Estimating Demographic Bias on Tests of Children's Early Vocabulary. (7th December 2022)

• Record Type: Journal Article
• Authors: Kachergis, George; Francis, Nathan; Frank, Michael C.
• Is Part Of: Topics in cognitive science. Volume 15:Number 2(2023)
• Page Start: 303
• Page End: 314
• Publication Date: 2022-12-07
• Subjects: Language acquisition -- Word learning -- Measuring instrument bias -- Demographic bias; Cognitive science -- Periodicals; Cognitive Science -- Periodicals; 153.05
• DOI: 10.1111/tops.12635
• Languages: English
• ISSNs: 1756-8757

Abstract

Abstract: Children's early language skill has been linked to later educational outcomes, making it important to measure early language accurately. Parent‐reported instruments, such as the Communicative Development Inventories (CDIs), have been shown to provide reliable and valid measures of children's aggregate early language skill. However, CDIs contain hundreds of vocabulary items, some of which may not be heard (and thus learned) equally often by children of varying backgrounds. This study used a database of American English CDIs to identify words demonstrating strong bias for particular demographic groups of children, on dimensions of sex (male vs. female), race (white vs. non‐white), and maternal education (high vs. low). For each dimension, many items showed bias; removing these items slightly reduced the magnitude of race‐ and education‐based group differences, but did not eliminate them. Additionally, we investigated how well the relative frequency of words spoken to young girls versus boys predicted sex‐based word learning bias, and discuss possible sources of demographic differences in early word learning. Abstract : Young children's language skill has been linked to educational outcomes years later, making it important to measure early language accurately. This study investigates a widely?used test of children's early vocabulary to identify items that show strong bias for particular demographic groups of children (i.e., by race, socioeconomic status, and sex), and provides recommendations to decrease test bias.