Rasch model and its extensions for analysis of aphasic deficits in syntactic comprehension

Roee Gutman, Gayle Dede, David Caplan, Jun S. Liu

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Aphasia is the loss of the ability to produce and/or comprehend language, due to injury to brain areas responsible for these functions. Aphasic patients' performance on comprehension tests has traditionally been related both to the patient's individual ability and to the difficulty of the test questions. The natural choice for analysis of these test results is the Rasch model. It assumes that the probability of a patient responding correctly to a question is the inverse-logit function of the difference between the individual patient's ability and the difficulty of the test question. This study first modeled the way aphasic patients process different sentence types, as well as their ability to accomplish tasks using Rasch models. However, several scientifically important features of the data, such as the correlation of correct responses between two different comprehension tasks, and the association between response patterns in control sentences and response patterns in experimental sentences, were found to be inadequately captured by such models. Alternatively, we used a full Bayesian approach, exploring a mixture of generalized linear mixed models that clustered patients into similar response patterns and abilities. The mixture model was found to better describe the experimental results than any other model examined. The mixture model also expresses the hypothesis that aphasic patients can be classified into different ability and response profile groups, and that patients utilize different cognitive resources in different comprehension tasks. These results are scientifically important and could not have been discovered by using the simple Rasch model. This article has supplementary material online.

Original languageEnglish (US)
Pages (from-to)1304-1316
Number of pages13
JournalJournal of the American Statistical Association
Volume106
Issue number496
DOIs
StatePublished - Dec 1 2011

Keywords

  • Bayesian analysis
  • GLMM
  • IRT
  • Mixture models
  • Similar tests

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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