Aphasia is a language disorder found in people with acquired brain injury due to stroke, disease, or trauma. One of the hallmarks of communication disorder in aphasia is a breakdown in the ability to find and use words. I treat people with aphasia (PwA) in my practice, and I recently sought to learn more about analysis of the kinds of words my clients were struggling to produce. Are nouns more problematic than verbs? Is pronoun agreement inconsistent? What types of words seem to trigger paraphasias, and what type of paraphasia is associated with different types of words? In my exploration of the literature, I happened upon the topic study that compared four different measures of lexical diversity.
Fergadiotis G., Wright H.H., West T.M. (2013). Measuring lexical diversity in narrative discourse of people with aphasia. American Journal of Speech-Language Pathology. 22, S397-S408. doi: 10.1044/1058-0360(2013/12-0083)
About the Study
The authors compared the effectiveness of four assessments of lexical diversity in discourse:
• Measure of Textual Lexical Diversity (MTLD; McCarthy, 2005)
• Moving-Average Type-Token Ratio (MATTR; Covington, 2007)
• D (McKee, Malvern, & Richards, 2000)
• Hypergeometric Distribution (HD-D; McCarthy & Jarvis, 2007)
The study was conducted with 101 PwA due to a single left-hemisphere cardiovascular accident (CVA). All participants provided narrative discourse samples, and their recounts of Cinderella (Grimes, 2005) were selected for analysis. Lexical diversity measures were obtained using the above-listed methods.
The results showed MTLD and MATTR provide less biased, potentially stronger measures of lexical diversity in PwA.
MTLD and MATTR are complicated measures. As the authors state on page S398, Type-Token Ratio (TTR) is the most common tool Speech-Language Pathologists use to measure lexical diversity. Unfortunately, the weakness of this method is that its accuracy is inversely related to sample length. In other words, lexical diversity declines as the sample length increases, so shorter utterances may score more favorably than longer ones produced from a limited lexicon.
I found this to be an interesting article, but I am not sure either of the methods described will give me the narrative discourse analysis tool I seek. My impression is that they would be time consuming to implement and are therefore not clinically practical for me; however, my understanding of the concepts might be enhanced given hands on teaching/practice using MTLD and MATTR to measure lexical diversity in PwA.
I had hoped this article would provide a useful, immediate addition to my therapy toolbox. Unfortunately, I conclude that I need to stick with my tried and true, TTR-style language sample measures combined with semantic feature analysis to inform my aphasia treatment plan. Other SLPs might read this article with a different take away message. If you do, I’d love to know your thoughts!
Cyndee Williams Bowen, MS, CCC-SLP owns Bowen Speech-Language Therapy, LLC in Clearwater, FL. She provides quality, creative, collaborative treatment to adults and adolescents with communication, swallowing, and related disorders.
Covington, M.A. (2007). MATTR user manual (CASPR research report 2007-05). Athens, GA.
Grimes, N. (2005). Walt Disney’s Cinderella. New York, NY: Random House.
McCarthy, P.M. (2005). An assessment of the range and usefulness of lexical diversity measures and the potential of the measure of textual, lexical diversity (Doctoral dissertation). Available from Proquest Dissertations and Theses. (UMI No. 3199485)
McCarthy, P.M. & Jarvis, S. (2007). Voc-D: A theoretical and empirical evaluation. Language Testing, 24(4), 459-488. doi:10.1177/0265532207080767.
McKee, G., Malvern, D., & Richards, B. (2000). Measuring vocabulary diversity using dedicated software. Literacy and Linguistic Computing, 15(3), 323-337.