There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Jessica Saurman |
| Institution: | University of Colorado Colorado Springs |
| Department: | Psychology |
| Country: | |
| Proposed Analysis: | Serial assessments are often used in neuropsychology to document disease progression, monitor recovery from injury, and evaluate efficacy of therapeutic interventions. Interpreting change accurately is a complex endeavor as one must account for factors that can impact variations in test performance, including reliability of the measure and practice effects. Practice effects are conceptualized as the amount of improvement related to prior exposure to the instrument. Measures in which an individual is tasked with learning new information and utilizing efficient strategies can be particularly impacted by practice effects because the measure is no longer novel at retest. Longitudinal measurement invariance suggests that a change in ability between time points can be validly inferred from an observed change in test scores, i.e., that practice effects are not having a significant impact on the validity of the testing at both time points. This study would aim to determine the longitudinal measurement invariance of Auditory Verbal Learning Test scores between baseline and 6-month follow-up in the ADNI dataset. To accomplish this goal, we plan to use confirmatory factor analysis on AVLT data to identify a latent variable model for AVLT performance. After an adequate model is found, we plan to complete formal longitudinal measurement invariance testing. |
| Additional Investigators | |
| Investigator's Name: | Brandon Gavett |
| Proposed Analysis: | Serial assessments are often used in neuropsychology to document disease progression, monitor recovery from injury, and evaluate efficacy of therapeutic interventions. Interpreting change accurately is a complex endeavor as one must account for factors that can impact variations in test performance, including reliability of the measure and practice effects. Practice effects are conceptualized as the amount of improvement related to prior exposure to the instrument. Measures in which an individual is tasked with learning new information and utilizing efficient strategies can be particularly impacted by practice effects because the measure is no longer novel at retest. Longitudinal measurement invariance suggests that a change in ability between time points can be validly inferred from an observed change in test scores, i.e., that practice effects are not significantly changing the validity of the change scores. This study aims to determine the longitudinal measurement invariance of AVLT scores. First, we plan to use confirmatory factor analysis to identify a latent variable model for Auditory Verbal Learning Test performance at baseline and 6 month follow-up. Once an adequate model is identified, we will complete formal longitudinal measurement invariance testing. |
