Disaggregating Associations of Between-Person Differences in Change over Time from Within-Person Associations in...

Area of Science:

• Psychometrics
• Quantitative Psychology
• Longitudinal Data Analysis

Background:

• Longitudinal designs allow examination of variable relationships over time.
• Distinguishing between-person (mean differences) and within-person (time-specific residuals) relations is crucial for time-varying predictors.
• Existing methods often overlook the distinct between-person relationships among individual change trajectories.

Purpose of the Study:

• To extend the understanding of longitudinal data analysis by introducing the distinction between-person relations among individual slopes.
• To demonstrate the implications of this distinction in both univariate and multivariate longitudinal models.
• To provide practical recommendations for researchers using longitudinal data.

Main Methods:

• Simulation methods were employed to illustrate the proposed distinctions.
• Analyses were conducted using univariate longitudinal models (multilevel/mixed-effects models).
• Analyses were also performed using multivariate longitudinal models (structural equation models).

Main Results:

• The study demonstrates how failing to distinguish between-person relations among individual slopes can lead to analytical inaccuracies.
• Simulation results highlight the importance of this distinction in both observed and latent variable models.
• The findings underscore the complexity of longitudinal data interpretation.

Conclusions:

• Researchers must differentiate between-person relationships among individual slopes from other longitudinal effects.
• Recommendations are provided for best practices in longitudinal data analysis.
• Caveats regarding lead-lag effect models in longitudinal research are discussed.