title: Development of the WAIS-III general ability index estimate (GAI-E).
abstract: The WAIS-III General Ability Index (GAI; Tulsky, Saklofske, Wilkins, & Weiss, 2001) is a recently developed, 6-subtest measure of global intellectual functioning. However, clinical use of the GAI is currently limited by the absence of a method to estimate premorbid functioning as measured by this index. The purpose of this study was to develop regression equations to estimate GAI scores from demographic variables and WAIS-III subtest performance. Participants consisted of those subjects in the WAIS-III standardization sample that has complete demographic data (N=2,401) and were randomly divided into two groups. The first group (n=1,200) was used to develop the formulas (i.e., Development group) and the second (n=1,201) group was used to validate the prediction algorithms (i.e., Validation group). Demographic variables included age, education, ethnicity, gender and region of country. Subtest variables included vocabulary, information, picture completion, and matrix reasoning raw scores. Ten regression algorithms were generated designed to estimate GAI. The GAI-Estimate (GAI-E) algorithms accounted for 58% to 82% of the variance. The standard error of estimate ranged from 6.44 to 9.57. The correlations between actual and estimated GAI ranged from r=.76 to r=.90. These algorithms provided accurate estimates of GAI in the WAIS-III standardization sample. Implications for estimating GAI in patients with known or suspected neurological dysfunction is discussed and future research is proposed.
authors.affiliation: Riverview Hospital, Coquitlam, British Columbia, Canada. rlange@bcmhs.bc.ca
authors.forename: Rael T
authors.initials: RT
authors.lastname: Lange
authors.forename: Mike R
authors.initials: MR
authors.lastname: Schoenberg
authors.forename: Gordon J
authors.initials: GJ
authors.lastname: Chelune
authors.forename: James G
authors.initials: JG
authors.lastname: Scott
authors.forename: Russell L
authors.initials: RL
authors.lastname: Adams
mesh_terms: D000043:Abstracting and Indexing; D017677:Age Distribution; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000704:Analysis of Variance; D016022:Case-Control Studies; D003710:Demography; D004522:Educational Status; D005069:Evaluation Studies as Topic; D005260:Female; D015577:Geriatric Assessment; D006801:Humans; D007360:Intelligence / Q000502:physiology*; D007361:Intelligence Tests / Q000592:standards* / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results