400
Views
33
CrossRef citations to date
0
Altmetric
Original Articles

Orthographic Knowledge Important in Comprehending Elementary Chinese Text by Users of Alphasyllabaries

, , &
Pages 237-271 | Published online: 12 May 2011
 

Abstract

Orthographic knowledge in Chinese was hypothesized to affect elementary Chinese text comprehension (four essays) by 80 twelve-year-old ethnic alphasyllabary language users compared with 74 native Chinese speakers at similar reading level. This was tested with two rapid automatized naming tasks; two working memory tasks; three orthographic knowledge tasks in Chinese; and equivalent tasks in English. Multivariate analyses of covariance showed that the two groups were differentiated on most of the linguistic and cognitive tasks. Confirmatory factor analyses found four factors as hypothesized: text comprehension, verbal working memory, orthographic knowledge in Chinese, and orthographic knowledge in English. Hierarchical multiple regression analyses showed that orthographic knowledge in Chinese explained a considerable amount of individual variation in elementary Chinese text comprehension.

Acknowledgments

The study was assisted in part with a grant from the Hong Kong Education Bureau (HKEDB). We thank HKEDB for its assistance. We also thank Hung Wai Ng, Man Ying Wong, and the principal, teachers, and students of the cooperating school. The views expressed in this article are ours and do not necessarily represent those of HKEDB.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 264.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.