Static and dynamic longitudinal structural analyses of cognitive changes in old age.

Area of Science:

• Psychology
• Gerontology
• Statistics

Background:

• Latent Growth Models (LGM) and Multilevel Models (MLM) represent significant advancements in analyzing longitudinal data.
• These statistical techniques are crucial for understanding developmental and change processes over time.

Purpose of the Study:

• To compare the advantages and disadvantages of LGM and MLM for longitudinal data analysis.
• To provide guidelines for selecting between LGM and MLM based on data completeness, theoretical model, error structure, covariate effects, and time-invariant covariates.

Main Methods:

• Illustrative examples using data from the Berlin Aging Study (BASE) and the Swiss Interdisciplinary Longitudinal Study on the Oldest Old (SWILSO-O).
• Application and comparison of LGM and MLM frameworks to analyze cognitive aging trajectories.

Main Results:

• Vocabulary test scores (crystallized intelligence) remained stable, while digit letter test scores (fluid intelligence) declined over six years, aligning with two-component lifespan cognition theories.
• Differential level effects were observed for both cognitive measures, with average and differential change effects noted only for fluid intelligence.
• A significant influence of fluid intelligence on the change in crystallized intelligence was detected, supporting the dedifferentiation hypothesis of cognitive abilities in advanced age.

Conclusions:

• Appropriate application of LGM and MLM can yield novel insights into cognitive aging.
• The selection and specification of LGM versus MLM should be guided by pre-defined theoretical and empirical considerations.