Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Area of Science:

• Psychology
• Statistics
• Developmental Science

Background:

• Accelerated longitudinal designs (ALDs) enable studying long-term developmental processes efficiently.
• Latent Change Score (LCS) models are commonly used for analyzing ALD data.
• Current occasion-based LCS models limit characterizing changes as a function of age or maturation.

Purpose of the Study:

• To extend occasion-based LCS models to incorporate age as a developmental metric.
• To improve the characterization of longitudinal changes in developmental research.
• To provide a more comprehensive analytical framework for ALDs.

Main Methods:

• Proposed an extension to occasion-based LCS models by including age differences at the first measurement.
• Conducted a Monte Carlo simulation study to evaluate the proposed model.
• Compared the performance of different transformations of the age variable within the model.

Main Results:

• Certain proposed age transformations accurately represented the developmental process across all study ages.
• The extended LCS model demonstrated excellent model fit.
• The new approach effectively overcomes limitations of traditional occasion-based LCS models.

Conclusions:

• The extended LCS model provides an accurate and effective method for analyzing developmental trajectories in ALDs.
• Researchers can now better link longitudinal changes to age and biological maturation.
• The study offers practical R code for implementing the enhanced analytical framework.