabulary production scores than those with lower levels of education attainment. We also report results from an effort to oversample non-white, lower-education participants via online recruitment (N = 241). These data showed similar demographic trends to the full sample but this effort resulted in a high exclusion rate. We conclude by discussing implications and challenges for the collection of large, population-representative datasets.