1. Reanalysis the EpiSLI dataset: Exploring phenotypes of language abilities in kindergarteners
Hope S. Lancaster  Stephen M. Camarata
Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN
INTRODUCTION
RESULTS
RESULTS
RESULTS
Language Impaired
Typically Developing
Language disorders, including primary language impairment (LI), are associated with long-term adverse academic outcomes if not properly identified and treated in early school-age children. The Office of Special Education Program (Data Accountability Center, 2012) reported that there is a current and present need to improve identification of children with LI. Specifically, we need to differentiate subgroups within the group of children who receive language services under IDEA (2004) so that they receive the best intervention possible given their strengths and needs. A lack of specificity presents a problem for educators because, if children are not appropriately identified, then effective services cannot be provided.
Tomblin et al. (1997) and many others (e.g, Bishop, 1997; Leonard, 2014) have suggested varying ways of differentiating the strengths and needs of subgroups of children with LI. However, there has been no empirical study of possible subgroups. One valuable outcome of empirically driven study of subgroups is a better, more robust understanding of different profiles of LI in children.
Figure 1 and Figure 4 show the dendrograms for the Ward's clusters. A dendrogram is a representation of how cases were merged to form clusters. The y-axis is the height value, the criteria used to merge cases.
Figure 2 and Figure 5 are the BIC curves for the clustering solutions tested. BIC was the criteria used to determine the optimal cluster sizes. The optimum cluster is the cluster with the smallest relative BIC value. The minimums are identified with a vertical line Both datasets had cluster solutions that were too large to interpret.
Language Impaired: 51 clusters
Typically Developing: 92 clusters
Figure 3 and Figure 6 are scatterplots of the data used for clustering. The axes are principal components derived from the language and cognitive variables used. Each principal component is a linear combination of variables that accounts for a certain percentage of variance. Components are independent. The scatterplots here show no distinct visual clusters in the data.
PURPOSE
SUMMARY
Figure 1. Dendogram for Language Impaired dataset.
Figure 4. Dendogram for Typical dataset.
The overall purpose of this study is to develop an empirically better understanding of the identifiable phenotypes of language ability of kindergarteners. Although, there are a variety of theories for subtyping children with language impairment (e.g., Leonard, 2000; Bishop, 1997; Tomblin et al., 1997), no single theory has more support than another. Therefore, it is useful to proceed without a specified number of phenotypes. There are two main research question:
Are there distinct clusters within the database? If so, how many?
Are there distinct clusters in the database? If so, how many
No, there is not an optimum number of clusters. The scatterplots and the dendrogram show no distinct clusters. The number of clusters identified was 51 (Language Impaired) and 92 (Typically Developing). Both of these solutions are too large to interpret meaningfully.
Limitations
There are a few important limitations to consider. A limitation is the extant data which limited the study to only the variables available. There are limitations associated with Ward's method. In Ward's method the process of chaining links two cases and this link cannot be changed later in the process. For Ward's method, the order of variables affects membership. This study did not examine how different orders of the variables would affect the results.
Key Finding
The key finding was that there were no meaningful clusters. An implication is that there no subtypes that are statistically identifiable in children with language impairment. A clinical implication is that individual strengths and weakness are probably more useful than a subtype profile.
METHODS
DATA
This project used the EpiSLI database. The database was reduced to smaller datasets. The datasets presented here are the Language Impaired and Typically Developing. Data were first transformed into z-scores and then a distance matrix was created.
Table 1
Variables used for clustering.
Notes. TOLD-P:2 = Test of Language Development – Primary 2nd edition (Newcomer & Hammill, 1988); WPPSI-R = Weschler Preschool and Primary Scales of Intelligence – Revised (Weschler, 1989).
ANALYSIS
All analyses were completed in R. Ward's method was used to cluster the data. The R function hclust was used for clustering. A plot of the clustering was obtained. The Bayesian Information Criterion (BIC) was used to determine the optimal number of clusters. The optimal cluster was the smallest BIC value. The BIC values were plotted. The distance matrix was plotted using the R function clusplot, which transformed the variables into components.
Figure 2. Bayesian Information Criterion curve for LI clusters 1 through 100.
Figure 5. Bayesian Information Criterion curve for Typical clusters 1 through 100.
Variable
Source
Language
Picture Vocabulary
TOLD-P:2
Oral Vocabulary
Grammatical Completion
Grammatical Understanding
Sentence Imitation
Word Articulation
Narrative recall
Culatta, et al. (1983)
Narrative comprehension
Cognition
Block Design
WPPSI-R
Picture Completion
KEY REFERENCES
Bishop, D. V. M., & Rosenbloom, L. (1987). Childhood language disorders: Classification and overview. Language Development and Disorders, 101, 16 – 41.
Data Accountability Center (February, 2012). Office of Special Education Programs IDEA, Part B (Revised). Rockville, MD: Westat.
Leonard, L. B. (2014). Children with Specific Language Impairment 2nd edition. Cambridge, MA: MIT Press.
Tomblin, J., Records, N., Buckwalter, P., Zhang, X., Smith, E., & O'Brien, M. (1997). Prevalence of specific language impairment in kindergarten children. Journal of Speech, Language, and Hearing Research, 40,
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 and supplement #3 P50 DC S1 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 Vanderbilt University.
Poster available at:
Figure 3. Scatterplot of distance values for LI data.
Figure 6. Scatterplot of distance values for Typical data.