Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)



First Advisor

W. Drew Gouvier


Early methods to estimate premorbid intelligence focused on the use of the WAIS or WAIS-R IQ scores or selected WAIS or WAIS-R subtests. It was expected that performance on such tests would remain stable after a brain injury. As further research showed this belief was not valid, researchers turned to the use of other tests of current ability, one being a measure of reading ability known as the National Adult Reading Test (NART). A second approach focused on the relationship found between certain demographic variables and intelligence. More recently, researchers have employed an approach that combined present abilities performance and demographic predictors in regression equations. Vanderploeg and Schinka (1995) used a combination of present ability as measured by the WAIS-R with certain demographic characteristics. Friedberg and Gouvier (1996) developed linear regression equations to estimate WAIS-R IQs using estimated Barona IQ (Barona, Reynolds, & Chastain, 1984) combined with error score on the NART. The present study compared the equations of Vanderploeg and Schinka (1995) with those of Friedberg and Gouvier (1996) using normal subjects and brain-injured individuals. Both sets of predictor equations found significant differences for the estimated IQ scores of the two groups with the control group having higher estimated scores than the head-injured group. Secondly, both sets of predictor equations noted a significantly greater difference between the estimated and obtained IQ scores for the CHI group than for the control group. This finding suggested that the obtained IQ scores were significantly decreased from premorbid levels, suggesting clinical utility for the equations as measures of premorbid intelligence. Finally, a comparison of the two sets of equations using a hierarchical regression pointed to the Vanderploeg and Schinka equations as better predictors of premorbid intelligence as they accounted for more of the variance than the Friedberg and Gouvier equations. However, this may be partly due to the fact that the predictor equations were derived from the same data as the criterion variable. This would suggest the need for further research using predictors that are independent of the data used in the criterion.