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    Area of Science:

    • Multivariate statistical modeling
    • Longitudinal data analysis
    • Psychophysiology

    Background:

    • Longitudinal research often involves measuring multiple variables over time.
    • Existing methods typically focus on analyzing change in a single variable at a time.
    • There is a need to understand interrelationships between changes in different variables.

    Purpose of the Study:

    • To present methods for studying relationships between patterns of change across multiple variables.
    • To extend the multilevel modeling framework to the multivariate case.
    • To explore the use of latent curve models in the multivariate context.

    Main Methods:

    • Extension of multilevel modeling to estimate covariances of parameters representing change in different variables.
    • Application of multivariate latent curve models.
    • Analysis of physiological responses during marital conflict in older married couples.

    Main Results:

    • Demonstrated the feasibility of estimating covariances between parameters of change for different variables.
    • Identified a substantial correlation between the rates of linear change in different stress-related hormones during marital conflict.
    • Showcased the relationship between multivariate multilevel models and multivariate latent curve models.

    Conclusions:

    • Multivariate multilevel modeling provides a robust framework for analyzing simultaneous changes in multiple variables.
    • Significant interdependencies exist in the physiological stress responses of older married couples during conflict.
    • The presented methods offer valuable tools for researchers investigating complex longitudinal data.