The Mayo Normative Studies (MNS) represents a robust dataset that provides demographically corrected norms for the Rey Auditory Verbal Learning Test. We report MNS application to an independent cohort to evaluate whether MNS norms accurately adjust for age, sex, and education differences in subjects from a different geographic region of the country. As secondary goals, we examined item-level patterns, recognition benefit compared to delayed free recall, and derived Auditory Verbal Learning Test (AVLT) confidence intervals (CIs) to facilitate clinical performance characterization.

Participants from the Emory Healthy Brain Study (463 women, 200 men) who were administered the AVLT were analyzed to demonstrate expected demographic group differences. AVLT scores were transformed using MNS normative correction to characterize the success of MNS demographic adjustment.

Expected demographic effects were observed across all primary raw AVLT scores. Depending on sample size, MNS normative adjustment either eliminated or minimized all observed statistically significant AVLT differences. Estimated CIs yielded broad CI ranges exceeding the standard deviation of each measure. The recognition performance benefit across age ranged from 2.7 words (SD = 2.3) in the 50–54-year-old group to 4.7 words (SD = 2.7) in the 70–75-year-old group.

These findings demonstrate generalizability of MNS normative correction to an independent sample from a different geographic region, with demographic adjusted performance differences close to overall performance levels near the expected value of T = 50. A large recognition performance benefit is commonly observed in the normal aging process and by itself does not necessarily suggest a pathological retrieval deficit.

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.

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.

A higher order model of cognition yielded the best fit to the data χ2(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.

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.