The executive function of bilingual and monolingual children: A technical efficiency approach.

Behav Res Methods

School of Languages, Literatures, Linguistics and Media, Bangor University, Bangor, UK.

Published: June 2022

AI Article Synopsis

  • This paper presents a new method for evaluating executive functioning skills in bilingual and monolingual children, addressing existing challenges in research.
  • The study employs various executive functioning tasks and data envelopment analysis to measure technical efficiency (TE) in 32 bilingual and 38 monolingual Greek children.
  • Findings indicate that bilingual children demonstrate about 6.5% more efficiency than monolinguals, with TE offering a more effective analysis compared to traditional methods like MANCOVA.

Article Abstract

This paper introduces a novel approach to evaluate performance in the executive functioning skills of bilingual and monolingual children. This approach targets method- and analysis-specific issues in the field, which has reached an impasse (Antoniou et al., 2021). This study moves beyond the traditional approach towards bilingualism by using an array of executive functioning tasks and frontier methodologies, which allow us to jointly consider multiple tasks and metrics in a new measure; technical efficiency (TE). We use a data envelopment analysis technique to estimate TE for a sample of 32 Greek-English bilingual and 38 Greek monolingual children. In a second stage, we compare the TE of the groups using an ANCOVA, a bootstrap regression, and a k-means nearest-neighbour technique, while controlling for a range of background variables. Results show that bilinguals have superior TE compared to their monolingual counterparts, being around 6.5% more efficient. Robustness tests reveal that TE yields similar results to the more complex conventional MANCOVA analyses, while utilising information in a more efficient way. By using the TE approach on a relevant existing dataset, we further highlight TE's advantages compared to conventional analyses; not only does TE use a single measure, instead of two principal components, but it also allows more group observations as it accounts for differences between the groups by construction.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9170628PMC
http://dx.doi.org/10.3758/s13428-021-01658-7DOI Listing

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