The United States is becoming increasingly more culturally and linguistically diverse. According to the U.S. Census Bureau’s 2017 American Community Survey, 22% of individuals live in a household where English is not spoken as the primary language. In contrast, only 6% of American Speech-Language-Hearing Association (ASHA) credentialed speech-language pathologists identify as bilingual service providers (ASHA, 2019). Due to the fact that schools are increasingly more linguistically diverse, it is essential for speech-language pathologists to use assessment practices that will accurately differentiate children as having a language disorder versus difference (Orellana et al., 2019).

Assessment of bilingual populations is a burgeoning area of study as researchers seek to gain greater understanding of the diagnostic accuracy of current assessment approaches for identification of bilingual children for speech and language services (Dollaghan & Horner, 2011). Speech-language pathologists may assess bilingual children for language impairment using standardized, norm-referenced tests, or they may assess children using an informal protocol, which may include dynamic assessment, language sampling, and nonword repetition (Arias & Friberg, 2017; Gutierrez-Clellen & Simon-Cereijido, 2010; Orellana et al., 2019; Peterson et al., 2017). However, standardized assessments are considered inherently biased against bilingual children, as they most often do not include normative samples with individuals of the same age, language, and cultural background, resulting in the potential for disproportionate representation of bilingual children in special education (Munoz et al., 2014). In contrast, dynamic assessment, language sampling and nonword repetition are often referred to as non-biased assessment practices as they do not rely on a normative sample (Arias & Friberg, 2017; Munoz et al., 2014).

Why does this matter?

If bilingual children are mislabeled as language impaired, then this may contribute to disproportionate representation of English language learners (ELL) in special education. Disproportionality refers to the common occurrence of underrepresentation or overrepresentation of minority students receiving special education services (Munoz et al., 2014). There are potentially serious consequences for mislabeling bilingual children as language impaired. These may include learning from a less rigorous curriculum, stigmatization, and reduced access to post-secondary education (Spinelli, 2008). Disproportionality in special education may be caused by a range of factors, including a student’s race, gender, socioeconomic status, and prior disciplinary actions (Sullivan & Bal, 2013). One factor that speech-language pathologists can influence to potentially reduce rates of disproportionality is through assessment practices (Munoz et al., 2014).

What does disproportionality in special education look like in Maryland?

In order to determine disproportionality in Maryland, risk ratios were calculated using the2016 data from the U.S. Department of Education’s National Center for Education Statistics and Child Count and Environments. Risk ratios were calculated by dividing the risk for English language learners to be placed in special education services by the risk for non-English language learners to be placed in special education (Robinson & Norton, 2019). Calculations were done using data for all disability categories in order to include children with language impairment who may have their primary disability category listed as another category, such as autism or developmental delay. In Maryland, ELLS are 0.88 times as likely to be enrolled in special education services than the total population. Therefore, in Maryland, ELLs are underrepresented in special education. This may be a useful exercise for clinicians to undertake within their own school and caseload.

What now?

Speech-language pathologists play a critical role in schools. With the rapidly increasing number of children who are bilingual, it has become even more imperative to use assessment practices that will determine if a child has a language difference or disorder. The literature supports the use of informal, non-biased measures of assessment when evaluating bilingual children with language impairment. These measures include dynamic assessment, language sampling, and nonword repetition. Furthermore, disproportionate representation of bilingual children in special education is a significant concern in schools nationwide. Bilingual children in school who are misidentified as language impaired may be negatively impacted long-term. Although there are many factors contributing to disproportionality, school-based speech-language pathologists should endeavor to reduce their contribution through more accurate identification of bilingual children (Munoz et al., 2014). The following are some factors to keep in mind when evaluating bilingual children for language impairment. 

  • Assess the student in both languages 
  • Use informal measures, like dynamic assessment, language sampling, and nonword repetition
  • When standardized assessments are required, examine tests for cultural and linguistic bias
  • Do not report scores when the student is not represented by the normative sample
  • When possible, refer the student to an ASHA-certified bilingual service provider; otherwise, use an interpreter
  • Conduct parent and teacher interviews
  • Observe the child in a variety of contexts

References

American Speech-Language-Hearing Association. (2019). Demographic profile of ASHA members providing bilingual services, year-end 2018. Retrieved from https://www.asha.org/uploadedFiles/Demographic-Profile-Bilingual-Spanish-Service-Members.pdf

Arias, G., & Friberg, J. (2017). Bilingual language assessment: Contemporary versus recommended practice in American schools. Language, Speech, and Hearing Services in Schools, 48(1), 1–15. doi:10.1044/2016_LSHSS-15-0090

Dollaghan, C., & Horner, E. (2011). Bilingual language assessment: A meta-analysis of diagnostic accuracy. Journal of Speech, Language, and Hearing Research, 54(4), 1077-88. doi:10.1044/1092-4388(2010/10-0093)

Gutierrez-Clellen, V., & Simon-Cereijido, G. (2010). Using nonword repetition tasks for the identification of language impairment in Spanish-English-speaking children: Does the language of assessment matter? Learning Disabilities Research & Practice, 25(1), 48-58.

Munoz, M., White, M., & Horton-Ikard, R. (2014). The identification conundrum. ASHA Leader, 19(11), 48-53. doi:10.1044/leader.FTR3.19112014.48

Orellana, C., Wada, R., & Gillam, R. (2019). The use of dynamic assessment for the diagnosis of language disorders in bilingual children: A meta-analysis. American Journal of Speech-Language Pathology, 28(3), 1298-1317. doi:10.1044/2019_AJSLP-18-0202

Petersen, D. B., Chanthongthip, H., Ukrainetz, T. A., Spencer, T. D., & Steeve, R. W. (2017). Dynamic assessment of narratives: Efficient, accurate identification of language impairment in bilingual students. Journal of Speech, Language, and Hearing Research, 60(4), 983–998. doi:10.1044/2016_JSLHR-L-15-0426 

Spinelli, C. (2008). Addressing the issue of cultural and linguistic diversity and assessment: Informal evaluation measures for English language learners. Reading & Writing Quarterly, 24(1), 101-118.

Sullivan, A. L., & Bal, A. (2013). Disproportionality in special education: Effects of individual and school variables on disability risk. Exceptional Children, 79(4), 475–494.

U.S. Department of Education, IDEA Section 618 Data Products: State Level Data Files, Part B (2017). Child count and educational environments, 2016. Retrieved from https://www2.ed.gov/programs/osepidea/618-data/state-level-data-files/index.html#bccee

U.S. Department of Education, National Center for Education Statistics, Common Core of Data (CCD) (2018). Local education agency universe survey, 2000-01 through 2016-17. Retrieved from https://nces.ed.gov/programs/digest/d18/tables/dt18_204.20.aspU.S. Department of Education, National Center for Education Statistics, Common Core of Data (CCD) (2018). State nonfiscal survey of public elementary/secondary education, 2016-17. Retrieved from https://nces.ed.gov/programs/digest/d18/tables/dt18_203.40.asp


About the Author

Karen Levine is part of the Cultural and Linguistic Diversity program for Speech-Language Pathology students at the University of Maryland. The program aims to broaden students’ understanding of culture and language in order to minimize disparities in service delivery to culturally and linguistically diverse populations.