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Language and literacy profiles: A mixture modeling approach

Mixture modeling was used to examined the: (a) heterogeneity and prevalence of the language and literacy profiles among 521 first grade students (Malaysian equivalent of Primary 1) and (b) predictors that optimize the classification of language and literacy profiles. Based on the Simple View of Read...

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Main Authors: Lee, Julia Ai Cheng, Stephanie, Al Otaiba
Format: Article
Language:English
English
Published: Elsevier 2013
Subjects:
L Education (General)
Online Access:http://ir.unimas.my/id/eprint/21665/2/S1877042813036562
http://ir.unimas.my/id/eprint/21665/3/Julia%20Lee.pdf
http://ir.unimas.my/id/eprint/21665/
http://www.sciencedirect.com
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spelling my.unimas.ir.216652022-01-25T06:44:11Z http://ir.unimas.my/id/eprint/21665/ Language and literacy profiles: A mixture modeling approach Lee, Julia Ai Cheng Stephanie, Al Otaiba L Education (General) Mixture modeling was used to examined the: (a) heterogeneity and prevalence of the language and literacy profiles among 521 first grade students (Malaysian equivalent of Primary 1) and (b) predictors that optimize the classification of language and literacy profiles. Based on the Simple View of Reading as a theoretical framework, five language and literacy profiles were identified. These were students with: a) the weakest performance on average in literacy but slightly higher language skills, b) weak performance in both language and literacy, c) average performance in language and literacy, d) above average performance on language and literacy, and e) the strongest performance in language and literacy. Unique predictors of class membership differentiation for all groups were phonological awareness, teacher judgment on academic achievement, and socioeconomic status. Elsevier 2013 Article PeerReviewed text en http://ir.unimas.my/id/eprint/21665/2/S1877042813036562 text en http://ir.unimas.my/id/eprint/21665/3/Julia%20Lee.pdf Lee, Julia Ai Cheng and Stephanie, Al Otaiba (2013) Language and literacy profiles: A mixture modeling approach. Procedia - Social and Behavioral Sciences, 97. 114 -121. ISSN 1877-0428 http://www.sciencedirect.com
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic L Education (General)
spellingShingle L Education (General)
Lee, Julia Ai Cheng
Stephanie, Al Otaiba
Language and literacy profiles: A mixture modeling approach
description Mixture modeling was used to examined the: (a) heterogeneity and prevalence of the language and literacy profiles among 521 first grade students (Malaysian equivalent of Primary 1) and (b) predictors that optimize the classification of language and literacy profiles. Based on the Simple View of Reading as a theoretical framework, five language and literacy profiles were identified. These were students with: a) the weakest performance on average in literacy but slightly higher language skills, b) weak performance in both language and literacy, c) average performance in language and literacy, d) above average performance on language and literacy, and e) the strongest performance in language and literacy. Unique predictors of class membership differentiation for all groups were phonological awareness, teacher judgment on academic achievement, and socioeconomic status.
format Article
author Lee, Julia Ai Cheng
Stephanie, Al Otaiba
author_facet Lee, Julia Ai Cheng
Stephanie, Al Otaiba
author_sort Lee, Julia Ai Cheng
title Language and literacy profiles: A mixture modeling approach
title_short Language and literacy profiles: A mixture modeling approach
title_full Language and literacy profiles: A mixture modeling approach
title_fullStr Language and literacy profiles: A mixture modeling approach
title_full_unstemmed Language and literacy profiles: A mixture modeling approach
title_sort language and literacy profiles: a mixture modeling approach
publisher Elsevier
publishDate 2013
url http://ir.unimas.my/id/eprint/21665/2/S1877042813036562
http://ir.unimas.my/id/eprint/21665/3/Julia%20Lee.pdf
http://ir.unimas.my/id/eprint/21665/
http://www.sciencedirect.com
_version_ 1724078510602977280
score 13.211869

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