Differential Diagnosis of Disordered Language in African American English-Speaking Children: A Comparison Between Computerized Black English Sentence Scoring and Developmental Sentence Scoring

Abstract

Background: Children who speak African American English (AAE) are disproportionately likely to be diagnosed with language disorders. Traditional language sample analysis (LSA) metrics, such as Developmental Sentence Scoring (DSS), are based on the morphosyntactic structures of Mainstream American English (MAE) and may not accurately reflect the language abilities of AAE-speaking children. This study examined the effectiveness of computerized Black English Sentence Scoring (BESS) system in distinguishing between TD and LI groups in AAE-speaking children compared to use of DSS. Method: Language samples from 88 children (22 LI, 66 TD) between the ages of 5;0 and 7;02, including 44 AAE-speaking children and 44 MAE-speaking children as the control group, were analyzed using Computerized Language Analysis (CLAN) DSS and BESS options. Results: Findings suggested BESS is a culturally responsive LSA metric for AAE-speaking children, minimizing linguistic bias seen in DSS. Results revealed neither DSS or BESS are statistically reliable ways to identify LI in children who speak either AAE or MAE. Discussion: Clinical ramifications and future directions are discussed.