A Tutorial on Multiblock Discriminant Correspondence Analysis (MUDICA): A New Method for Analyzing Discourse Data From Clinical Populations PurposeIn communication disorders research, clinical groups are frequently described based on patterns of performance, but researchers often study only a few participants described by many quantitative and qualitative variables. These data are difficult to handle with standard inferential tools (e.g., analysis of variance or factor analysis) whose assumptions are unfit ... Tutorial
Tutorial  |   October 01, 2010
A Tutorial on Multiblock Discriminant Correspondence Analysis (MUDICA): A New Method for Analyzing Discourse Data From Clinical Populations
 
Author Affiliations & Notes
  • Lynne J. Williams
    University of Western Ontario, London, Ontario, Canada
  • Hervé Abdi
    The University of Texas at Dallas, Richardson, TX
  • Rebecca French
    Southlake Regional Health Centre, Newmarket, Ontario, Canada
  • Joseph B. Orange
    University of Western Ontario, London, Ontario, Canada
  • Contact authors:
    Contact authors:×
  • Lynne J. Williams, who is now with the Kunin-Lunenfeld Applied Research Unit, Rotman Research Institute, Baycrest, 3560 Bathurst Street, Toronto, Ontario M6A 2E1, Canada. E-mail: lwilliams@klaru-baycrest.on.ca.
  • Hervé Abdi, The University of Texas at Dallas, GR4.1, 800 West Campbell Road, Richardson, TX 75080-3021. E-mail: herve@utdallas.edu.
    Hervé Abdi, The University of Texas at Dallas, GR4.1, 800 West Campbell Road, Richardson, TX 75080-3021. E-mail: herve@utdallas.edu.×
Article Information
Special Populations / Older Adults & Aging / Normal Language Processing / Language
Tutorial   |   October 01, 2010
A Tutorial on Multiblock Discriminant Correspondence Analysis (MUDICA): A New Method for Analyzing Discourse Data From Clinical Populations
Journal of Speech, Language, and Hearing Research, October 2010, Vol. 53, 1372-1393. doi:10.1044/1092-4388(2010/08-0141)
History: Received July 10, 2008 , Revised February 14, 2009 , Accepted February 14, 2010
 
Journal of Speech, Language, and Hearing Research, October 2010, Vol. 53, 1372-1393. doi:10.1044/1092-4388(2010/08-0141)
History: Received July 10, 2008; Revised February 14, 2009; Accepted February 14, 2010
Web of Science® Times Cited: 15

PurposeIn communication disorders research, clinical groups are frequently described based on patterns of performance, but researchers often study only a few participants described by many quantitative and qualitative variables. These data are difficult to handle with standard inferential tools (e.g., analysis of variance or factor analysis) whose assumptions are unfit for these data. This article presents multiblock discriminant correspondence analysis (MUDICA), which is a recent method that can handle datasets not suited for standard inferential techniques.

MethodMUDICA is illustrated with clinical data examining conversational trouble-source repair and topic maintenance in dementia of the Alzheimer’s type (DAT). Seventeen DAT participant/spouse dyads (6 controls, 5 participants with early DAT, 6 participants with moderate DAT) produced spontaneous conversations analyzed for co-occurrence of trouble-source repair and topic maintenance variables.

ResultsMUDICA found that trouble-source repair sequences and topic transitions are associated and that patterns of performance in the DAT groups differed significantly from those in the control group.

ConclusionMUDICA is ideally suited to analyze language and discourse data in communication disorders because it (a) can identify and predict clinical group membership based on patterns of performance, (b) can accommodate few participants and many variables, (c) can be used with categorical data, and (d) adds the rigor of inferential statistics.

Acknowledgments
This research was supported, in part, by grants from the R. Samuel McLaughlin Centre for Gerontological Health Research at McMaster University and from the Faculty of Health Sciences at the University of Western Ontario. This research is part of a larger study examining conversational trouble-source repair in DAT. For the current analysis, we used data collected at Time 1 of the larger study.
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