Hope S. Lancaster1  Stephen M. Camarata2 Analysis of racial composition of the EpiSLI database on cognitive variables: Issues and Implications Hope S. Lancaster1  Stephen M. Camarata2 1Arizona State University, Tempe, AZ  2Vanderbilt University, Nashville, TN ABSTRACT METHODS RESULTS This study sought to better understand how African American children scored on nonverbal measures in child language research. African American children often score lower on language (Craig & Washington, 2000; Stockman, 2010) and intelligence measures (Cronbach, 1975; Jensen, 2006; Rushton & Jensen, 2006) It is crucial to have a clear understanding about how African American children with language impairment score on nonverbal cognitive measures. This study used the EpiSLI data, because it contained a sufficient sample size of children with and without language impairment. We compared proportions of Caucasian and African American children in four diagnostic classification. We examined score distributions, skew, and kurtosis for two measures. This study found that in the EpiSLI data African American children were significantly overrepresented in low cognitive groups and scored lower than normative data for two nonverbal measures. Table 1 Demographic information by Tomblin et al.'s (1997) diagnostic classification We compared percentages to the full database (84.14% Caucasian and 13.17% African American) for diagnostic classifications. African American children were a larger percentage of the sample for Nonspecific Language Impairment and Low Normal. Risk ratios indicated African American children were 2.05 times likely to be identified as NLI or Low Normal when using population norms. Subsample N Age Gender Language Nonverbal IQ Typically Developing 1226 6 (3.8) 54% -0.08 (0.67) 0.39 (0.62) Low Normal 198 6 (3.7) 53% -0.55 (0.51) -1.09 (0.38) SLI 277 59% -1.52 (0.38) 0.12 (0.53) NLI 228 52% -1.76 (0.48) -1.27 (0.48) Notes. Mean (SD). For Age mean is reported in years and SD in months. Gender is reported as percent male. Language is a composite of five TOLD-P:2 (Newcomer & Hammill, 1988) and two narrative variables (Culatta et al., 1983) as described in Tomblin et al. (1996). Cognition is the average of z-scores of Block Design and Picture Completion (Weschler, 1988). percentage Black. Table 4 Racial percentages by diagnostic classification Typically Developing Specific Language Impaired Nonspecific Language Impaired Low Normal African American (n=254) 8.56 12.27 31.14*** 22.22** Caucasian (n=1623) 89.31 84.84 65.79 72.77 Notes. * p < .05; ** p < .01; *** p < .001. PURPOSE Previous research has indicated African American children score lower on measures of vocabulary (Craig & Washington, 2000; Qi, Kaiser, Milan, & Hancock, 2006) and other measures of language (Stockman, 2010 ) Intelligence has documented over the years lower scores in African American children and related these lower scores to SES and other environmentally issues (Cronbach, 1975; Jensen, 2006; Rushton & Jensen, 2006) Both language and nonverbal intelligence measures are used to identify children with language impairment and qualify them for services (Cole & Fey, 1996) This project examined the distribution of scores on nonverbal intelligence measures for African American and Caucasian children with and without language impairment This project also examined classification proportions to determine if there was over identification for African American children SUMMARY African American children have left-shifted but normal distributions for Block Design and Picture Completion Using left-shifted scores might result in an underestimation of ability African American children are 2.05 times more likely to be identified as having low nonverbal abilities Therefore some African American children may have been misidentified or failed to qualify for services if a discrepancy between verbal and nonverbal scores is required These analyses did not consider the impact of SES We examined classification based on population norms, the use of local norms might be different RESULTS KEY REFERENCES METHODS Craig, H. K. & Washington, J. A. (2000). An assessment battery for identifying language impairments in African American children. Journal of Speech, Language, and Hearing Research, 43, 366 – 379 Cronbach, L. J. (1975). Five decades of public controversy over mental testing. American Psychology, 30, 1 – 14. Jensen, A. R. (2006) Comments on correlations of IQ with skin color and geographic-demographic variables. Intelligence, 34, 128 – 131. Qi, C. H., Kaiser, A. P., Milan, S., & Hancock, T. (2006). Language performance of low-income African American and European American preschool children on the PPVT–III. Language, Speech, and Hearing Services in Schools, 37, 5-16. Rushton, J. P., & Jensen, A. R. (2006). The totality of available evidence shows the race IQ gap still remains. Psychological Science, 17, 921-922. Stockman, I. J. (2010). A review of developmental and applied language research on African American children: From a deficit to difference perspective on dialect differences. Language, Speech, and Hearing Services in Schools,41, 23-38. Tomblin, J. B. (2010). The EpiSLI database: A publicly available database on speech and language. Language, Speech, and Hearing Services in Schools, 41, 108 – 117. DATA This project used the EpiSLI database. This study used the EpiSLI kindergarten data (Tomblin, 2010). Chi-square tests were performed to compare racial proportions for four diagnostic categories to the full database. Skew and kurtosis were tested for the score distribution for Block Design and Picture Completion for Black children. The racial proportions for the full database were 84.14% Caucasian, 13.17% African American, and 2.7% Other minorities. See Table 1 for demographic data of the data used. Data were transformed into within group z-scores. ANALYSIS This project analyzed the Block Design and Picture Completion from the Wechsler Preschool and Primary Scales of Intelligence (Wechsler, 1989). The sample was divided into African American and Caucasian. Skew and kurtosis were analyzed. Additionally the group mean was compared to the normative mean to determine effect size. Figure 1. Histograms of scaled score on Block Design for African American children. Population curve is in black and Caucasian curve is in red. Figure 2. Histograms of scaled scores on Picture Completion for African American children. Population curve is overlaid in black and Caucasian curve in red. BLOCK DESIGN Mean (SD) Skew Kurtosis Effect Size African American 7.23 (2.93) 0.12 -0.55 0.93 Caucasian 9.61 (2.96) 0.01 0.28 0.13 PICTURE COMPLETION Mean (SD) Skew Kurtosis Effect Size African American 8.74 (2.93) 0.16 0.56 0.43 Caucasian 10.38 (2.86) -0.11 -0.18 -0.13 ACKNOWLEDGEMENTS This study was supported by a Preparation of Leadership Personnel grant (H325D080075; PI: Schuele) US Department of Education. Acknowledgement is given to original grant #N01-DC-1-2107 and supplement #3 P50 DC002746-08S1 from the National Institute on Deafness and Other Communication Disorders, a division of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the views of Arizona State University or Vanderbilt University. Poster available at: https://medschool.vanderbilt.edu/developmental-disabilities-lab Notes. Mean (Standard deviation). Effect size was calculated against normative data African American (n = 254) children scored close to 1 standard deviation lower compared to the normative data for Block Design, and roughly half a standard deviation for Picture Completion. Caucasian children scored within normal limits and did not differ from normative data. There was no evidence for skew or kurtosis for either group.