Modeling bivariate change in individual differences: Prospective associations between personality and life...

Area of Science:

• Psychology
• Quantitative Psychology
• Longitudinal Research

Background:

• Structural equation models (SEMs) are crucial for analyzing change and longitudinal associations between variables.
• Common SEMs include latent growth, autoregressive cross-lagged, autoregressive latent trajectory, and latent change score models.
• These models differ in their assumptions about the nature of change, impacting data interpretation.

Purpose of the Study:

• To overview and evaluate various SEMs for studying change and associations.
• To empirically examine the longitudinal association between personality traits and life satisfaction using different SEMs.
• To highlight how model choice influences conclusions about dynamic processes.

Main Methods:

• Overview of common structural equation models (SEMs).
• Empirical analysis of personality and life satisfaction data from a Dutch sample (N=8,320) over 8 years (5 waves).
• Application of various SEMs, including advanced models like autoregressive latent trajectory and extended latent change score models.

Main Results:

• Reproduced previous findings on the personality-life satisfaction relation.
• New findings indicate that intraindividual changes in life satisfaction predict subsequent intraindividual changes in personality traits.
• Advanced SEMs, applicable with 3+ assessment waves, allow for stronger inferences on dynamic associations.

Conclusions:

• The choice of SEM should be guided by theoretical assumptions about the change processes under investigation.
• Intraindividual changes in life satisfaction have a predictive influence on changes in personality traits.
• Advanced SEMs are essential for accurately modeling the complex, dynamic nature of psychological change processes.