Examining the utility of a clustering method for analysing psychological test data

The belief that certain disorders will produce specific patterns of cognitive strengths and weaknesses on psychological testing is pervasive and entrenched in the area of clinical neuropsychology, both with respect to expectations regarding the behaviour of individuals and clinical groups. However, there is little support in the literature for such a belief. To the contrary, studies examining patterns of cognitive performance in different clinical samples without exception find more than one pattern of test scores. Lange (2000) in his comprehensive analysis of WAIS-R/WMS-R data for a large sample of mixed clinical cases found that three to five profiles described variations in test performances within clinical diagnoses. Lange went on to show that these profiles occurred with approximately equal frequency in all diagnostic groups. He additionally found four profiles in an exploratory analysis of WAIS-III/WMS-III data from a similar sample. The goals of the current dissertation were to: a) replicate Lange’s findings in a larger clinical sample; b) extend the scope of these findings to a wider array of psychological tests; and c) develop a method to classify individual cases in terms of their psychological test profile. The first study assessed 849 cases with a variety of neurological and psychiatric diagnoses using hierarchical cluster and K-Means analysis. Four WAIS-III/WMS-III profiles were identified that included approximately equal numbers of cases from the sample. Two of these profiles were uniquely related to two of Lange’s profiles, while the remaining two demonstrated relationships with more than one of Lange’s clusters. The second study expanded the neuropsychological test battery employed in the analysis to include the Trail Making Test, Boston Naming Test, Wisconsin Card Sorting Test, Controlled Oral Word Association Test, and Word Lists from the WMS-III reducing the number of clinical cases to 420. In order to compensate for the impact of the reduced number of cases and increased number of variables on potential cluster stability, the number of test score variables was reduced using factor analysis. In this manner the 22 variables were reduced to six factor scores, which were then analysed with hierarchical cluster and K-Means analysis yielding five cognitive profiles. The third study examined the potential clinical utility of the five cognitive profiles by developing a single case methodology for allocating individual cases to cognitive profiles. This was achieved using a combination of a multivariate outlier statistic, the Mahalanobis Distance, and equations derived from a discriminant function analysis. This combination resulted in classification accuracies exceeding 88% when predicting the profile membership based upon the K-Means analysis. The potential utility of this method was illustrated with three age-, education-, gender-, and diagnostically-matched cases that demonstrated different cognitive test profiles. The implications of the small number of cognitive profiles that characterise test performance in a diverse sample of neurological and psychiatric cases as well as the clinical utility of an accurate classification method at the individual case level was discussed. The role of such a classification system in the design of individualised rehabilitation programmes was also highlighted. This research raises the intriguing possibility of developing a typology based on human behaviour rather than a medical nosology. In effect, replacing the medical diagnosis so ill-suited to encompassing the complexities of human behaviour, with a more appropriate “psychological diagnosis” based on cognitive test performance