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Developmental Psychology
Article . 2021 . Peer-reviewed
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Automaticity as an independent trait in predicting reading outcomes in middle-school.

Authors: Tanja C. Roembke; Eliot Hazeltine; Deborah K. Reed; Bob McMurray;

Automaticity as an independent trait in predicting reading outcomes in middle-school.

Abstract

Many middle-school students struggle with basic reading skills. One reason for this might be a lack of automaticity in word-level lexical processes. To investigate this, we used a novel backward masking paradigm, in which a written word is either covered with a mask or not. Participants (N = 444 [after exclusions]; nfemale = 264, nmale = 180) were average to struggling middle-school students from an urban area in Eastern Iowa that were all native speakers of English and were roughly equally from grades 6, 7, and 8 (average age: 13 years). Two-hundred-fifty-five students qualified for free or reduced-price lunch, a proxy for economic disadvantage. Participants completed different masked and unmasked task versions where they read a word and selected a response (e.g., a pictured referent). This was related to standardized measures of decoding, fluency, and reading comprehension. Decoding was uniquely predicted by knowledge (unmasked performance), whereas fluency was uniquely predicted by automaticity (masked performance). Automaticity was stable across two testing points. Thus, automaticity should be considered an individually reliable marker/reading trait that uniquely predicts some skills in average to struggling middle-school students. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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Keywords

Schools, Adolescent, Reading, Humans, Comprehension, Students, Language, Time

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
bronze