Discrepancy analysis and Australian norms for the Trail Making Test.

OBJECTIVE: Discrepancy analyses refer to comparison methods that evaluate the relationship or differences between two measures in the same individual. A common type of discrepancy analysis involves the comparison of two trials within a measure, such as, Trails A and B of the Trail Making Test (TMT). The TMT is well-suited to this role as the two measures are highly correlated, assess similar underlying constructs, and most importantly demonstrate differential vulnerability to the impact of pathology. While the inclusion of these types of data in the form of difference scores or ratios has become more frequent, this information has been presented only for demographically adjusted subgroups and has not taken into account the level of performance of the comparison trial, Trails A. METHOD: The role and advantages of discrepancy analysis with the TMT stratified by level of Trails A performance were demonstrated with an Australian normative sample of 647 participants and a heterogeneous clinical sample consisting of 2,292 Australian and U.S. RESULTS: The ability to differentiate between the influence of slowed visual scanning and/or graphomotor speed, and reduced mental flexibility was demonstrated by applying the normative data to clinical case discrepancies. The importance of accounting for the variability in discrepancy scores associated with the level of performance of Trails A was also highlighted. CONCLUSION: A simple, efficient, and effective approach to examining the basis for differences between TMT-A and TMT-B performances is provided to examine the relative contributions of perceptual/motor abilities, and mental flexibility.

Using Mahalanobis Distance to Evaluate Recovery in Acute Stroke.

OBJECTIVE: In the weeks immediately following a stroke, impairments across multiple cognitive domains are pervasive yet there is little literature that explores cognitive recovery during this period. This paper evaluates the use of Mahalanobis distance as a means of statistically evaluating cognitive change at the individual level. METHOD: A small battery of standardized neuropsychological tests was administered on five or six occasions across a 2-week period to the participants recovering from a stroke and a non-stroke control group. Mahalanobis distance was used to evaluate the change profile of those who were recovering from a stroke relative to the non-stroke control. RESULTS: The outcomes of three patients show that Mahalanobis distance could statistically differentiate recovery, no change, and deterioration from normal repetition effects. DISCUSSION: In the acute phase of stroke using Mahalanobis distance it is possible to distinguish between recovery, normal learning, and gerneralized learning deficits thereby identifying likely candidates for further cognitive assessment and rehabilitation.

How Small Is Big: Sample Size and Skewness.

Sample sizes of 50 have been cited as sufficient to obtain stable means and standard deviations in normative test data. The influence of skewness on this minimum number, however, has not been evaluated. Normative test data with varying levels of skewness were compiled for 12 measures from 7 tests collected as part of ongoing normative studies in Brisbane, Australia. Means and standard deviations were computed from sample sizes of 10 to 100 drawn with replacement from larger samples of 272 to 973 cases. The minimum sample size was determined by the number at which both mean and standard deviation estimates remained within the 90% confidence intervals surrounding the population estimates. Sample sizes of greater than 85 were found to generate stable means and standard deviations regardless of the level of skewness, with smaller samples required in skewed distributions. A formula was derived to compute recommended sample size at differing levels of skewness.