Predicting Arabic word reading: A cross-classified generalized random-effects analysis showing the critical role of morphology.

The distinctive features of the Arabic language and orthography offer opportunities to investigate multiple word characteristics at the item level. The aim of this paper was to model differences in word reading at the item level among 3rd grade native Arabic-speaking children (n = 303) using cross-classified generalized random-effects (CCGRE) analysis. The participants read 80 vowelized words that varied in multiple elements that may contribute to their decodability: number of letters, number of syllables, number of morphemes, ligaturing (connectivity), semantics (concrete vs. abstract), orthographic frequency, root type frequency, and part of speech. Morphological awareness (MA) was included as a person-level predictor. Results of individual models showed that MA, number of letters, number of syllables, number of morphemes, number of ligatures, orthographic frequency, and part of speech were significantly related to the probability of a correct response. However, when all predictors were entered simultaneously, only MA and number of morphemes remained significant. These results underscore the important role of morphology in the lexical structure of Arabic words and in Arabic word reading. Discussion focuses on the role of morphology in Arabic reading and the implications for intervention to improve word recognition in children learning to read Arabic.

Citation

Sana Tibi, Ashley A Edwards, Christopher Schatschneider, John R Kirby. Predicting Arabic word reading: A cross-classified generalized random-effects analysis showing the critical role of morphology. Annals of dyslexia. 2020 Jul;70(2):200-219

Mesh Tags

PMID: 32358771

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