The Uniform Dataset 3.0 Neuropsychological Battery: Factor Structure, Invariance Testing, and Demographically Adjusted Factor Score Calculation. (17th July 2020)

• Record Type: Journal Article
• Authors: Kiselica, Andrew M.; Webber, Troy A.; Benge, Jared F.
• Is Part Of: Journal of the International Neuropsychological Society. Volume 26:Number 6(2020)
• Page Start: 576
• Page End: 586
• Publication Date: 2020-07-17
• Subjects: Uniform Dataset, -- Neuropsychological tests, -- Factor analysis, -- Cognition, -- Demographic correction, -- Measurement invariance; Neuropsychology -- Periodicals; 616.8
• DOI: 10.1017/S135561772000003X
• Languages: English
• ISSNs: 1355-6177

Abstract

Abstract: Objective: The goals of this study were to (1) specify the factor structure of the Uniform Dataset 3.0 neuropsychological battery (UDS3NB) in cognitively unimpaired older adults, (2) establish measurement invariance for this model, and (3) create a normative calculator for factor scores. Methods: Data from 2520 cognitively intact older adults were submitted to confirmatory factor analyses and invariance testing across sex, age, and education. Additionally, a subsample of this dataset was used to examine invariance over time using 1-year follow-up data ( n = 1061). With the establishment of metric invariance of the UDS3NB measures, factor scores could be extracted uniformly for the entire normative sample. Finally, a calculator was created for deriving demographically adjusted factor scores. Results: A higher order model of cognition yielded the best fit to the data χ 2 (47) = 385.18, p < .001, comparative fit index = .962, Tucker-Lewis Index = .947, root mean square error of approximation = .054, and standardized root mean residual = .036. This model included a higher order general cognitive abilities factor, as well as lower order processing speed/executive, visual, attention, language, and memory factors. Age, sex, and education were significantly associated with factor score performance, evidencing a need for demographic correction when interpreting factor scores. A user-friendly Excel calculator was created to accomplish this goal and is available in the online supplementary materials. Conclusions: The UDS3NB is best characterized by a higher order factor structure. Factor scores demonstrate at least metric invariance across time and demographic groups. Methods for calculating these factors scores are provided.