Developing strategies for speakers of African American English (Maher et al., 2021)
datasetposted on 19.01.2021, 21:13 by Zachary K. Maher, Michelle E. Erskine, Arynn S. Byrd, Jeffrey R. Harring, Jan R. Edwards
Purpose: Many studies have found a correlation between overall usage rates of nonmainstream forms and reading scores, but less is known about which dialect differences are most predictive. Here, we consider different methods of characterizing African American English use from existing assessments and examine which methods best predict literacy achievement.
Method: Kindergarten and first-grade students who speak African American English received two assessments of dialect use and two assessments of decoding at the beginning and end of the school year. Item-level analyses of the dialect-use assessments were used to compute measures of dialect usage: (a) an overall feature rate measure based on the Diagnostic Evaluation of Language Variation–Screening Test, (b) a subscore analysis of the Diagnostic Evaluation of Language Variation–Screening Test based on items that pattern together, (c) an alternative assessment where children repeat and translate sentences, and (d) “repertoire” measures based on a categorical distinction of whether a child used a particular feature of mainstream American English.
Results: Models using feature rate measures provided better data–model fit than those with repertoire measures, and baseline performance on a sentence repetition task was a positive predictor of reading score at the end of the school year. For phonological subscores, change from the beginning to end of the school year predicted reading at the end of the school year, whereas baseline scores were most predictive for grammatical subscores.
Conclusions: The addition of a sentence imitation task is useful for understanding a child’s dialect and anticipating potential areas for support in early literacy. We observed some support for the idea that morphological dialect differences (i.e., irregular verb morphology) have a particularly close tie to later literacy, but future work will be necessary to confirm this finding.
Supplemental Material S1. A summary table of fixed effects and lme4 model specification for each model reported in the text; models with factor score predictors are also included.
Supplemental Material S2. Correlations between each pair of dialect measures at both baseline and post.
Maher, Z. K., Erskine, M. E., Byrd, A. S., Harring, J. R., & Edwards, J. R. (2021). African American English and Early Literacy: A Comparison of Approaches to Quantifying Nonmainstream Dialect. Language, Speech, and Hearing Services in Schools, 52(1), 118–130. https://doi.org/10.1044/2020_LSHSS-19-00115
Publisher Note: This article is part of the Forum: Serving African American English Speakers in Schools Through Interprofessional Education & Practice.
This research was supported in part by Institute of Education Sciences Grant R305A17013, awarded to Jan Edwards, and National Science Foundation Grant 1449815, awarded to Colin Phillips.
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African American EnglishliteracyearlycomparisonapproachquantifynonmainstreamdialectreadingAAEscorepredictivedifferencesassessmentmethodpredictachievementkindergartenfirst gradestudentsdecodingschool agefeatureratemeasureDiagnostic Evaluation of Language VariationScreening Testpatternrepeattranslatesentencesrepertoirecategorydistinctionmainstream American Englishphonologicalschool yeargrammarsentence imitationsupportearly literacymorphologicalirregular verb