Understanding Language Screeners for Low-Income Pre-Kindergartners
As a speech-language pathologist, making data-driven decisions is crucial, especially when working with vulnerable populations such as low-income prekindergartners who speak African American English (AAE) in the urban South. The recent study titled "Comparison of the Diagnostic Evaluation of Language Variation–Screening Test Risk Subtest to Two Other Screeners for Low-Income Prekindergartners Who Speak African American English and Live in the Urban South" offers valuable insights into the effectiveness of language screeners for this demographic.
The Importance of Accurate Screening
Screening serves as the first step in identifying children who may need further evaluation for language impairments. However, traditional screeners often fail to account for dialectal variations, leading to high fail rates and inconsistent outcomes. This study compared the Diagnostic Evaluation of Language Variation–Screening Test (DELV–Screening Test Risk) with two other screeners: the Fluharty Preschool Speech and Language Screening Test–Second Edition (FLUHARTY-2) and the Washington and Craig Language Screener (WCLS).
Key Findings
- The DELV–Screening Test Risk showed a 52% fail rate, similar to the WCLS's 48% fail rate.
- Fail rates for the FLUHARTY-2 varied significantly, ranging from 34% to 75% depending on scoring modifications.
- Inconsistent outcomes were observed across the screeners, with 44% of children receiving different pass/fail results.
- Sensitivity values for the DELV–Screening Test Risk, FLUHARTY-2, and WCLS were 1.0, 0.75, and 0.75, respectively, while specificity values were 0.51, 0.68, and 0.54.
Implications for Practitioners
The findings suggest that while the DELV–Screening Test Risk is designed for children speaking various dialects, including AAE, it may still lead to high fail rates. Practitioners should be cautious when using any single screener for children with similar sociolinguistic profiles as those studied. Instead, consider using multiple screeners and incorporating caregiver/teacher ratings and risk factors into the decision-making process.
Moreover, the study highlights the need for further research and development of screeners that account for dialectal variations and socioeconomic factors. By refining existing tools and collecting multiple normative data sets, we can improve the accuracy and reliability of language screenings for diverse populations.
Conclusion
As speech-language pathologists, our goal is to ensure that all children receive the services they need. By staying informed about the latest research and continuously evaluating our screening practices, we can make data-driven decisions that lead to better outcomes for children. To read the original research paper, please follow this link: Comparison of the Diagnostic Evaluation of Language Variation–Screening Test Risk Subtest to Two Other Screeners for Low-Income Prekindergartners Who Speak African American English and Live in the Urban South.
