Language difference among speakers of African American English (AAE) has often been considered language deficit, based on a lack of understanding about the AAE variety. Following Labov (1972), Wolfram (1969), Green (2002, 2011), and others, we define AAE as a complex rule-governed linguistic system and briefly discuss language structures that it shares with general American English (GAE) and others that are unique to AAE. We suggest ways in which mistaken ideas about the language variety add to children's difficulties in learning the mainstream dialect and, in effect, deny them the benefits of their educational programs. We propose that a linguistically informed approach that highlights correspondences between AAE and the mainstream dialect and trains students and teachers to understand language varieties at a metalinguistic level creates environments that support the academic achievement of AAE-speaking students. Finally, we present 3 program types that are recommended for helping students achieve the skills they need to be successful in multiple linguistic environments.

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PURPOSE In this study, the authors explored alternative gold standards to validate an innovative, dialect-neutral language assessment. METHOD Participants were 78 African American children, ages 5;0 (years;months) to 6;11. Twenty participants had previously been identified as having language impairment. The Diagnostic Evaluation of Language Variation-Norm Referenced (DELV-NR; Seymour, Roeper, & J. de Villiers, 2005) was administered, and concurrent language samples (LSs) were collected. Using LS profiles as the gold standard, sensitivity, specificity, and other measures of diagnostic accuracy were compared for diagnoses made from the DELV-NR and participants' clinical status prior to recruitment. In a second analysis, the authors used results from the first analysis to make evidence-based adjustments in the estimates of DELV-NR diagnostic accuracy. RESULTS Accuracy of the DELV-NR relative to LS profiles was greater than that of prior diagnoses, indicating that the DELV-NR was an improvement over preexisting diagnoses for this group. Specificity met conventional standards, but sensitivity was somewhat low. Reanalysis using the positive and negative predictive power of the preexisting diagnosis in a discrepant-resolution procedure revealed that estimates for sensitivity and specificity for the DELV-NR were .85 and .93, respectively. CONCLUSION The authors found that, even after making allowances for the imperfection of available gold standards, clinical decisions made with the DELV-NR achieved high values on conventional measures of diagnostic accuracy.

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