Person: Caplan, David
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Publication Rasch models of aphasic performance on syntactic comprehension tests
(Informa UK Limited, 2010) Gutman, Roee; DeDe, Gayle; Michaud, Jennifer; Liu, Jun; Caplan, DavidResponses of 42 people with aphasia to 11 sentence types in enactment and sentence–picture matching tasks were characterized using Rasch models that varied in the inclusion of the factors of task, sentence type, and patient group. The best fitting models required the factors of task and patient group but not sentence type. The results provide evidence that aphasic syntactic comprehension is best accounted for by models that include different estimates of patient ability in different tasks and different difficulty of all sentences in different groups of patients, but that do not include different estimates of patient ability for different types of sentences.
Publication Rasch Model and Its Extensions for Analysis of Aphasic Deficits in Syntactic Comprehension
(Informa UK Limited, 2011) Gutman, Roee; DeDe, Gayle; Caplan, David; Liu, JunAphasia 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.