Computerized adaptive test strategies for the matrix reasoning subtest of the Wechsler Adult Intelligence Scale, 4th Edition (WAIS-IV).

OBJECTIVE: Most neuropsychological tests were developed without the benefit of modern psychometric theory. We used item response theory (IRT) methods to determine whether a widely used test - the 26-item Matrix Reasoning subtest of the WAIS-IV - might be used more efficiently if it were administered using computerized adaptive testing (CAT). METHOD: Data on the Matrix Reasoning subtest from 2197 participants enrolled in the National Neuropsychology Network (NNN) were analyzed using a two-parameter logistic (2PL) IRT model. Simulated CAT results were generated to examine optimal short forms using fixed-length CATs of 3, 6, and 12 items and scores were compared to the original full subtest score. CAT models further explored how many items were needed to achieve a selected precision of measurement (standard error ≤ .40). RESULTS: The fixed-length CATs of 3, 6, and 12 items correlated well with full-length test results (with r = .90, .97 and .99, respectively). To achieve a standard error of .40 (approximate reliability = .84) only 3-7 items had to be administered for a large percentage of individuals. CONCLUSIONS: This proof-of-concept investigation suggests that the widely used Matrix Reasoning subtest of the WAIS-IV might be shortened by more than 70% in most examinees while maintaining acceptable measurement precision. If similar savings could be realized in other tests, the accessibility of neuropsychological assessment might be markedly enhanced, and more efficient time use could lead to broader subdomain assessment.

Construct identification in the neuropsychological battery: What are we measuring?

OBJECTIVE: Major obstacles to data harmonization in neuropsychology include lack of consensus about what constructs and tests are most important and invariant across healthy and clinical populations. This study addressed these challenges using data from the National Neuropsychology Network (NNN). METHOD: Data were obtained from 5,000 NNN participants and Pearson standardization samples. Analyses included variables from four instruments: Wechsler Adult Intelligence Scale, 4th Edition (WAIS-IV); Wechsler Memory Scale, 4th Edition (WMS-IV); California Verbal Learning Test, 3rd Edition (CVLT3); and Delis-Kaplan Executive Function System (D-KEFS). We used confirmatory factor analysis to evaluate models suggested by prior work and examined fit statistics and measurement invariance across samples. We examined relations of factor scores to demographic and clinical characteristics. RESULTS: For each instrument, we identified four first-order and one second-order factor. Optimal models in patients generally paralleled the best-fitting models in the standardization samples, including task-specific factors. Analysis of the NNN data prompted specification of a Recognition-Familiarity factor on the WMS-IV and an Inhibition-Switching factor on the D-KEFS. Analyses showed strong to strict factorial invariance across samples with expected differences in factor means and variances. The Recognition-Familiarity factor correlated with age more strongly in NNN than in the standardization sample. CONCLUSIONS: Factor models derived from healthy groups generally fit well in patients. NNN data helped identify novel Recognition-Familiarity and Inhibition-Switching factors that were also invariant across samples and may be clinically useful. The findings support efforts to identify evidence-based and optimally efficient measurements of neuropsychological constructs that are valid across groups. (PsycInfo Database Record (c) 2023 APA, all rights reserved).